Back Matter
  • 1 https://isni.org/isni/0000000404811396, International Monetary Fund
  • | 2 https://isni.org/isni/0000000404811396, International Monetary Fund
  • | 3 https://isni.org/isni/0000000404811396, International Monetary Fund

Appendix 1. Details of the Model from Chapter 4

The model is a dynamic general equilibrium model of a small open economy with multiple sectors. There are a large number of households that are heterogeneous, both within and across sectors. Urban and rural households differ with respect to their occupations as well as to their access to financial intermediaries. Within-sector heterogeneity is due to household specific shocks to productivity.

Economic Sectors

There are four types of occupations in the economy, three urban and one rural:

  • Agricultural workers (rural)

  • Entrepreneurs (urban)

  • Public-sector workers (urban)

  • Private-sector workers (urban)

Households are confined to their sectors and cannot easily switch occupations.

Production

Agricultural workers use their own labor to produce either maize, or other agricultural goods. Agricultural workers differ in their land holdings (some are small farmers and others own large plots) and employ land and labor and fertilizer to produce.

Table 12.

Production Structure

article image
Source: IMF staff.

Public-sector workers work for the government which does not produce marketable goods. Private-sector workers provide their labor to the entrepreneurs. Additionally, both private- and public-sector workers devote some of their time to producing services.1

The entrepreneurs produce manufacturing and services using capital, labor, and energy.2 Manufacturing goods can then either be sold to consumers or converted into capital, because they are tradable goods their price is determined in international markets. Services are produced only for the domestic market. Each entrepreneurial household owns its own capital stock which cannot be converted back into manufacturing goods. Capital depreciates over time, so that new investments are necessary to maintain the capital stock.

Entrepreneurs also produce energy goods using a capital-intensive technology. Energy (gas in the case of Republic of Congo) is also a tradable good, and therefore its price is determined in international markets. However, the domestic price of this input is administered by the government.

Besides the domestically produced food item, the industrial goods (services and manufacturing) and energy, there is also an agricultural export product. The production of this good (for example agricultural commodities) takes place in firms owned by entrepreneurs. It uses the food item as input which is then refined and packaged using labor.

Preferences and Household Decisions

Households live forever and are forward looking. In every period, they decide how much of their disposable income to consume and how much to save. Households face uncertainty regarding their future income and are risk averse: they want to avoid large fluctuations of their consumption over time. Having access to a financial intermediary allows them to accumulate a buffer of financial wealth as insurance against future drops in income. Households facing more severe shocks can borrow to smooth consumption if they have access to finance.3

Only private- and public-sector workers and a given fraction of agricultural workers have access to finance. The remaining farmers can neither save nor borrow.

Households also decide how to allocate their consumption expenditure over two food items (domestic agricultural goods and imported food) and the non-food goods (manufacturing, services and energy).

Workers also make a decision on how much of their time to devote to the formal labor market and how much to work in the informal sector.

Financial Intermediation and Financial Sector Policies

Financial intermediaries have two distinct roles in the economy:

  • They convert manufacturing and services goods into capital.

  • They allow households to save and borrow.

Fiscal Policy Parameters

The government in the model has access to a rich set of gas and non-gas taxes and transfers to pay the public sector workers, to finance subsidies, and to provide insurance to vulnerable households. Additionally, the government invests in infrastructure. These policies are captured by a set of exogenous policy parameters:

  • A tax on entrepreneurs’ capital income

  • A tax on private- and public-sector workers’ wage earnings

  • Royalties from energy sector

  • Sector-specific and means-tested transfers and subsidies

Idiosyncratic Shocks

Each non-entrepreneurial household’s productivity is subject to random changes over time, but these changes in productivity are different across households. At each point in time, some households are lucky while others are unlucky. There is no aggregate uncertainty and, given the large number of households, a law of large numbers applies, so that the distribution of shocks across households within each sector remains constant. That is, the number of unlucky households is always the same.

Equilibrium and Steady State

At each point in time, prices, wages, and interest rates are set to ensure that the markets for credit, labor and the goods produced only for domestic consumption clear. Moreover, given these prices (both in the present and future) and government policies, all household decisions are made to maximize the present value of lifetime utility. The prices of energy, agricultural exports and manufacturing goods are exogenously given.

The economy is in a steady state. Aggregate variables and prices are constant over time, as is the distribution of wealth, income, and consumption across households. The income, wealth, and consumption of individual households however changes over time with the realization of their idiosyncratic shocks.

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2

Tax data have been increasingly used to complement household survey data as an important source of income data. Available evidence suggests that the top 1 percent hold as much as 20 (Colombia, Chile, Mexico) to 25 percent (Brazil) of income (ECLAC 2019).

Part of the material in this chapter, as well as elements from Chapters 2, 3, and 6 formed the core of Chapter 5 of the April 2018 IMF Regional Economic Outlook: Western Hemisphere Departments.

1

The commodity boom for Latin America started during the first decade of the 2000s. While the peak in commodity terms of trade varies across countries, for comparability purposes we define the end of the boom as the start of the 2014 oil price shock.

2

Given data availability, country coverage includes Argentina, Belize, Bolivia, Brazil, Chile, Colombia, Costa Rica, the Dominican Republic, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, and Uruguay. Commodity exporters are determined according to whether net commodity exports surpass 10 percent of total exports plus imports at the time of the October 2015 World Economic Outlook, to which Brazil is added given it has the largest estimated natural resource reserves in the region. Hence, the full list of commodity exporters (with main commodity exports in brackets) is: Argentina (soybean meal, corn, soybean oil), Brazil (soybeans, iron ore, crude petroleum), Bolivia (petroleum gas, zinc ore, gold), Chile (copper ore, refined copper, fish), Colombia (crude petroleum, coal briquettes, gold), Ecuador (crude petroleum, bananas, crustaceans), Honduras (coffee, palm oil, bananas), Paraguay (soybeans, soybean meal, bovine meat), and Peru (copper ore, gold, refined petroleum).

3

Azevedo, Saavedra, and Winkler (2012) show that, on average, 45 percent of the reduction in the Gini coefficient can be attributed to changes in hourly labor income, which has ranged from 22 percent in Panama to 66 percent in Ecuador. Available evidence suggests that it is the skill premium, or the returns to education, that drives the decline in hourly labor income inequality (Barros and others 2010, 2012; Campos-Vazquez, Esquivel, and Lustig 2012; de la Torre, Messina, and Pienknagura 2012; Cruces and Gasparini 2011). In terms of the contributions of non-labor income, changes in government transfers contributed, on average, 14 percent of the observed regional decline in inequality, while changes in pensions contributed 7 percent. The contribution of changes in returns to capital in Argentina, Brazil, and Mexico is estimated to be small and mostly leading to increased inequality (Lustig, Lopez-Calva, and Ortiz-Juarez 2013). However, household surveys under-estimate income from capital so the effect may have been larger than current estimates indicate. See also Tsounta and Osueke (2014) for a paper exploring the drivers of lower inequality in Latin America.

4

To control for the initial level of poverty, the variable on the y axis is the residual of the regression of the change in poverty on the initial poverty ratio.

5

See Lustig, Lopez-Calva, and Ortiz-Juarez (2013) for another way of looking at this issue. They use a Datt-Ravalion decomposition to decompose the poverty reduction into a “growth” and “redistribution” components.

6

That poverty fell less in Chile than in other commodity exporters largely reflects the fact that Chile had relatively low poverty rates before the boom: poverty in 2000 stood at 10.3 percent and fell to 2.6 percent by 2013.

7

The mean poverty reduction during the boom period was statistically significantly larger in commodity exporters than nonexporters. For inequality, the mean reduction is also larger, but the result is not statistically significant.

8

Comparable cross-country data on poverty and inequality were available until the end of 2018 for most countries at the time of writing and until end-2019 for some.

9

As a measure of commodity terms of trade, we use an index built by Gruss (2014) and updated in Gruss and Kebhaj (2019). This captures the income gain or loss a country experienced during the period due to commodity price movements. This combines international prices and country-level data on export volumes for each individual commodity. In addition, an increase in the price of imported commodities (for example oil or primary intermediate inputs) is likely to reduce profit margins for firms and disposable income for households. To capture the net income effects from changes in commodity prices, the weights in the price index are based on net exports of each commodity so that commodity price increase would imply a positive (negative) income shock if the country is a net exporter (net importer) of that commodity. A special caveat applies with respect to natural gas exporters (Bolivia in the case of Latin America). Given that there is no worldwide reference price for natural gas, the commodity terms of trade database takes the average of three international hubs. In some instances, this is a poor proxy for the actual price obtained for natural gas exports. In the case of Bolivia, the gas export contracts link prices to crude oil prices. For all countries except Bolivia we use the latest vintage of the Gruss and Kebhaj database. But for Bolivia—when it is shown—we use an earlier vintage which shows higher co-movement with Bolivia’s actual export prices.

10

Note that Bolivia is omitted from Figure 10 due to the issues identified in footnote 9. Large movements in European natural gas prices lead to the impression of a very large negative commodity terms of trade movement in Bolivia between 2017 and 2019.

11

Data is until 2018 or 2019 depending on last available data point for each country. In general, when a country does not have data for a specific year, we use the closest available year instead.

12

Latinobarómetro is a Sanitago de Chile based non-governmental organization. Annual surveys on Latin American public opinion are available online at http://www.latinobarometro.org/lat.jsp for the time period 1996–2018. The margin of error per country and year is roughly +/- 3 percent.

13

For Brazil, survey data show that labor income losses were large across the income distribution but the bottom decile was most affected with a decline of around 30 percent. The poverty rate in May would have increased by as much as 8 percentage point based only on labor income. But preliminary evidence suggests that broad-based emergency cash transfers more than offset the labor income losses of the bottom 40 percent of the income distribution, avoiding any increase in poverty and inequality, at least temporarily (IMF 2020b). Underlying data come from IBGE’s PNAD-Covid.

1

While Honduras is classified as a commodity exporter given high net commodity exports, its commodity terms of trade declined since it exports non-extractive commodities and imports extractive ones that saw their prices increase by more. Consequently, commodity price changes led to a negative wealth effect for Honduras and poverty fell significantly less than in most other Latin American countries.

2

For the Gini index, we use the measure derived from disposable (post-tax and transfers) income (Gini Net) as it already controls for the impact of fiscal policies on households’ budgets. The annual weight of each commodity−used to construct the commodity terms of trade is given by the share of net exports in output: ωi,j,τ=xi,j,τmi,j,τGDPi,τ where xi,j( mi,j) denotes the exports (imports) value of commodity j of country i in year τ GDPi, expressed in US dollars; and GDPi denotes country i’s nominal GDP is US dollars in year τ (Gruss and Kebhaj, 2019 p. 10). A 1 percentage point change in the commodity terms of trade index can be interpreted as a change in aggregate disposable income equivalent to 1 percentage point of GDP.

3

See Solt (2016). The SWIID currently incorporates comparable Gini indices of disposable and market income inequality for 192 countries for as many years as possible from 1960 to the present; it also includes information on absolute and relative redistribution.

5

We only include commodity exporters in the sample here given that this is the focus of the analysis.

6

For example, in Bolivia nearly 40 percent of the population were below the poverty line in 2000.

7

See, for example, Alberola and Benigno (2017).

8

On the larger question of the long-term impact of natural resource abundance for GDP growth and development, there is no consensus. Van der Ploeg (2011), for example, shows that results supporting “the natural resource curse” are sensitive to sample periods and countries.

9

Oil and gas production, for example, is substantially less labor intensive than agriculture but is more intensive in skilled labor.

12

Allcott and Keniston (2018) demonstrate positive spillovers of the oil and gas sector to manufacturing in the US. Michaels (2011) finds a similar positive result for the US.

13

Of course, public and private investment can also expand supply not just demand.

14

Note that the vast majority of households in Latin America outside the highest-income segments do not receive any capital income so that transfer and labor income explain the overwhelming fraction of total income for them.

15

See Messina and Silva (2018) for a detailed discussion of demand and supply factors underlying the skill premium compression during the boom. They note that while an increase in high-skilled labor supply was an important factor, the demand factors tied to the commodity boom discussed in this chapter were also key drivers.

1

Simplifying somewhat, producing departments receive 1 percent, producing municipalities 3 percent, non-producer departments 8 percent and the remaining 20 percent stay with the central government.

3

There are some important methodological issues and associated caveats to note when using Shapley decompositions (see Sastre and Trannoy 2002).

4

On average, labor income per capita in the informal sector represented about 60 percent of its corresponding figure in the formal sector.

5

The census-based poverty measure is somewhat different from the household survey one given that the Bolivian census does not provide income information. The census-based measure is constructed by looking at the percentage of the population without access to basic necessities (sanitation, water, electricity, adequate living space, etc.). See Feres and Mancero (2001).

6

Production of natural gas is concentrated in the southern province of Tarija, while mining has traditionally been located in the highlands of Potosi and Oruro. Out of a total of 339 municipalities, there are 48 which produce either metal or hydrocarbons (extractive sector municipalities) in Bolivia. 24 produce hydrocarbons and 24 produce metals. See Toscani (2017) for additional details.

7

Entropy balancing achieves virtually perfect overlap both for the first and the second moment of the distribution. Like the now-popular synthetic control method, entropy balancing implicitly makes a strong linearity assumption, however. An alternative that could be explored in future work is to exclude municipalities that are adjacent to resource municipalities to alleviate concerns about spillovers.

8

Ideally, we would have explored the detailed impact of the boom on municipal fiscal revenues (similar to the analysis done for Brazil). Unfortunately, the necessary data was not available.

9

Brazil is a federal country with three layers of government: federal, state, and municipal.

10

Source: Brazilian Institute of Geography and Economics (IBGE).

11

We combine data from a number of sources in the analysis. Municipal-level data on poverty, income, inequality, income, and employment by sector comes from the 2000 and 2010 rounds of the population census provided by the Brazilian Institute of Geography and Economics (IBGE). Data on mineral production and mineral royalties by municipality are taken from the Brazilian Mining Ministry (DNPM). Oil royalties data by municipality is taken from the oil and gas regulator (ANP). Data on oil production by field are taken from ANP. It is then mapped to municipalities using information on geographic location of fields and municipalities. Municipal fiscal data comes from Ipeadata.

12

The per capita number should be the more relevant measure than the total value when thinking about determinants of poverty and inequality.

13

For mineral production, the earliest data we have is 2004. The value for 2000 is assumed to be the same as the one in 2004. This might bias our results to some degree but it should bias them against finding an impact, given that the change in the value of production was likely larger between 2000–10 than 2004–10.

14

Onshore oil production is also included in the regressions but since it is never found to be significant results are not reported to not clutter the tables. Recalling Table 2 shows why onshore oil production is not found to have any impact—the value of onshore oil production on average did not change between 2000 and 2010, in fact it slightly contracted as old fields were slowly winding down production. The level of royalties from onshore production is also a magnitude smaller than those from offshore.

15

The argument extends to general backward linkages (that is, demand for non-labor inputs) and not only labor demand.

16

Property taxes (IPTU) also increase significantly, but the magnitude is much smaller than for services taxes.

17

According to official reports, poverty in Peru fell by 14.6 percentage points during the same period. At least two methodological elements drive this difference. First, SEDLAC’s poverty measure is based on monetary income, while official measure is based on consumption; second, poverty lines are different.

18

It is worth noting that although extractive sectors—mining and oil and gas extraction—pay the highest salaries in the country, they absorb a limited share of workers. About 2 percent of households have mining sector related income. In the case of oil and gas, that share is 10 times smaller, representing about 0.2 percent of total households. Agriculture and services—mainly wholesale and retail, have been the sectors employing most of the workers in Peru. More than 50 percent of households in the country had their labor income linked to one of these sectors between 2007 and 2011. While these two sectors have been central as employment generators, their workers are less well-paid on average.

19

The household survey data are not representative at the municipal level. Having to work at the departmental level significantly reduces he power of the regressions. Additionally, the period of analysis is somewhat shorter here than in the analogous analysis for Bolivia and Peru. The results here should thus be taken more as a high-level exposition.

20

Our work complements a growing literature that looks at the impact of natural resource extraction by using microdata and exploiting local variation. Aragon and Rud (2013) show that a large gold mine in Peru leads to positive spillovers to the local economy through its demand for inputs, while Loayza and Rigolini (2016) find that the presence of gold mines in Peru reduces local poverty but increases consumption inequality. For Brazil, Cavalcanti, Da Mata, and Toscani (2019) find that the direct market effect (abstracting from the fiscal channel) of having an oil sector is beneficial for municipalities and leads to structural transformation away from subsistence agriculture toward the services sector over the long-term. Benguria, Saffie, and Urzua (2018) corroborate these finding for Brazil over the recent boom period by showing a compression in the wage premium as well as significant employment gains in the commodity sector with some positive spillovers to the nontradable sectors, combined with employment losses in the tradable sector in regions affected by the positive commodity price shock. For Chile, Pellandra (2015) shows that the commodity boom in the 2000s significantly reduced poverty and inequality by increasing unskilled workers’ wages and compressing the wage premiums. Alvarez Garcia, and S. Ilabaca (2017), also for Chile, come to a similar conclusion. Also see Cust and Poelhekke (2015) for an overview of the literature.

1

For the purpose of studying commodity cycles, the production and export of hydroelectric sector (by the so called Binationals) in Paraguay should be stripped out. While energy produced in Paraguay and then exported to Brazil and Argentina is a large share of GDP, prices are administered by international treaties and do not follow international price trends. Hence, they are not subject to commodity prices cycles that are the focus of this study. If we exclude the Binationals production from GDP, then the share of energy drops to 2 from close to 15 percent in Paraguay.

2

The trade balance is set to zero. This implies that the trade numbers (for example, for exports) can deviate from what is seen in the data, especially in the case of Paraguay where the average trade balance has been significantly different from zero.

3

For Bolivia, the official household survey is MECOVI. For Paraguay, the households survey is the Encuesta Permanente de Hogares (EPH), conducted by Dirección General de Estadística, Encuestas y Censos.

4

Note that migration is exogenous in the model but might well have been (partly) driven by the commodity sector as the expanding services sector in urban areas demanded more labor.

5

Another reason for the smaller effect than in the case of Paraguay is that there is a bigger share of smaller farmers in Paraguay than Bolivia. Smaller farmers are more likely to be poor and hence benefit from the positive agricultural commodity price shock.

6

Indeed, cash transfers have a much bigger impact on inequality than growth in the model.

7

To be consistent with the sectorial composition of the economy in the second steady-state the TFP of manufacturing had to increase by 14.7 percent (all other TFP remained at their levels in the steady-state).

8

The share of urban workers with low skills fell by more than 10 percentage points to slightly more than 50 percent.

9

If, for example, we decreased the concentration of land to a Gini of 78.0 in the rural sector, the impact of the increase in agricultural prices is higher and accounts for about 40 percent of the actual change of the Gini.

10

As a caveat, we do not dispose of data on the distribution of health care as in-kind transfers by income percentiles so we make some assumptions. We assume that the increase in health care spending provided by the central government (Ministry of Health) (most of the increase, 1.7 percent of GDP) would benefit disproportionally low-income categories while the increase observed at the Instituto de Prevision Social (IPS) (0.3 percent of GDP) benefitted the relatively better-off. The geographical distribution of health care center also affected the distribution of the transfer between urban and rural population, with a two third of the centers located in urban cities.

3

Brazil had the defined benefit system with the largest sustainability concerns but pension reform in late 2019 substantially improved the fiscal outlook.

4

Labor informality is often explicitly defined as work without social security contributions. In high-informality countries, many workers move between formal and informal sector jobs, leading to interrupted pension contribution careers. Freudenberg and Toscani (2019) show that in the case of Peru, workers on average contribute for 4–5 months out of a possible 12 months per year over their working life. This is similar to the level observed in the private sector in Mexico (IMF 2018c) and somewhat below the average in Chile (Benavides and Valdés, 2018).

5

Colombia’s royalty sharing arrangements are not fully integrated into the annual budget. A unified budget would be a preferable option for most countries.

6

Recall also the above discussion of pensions—higher labor formality, that is, higher contributive pension coverage, is a crucial component to raise old-age income across the region.

1

This assumption is made to capture large informal sectors in Bolivia and Paraguay.

2

Hence services are produced both within the entrepreneurs’ firms and informally by workers at home.

3

The model thereby highlights the role of financial inclusion not just as a measure of mobilizing resources for investment but also as an insurance mechanism that reduces consumption inequality.

Commodity Cycles, Inequality, and Poverty in Latin America
Author: Mr. Ravi Balakrishnan, Sandra Lizarazo, Marika Santoro, Mr. Frederik G Toscani, and Mr. Mauricio Vargas