Importing Inputs for Climate Change Mitigation: The Case of Agricultural Productivity

Rodrigo Garcia-Verdu; Alexis Meyer-Cirkel; Akira Sasahara; Hans Weisfeld

doi:10.5089/9781484393826.001.A001

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Importing Inputs for Climate Change Mitigation: The Case of Agricultural Productivity

Author: Mr. Rodrigo Garcia-Verdu, Alexis Meyer-Cirkel, Akira Sasahara¹, and Hans Weisfeld

¹ 0000000404811396https://isni.org/isni/0000000404811396International Monetary Fund

Contributor Notes

Author’s E-Mail Address: rgarciaverdu@imf.org, ameyerirkel@imf.org, sasahara@uidaho.edu, hweisfeld@imf.org

Publication Date:: 04 Feb 2019

eISBN:: 9781484393826

Language:: English

Keywords:: WP; growth rate; dummy variable; simple average; TFP estimate; TFP country; weather shock; agricultyral TFP; measure TFP

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This paper estimates agricultural total factor productivity (TFP) in 162 countries between 1991 and 2015 and aims to understand sources of cross-country variations in agricultural TFP levels and its growth rates. Two factors affecting agricultural TFP are analyzed in detail – imported intermediate inputs and climate. We first show that these two factors are independently important in explaining agricultural TFP – imported inputs raise agricultural TFP; and higher temperatures and rainfall shortages impede TFP growth, particularly in low-income countries (LICs). We also provide a new evidence that, within LICs, those with a higher import component of intermediate inputs seem to be more shielded from the negative impacts of weather shocks.

Abstract

I. Introduction

Agricultural productivity, as measured by total factor productivity (TFP), remains far below in low-income countries (LICs) compared to the levels registered in more advanced economies. Productivity in the agricultural sector is significantly lower than in the non-agricultural sector, and this difference is greater in LICs than in developed economies (Adamopoulos and Restuccia, 2018). It is thus not surprising that accelerations in agricultural TFP growth have often preceded episodes of aggregate economic growth (McArthur and McCord, 2017).

The goal of this paper is to understand the sources of cross-country variations in agricultural TFP and its growth rates by focusing on two key factors – imported intermediate inputs and weather shocks. These two variables are critical in explaining agricultural productivity. Trade in intermediate inputs covers 64 percent of world trade in 2014 according to the World Input-Output Table (Timmer et al., 2015 and Timmer et al., 2016) and a number of studies document economic benefits from expanding global value chains.² Guided by these, we aim to understand its implications in agricultural sectors. Moreover, climate change-related weather variations are an important ongoing issue (e.g., IMF, 2017) and agricultural productivity may suffer increasingly from a climate change-related deterioration in weather conditions. Therefore, it is important to understand their effects on agricultural productivity.

Using data from 162 countries during the period 1991–2015, we show that the two factors are independently important for countries’ agricultural sectors. Imported intermediate inputs boost productivity because they tend to be higher quality while being less expensive than domestic equivalents. Furthermore, we show that weather shocks play a role because higher temperatures and rainfall shortages reduce agricultural TFP in LICs.

These findings are new to the literature because we focus on their effects on agricultural TFP and none of the previous studies has investigated the impacts of these variables on agricultural TFP using a panel dataset with a large cross-section of countries. However, our results may not be surprising because previous work finds comparable estimates in different contexts.

One of the most interesting results comes from interactions between the two key factors we focus. Within LICs where we find significant effects of weather shocks, stronger weather effects come from countries employing less imported inputs. Higher temperatures and rainfall shortages do not seem to have significant effects on countries employing greater imported inputs. These results imply that using imported intermediate inputs reduces negative effects of weather shocks.

There are three main reasons to believe imported inputs have such effects. First, imported inputs tend to be higher quality and embed better technologies. As a result, these work to reduce producers’ sensitively to weather shocks. Second, a greater share of imported inputs to total intermediate inputs makes the overall quality of inputs less sensitive to local weather shocks, because local climate has no effects on the quality of imported inputs.³ Third, local final good producers are intermediate good suppliers because there are sectoral linkages. Local final good producers’ productivity gains through imported inputs have positive effects on domestic intermediate goods. This contributes to make domestic input quality less climate sensitive, which in turn leads to more climate-robust agricultural sectors.

This paper contributes to two different strands of literature. First, it is related with the literature on productivity gains from imported intermediate inputs. It finds that imported inputs increase firms’ productivity in manufacturing industries because those inputs tend to be higher quality and less expensive (e.g., Amiti and Konings, 2007; Topalova and Khandelwal, 2011).⁴ To the best of our knowledge, all prior studies focuse on manufacturing industries, with a few exceptions, such as Chevassus-Lozza et al. (2013) focusing on the French food agriculture industry, and Olper et al. (2017) analyzing the data from the French and Italian food processing industry.⁵ The current paper is the first to shed light on agricultural industry in general in the context of gains from imported inputs.⁶

Second, this paper contributes to the literature on the impacts of weather shocks on agricultural sectors. The previous work on this issue focuses on certain areas of the world (e.g., Deschenes and Greenstone, 2007, for the U.S., Aschenfelter and Storchmann, 2006, for Germany, and Wang et al., 2009, for China) and they are silent about cross-country differences in the effect of weather shocks. In contrast, by employing a large panel dataset we find that countries’ income levels play a role in explaining countries’ sensitivities to weather shocks. In particular, we find that only LICs are negatively impacted by higher temperatures and rainfall shortages. In this regard, this paper is attuned to recent studies finding significant effects of weather shocks in lower income countries (e.g., Dell et al., 2012, for GDP growth rate; and Cattaneo and Peri, 2016, for emigration from countries).

Our contribution is three-fold. First, our results imply that an increase in imported intermediate inputs, instrumented by tariff cuts and inward FDI, has a positive effect on agricultural TFP. A one percentage point increase in the share of imported inputs to total value of intermediate goods raises TFP by 3–4 percent. This result is robust to wide range of specifications and samples. This study is the first to show the positive effect of imported inputs on agricultural TFP using a large panel dataset.

Second, by exploiting plausibly exogenous year-to-year fluctuations in temperatures and rainfalls, we find that for LICs, higher temperatures have a negative impact on TFP and greater rainfalls have a positive one. This is consistent with prior articles arguing that agricultural production in developing countries are more sensitively affected by weather shocks because these countries tend to have lower capital-to-labor ratios and their technologies are more climate sensitive (Mendelsohn et al., 2001, 2006). We are the first to show this using a panel dataset on agricultural TFP, which makes it possible to overcome bias coming from time-invariant omitted variables as in recent studies such as Dell et al. (2012) and Cattaneo and Peri (2016).

Third, we go beyond the existing literature by finding interactions between imported inputs and climate effects in explaining agricultural TFP. While previous studies have found that income-levels explain countries’ sensitivity to climate, we are the first to document that prevalence of imported inputs reduces countries’ vulnerability to weather shocks.

The rest of the paper is organized as follows. The next section conducts a growth accounting exercise and estimates agricultural TFP. Section III presents summary of data and discusses our motivations. Section VI empirical assesses the effect of imported inputs and weather shocks on agricultural TFP. It also considers interactions between these two variables in explaining the impact of weather shocks. Section V conducts counterfactual exercises to understand economic magnitudes of the estimated impacts. Section VI concludes.

II. Agricultural TFP

A. The Method Estimating Agricultural TFP

We start from estimating agricultural TFP. Agricultural value-added is decomposed into TFP and of three inputs: capital stock, labor force, and land area in the agricultural industry. We first discuss the methodology, followed by a description of data sources, and then results are presented.

As in Herrendorf et al. (2015) and many others,⁷ country i’s agricultural production function in year t is described by a Cobb-Douglas production function subject to constant returns to scale (CRS):⁸

\begin{matrix} Y_{i t} = A_{i t} {(K_{i t})}^{α_{i t}^{K}} {(L_{i t})}^{α_{i t}^{L}} {(T_{i t})}^{α_{i t}^{T}} w i t h α_{i t}^{K} + α_{i t}^{L} + α_{i t}^{T} = 1, & (1) \end{matrix}

where Y_it, A_it, K_it, L_it and T_it are value-added, TFP, capital stock, employment, and land area in the agricultural industry, respectively. $α_{i t}^{K}, α_{i t}^{L} a n d α_{i t}^{T}$ are the income shares of capital stock, labor, and land, respectively. Note that these income shares have country and year subscripts, meaning that these are different across countries and across time.

Data on agricultural value-added, agricultural capital stock, and agricultural land area are taken from FAO (2018) and data on agricultural employment come from the World Bank (2018a). We take the income share and the labor share data from the EORA database (Lenzen et al., 2012, 2013). It provides the data on payments to capital (consumption of fixed capital), payments to labor (compensation of labor), and value-added.⁹ We compute the capital share as $α_{i t}^{K} = \frac{p a y m e n t s t o c a p i t a l_{i t}}{v a l u e - a d d e d_{i t}}$ and the labor share as $α_{i t}^{L} = \frac{p a y m e n t s t o l a b o r_{i t}}{v a l u e - a d d e d_{i t}}$ . By the CRS assumption, the land share is $α_{i t}^{T} = 1 - α_{i t}^{K} - α_{i t}^{L}$ .

TFP is then obtained as a residual: $A_{i, t} = Y_{i, t} / {(K_{i t})}^{α_{i t}^{K}} {(L_{i t})}^{α_{i t}^{L}} {(T_{i t})}^{α_{i t}^{T}}$ . ¹⁰ Annualized long-run growth rates of value added of country i from 1991 to 2015, $g_{i, 1991 - 2015}^{V A} = 100 \times [\ln (V A_{i, 2015}) - \ln (V A_{i, 1991})] / 24$ , are decomposed into four components:

\begin{matrix} T F P : g_{i, 1991 - 2015}^{T F P} = 100 \times [\ln (A_{i, 2015}) - \ln (A_{i, 1991})] / 24, \\ Capital stock: g_{i, 1991 - 2015}^{K} = 100 \times α_{i t}^{K} [\ln (K_{i, 2015}) - \ln (K_{i, 1991})] / 24, \\ Employment: g_{i, 1991 - 2015}^{L} = 100 \times α_{i t}^{L} [\ln (L_{i, 2015}) - \ln (L_{i, 1991})] / 24, \\ L a n d a r e a : g_{i, 1991 - 2015}^{T} = 100 \times α_{i t}^{T} [\ln (T_{i, 2015}) - \ln (T_{i, 1991})] / 24. \end{matrix}

This decomposition exercise is conducted for each of the countries available.

Our sample includes 162 countries in the world. However, not all countries have complete data from 1991 to 2015. The growth accounting exercise focuses on countries where complete data from 1991 to 2015 are available. As a result, the sample size is restricted to 135 countries – 25 LICs, 35 lower-middle-income countries, 34 upper-middle-income countries, and 41 high-income countries.

We also provide alternative TFP estimate based on factor shares obtained by estimating a log-linearized Cobb-Douglas production function, which we call TFPb. The productivity measure TFP_b is based on a strong assumption that all countries have the same factor shares. However, this measure of TFP covers a slightly greater number of countries – 27 LICs, 37 lower-middle income countries, 38 upper-middle income countries, 42 high-income countries, totaling 144 countries. TFPb estimates are used for robustness checks of regression analyses.¹¹

B. Results from Growth Accounting

Table 1 presents results from the growth accounting exercise for four groups of countries. It shows simple averages of the growth rates of agricultural value-added and those of four decomposed components. TFP grew the most in lower-middle income countries – the annual average growth rate is 2.3 percent over the period 1991–2015. Upper-middle income countries (2.16%), high-income countries (1.93%), and LICs (1.87%) follow.

Table 1:

Growth Accounting Results, Countries Grouped by Income Level, 1991–2015

Notes: The table shows the decomposition of the annual average growth in agricultural value-added over 24 years, from 1991 to 2015. The growth accounting exercise is conducted at the country-level first and then the simple average of each country’s growth rates are found. Countries’ income levels are based on the World Bank’s classification. See the main text for data sources.

Table 1:

Growth Accounting Results, Countries Grouped by Income Level, 1991–2015

	Value-added	Decomposition
	Value-added	TFP	Capital stock	Labor	Land
Low-income countries	3.32	1.87	0.86	0.69	0.30
Lower-middle income countries	3.42	2.29	1.43	-0.03	0.26
Upper-middle income countries	3.01	2.16	1.49	-1.27	0.42
High-income countries	1.08	1.93	0.39	-1.22	-0.02

Table 1:

Growth Accounting Results, Countries Grouped by Income Level, 1991–2015

	Value-added	Decomposition
	Value-added	TFP	Capital stock	Labor	Land
Low-income countries	3.32	1.87	0.86	0.69	0.30
Lower-middle income countries	3.42	2.29	1.43	-0.03	0.26
Upper-middle income countries	3.01	2.16	1.49	-1.27	0.42
High-income countries	1.08	1.93	0.39	-1.22	-0.02

Agricultural value-added growth rate in LICs, 3.32 percent, is higher than that from richer countries. However, relatively higher input growth rate led to a small contribution of TFP. High-income countries have a lower value-added growth rate than other groups of countries, 1.08 percent. However, the TFP growth rate is estimated to be fairly high due to the fact that there is a decrease in inputs such as labor (-1.22%) and land (-0.02%).

Figure 1 summarizes results from each of LICs over the 24-year period 1991–2015.¹² Out of the 27 countries, Mali, Chad, and Liberia have the highest value-added growth rates: annual average growth rates of 7.7 percent, 6.8 percent, and 6.2 percent, respectively. TFP contributes the most in Mali and Chad: 3.5 percent and 3.6 percent, respectively. On the other hand, the growth in the capital stock explains the largest part of the agricultural value-added growth in Liberia, 3.5 percent. Among the LICs, Central African Republic, Burundi, and Haiti have the smallest value-added growth rate over the period: 0 percent, -0.14 percent, and -0.27 percent, respectively. All of these three countries have non-positive TFP growth rates and negative capital stock growth rates.

We are also interested in agricultural productivity levels and their gaps across countries. Figure 2 shows the average agricultural TFP for the four groups of countries. Panel A presents average TFP levels and shows that TFP levels have been increasing in all groups of countries over the period 1991–2015. Panel B displays the TFP levels normalized so as to make the TFP levels from 1991 to be one. It shows that among these four groups of countries, TFP levels increased almost at the same rate for all of the four groups of counties. We seek to disentangle the sources of this productivity gap.

III. Stylized Facts on Imported Inputs, and Weather Shocks

We focus on two variables, imported inputs and weather shocks, to explain cross-country variations in agricultural TFP. This section presents empirical observations on these variables by showing their time-series variations by country income group.

Figure 3 shows the share of imported inputs to total purchase of intermediate goods in the agricultural sector. It indicates that high-income countries consistently have a higher share of imported inputs among the four groups of countries after 1995, and LICs always have the lowest share except for the year 2000. In terms of time-series variation, there is a slight declining trend of the share of imported inputs in the 1990s and it is increasing since early 2000s. There are sharp declines in the share of imported inputs during 2008–2010 due to the 2008–09 global financial crisis.

We display average temperatures and rainfalls across the four groups of countries in Figure 4. Panel A shows that lower income countries tend to have higher average temperatures. Average temperatures are rising over the period 1991–2015. Panel B indicates that middle-income countries have greater rainfalls on average. LICs and high-income countries have similar levels of rainfalls.

Figure 5 shows kernel density estimates of average of temperatures and rainfalls using the data from 2015. Panel A of Part I indicates that average temperatures are right-skewered in LICs and middle-income countries. The modes of the distributions are above 25 degrees Celsius. On the other hand, average temperatures for high-income countries is almost normally distributed and the mode is about 10 degrees Celsius. Panel B of Part I shows the long-run changes in average temperatures between 1990 and 2015. Strikingly, most countries experienced a rise in temperatures. The modes are above zero for all groups of countries. Panel A of Part II presents kernel density estimates of average monthly rainfalls and their long-run changes during 1990–2015 are presented in Panel B. Long-run changes in rainfalls are almost symmetrically distributed with mean zero.

IV. Regression Analysis

A. Imported Inputs and Agricultural TFP Level

This section examines the role of imported inputs in determining agricultural TFP. By closely following prior studies investigating determinants of TFP, we estimate the following regression model:¹³ ¹⁴

\begin{matrix} \ln (T F P_{i, t}) = β_{i} + β_{1} Im I n p u t s_{i, t} + 𝕏_{i, t} β_{2} + e_{i, t}, & (2) \end{matrix}

where ln(TFP_i,t) denotes natural log of TFP in country i in year t; β_i indicates country fixed effects; Imlnputs_i,t = 100 x Imported Inputs_i,t /Total Inputs_i,t is the value of imported intermediate inputs divided by the value of total intermediate inputs times 100; 𝕏_i,t is a vector of control variables including the consumption of fertilizers and pesticides, the capital-to-labor ratio, the production taxes-to-value added ratio, the production subsidies-to-value added ratio, the political instability index, the expenditure share on research and development, and temperatures and rainfalls¹⁵; e_i,t is an error term; β₁ and β₂ are a scalar parameter and a vector of parameters to be estimated, respectively.

OLS estimates would lead to a bias because there is reverse causality from the level of TFP to countries’ decisions to import. For example, productive countries may be more likely to import inputs from abroad because they have a greater incentive to remain competitive and increase their global market share. Alternatively, less productive countries may be less likely to import because they often have a set of stringent industrial policy design setups biased towards domestically produced inputs. If the former story were true, β₁ would have an upward bias; on the other hand, β₁ would have a downward bias if the latter story were true.

In order to overcome this potential endogeneity, we employ tariffs applied by importing countries and inward FDI (as a share of agricultural value-added) as instruments. These variables are valid instruments because they satisfy the relevancy condition and the exclusion restriction. First, a decline in tariffs increases imported inputs but it does not affect agricultural TFP other than through changes in the value of imported inputs. Second, an increase in inward FDI to the agricultural sector increases imported inputs because these foreign-owned agricultural entities are more likely to use imports from abroad. An increase in inward FDI may increase agricultural TFP directly if there are some spillovers from foreign-owned entities. However, econometric tests suggest that our instruments satisfy the exclusion restriction.

The data come from various sources. Section II laid out the underlying sources of data used to calculate TFP. The data on imported inputs come from the EORA Input-Output tables (Lenzen et al. 2012; Lenzen et al., 2013). The share of imported intermediate goods to the total intermediate good used is computed for the agricultural sector for all EORA 189 countries and then the data on imported inputs are matched with our agricultural productivity dataset. The data on fertilizer consumption per area and R&D expenditures comes from the WDI. Pesticide consumptions per area are from FAO. We obtain the political instability index from the Freedon House. The data on the capital-to-labor ratio, production taxes, and production subsidies are from the EORA. Temperature and rainfall are taken from the World Bank Climate Change Knowledge Portal (World Bank, 2018b). See Appendix B for more details.

Table 2 reports regression results. The first two columns employ OLS – column (1) regresses log of TFP on imported inputs only and column (2) introduces other control variables. The results show that the imported inputs-to-total inputs ratio does not have a significant effect on TFP levels. These insignificant coefficients are presumably because there are endogeneity issues, leading to bias in both ways – negative and positive. As a result, we obtain zero point estimates.¹⁶

Table 2:

Determinants of TFP, Baseline Results Dependent Variable = 100×ln(TFP)

Notes: All regressions include country fixed effects. Standard errors are in parentheses. ***, **, and * indicate the statistical significance at the 1%, 5%, and 10% level, respectively. Instruments include weighted average tariffs on all products and the share of inward FDI to the agricultural sector to the agricultural value-added. See the main text for data sources.

Table 2:

Determinants of TFP, Baseline Results Dependent Variable = 100×ln(TFP)

	OLS		2SLS
	(1)	(2)	(3)	(4)	(5)	(6)
Imported inputs/Total inputs × 100	0.101 (0.246)	-0.048 (0.371)	8.863*** (1.093)	4.399*** (1.290)	4.023** (1.677)	3.995*** (1.114)
Controls
Fertilizer & pesticides		0.465* (0.274)		4.122*** (1.304)	4.590*** (1.344)	3.924*** (1.281)
Capital-labor ratio		-0.020 (0.165)		0.344*** (0.096)	0.426*** (0.124)	0.368*** (0.089)
Taxes		-0.420*** (0.038)		-1.606 (1.241)	-1.433 (1.182)	-1.717 (1.209)
Subsidies		0.003*** (0.000)		0.475 (0.593)	0.421 (0.581)	0.600 (0.581)
Political instability index		1.445 (2.821)		-7.596*** (2.520)	-4.011 (2.495)	-5.734** (2.569)
Research & development					2.237 (6.725)
Temperatures						-1.752 (1.738)
Rainfalls						0.023 (0.051)
Country fixed effects	Yes	Yes	Yes	Yes	Yes	Yes
Observations	3,914	1,511	2,654	455	371	424
Countries	162	111	161	61	54	56
Cragg-Donald Wald F- statistic			44.65	12.90	8.43	15.73
Sargan statistic			1.285	0.045	0.021	0.025
p-value of Sargan statistic			0.257	0.831	0.885	0.874
Hausman statistic			123.74	16.03	8.20	15.83
p-value of Hausman statistic			0.000	0.000	0.004	0.000

Table 2:

Determinants of TFP, Baseline Results Dependent Variable = 100×ln(TFP)

	OLS		2SLS
	(1)	(2)	(3)	(4)	(5)	(6)
Imported inputs/Total inputs × 100	0.101 (0.246)	-0.048 (0.371)	8.863*** (1.093)	4.399*** (1.290)	4.023** (1.677)	3.995*** (1.114)
Controls
Fertilizer & pesticides		0.465* (0.274)		4.122*** (1.304)	4.590*** (1.344)	3.924*** (1.281)
Capital-labor ratio		-0.020 (0.165)		0.344*** (0.096)	0.426*** (0.124)	0.368*** (0.089)
Taxes		-0.420*** (0.038)		-1.606 (1.241)	-1.433 (1.182)	-1.717 (1.209)
Subsidies		0.003*** (0.000)		0.475 (0.593)	0.421 (0.581)	0.600 (0.581)
Political instability index		1.445 (2.821)		-7.596*** (2.520)	-4.011 (2.495)	-5.734** (2.569)
Research & development					2.237 (6.725)
Temperatures						-1.752 (1.738)
Rainfalls						0.023 (0.051)
Country fixed effects	Yes	Yes	Yes	Yes	Yes	Yes
Observations	3,914	1,511	2,654	455	371	424
Countries	162	111	161	61	54	56
Cragg-Donald Wald F- statistic			44.65	12.90	8.43	15.73
Sargan statistic			1.285	0.045	0.021	0.025
p-value of Sargan statistic			0.257	0.831	0.885	0.874
Hausman statistic			123.74	16.03	8.20	15.83
p-value of Hausman statistic			0.000	0.000	0.004	0.000

The last four columns in Table 2 show results from 2SLS. Column (3) employs the imported inputs-to-total inputs ratio as the only explanatory variables and shows that a one percentage point increase in the share of imported inputs raises TFP by 8.9 percent. Columns (4)-(6) introduce additional control variables. Column (4) includes the same set of regressors as for column (2). All of the additionally introduced variables have expected signs.¹⁷ After controlling for these, the point estimate for the effect of imported inputs becomes 4.4. Column (4) is our preferred specification because the first-stage F-statistic is great enough and the Sargan test suggests that the exclusion restriction is satisfied.

Column (5) adds the expenditure on R&D. This is potentially an important variable in explaining agricultural TFP. However, this variable includes many missing observations, which reduces our sample size from 455 to 371. Moreover, the first-stage F-statistic becomes smaller. Column (6) introduces climate variables – the level of average temperature in degree Celsius and the level of average monthly rainfall – in order to control for climatic conditions. Temperature and rainfall are expected to have negative and positive signs, respectively, as document in the previous literature (e.g., Barrios et al., 2010; Dell et al., 2012). The result shows that we have expected signs but these are not statistically significant.¹⁸ Overall, the results suggest that a one percentage point increase in the share of imported inputs raises agricultural TFP by about 4 percent.

Table 3 conducts several robustness checks to show that our baseline results are robust. Columns (1) and (2) employ natural log of agricultural value-added and natural log of TFPb as the dependent variables, respectively, using our baseline specification, column (4) of Table 2.¹⁹ We use these dependent variables in order to show that our baseline results do not come from particular assumptions we make to estimate TFP. Indeed, results remain qualitatively the same. A one percentage point increase in the share of imported inputs raises value-added by 5.4 percent and TFPb by 3.9 percent.

Table 3:

Determinants of TFP, Robustness Checks Dependent Variable = 100×ln(TFP) or 100×ln(Value-Added), or 100×ln(TFP_b)

Notes: The first two columns use the baseline specification presented in column (4) of Table 2. The definition of high-income countries follows the World Bank. Oil producers are countries where their oil rents as a share of GDP is greater than the 90^th percentile of the sample in 1990 (16 percent). The period of commodity price hikes are defined as years when the food price index in December of that year is greater than 12 percent of the price index in December in the previous year. The excluded years as the period of commodity price hikes are 1991, 1994, 2002, 2005, 2006, 2009, and 2010. Instruments include weighted average tariffs on all products and the share of inward FDI to the agricultural sector to the agricultural value-added. In addition to these instruments, the real effective exchange rate is added as an instrument in column (6). All regressions include country fixed effects. Standard errors are in parentheses. ***, **, and * indicate the statistical significance at the 1%, 5%, and 10% level, respectively. See the main text for data sources.

Table 3:

Determinants of TFP, Robustness Checks Dependent Variable = 100×ln(TFP) or 100×ln(Value-Added), or 100×ln(TFP_b)

	Baseline specification & baseline sample		Excluding high income countries	Excluding oil producers	Excluding the period of commodity price hikes	Adding the effective exchange rate as an instrument
Dependent variable	Value-added	TFPb	TFP
	(1)	(2)	(3)	(4)	(5)	(6)
Imported inputs/Total inputs × 100	5.408*** (1.334)	3.871*** (1.115)	4.178*** (1.447)	4.258*** (1.330)	4.006*** (1.526)	4.116** (1.627)
Controls
Fertilizer & pesticides	4.587*** (1.348)	3.211*** (1.127)	3.652** (1.693)	3.653*** (1.352)	5.625*** (2.125)	4.396** (2.070)
Capital-labor ratio	-0.012 (0.099)	-0.026 (0.083)	-1.819*** (0.693)	0.292*** (0.096)	0.534*** (0.154)	0.266** (0.105)
Taxes	-1.824 (1.283)	-1.204 (1.073)	-1.189 (1.325)	-1.565 (1.267)	-8.877 (8.146)	-14.75* (8.195)
Subsidies	0.388 (0.613)	-0.244 (0.513)	-1.064 (1.576)	0.777 (0.659)	0.198 (0.738)	2.18 (1.335)
Political instability index	-6.801*** (2.605)	-4.538** (2.178)	-7.026* (3.874)	-9.151*** (2.886)	-6.284* (3.474)	-8.046** (3.733)
Country fixed effects	Yes	Yes	Yes	Yes	Yes	Yes
Observations	455	455	236	385	246	181
Countries	61	61	35	52	55	50
Cragg-Donald Wald F- statistic	7.45	11.40	7.78	5.95	12.90	12.90
Sargan statistic	0.073	0.009	0.008	3.997	0.142	0.007
p-value of Sargan statistic	0.787	0.924	0.930	0.136	0.707	0.936
Hausman statistic	17.65	15.07	9.72	6.19	29.19	14.82
p-value of Hausman statistic	0.000	0.000	0.002	0.013	0.000	0.000

Table 3:

Determinants of TFP, Robustness Checks Dependent Variable = 100×ln(TFP) or 100×ln(Value-Added), or 100×ln(TFP_b)

	Baseline specification & baseline sample		Excluding high income countries	Excluding oil producers	Excluding the period of commodity price hikes	Adding the effective exchange rate as an instrument
Dependent variable	Value-added	TFPb	TFP
	(1)	(2)	(3)	(4)	(5)	(6)
Imported inputs/Total inputs × 100	5.408*** (1.334)	3.871*** (1.115)	4.178*** (1.447)	4.258*** (1.330)	4.006*** (1.526)	4.116** (1.627)
Controls
Fertilizer & pesticides	4.587*** (1.348)	3.211*** (1.127)	3.652** (1.693)	3.653*** (1.352)	5.625*** (2.125)	4.396** (2.070)
Capital-labor ratio	-0.012 (0.099)	-0.026 (0.083)	-1.819*** (0.693)	0.292*** (0.096)	0.534*** (0.154)	0.266** (0.105)
Taxes	-1.824 (1.283)	-1.204 (1.073)	-1.189 (1.325)	-1.565 (1.267)	-8.877 (8.146)	-14.75* (8.195)
Subsidies	0.388 (0.613)	-0.244 (0.513)	-1.064 (1.576)	0.777 (0.659)	0.198 (0.738)	2.18 (1.335)
Political instability index	-6.801*** (2.605)	-4.538** (2.178)	-7.026* (3.874)	-9.151*** (2.886)	-6.284* (3.474)	-8.046** (3.733)
Country fixed effects	Yes	Yes	Yes	Yes	Yes	Yes
Observations	455	455	236	385	246	181
Countries	61	61	35	52	55	50
Cragg-Donald Wald F- statistic	7.45	11.40	7.78	5.95	12.90	12.90
Sargan statistic	0.073	0.009	0.008	3.997	0.142	0.007
p-value of Sargan statistic	0.787	0.924	0.930	0.136	0.707	0.936
Hausman statistic	17.65	15.07	9.72	6.19	29.19	14.82
p-value of Hausman statistic	0.000	0.000	0.002	0.013	0.000	0.000

Column (3) excludes observations from high-income countries because one may argue that these countries are different from other lower income countries in terms of the way they produce agricultural goods. However, excluding these countries does not change our results much. Column (4) drops oil producers.²⁰ However, again, the results are similar to those reported in Table 2. We drop periods of commodity price increases in column (5) because an exceptional increase in commodity prices may increase the value of agricultural output and therefore value-added and TFP. However, the result in column (5) is similar to those in other columns.

Lastly, one may claim that the real effective exchange rate can also be used as instruments because changes in real exchange rates alter the relative prices of imported inputs to domestic inputs, affecting countries’ decitions to import intermediate inputs. Therefore, column (6) adds the real effective exchange rate as an additional instrument. However, results do not change qualitatively.

We compare our results with previous empirical findings. Halpern et al. (2015), Topalova and Khaldelwal (2011), and Amiti and Konings (2007) find that a 10 percent decrease in input tariffs raises TFP by 1.2–1.5 percent, 4.8 percent, and 12 percent, respectively. ²¹ In order to compare with these figures, we combine our first-stage and second-stage results. The first-stage regressions indicate that a 10 percentage point decline in tariffs increases the share of imported inputs to total inputs, $\frac{I m p o r l e d i n p u l s}{T o l a l i n p u l s}$ , by 3 percentage points. The second-stage results show that a 1 percentage point increase in $\frac{I m p o r l e d i n p u l s}{T o l a l i n p u l s}$ raises TFP by 4 percent. Combining these implies that a 10 percentage point decrease in tariffs is associated with a 12 percent increase in the level of TFP. This number is almost the same as Amiti and Konings (2007)’s result.

B. Weather Shocks and Agricultural TFP Growth

The second key determinant of agricultural TFP is weather shocks, i.e., temperatures and rainfalls. Agricultural sectors are known to be more sensitively affected by weather shocks and climate change (Mendelsohn et al., 2001; and Mendelsohn et al., 2006). Moreover, previous studies find that countries’ responses to weather shocks vary substantially depending upon income levels of countries (e.g., Dell et al., 2012; Cattaneo and Peri, 2016). Guided by these, this section seeks to understand if there are similar cross-country differences in the impacts of weather shocks on agricultural TFP.

We closely follow the literature to setup our regression model. Previous studies investigate the impact of weather shocks on the GDP growth rate by implicitely assuming that weather shocks affect the current level of GDP by changing its growth path from the previous year (Dell et al., 2012; Hsiang and Jina, 2014; Moore and Diaz, 2015; IMF, 2017).²² We assume that a similar argument applies in the context of agricultyral TFP. Therefore, our baseline regression model is:²³

\begin{matrix} g_{i, t}^{T F P} = γ_{0} + γ_{1} d . T e m p_{i, t} + γ_{1}^{L o w} [d . T e m p_{i, t} D_{i}^{L o w}] + γ_{1}^{M i d d l e} [d . T e m p_{i, t} D_{i}^{M i d d l e}] + γ_{2} d . R a i n_{i, t} + γ_{2}^{L o w} [d . R a i n_{i, t} D_{i}^{L o w}] + γ_{2}^{M i d d l e} [d . R a i n_{i, t} D_{i}^{M i d d l e}] + D_{i}^{L o w} θ_{t} + D_{i}^{M i d d l e} θ_{t} + ε_{i, t}, & (3) \end{matrix}

where $g_{i, t}^{T F P} = 100 \times (T F P_{i, t} - T F P_{i, t - 1}) / T F P_{i, t - 1}$ denotes the annual growth rate of TFP of country i in year t; d. Temp_i,t = Temp_i,t — Temp_i,t-1 is the annual change in average temperatures in degree Celsius; d. Rain_i,t = Rain_i,t — Rain_t-1 is the annual change in average monthly rainfalls in 100 mm²⁴; $D_{i}^{L o w} a n d D_{i}^{M i d d l e}$ are dummy variables taking unity if country i is a LIC and a middle-income country, respectively; θ_t and ε_i,t denote year fixed effects and an error term, respectively; $γ_{0}, γ_{1}^{L o w}, γ_{1}^{M i d d l e}, γ_{2}, γ_{2}^{L o w}, a n d γ_{2}^{M i d d l e}$ are coefficients to be estimated.

Climate variables, d. Temp_i,t and d. Rain_i,t, are interacted with income-level dummies in order to capture heterogeneous responses to weather shocks across the three groups of countries – low-income countries, middle-income countries, and high-income countries. With these dummies and all observations from the world, coefficients γ₁ and γ₂ measure the impact of weather shocks on TFP in high-income countries. $γ_{1}^{L o w} a n d γ_{1}^{M i d d l e}$ capture the difference in the impact of changes in temperatures, comparing with high-income countries, on TFP in LICs and middle-income countries, respectively. The overall impact of changes in temperatures on LICs, for example, is a linear combination of two coefficients: $γ_{1} + γ_{1}^{L o w}$ .²⁵

Table 4 summarizes results from estimating equation (2). Column (1) regresses TFP growth rate on dTemp only, assuming that all countries respond to weather shocks in the same way. The estimated coefficient is negative, -0.6, as expected, but it is not statistically significant. This is because the model does not allow different responses to weather shocks across countries. As a result, positive responses and negative responses worked in difference directions, resulting in an insignificant coefficient.

Table 4:

The Impact of Weather Shocks, Baseline Results Dependent Variable = 100 times Annual Agricultural TFP Growth Rate

Notes: All regressions include income-level dummies interacted with year fixed effects. Robust standard errors, clustered in two ways, at the country-level and the region-level, are in parentheses. Country classifications are based on the World Bank’s classification. Hot countries are defined as countries having above median average temperature in 1990. Agricultural countries are defined as those having a share of agricultural value-added to GDP above the 75th percentile in 1990. Temperatures are in degrees Celsius and rainfalls are in units of 100 mm per month. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level, respectively. See the main text for data sources.

Table 4:

The Impact of Weather Shocks, Baseline Results Dependent Variable = 100 times Annual Agricultural TFP Growth Rate

	(1)	(2)	(3)	(4)	(5)
d.Temperature	-0.606 (0.447)	-0.215 (0.614)	-0.080 (0.426)	-0.290 (0.232)	-0.080 (0.428)
Low-income country dummy × d.Temperature		-2.482** (1.121)	-2.340** (0.967)	-3.073*** (1.107)	-3.601*** (0.970)
Middle-income country dummy × d.Temperature		-0.404 (0.296)	-0.451 (0.390)	-0.608 (0.790)	-0.650 (0.543)
Hot country dummy × d.Temperature				1.619* (0.848)
Agricultural country dummy × d.Temperature					1.742 (1.464)
d.Rainfalls			-2.069 (7.648)	2.051 (5.846)	-2.069 (7.680)
Low-income country dummy × d.Rainfalls			7.919 (9.131)	9.074 (9.602)	8.494 (9.156)
Middle-income country dummy × d.Rainfalls			3.324 (7.957)	6.163 (9.483)	3.390 (7.988)
Hot country dummy × d.Rainfalls				-7.839* (4.681)
Agricultural country dummy × d.Rainfalls					-0.930 (2.583)
Observations	3,266	3,266	3,242	3,242	3,242
Countries	141	141	141	141	141
R-squared	0.011	0.012	0.013	0.029	0.016
Linear combination of coefficients, Temperature effects
Low-income countries		-2.697*** (0.666)	-2.419*** (0.661)	-3.363*** (0.916)	-3.680*** (0.868)
Middle-income countries		-0.618 (0.633)	-0.530 (0.651)	-0.898 (0.693)	-0.730 (0.771)
Linear combination of coefficients, Rainfall effects
Low-income countries			5.850** (3.385)	11.12** (5.077)	6.425 (5.327)
Middle-income countries			1.254 (1.357)	8.213 (5.220)	1.321 (1.595)

Table 4:

The Impact of Weather Shocks, Baseline Results Dependent Variable = 100 times Annual Agricultural TFP Growth Rate

	(1)	(2)	(3)	(4)	(5)
d.Temperature	-0.606 (0.447)	-0.215 (0.614)	-0.080 (0.426)	-0.290 (0.232)	-0.080 (0.428)
Low-income country dummy × d.Temperature		-2.482** (1.121)	-2.340** (0.967)	-3.073*** (1.107)	-3.601*** (0.970)
Middle-income country dummy × d.Temperature		-0.404 (0.296)	-0.451 (0.390)	-0.608 (0.790)	-0.650 (0.543)
Hot country dummy × d.Temperature				1.619* (0.848)
Agricultural country dummy × d.Temperature					1.742 (1.464)
d.Rainfalls			-2.069 (7.648)	2.051 (5.846)	-2.069 (7.680)
Low-income country dummy × d.Rainfalls			7.919 (9.131)	9.074 (9.602)	8.494 (9.156)
Middle-income country dummy × d.Rainfalls			3.324 (7.957)	6.163 (9.483)	3.390 (7.988)
Hot country dummy × d.Rainfalls				-7.839* (4.681)
Agricultural country dummy × d.Rainfalls					-0.930 (2.583)
Observations	3,266	3,266	3,242	3,242	3,242
Countries	141	141	141	141	141
R-squared	0.011	0.012	0.013	0.029	0.016
Linear combination of coefficients, Temperature effects
Low-income countries		-2.697*** (0.666)	-2.419*** (0.661)	-3.363*** (0.916)	-3.680*** (0.868)
Middle-income countries		-0.618 (0.633)	-0.530 (0.651)	-0.898 (0.693)	-0.730 (0.771)
Linear combination of coefficients, Rainfall effects
Low-income countries			5.850** (3.385)	11.12** (5.077)	6.425 (5.327)
Middle-income countries			1.254 (1.357)	8.213 (5.220)	1.321 (1.595)

Column (2) introduces interaction terms with income-level dummies. Linear combinations of coefficients reported in the bottom of the table show that a 1°C rise in temperatures reduces the TFP growth rate by 2.7 percent in LICs. Middle-income countries also have a negative coefficient, but the magnitude is small and statistically insignificant. These negative temperature effects in LICs are consistent with previous empirical results. For example, Dell et al. (2012) show that rising temperatures had reduced the GDP growth rate of LICs. Cattaneo and Peri (2016) find that an increase in temperatures increased emigration from middle-income countries, possibly because agriculture productivity declined due to higher temperatures, which led to a greater incentive to emigrate from the countries.

The significant weather effects are presumably because LICs employ agricultural technologies that are more sensitive to climatic conditions, in the sense that they use less machinery capital, fertilizers, and are less able to hedge against commodity price risk compared to richer countries. Mendelsohn et al. (2001) and Mendelsohn et al. (2006) argue that economic development reduces vulnerability of agricultural production to climatic changes. Another possible explanation is irrigation. Previous articles find that irrigated farms are less sensitive to weather shocks (e.g., Wang et al., 2009; Kurukulasuriya et al., 2006). LICs may have less irrigation, which possibly led to a sensitive reaction to weather shocks.

One may claim that higher temperatures negatively affect LICs just because they are located in hot areas such as Sub-Saharan Africa. In order to control for the level of temperatures, by following Dell et al. (2012), we introduce interaction terms between climate variables and a dummy variable taking unity if the country is a “hot country”. Hot countries are defined as those having above median average temperature in the start year of the sample (1991). Column (4) indicates that adding the interaction terms does not change our baseline result qualitatively.

The next concern comes from the level of importance of agriculture in each country. The significant climate effects in LICs may be just because those countries are more agricultural-based than other countries. In order to examine if that is the case, we introduce interaction terms with a dummy variable taking unity if the share of value-added from the agricultural sector in GDP is greater than the 75th percentile of the sample in 1990.²⁶ The last column shows that adding the interaction terms does not change our baseline results much.

Next, we show that our results are robust to a wide range of different samples and specifications. Table 5 addresses various concerns that might affect our conclusion. The first two columns show results from estimating the baseline model by replacing the dependent variable with the agricultural value-added growth and the TFPb growth rate as in the previous section. Although the coefficients change slightly, we obtain essentially the same results.

Table 5:

The Impact of Weather Shocks, Robustness Checks Dependent Variable = 100 times Annual Agricultural TFP Growth Rate or 100 times Annual Agricultural Value-Added Growth Rate

Notes: All regressions include income-level dummies interacted with year fixed effects. Robust standard errors, clustered in two ways, at the country-level and the region-level, are in parentheses. Temperatures are in degrees Celsius and rainfalls are in units of 100 mm per month. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level, respectively. See the main text for data sources.

Table 5:

The Impact of Weather Shocks, Robustness Checks Dependent Variable = 100 times Annual Agricultural TFP Growth Rate or 100 times Annual Agricultural Value-Added Growth Rate

	Baseline specification & baseline sample		Excluding oil producers	Excluding the period of commodity price hikes	Income-level groups based on percentiles	Controlloing for other determinants of TFP
Dependent variable	Value-added growth rate	TFP_b growth rate	TFP growth rate
	(1)	(2)	(3)	(4)	(5)	(6)
d.Temp.	0.290 (0.239)	0.231 (0.183)	0.073 (0.415)	0.012 (0.425)	0.498 (0.487)	-0.423 (0.379)
Low-income country dummy × d.Temp.	-2.876*** (1.092)	-2.519*** (0.673)	-1.567* (0.951)	-1.744** (0.695)	-2.375*** (0.428)	-2.378*** (0.604)
Middle-income country dummy × d.Temp.	-0.992** (0.473)	-0.489 (0.482)	-0.965*** (0.320)	-0.637* (0.353)	-1.217** (0.598)	-0.884 (1.661)
d.Rainfalls	2.856* (1.617)	2.210 (2.176)	-5.792 (7.315)	-4.479 (8.954)	-6.107 (7.538)	4.355*** (1.570)
Low-income country dummy × d.Rainfalls	3.235 (2.201)	3.152 (3.396)	11.70 (7.462)	10.79 (10.140)	10.45 (7.893)	9.867 (6.912)
Middle-income country dummy × d.Rainfalls	-2.037** (1.028)	-0.837 (2.005)	6.552 (7.614)	6.151 (9.207)	8.087 (7.442)	-4.732** (2.045)
Observations	4,066	3,410	2,661	2,423	3,242	1,382
Countries	158	147	141	141	141	61
R-squared	0.025	0.023	0.017	0.012	0.013	0.038
Linear combination of coefficients, Temperature effects
Low-income countries	-2.586** (1.044)	-2.288*** (0.636)	-1.494* (0.842)	-1.732*** (0.427)	-1.877*** (0.383)	-2.801*** (0.473)
Middle-income countries	-0.702 (0.521)	-0.258 (0.551)	-0.892 (0.489)	-0.625 (0.267)	-0.719 (0.732)	-1.307 (1.854)
Linear combination of coefficients, Rainfall effects
Low-income countries	6.092** (2.648)	5.361** (2.722)	5.907*** (1.302)	6.311*** (3.377)	4.345** (1.769)	14.222** (6.739)
Middle-income countries	0.820 (1.014)	1.373 (1.125)	0.760 (1.300)	1.672 (1.805)	1.980 (1.700)	-0.377 (1.312)

Notes: All regressions include income-level dummies interacted with year fixed effects. Robust standard errors, clustered in two ways, at the country-level and the region-level, are in parentheses. Temperatures are in degrees Celsius and rainfalls are in units of 100 mm per month. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level, respectively. See the main text for data sources.

Table 5:

The Impact of Weather Shocks, Robustness Checks Dependent Variable = 100 times Annual Agricultural TFP Growth Rate or 100 times Annual Agricultural Value-Added Growth Rate

	Baseline specification & baseline sample		Excluding oil producers	Excluding the period of commodity price hikes	Income-level groups based on percentiles	Controlloing for other determinants of TFP
Dependent variable	Value-added growth rate	TFP_b growth rate	TFP growth rate
	(1)	(2)	(3)	(4)	(5)	(6)
d.Temp.	0.290 (0.239)	0.231 (0.183)	0.073 (0.415)	0.012 (0.425)	0.498 (0.487)	-0.423 (0.379)
Low-income country dummy × d.Temp.	-2.876*** (1.092)	-2.519*** (0.673)	-1.567* (0.951)	-1.744** (0.695)	-2.375*** (0.428)	-2.378*** (0.604)
Middle-income country dummy × d.Temp.	-0.992** (0.473)	-0.489 (0.482)	-0.965*** (0.320)	-0.637* (0.353)	-1.217** (0.598)	-0.884 (1.661)
d.Rainfalls	2.856* (1.617)	2.210 (2.176)	-5.792 (7.315)	-4.479 (8.954)	-6.107 (7.538)	4.355*** (1.570)
Low-income country dummy × d.Rainfalls	3.235 (2.201)	3.152 (3.396)	11.70 (7.462)	10.79 (10.140)	10.45 (7.893)	9.867 (6.912)
Middle-income country dummy × d.Rainfalls	-2.037** (1.028)	-0.837 (2.005)	6.552 (7.614)	6.151 (9.207)	8.087 (7.442)	-4.732** (2.045)
Observations	4,066	3,410	2,661	2,423	3,242	1,382
Countries	158	147	141	141	141	61
R-squared	0.025	0.023	0.017	0.012	0.013	0.038
Linear combination of coefficients, Temperature effects
Low-income countries	-2.586** (1.044)	-2.288*** (0.636)	-1.494* (0.842)	-1.732*** (0.427)	-1.877*** (0.383)	-2.801*** (0.473)
Middle-income countries	-0.702 (0.521)	-0.258 (0.551)	-0.892 (0.489)	-0.625 (0.267)	-0.719 (0.732)	-1.307 (1.854)
Linear combination of coefficients, Rainfall effects
Low-income countries	6.092** (2.648)	5.361** (2.722)	5.907*** (1.302)	6.311*** (3.377)	4.345** (1.769)	14.222** (6.739)
Middle-income countries	0.820 (1.014)	1.373 (1.125)	0.760 (1.300)	1.672 (1.805)	1.980 (1.700)	-0.377 (1.312)

Notes: All regressions include income-level dummies interacted with year fixed effects. Robust standard errors, clustered in two ways, at the country-level and the region-level, are in parentheses. Temperatures are in degrees Celsius and rainfalls are in units of 100 mm per month. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level, respectively. See the main text for data sources.

Column (3) reports a result from estimating the baseline model with excluding countries with greater share of oil production. Column (4) excludes all samples from commodity price hikes. Column (5) employs different income-level classification – the baseline specification uses the income-level classification from the World Bank while column (3) uses our own definitions based on income-level percentiles from 1995.²⁷ Column (6) adds explanatory variables from Table 2 to control for other possible determinants of TFP.²⁸ Overall, Table 5 shows that our results are robust.

C. Importing Inputs Mitigates the Negative Weather Effects: Theory

The previous sections consider the impact of imported inputs and weather shocks individually, by closely following regression models from the literature. We further investigate interactions between these two factors in explaining agricultural TFP. This section presents a simple theoretical model helps clarify how imported inputs and weather shocks interact to affect TFP.

We start from the agricultural production function in Section II:

Y_{i t} = A_{i t} {(K_{i t})}^{α_{i t}^{K}} {(L_{i t})}^{α_{i t}^{L}} {(T_{i t})}^{α_{i t}^{T}},

where agricultural TFP, A_it, is now described as a function of local temperatures Temp_it, local rainfalls Rain_it, and quality of intermediate inputs ϕ_it:²⁹

A_{i t} = A (T e m p_{i t}, R a i n_{i t}, ϕ_{i t}) .

The overall quality of intermediate inputs ϕ_it is a weighted average of quality of domestic inputs $ϕ_{i t}^{D}$ and that of imported inputs $ϕ_{i t}^{Im}$ :

ϕ_{i t} = ϑ_{i t}^{D} ϕ_{i t}^{D} + ϑ_{i t}^{Im} ϕ_{i t}^{Im},

where the weights are the share of domestic inputs to the total value of inputs, $ϑ_{i t}^{D} = I_{i t}^{D} / (I_{i t}^{D} + I_{i t}^{I m}) a n d θ_{i t}^{I m} = I_{i t}^{I m} / (I_{i t}^{D} + I_{i t}^{I m})$ is the share of imported inputs.

We argue that a higher share of imported inputs reduces TFP’s sensitivity to weather shocks. In other words, because higher temperatures reduce TFP, ∂A_it/∂Temp_it < 0, and rainfalls increase TFP, ∂A_it/∂Temp_it > 0, we have $\frac{\partial^{2} A_{i t}}{\partial T e m p_{i t} \partial θ_{i t}^{I m}} > 0 a n d \frac{\partial^{2} A_{i t}}{\partial R a i n_{i t} \partial ϑ_{i t}^{I m}} < 0$ . Although the directions of the effects are opposite between the two weather shocks, the exact same discussions apply to these two. Therefore, this section focuses on the effect of temperature shocks only.

The effect of rising temperatures on agricultural TFP is obtained by differentiating TFP A_it with respect to Temp_it:

\frac{\partial A_{i t}}{\partial T e m p_{i t}} = \frac{\partial A}{\partial T e m p_{i t}} + \frac{\partial A}{\partial ϕ_{i t}} (1 - ϑ_{i t}^{I m}) \frac{\partial ϕ_{i t}^{D}}{\partial T e m p_{i t}} + \frac{\partial A}{\partial ϕ_{i t}} ϑ_{i t}^{I m} \frac{\partial ϕ_{i t}^{M}}{\partial T e m p_{i t}},

where we plugged $ϑ_{i t}^{D} = 1 - ϑ_{i t}^{I m}$ . The first term is the direct effect of rising temperatures on agricultural TFP; the second term indicates the indirect effect through the quality domestic inputs; and the third term is the indirect effect through the quality of imported inputs. Assuming that local temperature shocks do not affect quality of imported inputs, $\partial ϕ_{i t}^{M} / \partial T e m p_{i t} = 0$ , the previous equation becomes:

\frac{\partial A_{i t}}{\partial T e m p_{i t}} = \frac{\partial A}{\partial T e m p_{i t}} + \frac{\partial A}{\partial ϕ_{i t}} (1 - ϑ_{i t}^{I m}) \frac{\partial ϕ_{i t}^{D}}{\partial T e m p_{i t}} .

By differentiating this equation with respect to $ϑ_{i t}^{I m}$ , we obtain

\frac{\partial^{2} A_{i t}}{\partial T e m p_{i t} \partial θ_{i t}^{I m}} = \frac{\partial^{2} A}{\underset{D i r e c t p r o d u c t i v i t y e f f e c t}{\underset{︸}{\partial T e m p_{i t} \partial θ_{i t}^{I m}}}} + (\underset{D i v e r s i f i c a t i o n e f f e c t}{\underset{︸}{- \frac{\partial A}{\partial ϕ_{i t}} \frac{\partial ϕ_{i t}^{D}}{\partial T e m p_{i t}})}} + \underset{S y n \arg i e s b e t w e e n d o m e s t i c a n d i m p o r t e d i n p u t s}{\underset{︸}{\frac{\partial A}{\partial ϕ_{i t}} (1 - θ_{i t}^{I m}) \frac{\partial^{2} ϕ_{i t}^{D}}{\partial T e m p_{i t} \partial ϑ_{i t}^{I m}}}} .

where we assume $\partial^{2} A / (\partial ϕ_{i t} \partial θ_{i t}^{I m}) = 0$ .³⁰ Because higher temperatures reduce agricultural TFP, ∂A_it/∂Temp_it < 0, and a greater share of imported inputs reduces the negative temperature effects, we argue $\partial^{2} A_{i t} / (\partial T e m p_{i t} \partial θ_{i t}^{I m}) > 0$ .

This positive cross derivative comes from three effects. First, a greater share of imported inputs directly reduces the negative temperature effects, $\partial^{2} A / (\partial T e m p_{i t} \partial θ_{i t}^{I m}) > 0$ . Better production technologies embedded in imported inputs increase productivity, making agricultural production technology less sensitive to weather shocks. As shown in Section IV, a greater share of imported inputs increases agricultural TFP. Although we do not examine the direct effect on the climate sensitivity, we suppose a greater TFP makes agricultural production less sensitive to weather shocks. We refer to this effect as the direct productivity effect.

Second, a greater share of imported inputs increases the share of inputs that are not affected by local temperature shocks. As a result, this de-localization of inputs reduces the sensitivity of agricultural TFP to weather shocks, reflected in the second term: $- \frac{\partial A}{\partial ϕ_{i t}} \frac{\partial ϕ_{i t}^{D}}{\partial T e m p_{i t}}$ , which is positive because $\partial ϕ_{i t}^{D} / \partial T e m p_{i t} < 0$ . This is the same mechanism as Caselli et al. (2015), showing that a country can reduce exposure to domestic shocks therefore income volatility by diversifying source countries of imports. Their analyses include all macroeconomic shocks but there must be similar mechanisms in the context of weather shocks. We call this second channel the diversification effect.

Third, the last term of the previous equation is positive if $\partial^{2} ϕ_{i t}^{D} / (\partial T e m p_{i t} \partial ϑ_{i t}^{I m}) > 0$ because $\frac{\partial A}{\partial ϕ_{i t}} (1 - ϑ_{i t}^{I m}) > 0$ . This captures synergies between domestic inputs and imported inputs. A local final good producer is an intermediate good provider for other local final good producers. Therefore, increased productivity of domestic intermediate good producers raises productivity of domestic final good producers, making them less sensitive to weather shocks.³¹ We refer to this as synergies between imported inputs and domestic inputs.

D. Importing Inputs Mitigates the Negative Weather Effects: Evidence

We have clarified the channels a higher share of imported inputs makes countries less sensitive to weather shocks. This section investigates if imported inputs have such effects by only using observations from LICs where we find significant effects of weather shocks.

In order to test the theoretical possibilities, we estimate the following equation:

\begin{matrix} g_{i, t}^{T F P} = π_{0} + π_{1} d . T e m p_{i t} + π_{1}^{L o w I m} [d . T e m p_{i, t} D_{i}^{L o w I m}] + π_{2} d . R a i n_{i, t} + π_{2}^{L o w I m} [d . R a i n_{i, t} D_{i}^{L o w}] + D_{i}^{L o w I m} + D_{i}^{L o w I M} θ_{t} + u_{i, t} & (4) \end{matrix}

where $g_{i, t}^{T F P}$ , d.Tempi_i,t, and d. Rairi_i,t follow the same definitions as for equation (3). u_i,t denotes an error term. $D_{i}^{L o w I m}$ is a dummy variable taking unity if country i’s imported inputs-to-total inputs share is less than the 50^th percentile of LICs in the start year of the sample (1991). We use the data from 1991 to construct $D_{i}^{L o w I m}$ in order to deal with possible endogenous changes in the share of imported inputs due to weather shocks. Interaction terms between $D_{i}^{L o w I M}$ and year dummies θ_t are also introduced. $π_{0}, π_{1}^{L o w I m}, π_{2}, a n d π_{2}^{L o w I m}$ are coefficients to be estimated.

Because we use a sample from LICs only and introduce the interaction term, $d . T e m p_{i, t} D_{i}^{L o w I m}$ , the coefficient π₁ measures the temperature effect in LICs with higher share of imported inputs. $π_{1}^{L o w I m}$ captures “the temperature effect for countries with lower shares of imported inputs” minus “that for those with higher share of imported inputs”. As a result, a linear combination of coefficients, $π_{1} + π_{1}^{L o w I m}$ , is the temperature effect for LICs with lower shares of imported inputs. A similar interpretation applies to the rainfall variables.

Table 6 presents estimation results. Column (1) shows that a 1℃ increase in average temperatures reduces the TFP growth rate by 4.3 percent in countries with lower shares of imported inputs (see the linear combination of coefficients in the bottom of the table). Moreover, a 100 mm increase in monthly rainfalls increases the TFP growth rate by 13.6 percent. The results also suggest that weather shocks have no significant effects on countries with higher share of imported inputs even though all countries in the sample are from LICs.

Table 6:

Weather Shocks and Imported Inputs, LICs

Notes: All regressions include country dummies interacted with year dummies and use observations from LICs only. Robust standard errors, clustered at the country-level, are in parentheses. Temperatures are in degrees Celsius and rainfalls are in units of 100 mm per month. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level, respectively. See the main text for data sources.

Table 6:

Weather Shocks and Imported Inputs, LICs

	Baseline specification & baseline sample			Excluding oil producers	Excluding the period of commodity price hikes	Controlloing for other determinants of TFP
Dependent variable	TFP growth rate	Value-added growth rate	TFP_b growth rate	TFP growth rate
	(1)	(2)	(3)	(4)	(5)	(6)
d.Temp.	0.631 (0.849)	0.554 (0.639)	0.479 (0.755)	0.898 (0.612)	-0.525 (0.493)	0.618 (0.811)
Lower share of imported inputs × d.Temp.	-4.915*** (0.977)	-5.429*** (1.780)	-4.546*** (1.065)	-4.790*** (0.402)	-1.811** (0.817)	-4.963*** (0.971)
d.Rainfalls	1.593 (2.465)	3.646 (3.792)	1.578 (3.118)	0.748 (0.844)	1.198 (2.529)	1.580 (2.466)
Lower share of imported inputs × d.Rainfalls	11.96*** (4.563)	8.574* (4.905)	12.02** (4.689)	8.142* (4.576)	14.27*** (4.904)	11.99*** (4.587)
Lower share of imported inputs dummy	-0.377 (0.626)	0.356 (0.567)	-0.381 (0.567)	-0.051 (0.576)	-0.890* (0.482)	-0.245 (0.388)
Observations	557	621	557	498	415	557
Countries	24	24	24	21	24	24
R-squared	0.086	0.086	0.086	0.096	0.069	0.094
Linear combination of coefficients, Temperature effects
Lower share of imported inputs	-4.284*** (0.850)	-4.875*** (1.559)	-4.067*** (0.940)	-3.891*** (0.471)	-2.336*** (0.678)	-4.345*** (0.868)
Linear combination of coefficients, Rainfall effects
Lower share of imported inputs	13.56*** (3.943)	12.22*** (3.639)	13.60*** (3.751)	8.889* (4.802)	15.46*** (4.920)	13.57*** (4.001)

Table 6:

Weather Shocks and Imported Inputs, LICs

	Baseline specification & baseline sample			Excluding oil producers	Excluding the period of commodity price hikes	Controlloing for other determinants of TFP
Dependent variable	TFP growth rate	Value-added growth rate	TFP_b growth rate	TFP growth rate
	(1)	(2)	(3)	(4)	(5)	(6)
d.Temp.	0.631 (0.849)	0.554 (0.639)	0.479 (0.755)	0.898 (0.612)	-0.525 (0.493)	0.618 (0.811)
Lower share of imported inputs × d.Temp.	-4.915*** (0.977)	-5.429*** (1.780)	-4.546*** (1.065)	-4.790*** (0.402)	-1.811** (0.817)	-4.963*** (0.971)
d.Rainfalls	1.593 (2.465)	3.646 (3.792)	1.578 (3.118)	0.748 (0.844)	1.198 (2.529)	1.580 (2.466)
Lower share of imported inputs × d.Rainfalls	11.96*** (4.563)	8.574* (4.905)	12.02** (4.689)	8.142* (4.576)	14.27*** (4.904)	11.99*** (4.587)
Lower share of imported inputs dummy	-0.377 (0.626)	0.356 (0.567)	-0.381 (0.567)	-0.051 (0.576)	-0.890* (0.482)	-0.245 (0.388)
Observations	557	621	557	498	415	557
Countries	24	24	24	21	24	24
R-squared	0.086	0.086	0.086	0.096	0.069	0.094
Linear combination of coefficients, Temperature effects
Lower share of imported inputs	-4.284*** (0.850)	-4.875*** (1.559)	-4.067*** (0.940)	-3.891*** (0.471)	-2.336*** (0.678)	-4.345*** (0.868)
Linear combination of coefficients, Rainfall effects
Lower share of imported inputs	13.56*** (3.943)	12.22*** (3.639)	13.60*** (3.751)	8.889* (4.802)	15.46*** (4.920)	13.57*** (4.001)

Columns (2) and (3) use the same sample and the same explanatory variables as for column (1) but they use the value-added growth rate and the TFPb growth rate. respectively. Results are essentially the same as for column (1). Columns (4)-(6) use the same dependent variable as for column (1) but they employ different samples of observations or controlling for additional explanatory variables as we have done in the previous section.³² Again, results are robust.

One may claim that imported inputs actually do not mitigate weather shocks and the variable is just working as a proxy of something else. We consider three possibilities that our baseline results are spurious. First, it is possible that (Imported inputs)/(Total inputs) merely captures the countries’ openness to import. Because imports in general have pro-competitive effects and increase productivity, the results may just be capturing countries’ propensity to import from abroad, not the impact of imported inputs.

Second, possibly relatively richer countries within the LICs tend to use more imported inputs and these countries are less sensitive to weather shocks for some other reason. If that is the case, our baseline results could be coming from countries’ initial income levels, not the share of imported inputs. Third, a higher share of imported inputs may be related with countries’ initial technology levels and countries with better production technologies are possibly less vulnerable to weather shocks. If so, the results may just be showing different temperature effects stemming from countries’ differences in initial technology levels.

In order to examine if these concerns are valid, we estimate the following equation:

\begin{matrix} g_{i, t}^{T F P} = ρ_{1} d . T e m p_{i, t} + ρ_{1}^{L o w I m} [d . T e m p_{i, t} D_{i}^{L o w I m}] + ρ_{2} d . R a i n_{i, t} + ρ_{2}^{L o w I m} [d . R a i n_{i, t} D_{i}^{L o w I m}] + ρ_{1}^{L o w A g g I m} [d . T e m p_{i, t} D_{i}^{L o w A g g I m}] + ρ_{2}^{L o w A g g I m} [d . R a i n_{i, t} D_{i}^{L o w A g g I m}] + D_{i}^{L o w I m} + D_{i}^{L o w A g g I m} + ρ_{0} + {\tilde{u}}_{i, t}, & (4) \end{matrix}

where $D_{i}^{L o w A g g I m}$ denotes a dummy variable taking unity if the country’s aggregate imports-to- GDP ratio is less than the 50* percentile among LICs in 1991; $ρ_{0}, ρ_{1}, ρ_{1}^{L o w I m}, ρ_{1}^{L o w A g g Im}, ρ_{2}, ρ_{2}^{L o w I m}, a n d ρ_{2}^{L o w A g g Im}$ are parameters to be estimated; ũ_i,t indicates an error term.

Estimating equation (4) answers if the first story is the main cause of the baseline results. In order to examine if the second and third stories are true, we make a dummy variable taking unity if the country’s initial GDP per capita is less than the 50^th percentile among LICs, $D_{i}^{L o w G D P p c}$ , and a dummy variable taking unity if the country’s initial TFP level is less than the 50^thpercentile among the group of countries, $D_{i}^{L o w T F P}$ . Estimating equation (4) by replacing $D_{i}^{L o w A g g I m} w i t h D_{i}^{L o w G D P p c} (o r D_{i}^{L o w T F P})$ answers if the second (or the third) concern is valid or not. ³³ These dummy variables are constructed based on the data from the WDI and our TFP estimates.³⁴

Regression results are shown in Table 7. Columns (1) and (2) display results from estimating regressions controlling for the aggregate imports-to-GDP ratio. Column (1) introduces interaction terms with the aggregate imports-to-GDP ratio only and shows temperature effects are not statistically different across the two groups of countries – countries with higher aggregate imports-to-GDP ratio and those with lower ones. It also shows that the rainfall effects are greater for countries with lower aggregate imports-to-GDP ratio. Column (2) controls for both the imported inputs-to-total inputs ratio and the aggregate imports-to-GDP ratio. However, the effect of imported inputs remain significant. These results imply that our results are not coming from cross-country differences in propensity to import from abroad in general.

Table 7:

Weather Shocks and Imported Inputs, LICs, Robustness Checks Dependent Variable = 100 times Annual Agricultural TFP Growth Rate

Notes: The dependent variable is the TFP growth rate. All regressions include a constant term and interaction terms between year dummies and each of the dummy variables. It uses observations from LICs only. Robust standard errors, clustered at the country-level, are in parentheses. Temperatures are in degrees Celsius and rainfalls are in units of 100 mm per month. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level, respectively. See the main text for data sources.

Table 7:

Weather Shocks and Imported Inputs, LICs, Robustness Checks Dependent Variable = 100 times Annual Agricultural TFP Growth Rate

	Imports-to-GDP ratio		Initial income levels		Initial TFP levels
	(1)	(2)	(3)	(4)	(5)	(6)
d.Temp.	-4.281* (2.529)	-1.001 (3.014)	-0.947 (1.168)	1.234* (0.747)	-2.619 (1.763)	0.568 (2.157)
Lower share of imported inputs × d.Temp.		-4.619*** (1.298)		-4.568*** (1.335)		-4.933*** (0.961)
Lower imports-to-GDP ratio × d.Temp.	3.259 (2.283)	2.580 (2.710)
Lower initial income level × d.Temp.			-3.625*** (1.302)	-1.809 (1.303)
Lower initial TFP level × d.Temp.					0.421 (3.224)	0.178 (3.067)
d.Rainfalls	15.96*** (4.684)	12.03*** (4.287)	7.688 (7.803)	5.752 (6.199)	5.778 (4.280)	1.826 (3.128)
Lower share of imported inputs × d.Rainfalls		12.20*** (2.944)		17.11*** (5.899)		11.99** (4.767)
Lower imports-to-GDP × d.Rainfalls	-14.90** (6.049)	-15.41*** (5.144)
Lower initial income level × d.Rainfalls			-3.900 (7.447)	-12.02* (6.588)
Lower initial TFP level × d.Rainfalls					-0.032 (8.487)	-1.724 (7.708)
Observations	557	557	557	557	557	557
Countries	24	24	24	24	24	24
R-squared	0.084	0.098	0.077	0.092	0.071	0.086
Linear combination of coefficients, Temperature effects
Lower share of imported inputs		-5.620** (2.853)		-3.334*** (1.283)		-4.366** (1.841)
Lower imports-to-GDP ratio	-1.022*** (0.271)	1.579 (0.812)				0.746 (1.134)
Lower initial income levels			-4.573*** (0.332)	-0.575 (1.435)
Lower initial TFP levels					-2.198 (1.771)
Linear combination of coefficients, Rainfall effects
Lower share of imported inputs		24.23*** (5.155)		22.86*** (5.922)		13.82*** (4.797)
Lower imports-to-GDP ratio	1.062 (2.280)	-3.375*** (1.284)
Lower initial income levels			3.788*** (1.325)	-6.270 (6.060)
Lower initial TFP levels					5.746 (5.876)	0.102 (5.897)

Table 7:

Weather Shocks and Imported Inputs, LICs, Robustness Checks Dependent Variable = 100 times Annual Agricultural TFP Growth Rate

	Imports-to-GDP ratio		Initial income levels		Initial TFP levels
	(1)	(2)	(3)	(4)	(5)	(6)
d.Temp.	-4.281* (2.529)	-1.001 (3.014)	-0.947 (1.168)	1.234* (0.747)	-2.619 (1.763)	0.568 (2.157)
Lower share of imported inputs × d.Temp.		-4.619*** (1.298)		-4.568*** (1.335)		-4.933*** (0.961)
Lower imports-to-GDP ratio × d.Temp.	3.259 (2.283)	2.580 (2.710)
Lower initial income level × d.Temp.			-3.625*** (1.302)	-1.809 (1.303)
Lower initial TFP level × d.Temp.					0.421 (3.224)	0.178 (3.067)
d.Rainfalls	15.96*** (4.684)	12.03*** (4.287)	7.688 (7.803)	5.752 (6.199)	5.778 (4.280)	1.826 (3.128)
Lower share of imported inputs × d.Rainfalls		12.20*** (2.944)		17.11*** (5.899)		11.99** (4.767)
Lower imports-to-GDP × d.Rainfalls	-14.90** (6.049)	-15.41*** (5.144)
Lower initial income level × d.Rainfalls			-3.900 (7.447)	-12.02* (6.588)
Lower initial TFP level × d.Rainfalls					-0.032 (8.487)	-1.724 (7.708)
Observations	557	557	557	557	557	557
Countries	24	24	24	24	24	24
R-squared	0.084	0.098	0.077	0.092	0.071	0.086
Linear combination of coefficients, Temperature effects
Lower share of imported inputs		-5.620** (2.853)		-3.334*** (1.283)		-4.366** (1.841)
Lower imports-to-GDP ratio	-1.022*** (0.271)	1.579 (0.812)				0.746 (1.134)
Lower initial income levels			-4.573*** (0.332)	-0.575 (1.435)
Lower initial TFP levels					-2.198 (1.771)
Linear combination of coefficients, Rainfall effects
Lower share of imported inputs		24.23*** (5.155)		22.86*** (5.922)		13.82*** (4.797)
Lower imports-to-GDP ratio	1.062 (2.280)	-3.375*** (1.284)
Lower initial income levels			3.788*** (1.325)	-6.270 (6.060)
Lower initial TFP levels					5.746 (5.876)	0.102 (5.897)

Finally, columns (5) and (6) consider the initial agricultural TFP levels. Results in column (5) imply that there is no systematic difference in weather shocks across low TFP countries and high TFP countries within the LICs. Furthermore, column (6) shows that, even after controlling for the initial TFP levels, the effects of imported inputs are similar to the baseline result. These considerations support the idea that our baseline results are caused by cross-country differences in the share of imported inputs. Appendix G conducts more robustness checks using different samples and concerning the way we construct the dummy variables.

Figure 6 visually describe the baseline results, where Panel A shows the relationship between the TFP growth rate and annual changes in temperatures and Panel B presents the one for rainfalls. It indicates that steeper temperature effects and rainfall effects come from countries employing lower shares of imported inputs.

We acknowledge that our results come from reduced-form regression analyses, exploiting historical variations in weather and agricultural TFPs. Therefore, the analysis focuses on the impact of weather shocks on a particular aspect of the economies – agricultural TFP – and the estimated impacts are considered as the short-run effects because we estimate countries’ contemporaneous responses to short-run fluctuations in weather. In this sense, our analysis differs from ones in natural science fields employing estimates of future climate change and a General Circulation Model (GCM). These studies tend to find more pessimistic projections regarding the impact of climate change in the future. See Dell et al. (2014) and Auffhammer (2018) for more details.

V. Counterfactuals

The last set of analyses examines the magnitude of estimated impacts of imported inputs and weather shocks. Our analysis is simple. First, we estimate the regression ln(TFP_i,t) = β₀ + β₁Inputs_i,t + 𝕏_i,t β₂ + e_i,t with our baseline model using IV. Second, we find counterfactual TFP levels, keeping Inputs_i,t at their 1991 level, ${\hat{y}}_{i, t}^{1991} = {\hat{β}}_{0} + {\hat{β}}_{2} I n p u t s_{i, 1991} + 𝕏_{i, t} {\hat{β}}_{2} + {\hat{e}}_{i, t}$ .³⁵ Third, the gap between the counterfactual TFP and the actual TFP is computed ${G a p}_{i, t}^{1991} = 100 \times [{\hat{y}}_{i, t}^{1991} - \ln (T F P_{i, t})]$ , which is a percentage deviation from the actual TFP level. If the gap is positive, then it means that actual changes in the share of imported inputs worked to reduce agricultural TFP and vice versa. We use the regression coefficients from column (4) of Table 2 to find counterfactual TFPs.

Figure 7 shows the estimated gap between counterfactual TFPs and actual TFPs for the four groups of countries. It shows that changes in the share of imported inputs in the 1990s worked to reduce agricultural TFP in lower income countries. In 2002, for example, if the share of imported inputs stayed at the 1991 level, upper-middle income countries would have had 20 percent higher agricultural TFP and low-income and lower-middle income countries would have had 10 percent greater TFP than the actual TFP.

The gap between the counterfactual and actual TFPs turned to be negative around 2004 for lower-middle income countries, and around 2010 for LICs and upper-middle-income countries. In 2014, LICs and middle-income countries would have about 20 percent lower TFP if the share of imported inputs stayed at the 1991 level. These results come from the fact that the share of imported inputs was declining in 1990s and it started to increase in early 2000s as shown in Figure 3. For high-income countries, the share of imported inputs continuously increased throughout the period, which contributed to the increase in TFP by about 60 percent in 2014.

We conduct a similar counterfactual analysis for weather shocks. First, we estimate equation (2) and find parameter estimates. Second, find counterfactual TFP growth rate when climatic conditions stayed at the 1991 level by assuming d. Temp_i,t = 0 and d. Rain_i,t = 0. Third, we find counterfactual TFP level in 1992, ${\hat{T F P}}_{i, 1992}^{1991}$ , by using the counterfactual TFP growth rate in 1992 and the actual TFP level in 1991: ${\hat{T F P}}_{i, 1992}^{1991} = (1 + {\hat{g}}_{i, 1992}^{T F P} / 100) \times T F P_{i, 1991}$ and then find TFP levels in the following years as follows: ${\hat{T F P}}_{i, t}^{1991} = (1 + {\hat{g}}_{i, t}^{T F P} / 100) \times T F P_{i, t - 1}$ for t = 1993, 1994, ..., 2015. Forth, the gap between the counterfactual TFP and actual TFP is computed $G a p_{i, t}^{1994} = 100 \times [\ln ({\hat{T F P}}_{i, t}^{1991}) - \ln (T F P_{i, t})]$ , which is a percentage deviation from the actual TFP level.

Counterfactuals are found only for LICs where we find significant effects of weather shocks. We consider three scenarios. Scenarios 1 and 2 are the cases where temperatures and rainfalls did not change since 1991, respectively. Scenario 3 is when both temperatures and rainfalls did not change since 1991. Figure 8 shows results and suggests that weather shocks worked to reduce agricultural TFP in LICs. About 2 percent agricultural TFP were lost in 2005 and 2010 because these two years had the warmest average temperatures (NOAA National Centers for Environmental Information, 2011). The figure shows that the temperature effect is much more sizable than the rainfall effect. Scenario 1 (no change in temperatures) and Scenario 3 (no change in temperatures and rainfalls) imply similar results while Scenario 2 (no change in rainfalls) leads to a relatively smaller difference in actual TFP and hypothetical TFP.

In order to quantify its effects on agricultural value-added, we estimate hypothetical agricultural value-added based on counterfactuals under Scenario 1 (no change in temperatures). The hypothetical agricultural value-added is estimated by plugging the counterfactual TFP to the Cobb-Douglas production function: $Y_{i, t}^{C} = A_{i, t}^{C} {(K_{i t})}^{α_{i t}^{K}} {(L_{i t})}^{α_{i t}^{L}} {(T_{i t})}^{α_{i t}^{T}}$ . Table 8 presents results for each of LICs from the year where the difference between the actual value-added Y_i,t and the hypothetical value-added $Y_{i, t}^{C}$ is the largest. In many LICs, damages from higher temperatures are the greatest mostly in the year 2010 because the global average temperature was the record high in the year.

Table 8:

Actual Agricultural Value-Added and Counterfactual Value-Added under Scenario 1

Notes: The table shows actual agricultural value added (million USD, constant 2005 prices) and counterfactual agricultural value added based on counterfactual TFPs estimated based on Scenario 1 for LICs. Some LICs are missing from the table due to data availability constraint.

Table 8:

Actual Agricultural Value-Added and Counterfactual Value-Added under Scenario 1

		Year	Actual agricultural value-added (million USD)	Hypothetical agricultural value-added (million USD)	Difference, (2) minus (1) (million USD)	Percentage difference, [(2) – (1)]/(1)×100
			(1)	(2)	(3)	(4)
Afghanistan	AFG	2010	2,639	2,772	133	5.0%
Burundi	BDI	2005	456	471	14	3.2%
Benin	BEN	2010	1,388	1,420	32	2.3%
Burkina Faso	BFA	2010	2,530	2,587	58	2.3%
Central African Rep.	CAF	2010	660	675	15	2.3%
Gambia, The	GMB	2010	222	227	5	2.1%
Haiti	HTI	2015	902	918	16	1.7%
Liberia	LBR	2010	707	716	9	1.2%
Madagascar	MDG	2009	1,053	1,122	69	6.6%
Mali	MLI	2010	3,583	3,719	136	3.8%
Malawi	MWI	2010	1,545	1,594	49	3.2%
Niger	NER	2010	2,009	2,064	56	2.8%
Nepal	NPL	2010	3,193	3,319	126	3.9%
Rwanda	RWA	2010	1,258	1,288	30	2.4%
Senegal	SEN	2010	1,570	1,607	37	2.3%
Sierra Leone	SLE	2010	1,124	1,139	15	1.3%
Syria	SYR	2010	5,219	5,479	260	5.0%
Chad	TCD	2010	3,415	3,483	68	2.0%
Togo	TGO	2010	1,032	1,055	23	2.2%
Tanzania	TZA	2010	6,421	6,569	148	2.3%
Uganda	UGA	2010	3,297	3,413	117	3.5%
Total			44,223	45,636	1,413	3.2%

Table 8:

Actual Agricultural Value-Added and Counterfactual Value-Added under Scenario 1

		Year	Actual agricultural value-added (million USD)	Hypothetical agricultural value-added (million USD)	Difference, (2) minus (1) (million USD)	Percentage difference, [(2) – (1)]/(1)×100
			(1)	(2)	(3)	(4)
Afghanistan	AFG	2010	2,639	2,772	133	5.0%
Burundi	BDI	2005	456	471	14	3.2%
Benin	BEN	2010	1,388	1,420	32	2.3%
Burkina Faso	BFA	2010	2,530	2,587	58	2.3%
Central African Rep.	CAF	2010	660	675	15	2.3%
Gambia, The	GMB	2010	222	227	5	2.1%
Haiti	HTI	2015	902	918	16	1.7%
Liberia	LBR	2010	707	716	9	1.2%
Madagascar	MDG	2009	1,053	1,122	69	6.6%
Mali	MLI	2010	3,583	3,719	136	3.8%
Malawi	MWI	2010	1,545	1,594	49	3.2%
Niger	NER	2010	2,009	2,064	56	2.8%
Nepal	NPL	2010	3,193	3,319	126	3.9%
Rwanda	RWA	2010	1,258	1,288	30	2.4%
Senegal	SEN	2010	1,570	1,607	37	2.3%
Sierra Leone	SLE	2010	1,124	1,139	15	1.3%
Syria	SYR	2010	5,219	5,479	260	5.0%
Chad	TCD	2010	3,415	3,483	68	2.0%
Togo	TGO	2010	1,032	1,055	23	2.2%
Tanzania	TZA	2010	6,421	6,569	148	2.3%
Uganda	UGA	2010	3,297	3,413	117	3.5%
Total			44,223	45,636	1,413	3.2%

In terms of absolute value, the largest losses in agricultural value-added come from Syria, Tanzania, and Mali – 260 million USD, 148 million USD, and 136 million USD agricultural value-added were lost, respectively. In terms of percentage, the largest losses are from Madagascar (6.6%), Afghanistan (5.0%), Syria (5.0%), and Nepal (3.9%). In LICs as a whole, 3.2 percent of total agricultural value-added, which is equivalent to 1.4 billion USD, were lost if we collect the largest damages throughout the sample period 1991–2015. These results suggest that rising temperatures have economically sizable effects on agricultural value-added.

VI. Conclusions

This paper has estimated agricultural TFP for 162 countries from 1990 to 2015 and examined the determinants of TFP by focusing on the role of imported inputs and weather shocks. We have three major findings – (1) An increase in usage of imported inputs has a significant impact on the level of TFP; (2) rising temperatures and rainfall shortages negatively influenced the agricultural TFP growth rate; (3) within LICs, a greater share of imported inputs works to reduce the negative effects of weather shocks.

While these results may imply that an optimistic view on the impact of future climate change because importing inputs would help LICs to deal with negative effects of weather shocks. However, we once again acknowledge that our results come from reduced-form regressions relating annual TFP growth rates with short-run fluctuations in weather. Therefore, this paper is silent about the impact of future climate change, which is projected to lead to more severe rises in temperatures and more radical changes in precipitation patterns compared with historical variations in the last two decades.

We have also conducted counterfactual analyses to understand the economic magnitudes of these impacts. The results suggest that an increase in the share of imported inputs explain at most 60 percent of agricultural TFP in high-income countries and 20 percent of that in low-income and middle-income countries. The economic magnitude of the impact of weather shocks is also sizable. Our results suggest that, colleting the cumulative losses in the warmest years during the sample period, in total 3.2 percent of agricultural value-added, which is equivalent to 1.4 billion USD, were lost due to a rise in temperatures in LICs as a whole.

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A. List of Countries

We follow the World Bank’s classification of income-level of countries. In a broader definition, lower-middle income and upper-middle countries are classified as middle-income countries.

Low-income countries (LICs)				Lower-middle-income countries
No.	ISO	Country	Region	No.	ISO	Country	Region
1	AFG	Afghanistan	South Asia	1	AGO	Angola	Sub-Saharan Africa
2	BDI	Burundi	Sub-Saharan Africa	2	BGD	Bangladesh	South Asia
3	BEN	Benin	Sub-Saharan Africa	3	BOL	Bolivia	Latin America & Caribbean
4	BFA	Burkina Faso	Sub-Saharan Africa	4	BTN	Bhutan	South Asia
5	CAF	Central African Rep.	Sub-Saharan Africa	5	CIV	Cote d’Ivoire	Sub-Saharan Africa
6	COD	Congo, Dem. Rep.	Sub-Saharan Africa	6	CMR	Cameroon	Sub-Saharan Africa
7	ERI	Eritrea	Sub-Saharan Africa	7	COG	Congo, Rep.	Sub-Saharan Africa
8	ETH	Ethiopia	Sub-Saharan Africa	8	CPV	Cabo Verde	Sub-Saharan Africa
9	GIN	Guinea	Sub-Saharan Africa	9	DJI	Djibouti	Middle East & North Africa
10	GMB	Gambia, The	Sub-Saharan Africa	10	EGY	Egypt, Arab Rep.	Middle East & North Africa
11	HTI	Haiti	Latin America & Caribbean	11	GEO	Georgia	Europe & Central Asia
12	LBR	Liberia	Sub-Saharan Africa	12	GHA	Ghana	Sub-Saharan Africa
13	MDG	Madagascar	Sub-Saharan Africa	13	HND	Honduras	Latin America & Caribbean
14	MLI	Mali	Sub-Saharan Africa	14	IDN	Indonesia	East Asia & Pacific
15	MOZ	Mozambique	Sub-Saharan Africa	15	IND	India	South Asia
16	MWI	Malawi	Sub-Saharan Africa	16	KEN	Kenya	Sub-Saharan Africa
17	NER	Niger	Sub-Saharan Africa	17	KGZ	Kyrgyz Republic	Europe & Central Asia
18	NPL	Nepal	South Asia	18	KHM	Cambodia	East Asia & Pacific
19	RWA	Rwanda	Sub-Saharan Africa	19	LAO	Lao PDR	East Asia & Pacific
20	SEN	Senegal	Sub-Saharan Africa	20	LKA	Sri Lanka	South Asia
21	SLE	Sierra Leone	Sub-Saharan Africa	21	LSO	Lesotho	Sub-Saharan Africa
22	SYR	Syria	Middle East & North Africa	22	MAR	Morocco	Middle East & North Africa
23	TCD	Chad	Sub-Saharan Africa	23	MDA	Moldova	Europe & Central Asia
24	TGO	Togo	Sub-Saharan Africa	24	MMR	Myanmar	East Asia & Pacific
25	TJK	Tajikistan	Europe & Central Asia	25	MNG	Mongolia	East Asia & Pacific
26	TZA	Tanzania	Sub-Saharan Africa	26	MRT	Mauritania	Sub-Saharan Africa
27	UGA	Uganda	Sub-Saharan Africa	27	NGA	Nigeria	Sub-Saharan Africa
28	YEM	Yemen, Rep.	Middle East & North Africa	28	NIC	Nicaragua	Latin America & Caribbean
				29	PAK	Pakistan	South Asia
				30	PHL	Philippines	East Asia & Pacific
				31	PNG	Papua New Guinea	East Asia & Pacific
				32	SLV	El Salvador	Latin America & Caribbean
				33	STP	Sao Tome and Principe	Sub-Saharan Africa
				34	SWZ	Swaziland	Sub-Saharan Africa
				35	TUN	Tunisia	Middle East & North Africa
				36	UKR	Ukraine	Europe & Central Asia
				37	UZB	Uzbekistan	Europe & Central Asia
				38	VNM	Vietnam	East Asia & Pacific
				39	VUT	Vanuatu	East Asia & Pacific
				40	ZMB	Zambia	Sub-Saharan Africa

Low-income countries (LICs)				Lower-middle-income countries
No.	ISO	Country	Region	No.	ISO	Country	Region
1	AFG	Afghanistan	South Asia	1	AGO	Angola	Sub-Saharan Africa
2	BDI	Burundi	Sub-Saharan Africa	2	BGD	Bangladesh	South Asia
3	BEN	Benin	Sub-Saharan Africa	3	BOL	Bolivia	Latin America & Caribbean
4	BFA	Burkina Faso	Sub-Saharan Africa	4	BTN	Bhutan	South Asia
5	CAF	Central African Rep.	Sub-Saharan Africa	5	CIV	Cote d’Ivoire	Sub-Saharan Africa
6	COD	Congo, Dem. Rep.	Sub-Saharan Africa	6	CMR	Cameroon	Sub-Saharan Africa
7	ERI	Eritrea	Sub-Saharan Africa	7	COG	Congo, Rep.	Sub-Saharan Africa
8	ETH	Ethiopia	Sub-Saharan Africa	8	CPV	Cabo Verde	Sub-Saharan Africa
9	GIN	Guinea	Sub-Saharan Africa	9	DJI	Djibouti	Middle East & North Africa
10	GMB	Gambia, The	Sub-Saharan Africa	10	EGY	Egypt, Arab Rep.	Middle East & North Africa
11	HTI	Haiti	Latin America & Caribbean	11	GEO	Georgia	Europe & Central Asia
12	LBR	Liberia	Sub-Saharan Africa	12	GHA	Ghana	Sub-Saharan Africa
13	MDG	Madagascar	Sub-Saharan Africa	13	HND	Honduras	Latin America & Caribbean
14	MLI	Mali	Sub-Saharan Africa	14	IDN	Indonesia	East Asia & Pacific
15	MOZ	Mozambique	Sub-Saharan Africa	15	IND	India	South Asia
16	MWI	Malawi	Sub-Saharan Africa	16	KEN	Kenya	Sub-Saharan Africa
17	NER	Niger	Sub-Saharan Africa	17	KGZ	Kyrgyz Republic	Europe & Central Asia
18	NPL	Nepal	South Asia	18	KHM	Cambodia	East Asia & Pacific
19	RWA	Rwanda	Sub-Saharan Africa	19	LAO	Lao PDR	East Asia & Pacific
20	SEN	Senegal	Sub-Saharan Africa	20	LKA	Sri Lanka	South Asia
21	SLE	Sierra Leone	Sub-Saharan Africa	21	LSO	Lesotho	Sub-Saharan Africa
22	SYR	Syria	Middle East & North Africa	22	MAR	Morocco	Middle East & North Africa
23	TCD	Chad	Sub-Saharan Africa	23	MDA	Moldova	Europe & Central Asia
24	TGO	Togo	Sub-Saharan Africa	24	MMR	Myanmar	East Asia & Pacific
25	TJK	Tajikistan	Europe & Central Asia	25	MNG	Mongolia	East Asia & Pacific
26	TZA	Tanzania	Sub-Saharan Africa	26	MRT	Mauritania	Sub-Saharan Africa
27	UGA	Uganda

Topics

Countries

Series

About

Resources

Contributor Notes

Abstract

I. Introduction

II. Agricultural TFP

A. The Method Estimating Agricultural TFP

B. Results from Growth Accounting

III. Stylized Facts on Imported Inputs, and Weather Shocks

IV. Regression Analysis

A. Imported Inputs and Agricultural TFP Level

B. Weather Shocks and Agricultural TFP Growth

C. Importing Inputs Mitigates the Negative Weather Effects: Theory

D. Importing Inputs Mitigates the Negative Weather Effects: Evidence

V. Counterfactuals

VI. Conclusions

References

A. List of Countries