Abstract
This Selected Issues paper reviews Pakistan's tax regime, evaluates the level and composition of tax revenues, and estimates tax buoyancy and efficiency. Despite recent progress under the program, Pakistan's tax revenue remains very low relative to comparator developing countries and the tax effort expected for the country's level of development. This reflects narrow tax bases, overgenerous tax concessions and exemptions, weak and fragmented revenue administrations, and structural features of the economy. The findings suggest that unlocking tax revenue potential requires broadening tax bases, strengthening revenue administration and taxpayer compliance, eliminating distortionary tax expenditures, and rationalizing tax policy for greater efficiency and equity through a comprehensive and front-loaded reform agenda.
Macroeconomic Gains from Raising Female Labor Force Participation in Pakistan1
A. Background
1. Women make up half of Pakistan’s population; however, their contribution to income is far below its potential. With the roots of gender inequality lying to a large extent in Pakistan’s cultural context, addressing the issue will continue to be a gradual process. Nonetheless, the potential gains from greater inclusion of women in the economy are large: closing the gender gap in Pakistan could boost GDP by about 30 percent (Chart 1).2
2. Pakistan has made significant progress over the last decade in promoting gender equality (Chart 2). Female labor force participation increased by around 10 percentage points since 1990. That said, there remains ample scope for further progress. Pakistan’s female labor force participation (FLFP) in 2012 remains low at 24 percent (32 percent in South Asia, 69 percent in low-income countries).3
GDP Losses due to Economic Gender Gaps in Selected Countries
(In percent of GDP) 1/
Citation: IMF Staff Country Reports 2016, 002; 10.5089/9781513582412.003.A003
Source: Estimates by Cuberes and Teignier (2014).1/ Losses are estimated for a particular year for each country and can thus be interpreted as a one-off increase in GDP if gender gaps were to be removed.3. Historically female labor force participation has remained lower than male participation. Despite an increase in the participation rates, women account for most unpaid work (64 percent of female employment is in unpaid family work, double the South Asia average). They also face significant wage differentials—18 percent—vis-à-vis their male colleagues (WEF, 2014).
Labor Force Participation (LFP) Rates
(In percent)
Citation: IMF Staff Country Reports 2016, 002; 10.5089/9781513582412.003.A003
Sources: Gonzales et al. (2015).Global Gender Gap
(Pakistan rankings out of 142 countries)
Citation: IMF Staff Country Reports 2016, 002; 10.5089/9781513582412.003.A003
Source: World Economic Forum, Global Gender Gap Report (2014).4. Pakistan ranks second to the last in global gender gap index.4 The Index looks into the gap between men and women in four categories: Economic Participation and Opportunity (labor for participation, wages, senior managerial and technical positions), Educational Attainment (literacy and educational enrollment), Health and Survival (Sex ratio at birth and healthy life expectancy) and Political Empowerment (parliament seats, ministers and length of heads of states). Gender gap in Pakistan is particularly stark in economic opportunities and participation, education, and health.5
5. Gender gap is large in Pakistan’s public service. Total female legislators, senior officials, and managers is only 3 percent of the total (World average is 29 percent). However, female representation in Pakistan National Assembly has increased (thanks to quota) in line with the increasing trend in the World—outperforming World Average and the South Asian countries (Chart 4).
6. Gender gaps in education have been declining in Pakistan (Chart 5). The ratio of girls to boys in enrollment in primary and secondary education is 82 percent. However, there is room for improvement as Pakistan still remains well below the low income country average of 93 percent.
Women in Parliaments
(In percent of total seats)
Citation: IMF Staff Country Reports 2016, 002; 10.5089/9781513582412.003.A003
Sources: World Bank WDI Database; and IMF staff calculations.Ratio of Girls to Boys Enrollments in Primary and Secondary Education
(In percent)
Citation: IMF Staff Country Reports 2016, 002; 10.5089/9781513582412.003.A003
Sources: World Bank WDI Database; and IMF staff calculations.B. International Evidence for FLP Determinants
7. Legal and resource restrictions negatively affect FLFP and growth rates across countries. Based on the World Bank and OECD,6 restrictions on women’s rights to inheritance and property, as well as legal impediments to undertaking economic activities such as opening a bank account or freely pursuing a profession, are strongly associated with larger gender gaps in labor force participation.
Resource and Legal Restrictions
Citation: IMF Staff Country Reports 2016, 002; 10.5089/9781513582412.003.A003
8. Recent empirical work has identified demographic and legal characteristics as drivers of female labor force participation.7 Building on the simple associations given in [¶7], Gonzales et al., (2015) estimated panel regressions on 90 emerging and developing countries8 over the 1995–2010 period. The regression provides a good fit for Pakistan (Chart 7). As expected fertility, educational attainment, daughter inheritance rights, being the head of household and guaranteed equality provide a good fit at predicting male-female gender gap. Higher fertility rate is associated with higher labor force participation gaps. On the other hand, higher female educational attainment, the presence of daughters’ inheritance rights, being the head of the household and guaranteed equality help reduce the gap.
Male Minus Female Participation Rates
(In percent, regression results and actual gap)
Citation: IMF Staff Country Reports 2016, 002; 10.5089/9781513582412.003.A003
Source: Gonzales et al. (2015).9. Recent work also highlighted that availability of infrastructure and access to finance help increase FLFP. Availability of transportation, better roads and mobile networks help women access work.9 Presence of support networks among female entrepreneurs and availability of finance help raise the productivity of female owned/managed enterprises.10
C. Evidence from Pakistan’s Micro Data
10. Pakistan Social and Living Standards Measurement and Household Integrated Economic Surveys provide useful information to explore Pakistan’s FLFP determinants. Cross section data is constructed through 2011/12survey results. Of the total 25486 observations, 18 percent of women (within the 15–49 age group) are employed, with only 11 percent in paid work. Roughly half the population lives in rural areas. Average household size is eight (with two children on average per household). Average age of women is 28, with 63 percent married, and only 4 percent are heads of household. Average education of women is three years. Half the population owns a car, 85 percent owns a computer or a cell phone, 86 percent own a house and 88 percent own land. Average household income is PRs 174000.
11. The following probit regressions are estimated:
FLFP is the female labor force participation that takes values (1,0) for employed and unemployed, respectively. Demographics is a vector of variables to represent years of education, age, household size, marital status, women’s head of household status, number of children, and who in household decides whether a female member can seek or continue to remain in paid employment. Household income, ownership of computer and cell phones, number of home appliances, ownership of transport, home, land are used as control variables. Regressions are run to differentiate between urban or rural life and variation across provinces.
12. Results show that income, education, marital status, household size and being the head of household are good predictors of FLFP.11 Log income is used as control variable and negatively correlated in line with the U-shaped pattern across countries i.e. with higher household income and increasing social protection; women can withdraw from the market in favor of household work and childcare.12 In those families with higher household income and large household size, FLFP declines in both urban and rural areas. More educated women are more likely to participate in the labor force in urban areas; however this correlation is negative in rural areas. Marital status however is only significant in rural areas where married women are less likely to work. Female head of the household status makes it more likely for higher FLFP rates as women are the main breadwinners of the households. The table below summarizes the results. The assessment is robust to different use of control variables and the choice of FLFP to include only paid employment.
p<0.01
p<0.05
p<0.1
| VARIABLES | (1) FLFP determinants Urban |
(2) FLFP determinants Rural |
(3) FLFP determinants Urban |
(4) FLFP determinants Rural |
|---|---|---|---|---|
| Income | −0.19*** | −0.02 | ||
| (0.031) | (0.015) | |||
| Marital Status | −0.04 | 0.29*** | −0.17*** | 0.20*** |
| (0.071) | (0.033) | (0.032) | (0.025) | |
| Head of Household | 0.87*** | 0.75*** | 0.36*** | −0.04 |
| (0.227) | (0.108) | (0.082) | (0.056) | |
| Education (yrs) | 0.05*** | −0.05*** | 0.03*** | −0.03*** |
| (0.006) | (0.004) | (0.003) | (0.003) | |
| Household Size | −0.04*** | −0.03*** | −0.03*** | −0.03*** |
| (0.010) | (0.004) | (0.005) | (0.003) | |
| Constant | 1.07*** | −0.04 | −1.08*** | −0.54*** |
| (0.355) | (0.164) | (0.047) | (0.034) | |
| Observations | 2,273 | 7,565 | 11,217 | 14,260 |
p<0.01
p<0.05
p<0.1
| VARIABLES | (1) FLFP determinants Urban |
(2) FLFP determinants Rural |
(3) FLFP determinants Urban |
(4) FLFP determinants Rural |
|---|---|---|---|---|
| Income | −0.19*** | −0.02 | ||
| (0.031) | (0.015) | |||
| Marital Status | −0.04 | 0.29*** | −0.17*** | 0.20*** |
| (0.071) | (0.033) | (0.032) | (0.025) | |
| Head of Household | 0.87*** | 0.75*** | 0.36*** | −0.04 |
| (0.227) | (0.108) | (0.082) | (0.056) | |
| Education (yrs) | 0.05*** | −0.05*** | 0.03*** | −0.03*** |
| (0.006) | (0.004) | (0.003) | (0.003) | |
| Household Size | −0.04*** | −0.03*** | −0.03*** | −0.03*** |
| (0.010) | (0.004) | (0.005) | (0.003) | |
| Constant | 1.07*** | −0.04 | −1.08*** | −0.54*** |
| (0.355) | (0.164) | (0.047) | (0.034) | |
| Observations | 2,273 | 7,565 | 11,217 | 14,260 |
p<0.01
p<0.05
p<0.1
13. Attendance to higher education13 in both urban and rural areas significantly affects higher FLFP. However, the impact is double in urban areas compared to rural areas. In urban areas, primary and secondary education attainment is not correlated with FLFP, however, in rural areas below high school education makes it more likely for women to stay out of labor force.
| VARIABLES | (1) High School and Above |
(2) Below High School |
(3) High School and Above Urban |
(4) Below High School Urban |
(5) High School and Above Rural |
(6) Below High School Rural |
|---|---|---|---|---|---|---|
| Education (years) | 0.13*** | −0.07*** | 0.20*** | 0.00 | 0.10*** | −0.07*** |
| (0.018) | (0.007) | (0.026) | (0.016) | (0.026) | (0.007) | |
| Income | −0.15*** | −0.08*** | −0.17*** | −0.26*** | −0.14*** | −0.06*** |
| (0.033) | (0.013) | (0.049) | (0.040) | (0.047) | (0.015) | |
| Constant | −0.71* | 0.51*** | −1.38** | 1.65*** | −0.37 | 0.39** |
| (0.419) | (0.152) | (0.608) | (0.449) | (0.601) | (0.166) | |
| Observations | 1,558 | 8,280 | 802 | 1,471 | 756 | 6,809 |
| VARIABLES | (1) High School and Above |
(2) Below High School |
(3) High School and Above Urban |
(4) Below High School Urban |
(5) High School and Above Rural |
(6) Below High School Rural |
|---|---|---|---|---|---|---|
| Education (years) | 0.13*** | −0.07*** | 0.20*** | 0.00 | 0.10*** | −0.07*** |
| (0.018) | (0.007) | (0.026) | (0.016) | (0.026) | (0.007) | |
| Income | −0.15*** | −0.08*** | −0.17*** | −0.26*** | −0.14*** | −0.06*** |
| (0.033) | (0.013) | (0.049) | (0.040) | (0.047) | (0.015) | |
| Constant | −0.71* | 0.51*** | −1.38** | 1.65*** | −0.37 | 0.39** |
| (0.419) | (0.152) | (0.608) | (0.449) | (0.601) | (0.166) | |
| Observations | 1,558 | 8,280 | 802 | 1,471 | 756 | 6,809 |
D. Policy Recommendations
14. A range of revenue, expenditure and legal measures could be used to promote greater FLFP. An integrated set of policies is needed to help FLFP that has significant prospective growth and development implications.14 In this respect, comprehensive policies can be effective in boosting women’s economic participation.15 Fiscal consolidation will free up resources for higher infrastructure spending16 and higher investment in education, and business climate reforms will help financial inclusion for women.
15. Among expenditure measures, increased social spending under the BISP provides unconditional cash transfers to women. The transfers will promote continued female school attendance through conditional cash transfers.17 Budgetary resources could be allocated to provide access to comprehensive, affordable and high-quality daycare services18 which would free up women’s time for caring young and elderly and facilitate and increase in female labor force participation.19 In addition, publicly financed parental leave schemes,20 promoting parity in paternity and maternity leave and flexible work arrangements21 can also complement policies to balance family and work responsibilities.22 Rural infrastructure spending on access to clean water and transportation could also reduce the time women spend on domestic tasks and facilitate their access to markets.23
16. Impediments to access to finance for women could be removed to help to raise the productivity of enterprises owned and managed by women. Pakistani women are entitled to obtain bank loans and other forms of credit, and a number of credit institutions target women. However, their access is limited by their inability to provide the required collateral.24 In order to raise the productivity of women-owned and -managed enterprises, access to finance should be improved and training and support networks among female entrepreneurs should be developed.25 In this regard, swift implementation of credit bureau would be crucial.
17. Efforts to mitigate resource restrictions can increase FLFP in Pakistan. Finding opportunities to strengthening female inheritance rights on immoveable property can enhance economic opportunity to women.
18. Quotas for senior positions could help boost FLFP. In both private and public sectors, targeted search for female candidates for senior positions can provide opportunity and acceptance for female leadership.26
References
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Prepared by Ferhan Salman (MCD) with research assistance from Hiba Zaidi and Yi Liu (both MCD).
The Global Gender Gap Index was first introduced by the World Economic Forum in 2006 as a framework for capturing the magnitude of gender-based disparities and tracking their progress (WEF, 2014).
World Economic Forum, Global Gender Gap Report (2014).
The World Bank’s Women, Business and the Law Database comprises 143 countries identifying legal and regulatory barriers to women’s economic participation and entrepreneurial activity. The focus of the database is on seven indicators of gender-related differences in the legal and institutional framework. OECD’s social institutions and gender index has 160 countries that combines both the de jure and de facto discrimination of social institutions, through information on laws, attitudes and practices. Discriminatory social institutions are defined as the formal and informal laws, attitudes and practices that restrict women’s and girls’ access to rights, justice and empowerment opportunities (OECD, 2014).
Including Pakistan.
Results are in line with international evidence.
Attendance to high school grades and above.
section C, World Bank (2011).
Section 18 of the Constitution grants all citizens the right to conduct any lawful trade or business, and the government reported that all of the services of the formal banking sector are available to women.
