rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 12: Sector and industry of employment of youth the Northern and Eastern provinces**

<!-- Start of picture text --> a. Type of employment of 15‐24 year‐olds<br>23% 19% 23% 13% 29%<br>77% 81% 77% 87% 71%<br>Sri Lanka Northern Eastern Western Other<br>Province Province Province Provinces<br>Paid employee/employer Self‐employed/non‐paid employee<br>Source: Labor Force Survey 2015 (Sample: 15‐24 year olds)<br><!-- End of picture text --> <!-- Start of picture text --> b. Sector of employment of 15‐24 year‐olds<br>48% 45% 46% 43%<br>56%<br>36% 33% 32% 35%<br>41%<br>16% 22% 21% 22%<br>3%<br>Sri Lanka Northern Eastern Western Other<br>Province Province Province Provinces<br>Agriculture Industry Services<br>Source: Labor Force Survey 2015 (Sample: 15‐24 year olds)<br><!-- End of picture text -->

# 4 Explaining employment gaps: Supply or demand?

To better understand the causes of low employment rates for women and youth in the Northern and Eastern provinces, we next attempt to shed light on whether demand or supply factors are driving these differences. Factors that affect the demand for labor include variables, such as education or experience, that determine the value of the marginal product of labor. Factors that affect the supply of labor include variables that affect the reservation wage of women. These could include factors such as fertility, availability of child care, husband’s income, transfer income, substitutes for women’s time (such as household appliances), and preferences for work. Since the data available to us allow us to better examine demand‐side factors, we leave close analysis of supply‐side issues for future research.

## 4.1 Women

We begin this section by decomposing gender differences in employment rates into variables that determine employment rates. This will allow us to examine if gender differences in employment rates vary between the Northern and Eastern provinces and the rest of the country because of differences in the prevalence of characteristics or in their relationship with the probability of an individual being employed. We then examine some of these factors to shed light on the geographic differences that we observe.

### 4.1.1 Decomposing gender differences in employment rates

Female labor force participation in the North and East can be low either because women have different characteristics or because the returns to these characteristics are different. To shed light on this question, we consider an Oaxaca‐Blinder decomposition, which can be used to decompose the source of differences in an outcome variable (such as the labor force participation rate) between groups of workers (Oaxaca 1973, Blinder 1973). In other words, it indicates what portion of the disparity in an outcome variable is due to differences in levels of observed characteristics, as opposed to returns to those characteristics.

15
# **The Labor Productivity Gap between Female and Male-Managed Firms in the Formal Private Sector**

Asif Islam Isis Gaddis Amparo Palacios Lopez Mohammad Amin<sup>1</sup>

**JEL:** J16, L25, L26, D22, O17

**Keywords** : Entrepreneurship, gender, labor productivity, women entrepreneurs, formal sector

> 1 Enterprise Analysis Unit, DECIG, World Bank; Gender Group, World Bank; Living Standards Measurement Study (LSMS), Development Data Group, World Bank; Enterprise Analysis Unit, DECIG, World Bank

> **Acknowledgments** : The authors are grateful to Jorge Rodriguez Meza for invaluable comments and suggestions.
# **INCOME INEQUALITY**

Part of the reason why the poverty indicators of Bahia and those of the nation as a whole are worse than in other countries with similar per-capita incomes is because of income inequality. Both Brazil and Bahia have an extremely unequal income distribution. Moreover, Bahia’s income inequality has not changed during the last decade and is stubbornly high. In 2000, the Gini coefficient for Bahia was 0.61, slightly below the coefficient for the Northeast region (0.62), and in line with Brazil as a whole (Figure 4.4). It is worth noting that international research shows that the more unequal income is distributed the less effective is economic growth in reducing poverty (Lustig et al 2001).

**<u>Figure 4.4:  Gini Index of Income Inequality, 1991 and 2000</u>**

<!-- Start of picture text --> 0.680<br>0.660<br>1991 2000<br>0.640<br>0.620<br>0.600<br>0.580<br>0.560<br>0.540<br>0.520<br>Source: Census 1991 and 2000.<br>North Northeast Rio Grande do Norte Pernambuco Alagoas Sergipe Bahia Southeast South Center-West Brazil<br><!-- End of picture text --> Changes in inequality are typically very slow, except during periods of radical social and institutional change. Where inequality has fallen it has usually happened in association with major expansion and equalization in educational attainment, as in Korea and Malaysia in the 1970s and 1980s. Bahia’s expansion in education (reduction in education inequalities) may have been too recent to have a significant effect on the composition of skills, and occurred during a period in which the overall returns to high levels of skills were rising and returns to basic skills were falling in Brazil.

In Bahia, the income distribution reveals that both the top and the very bottom of the income distribution receive a smaller share of total income in 2001 than they received in 1995. However, in 2001, the top 10 percent of the income distribution still obtain 49 percent of the total income compared to the 12 percent obtained by the lowest 50 percent of the distribution (Figure 4.5). Education is also unequally distributed and international research shows that this can more easily be reduced than income inequality. However, research also shows that a reduction in education inequality affects the income distribution very little in the short run (Ferreira 2002).

17
_Table 8: Regression estimates of the probability of delivering in a hospital_

||**IV-PROBIT**|**PROBIT**|
|---|---|---|
|Years of schooling|0.1908|0.095|
||(0.1056)|(0.0092)**|
|Log of household consumption per capita (Rp.'000000)|0.0002<br>|0.0015<br>|
||(0.0016)|(0.0004)**|
|Age at childbirth|0.0206<br>|0.0267<br>|
||(0.0148)|(0.0122)*|
|**_Parity (reference group: first birth)_**|||
|Low parity: 2nd or 3rd birth|0.0173|-0.0368|
||(0.1182)|(0.1030)|
|Medium parity: 4th or 5th birth|0.1322|-0.1122|
||(0.3165)|(0.1259)|
|High parity: 6th birth or higher|0.1270|-0.1805|
||(0.4014)|(0.1576)|
|Outcome of previous pregnancy: alive|-0.0583|-0.2065|
||(0.2093)|(0.0966)*|
|Urban area of residence|0.2808|0.464|
||(0.2489)|(0.0618)**|
|Time to nearest health centre or midwife - hrs|0.0325<br>(0.1240)|-0.0486<br>(0.0887)|
|**_Year of childbirth (reference group: 1985-89)_**|||
|1990-1994|-0.1003|-0.0574|
||(0.0897)|(0.0800)|
|1995-1997|-0.1035|-0.0009|
||(0.1621)|(0.1152)|
|1998-2000|-0.1150|-0.0282|
||(0.1767)|(0.1501)|
|**_Region fixed effects_**|||
|Sumatera|-0.2713|-0.2195|
||(0.1402)|(0.1382)|
|Java|-0.2044|-0.2265|
||(0.1243)|(0.1250)|
|Jakarta|0.0256|0.046|
||(0.1517)|(0.1545)|
|Bali|0.3653|0.3151|
||(0.1838)*|(0.1849)|
|Nusa|-0.2762|-0.3371|
||(0.1918)|(0.1799)|
|Kalimantan|-0.1821|-0.2323|
||(0.1926)|(0.1885)|
|**_Cohort of birth fixed effects_**|||
|Cohort 1: 1-6yrs in 1974|-0.3617|-0.5042|
||(0.7611)|(0.7524)|

50
Table 1: Descriptive Statistics for Mother’s Fertilty in Albania (2002 - 2012)

|Variables|Observations|Mean|Standard<br>Deviation|Min|Max|
|---|---|---|---|---|---|
|_Sample: Mothers aged 20 to 49 years old with _<br>Fertility|_2 or more child_<br>7480|_ren youn_<br>2.600|_ger than 18 _<br>(0.871)|_years _<br>2|_old_<br>9|
|_(Number of children ever born)_||||||
|More than 2 children|7480|0.418|(0.493)|0|1|
|_(=1 if mother had 3 or more children)_||||||
|First child boy|7480|0.492|(0.499)|0|1|
|_(=1 if frst child was a boy)_||||||
|First child girl<br>_(=1 if frst child was a girl)_|7480|0.508|(0.499)|0|1|
|Second child boy|7480|0.514|(0.499)|0|1|
|_(=1 if second child was a boy)_||||||
|Two boys|7480|0.254|(0.435)|0|1|
|_(=1 if frst two children were boys)_||||||
|Two girls<br>_(=1 if frst two children were girls)_|7480|0.247|(0.431)|0|1|
|Same sex<br>_(=1 if frst two children have the same sex)_|7480|0.502|(0.500)|0|1|
|Mixed sex<br>_(=1 if frst two children have diferent sex)_|7480|0.497|(0.500)|0|1|

_Notes:_ The data used are the 2002 LSMS, the 2005 LSMS, the 2008-09 DHS and the 2012 LSMS 34
– capita the remaining covariates are sourced from the 2009 employment survey conducted by the NSS and we interact each covariate with a 2015 indicator to avoid collinearity with the district Our primary enterprise outcomes of interest are value-addition, revenues, employment and wage per worker. In additional specifications, we also identify the impact of rainfall shocks on binary measures of credit, registration status, and qualitative measures of firm – performance. Our coefficient of interest is _β_ the impact of a positive rainfall shock on enterprise outcomes. We exclude _NegShock_ as no district in our sample faced a negative shock in the years 2010 and 2015. _β_ therefore estimates the change in firm outcomes if the district receives a positive rainfall shock, relative to the “zero shock” case – when the district receives neither a positive, nor a negative rainfall shock.

## **3.3 Rainfall Shocks and Household Consumption**

The empirical specification used to identify the impact of weather-induced shocks on household consumption is: The unit of observation in (4) is the household with _α_ and _δ_ now denoting household and year fixed effects. _PosShock_ is defined as before while **X** denotes household-specific time-varying covariates such as average years of education, average age, household size, and employment characteristics.

The outcome of interest is logged per capita monthly expenditures, where monthly expenditures are computed after taking the average across all months in the year. Additional outcomes of interest are monthly food, essential food and luxury expenditures per capita. As the outcomes are logged, the _β_ coefficient estimates the percentage change in per capita monthly household expenditures due to a positive rainfall shock, relative to years in which there is neither a positive nor negative shock. As no district faced a negative shock in the years 2014, 2015 and 2016, we do not have negative rainfall shocks in this specification.

# **4 Results**

## **4.1 Precipitation Shocks and Farm Productivity**

We begin by identifying the impact of precipitation shocks on farm productivity, estimated using specification (2). We start by depicting the raw correlation in the data in the form of binned scatterplots in Figure 1. The horizontal axis represents 20 equally spaced bins of standardized annual district rainfall and the vertical axis denotes aggregate district farm productivity. Aggregate farm productivity in the left-hand figure is measured using all crops while aggregate farm productivity in the right hand figure includes only 7 major crops. Each point on the figure represents the unconditional mean farm productivity corresponding to that bin of standardized annual rainfall. In both figures, we see (expectedly) a strong positive correlation between annual rainfall and farm productivity.

9
# 1 Introduction

As is well-known by now, fiscal policy tends to be procyclical (i.e., expansionary in good times and contractionary in bad times) in emerging markets and developing economies (EMDE) and acyclical/countercyclical in advanced economies (AE).<sup>1</sup> Figure 1, updated from Kaminsky et al. (2004), shows the correlations for the HP-cyclical components of GDP and government spending for 189 countries (152 EMDE and 37 AE) for the period 1980-2020.<sup>2</sup> Red bars identify EMDE and black bars AE. A positive correlation indicates procyclicality, while a negative correlation indicates countercyclicality. The visual impression is striking. We see a large red mass on the right-side of the figure illustrating the fact that, on average, most EMDE have been procyclical over the past 40 years. In contrast, most black bars lie on the left-side of the picture, indicating that industrial countries have been typically countercyclical.<sup>3</sup> The average correlation for EMDE is 0.42 (and significant at the 1 percent level). For AE, the average is slightly negative but not significantly different from zero. Procyclical fiscal policy in EMDE thus amplifies an already volatile business cycle (especially in emerging markets). As government spending often spirals out of control in good times, the inability to borrow in bad times makes the inevitable adjustment even more painful. Ironically, fiscal policy thus becomes a source of instability, contrary to its intended stabilizing role in a Keynesian world.

The role of fiscal policy as a possible source of macroeconomic instability is particularly relevant in commodity exporters, given the significant response of fiscal variables to commodity shocks. In an early policy analysis, Cuddington (1989) examines fiscal management in five developing countries (Colombia, Cameroon, Kenya, Nigeria, and Jamaica) in the 1970s in response to commodity price booms. While Colombia and Cameroon avoided large increases in fiscal revenues and government expenditures, fiscal authorities in the other three countries essentially lost control over public finances, with government spending increasing markedly as a proportion of GDP and actually

> 1For an early empirical look at the fiscal procyclicality phenomenon, see Kaminsky et al. (2004) and, more recently, Frankel et al. (2013), Carneiro and Garrido (2016), Richaud et al. (2019), and the references therein.

> 2See Table A1 for details.

> 3As shown by Vegh and Vuletin (2015), the same pattern emerges on the taxation side based on an analysis of tax rates.

1
# Table 6: Legal Gender Disparities and Proportion of Seats Held by Women in National Parliaments

|||||**O**|**LS**||||
|---|---|---|---|---|---|---|---|---|
|**Dependent Variable**||**Proportio**|**n of seats he**|**ld by women**|**in national**|**parliaments**|**(%), 2013**||
||**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|
|Legal disparities: Overall measure|-0.355***||||||||
||(0.137)||||||||
|Legal disparities: Accessing Institutions||-0.468|||||||
|||(0.321)|||||||
|Legal disparities: Using Property|||-0.278<br>(0.886)||||||
|Legal disparities: Getting a Job||||-0.418*<br>(0.249)|||||
|Legal disparities: Going to Court|||||-0.983||||
||||||(1.823)||||
|Legal disparities: Providing Incentives to<br>Work||||||0.017|||
|||||||(1.774)|||
|Legal disparities: Building Credit|||||||-2.103***||
||||||||(0.763)||
|Legal disparities: Violence Protection||||||||-1.855***|
|||||||||(0.643)|
|Log of GDP per capita (constant 2005<br>US$)|-0.029|-0.008|-0.192|-0.167|-0.156|-0.171|-0.307|0.204|
||(1.438)|(1.492)|(1.514)|(1.510)|(1.506)|(1.524)|(1.470)|(1.368)|
|Rule of law|1.464|2.436|2.625|1.876|2.597|2.660|2.256|1.569|
||(1.962)|(1.998)|(1.995)|(2.040)|(2.012)|(2.001)|(1.986)|(1.874)|
|region==East Asia & Pacific|0.785|0.472|0.011|-0.325|-0.212|-0.165|3.345|-1.802|
||(3.496)|(3.357)|(3.469)|(3.684)|(3.503)|(3.574)|(3.449)|(3.946)|
|region==Europe & Central Asia|5.955*|6.586**|6.407**|5.743*|6.419**|6.469**|9.038***|4.966|
||(3.104)|(2.670)|(2.907)|(3.215)|(2.907)|(2.906)|(3.065)|(3.647)|
|region==Latin America & Caribbean|2.490|2.425|2.409|2.254|2.360|2.445|6.900*|0.384|
||(3.669)|(3.434)|(3.634)|(3.812)|(3.643)|(3.647)|(3.751)|(4.093)|
|region==Middle East & North Africa|3.631|0.736|-1.754|-1.107|-2.044|-2.625|2.034|-1.383|
||(4.582)|(4.320)|(4.540)|(3.942)|(3.777)|(3.746)|(3.971)|(4.299)|
|region==South Asia|2.824|1.721|0.999|0.560|0.863|0.460|5.361|-0.869|
||(5.808)|(5.370)|(5.533)|(5.503)|(5.351)|(5.259)|(5.426)|(5.838)|
|region==Sub-Saharan Africa|7.977*|7.561*|6.644|5.916|6.425|6.394|11.100**|6.358|
||(4.498)|(4.377)|(4.484)|(4.640)|(4.405)|(4.412)|(4.593)|(4.711)|
|Constant|22.579*|17.852|19.040|22.921*|18.702|18.756|20.866|19.336|
||(13.055)|(13.209)|(13.548)|(13.927)|(13.431)|(13.635)|(13.117)|(12.566)|
|Number of observations|150|150|150|150|150|150|150|150|
|Adjusted R2|0.117|0.090|0.079|0.093|0.080|0.078|0.111|0.134|

note: *** p<0.01, ** p<0.05, * p<0.1, Huber/White standard errors reported Omitted region: North America 30
welfare effect of the depreciation, which could be substantially smaller than what is reported here.

Third, I stress that I am looking at the welfare effects of depreciation-induced changes in consumer prices over a fairly short period with a fairly large depreciation, and this time horizon drives the finding of significant welfare losses. However, looking at exchange rate changes over other horizons would naturally lead to different conclusions. For example, the depreciation in the trade-weighted nominal exchange rate between 2000 and 2005 was preceded by an even larger trade-weighted nominal _appreciation_ in the previous five years between 1995 and 2000. In fact, for the entire period between 1995 and 2005, the trade-weighted nominal exchange rate _appreciated_ by about 20 percent. If the pattern of exchange rate pass-through to disaggregated consumer prices was similar during this earlier period, then one can interpret the welfare losses sustained between 2000 and 2005 as just a partial reversal of the welfare gains experienced during the appreciation between 1995 and 2000.

# **2.  The Depreciation and Consumer Price Changes**

Figure 1 shows the evolution of the nominal exchange rate and consumer price index in Egypt between 1995 and 2005. The pound was fixed against the US dollar between 1995 and 1999, followed by a moderate depreciation during 2000 and 2001. In 2002 the pound was again fixed against the US dollar, but during 2003 it depreciated sharply by 31 percent against the dollar, and by 41 percent in trade-weighted terms.<sup>4</sup> The consumer price index increased by 6.2 percent during 2003 and by another 10.8 percent during 2004. As shown in Table 1, the trade-weighted exchange rate

> 4 The trade-weighted exchange rate index used here is constructed using data from Egypt's 25 largest trading partners in 2000.  I use fixed weights based on these countries' shares in Egypt's imports in 2000.  During this period there were some exchange controls in place and the parallel exchange vis-a-vis the US dollar diverged significantly from the official rate (in levels).  This raises the question of whether it is more appropriate to use the parallel market rate.  For the analysis that follows, what matters is the exchange rate at which importers actually transact.  If they have access to foreign currency at official (parallel) rates then the official (parallel) rates are appropriate.  Absent information on this, and absent data on parallel rates vis-a-vis all trading partners, I use the trade-weighted official rates.  However, in unreported results I obtain very similar estimates of pass-through using the parallel market rate vis-a-vis the US dollar.  This because although the two series diverge somewhat in levels, in differences they track each other quite closely over the period I consider.

3
However, the literature also shows that in a large cross-section of countries, higher government ownership of banks is associated with slower subsequent financial development and lower income per capita growth and productivity (La Porta et al, 2002; Barth et al. 2001), as well as poorer financial access by small firms (e.g. Beck, Demirguc-Kunt and Maksimovic, 2005). Our findings are also consistent with those of Boyreu-Debray (2003) and Boyreau-Debray and Wei (2005) who show that the depth of the financial sector does not promote growth of provinces in China, another country where the banking system is largely state owned and controlled. Hence, our findings on India provide additional evidence that state-ownership in banking hampers the impact of finance on growth.

The finding that differences in financial depth in a state-owned financial system do not affect the life cycles of firms does not imply that there are no qualitative differences in financial access within those systems or that they do not benefit individual firms. For example, Cole’s (2009) Indian study uses a regression discontinuity design and finds that government ownership of banks adversely affects the development of small enterprises in villages in which these banks have branches. In another study of Chinese banks and enterprises, Ayyagari, Demirguc-Kunt and Makimovic (2010) find that controlling for local financial development, firms that receive bank financing outperform firms that do not. However, Ayyagari et al. do not address the question of whether differences in financial depth at the province level materially affect the aggregate province level growth or the life cycles of the firms comprising the whole of the manufacturing sector.

Our results also contribute to the large literature on firm size and age (Dunne, Roberts, and Samuelson, 1989; Davis, Haltiwanger, and Schuh,1996; Cabral and Matta, 2003, Foster, Haltiwanger, and Syverson, 2012). Given the idiosyncratic distortions in the business 7
# **Table 3 Main Factors Underlying Debt Reductions, 1990-2003**

|**Country, Time Period**|**Total**<br>|**Initial**<br>||**M**|**ain Contributing**|**Factors**(% of|GDP)||
|---|---|---|---|---|---|---|---|---|
||**Change**<br>(% of GDP)|**Level**<br>(% of GDP)|Primary<br>Balance|GDP<br>Growth|Real Exchange<br>Rate|Real Interest<br>Rate|Privatization|Other<br>Factors|
|Chile, 1991-1998|-30.2|42.7|**_–11.5_**|**_–15.6_**|||||
|Indonesia, 2001-2003|-22.3|90.3|**_-8.3_**|**_-9.5_**|**_-4.8_**||||
|Lebanon, 1991-1993|-48.5|98.4|15.8|**_-33.4_**||**_-45.5_**||18.6|
|Malaysia, 1991-1996|-41.4|91.4|**_-32.3_**|**_-37.3_**||16.5||17.8|
|Mexico, 1991-1993|-22.8|50.2|**_-12.9_**|**_-3.9_**|**_-5.9_**||||
|Pakistan, 2000-2003|-9.5|109.1|**_-11.1_**|**_-16.5_**||14.8|||
|Philippines, 1994-1997|-25.3|93.5|**_-22.4_**|**_-15.1_**|**_-7.5_**|13.0||8.2|
|Poland, 1992-2000|-42.8|86.7|2.1|**_-25.5_**|**_-9.0_**|**_-9.5_**|**_-11.7_**|10.8|
|Russia, 2000-2003|-55.2|88.7|**_-16.7_**|**_-15.4_**|**_-19.3_**||**_-6.5_**||
|Turkey, 2002-2003|-24.9|91.0|**_-10.3_**|**_-11.3_**|**_-8.2_**|8.9|||

_Based on Budina, Fiess and others (2004)_ 19