rafmacalaba/datause-encoder-data huggingface dataset:
their daughters, and a composite pro-growth, pro-gender adjustment program involving some element of tax reform.<sup>4</sup> Section 6 offers some concluding remarks.

# 2 Background

In the past two decades, Brazil made much progress in reducing gender inequality. According to the results of a 2010 study by Brazilian Institute of Geography and Statistics (IBGS), illiteracy rates for women 15 years old and above fell from 20.3 percent in 1991 to 13.5 percent in 2000 and 9.8 percent in 2008. Brazilian women are now generally more educated, with female participation in tertiary education exceeding significantly male participation. As a result, the share of the female labor force with tertiary education increased from 7.4 percent in 1992 to 8.5 percent in 1999 and 11.9 percent in 2007, compared to 5.3, 6.2, and 7.3 percent for males, respectively. Working women have an average of 8.8 years of schooling, while their male counterparts have an average of 7.7 years. This is important because, as discussed in the next section, educated mothers tend to have greater bargaining power within the household over intrafamily allocation of monetary resources, be better able to act on their preference for investing in children, and have a greater impact on family decisions regarding the allocation of children’s time to household chores. Professions that traditionally were dominated by males, such as law, medicine, and engineering, are becoming more balanced in terms of gender, and some already have more women than men. The proportion of women in the workforce rose from 52.8 percent to 57.6 percent between 1998 and 2009, whereas the share of women in wage employment in the non-agricultural sector rose from 35.1 percent in 1990 to 41.6 percent in 2007. The ratio of female to male labor force participation rate increased from 52.2 in 1990 to 63.9 in 1995, 66.7 in 2000, and 73.3 in 2010.

> 4In Brazil, under the Bolsa Família program, families with income less than R$60 (roughly US$38) per capita receive R$62, plus R$20 per child (aged 15 and lower) up to three children, and R$ 30 per adolescent (aged 16 and 17) up to two children. Therefore, poor families can receive up to R$182, which is about half of the minimum wage in Brazil (Cavalcanti and Correa (2010)). The conditionalities associated with these transfers ensure the compliance of beneficiary families with requirements such as school attendance, vaccine, and pre-natal visits. The total coverage is large: roughly 11.1 million families or about 44 million people. In the Brazilian Northeast (the poorest region of the country) about half of all families are enrolled in this program.

5
considerably by gender and rural/urban residence. Due to the higher drop-out rates of girls and rural residents in lower grades, those who make it to higher grades among these groups are likely to be a more select group with stronger school attachment.

# _III. DATA_

The data for this study come from the 2003 and 2008 Turkish Demographic and Health Surveys (DHS), both of which are nationally representative. The main advantage of DHS over other data sources for the purposes of this study is that it provides information on the location of residence at age 12, which allows us to identify rural/urban residence at ages that are pertinent to the schooling decisions examined in this study. Another advantage of DHS is that it provides information not only on the highest schooling level but also on the highest grade completed, which is lacking in other Turkish data sources.

Students who complete grade 4 or a lower grade in the 1996–97 school year are covered by the policy (i.e., students who do not have a primary school diploma by the beginning of the 1997–98 school year). This means that the first cohort affected by the policy is the one that begins grade 1 in the 1993–94 school year. Most children in Turkey start school at age 6. In that case, children who are born after September 1986 would be affected by the policy. However, a considerable fraction of children delay starting school to age 7. Among these children, those who are born after September 1985 would be affected by the policy. Our sample covers those who are born between 1975 and 1996 with the exception of those born in 1985 and in 1986. We drop these two cohorts because of the fuzziness in their treatment status, as explained above. Hence, our sample includes 10 birth-cohorts who are not affected by the policy (1975–84) and 10 birth-cohorts who are affected (1987–96). The female sample in our analysis is drawn from the 2003 and 2008 surveys, whereas the male sample is drawn only from the 18
# **5.7 Evidence of Pro-Local Policies**

## **5.7.1 Improvement in Local Educational Achievement**

We now present evidence that the counties led by YRD-affiliated cadres indeed adopted pro-local policies that facilitated local development. Education, a key component of human capital, can be an important source of local growth. Table 17 regresses the log changes in the average years of schooling for individuals born in 1950-1972 from those born prior to 1930, as observed in the 1990 Census, on the county’s YRD affiliation, Guerrilla presence and their interactions. As before, we control for the initial conditions and geography. Note that those born prior to 1930 would have been 22 years old in 1952, and would mostly have finished their formal schooling by then. Similarly, cohorts born between 1950 and 1972 would be at least 18 by 1990 and would have completed their formal schooling by then. We use the log differences in the average schooling of the 1950-1972 cohorts and the cohorts prior to 1930 as the proxy for the improvement in local educational improvement.

In Columns (1) and (2) of Table 17 where all the counties (with non-missing variables) are included in the analysis, we find that counties with YRD affiliations and with Guerrilla presence are associated with larger percentage increases in educational achievement, though the differences are not statistically significant. In Column (3) where we interact the YRD and the Guerrilla dummies, we find that counties with both YRD affiliation and Guerrilla presence are associated with 37.5 percentage points higher increase in average education, and the effect is statistically significant at 10% level.

In the right panel of Table 17, we trim the tail 5% of the counties from the analysis. In Column (4), we find that, relative to counties led by FD3 cadres, those led by YRD cadres had a 30.1 percentage points higher increase in average schooling levels; in Column (5), we include both YRD and Guerrilla dummies in the regression, and the results indicate that the YRD premium in education increase is 28.7 percentage points, statistically significant at 10% level. Guerrilla presence is also associated with a premium in education increase of 13 percentage points, though not statistically significant. In Column (6), we include the YRD and Guerrilla interactions. We find that most of the YRD effect on educational improvement result from the counties led by YRD-affiliated cadres that also had guerrilla presence, and the premium is 38 percentage points (relative to FA3 counties). In counties led by YRD-affiliated cadres that had no guerrilla presence, the educational achievement growth is also faster than that in FA3 counties (by 22 percentage points), but the coefficient estimate is not statistically significant.

The results in Table 17 confirm that counties led by cadres from weaker factions that in addition had stronger local accountability did pursue policies that led to faster improvement in local educational achievement. We believe that these are likely levers through which factional competition and local accountability impact the local development.

43
variation across regions in rice prices<sup>7</sup> and natural disasters in Vietnam. Results indicate that child labor lowers school performance. However, Ravallion and Wood find that the schooling subsidy, while strongly raising school attendance, reduced child labor to a much lower extent, since school parallel to labor is feasible due to the short duration of daily class time. Orazem and Gunnarsson (2004) provide an overview of the literature on the impact of labor on school outcomes, and in particular a discussion of instruments.

Instrumental variables has also been the empirical approach followed by Chatterji and DeSimone (2005), who used alcohol consumption in the previous month as instrument in their study of the impact of alcohol consumption on high school drop-out; similarly, Roebuck et al (2004) used indicators of religiosity to study the impact of drug use on schooling outcomes. We will follow this methodological line to study the factors pushing children out of school.

# **4. The neighborhoods under study and data collection procedures**

# _4.1. Fortaleza, Northeast Brazil_

Headcount poverty in the Northeast is among the highest in Brazil.<sup>8</sup> Moreover, the state of Ceará, in particular, has one of the lowest HDI (Human Development Indicator, developed by United Nations Development Program) among Brazil's 27 states, even though it is the state that has improved the most this indicator, from 0.593 in 1991 to 0.700 in 2000.

The capital of the state, Fortaleza, is the fifth largest city in Brazil, with 2.1 million inhabitants, adding up to 2.8 million people when the metropolitan area is included. It has 402 _favelas_ , of which 82 were classified as risk areas by the Military Police. During 1991-2000, the municipality of Fortaleza improved its HDI slightly less than the state as a whole, from 0.717 to 0.786. However, it is important to note that large disparities are observed when disaggregating the HDI by neighborhood. The most developed neighborhood in Fortaleza has a HDI comparable to that of industrialized countries (0.916), while the least developed neighborhoods have a level of development comparable to that of countries in Sub-Saharan Africa (0.338).

The three neighborhoods included in the survey are among the poorest in Fortaleza and this study thus covers a relatively homogenous urban area, in terms for example of low provision of infrastructure and public services.

> 7 Which reduces child labor.

> 8 This section draws heavily on Verner and Alda (2004).

5
The analysis uses the tariff cut proposal that has shaped the negotiations—the proposal by the G-20 of a tiered formula with four bands and three inflexion points (G-20 2005). For the industrial countries, this involves proportional cuts in bound tariffs that increase through four tiers to reach 75 percent on tariffs above 75 percent. For developing countries, the cuts rise to 40 percent on tariffs above 130 percent. Tariffs are capped at 100 percent for developed countries and 150 percent for developing countries. Leastdeveloped countries are not required to undertake any reduction commitments.

Bound tariffs on sensitive products can be cut by one third or two thirds of the formula cut, with increases in TRQs required to compensate trading partners for the resulting loss of market access (WTO 2008). We assume that the combined effect of the tariff cut and TRQ expansion for a sensitive product with a TRQ is one-half the formula cut.

# **IV. Experiments and Impacts on Average Tariffs**

We used four different approaches to identifying sensitive products. The first was to solve equation (1') using nonlinear integer programming. However, we encountered multiple solutions using this nonlinear approach. We therefore turned to approaches based on the second-order approximations discussed in Section II. Our initial results were obtained by solving equation (4'') using the SBB (Branch & Bound) GAMS® solver for Mixed Integer Nonlinear Programming (MINLP) (see GAMS 2010) with up to 2 percent of products allowed as sensitive. This was complemented by simple one-product-at-atime selections using equation (6), and equation (7) with the own-price elasticities of Kee, Nicita and Olarreaga (2008).

12
**Table 2. Central Government Expenditure, 2016**

|**Source**|Million<br>Turkish Lira<br>(TL)|Percent of<br>GDP|Share of<br>Government<br>Expenditure|
|---|---|---|---|
|**Central Government Expenditure (A+B)**|**805,205**|**30.9**|**100**|
|**A) Social Spending (I+II+III+IV+V)**|**465,434**|**17.8**|**57.8**|
|**I. Non-contributory Benefits**|**32,007**|**1.2**|**4.0**|
|Old-Age Benefit|1,580|0.1|0.2|
|Family Benefit|3,253|0.1|0.4|
|Disability Benefit|3,182|0.1|0.4|
|Home Care Benefit|5,039|0.2|0.6|
|Scholarship Benefit|2,357|0.1|0.3|
|Near Cash Benefit|5,387|0.2|0.7|
|Health Premium Fee Waiver|7,003|0.3|0.9|
|Widow Transfer for Women|810|0.0|0.1|
|Birth Support|512|0.0|0.1|
|Others|2,884|0.1|0.4|
|**II. Contributory Benefit**|**205,691**|**7.9**|**25.5**|
|Disability Transfers|3,128|0.1|0.4|
|Unemployment Transfers|3,713|0.1|0.5|
|Retirement Pension|160,372|6.1|19.9|
|Widow and Orphan Pension|38,478|1.5|4.8|
|**III. Agricultural Support**|**3,244**|**0.1**|**0.4**|
|**IV. Minimum Subsistence Allowance (AGI)**|**23,864**|**0.9**|**3.0**|
|**V. In-kind Transfers**|**200,628**|**7.7**|**24.9**|
|Education|106,616|4.1|13.2|
|Health|94,012|3.6|11.7|
|**B) Other Spending* **|**339,771 **|**13.0**|**42.2 **|

Source: Ministry of Treasury and Finance.

*Other Spending includes: General public services, defense, public order and safety, economic affairs, environmental protection, housing and community amenity, recreation, culture and religious services, and social security and aid.

_Contributory Benefits_ 9
in 1996; by contrast, the STSM and merged approaches suggest that there is a strong cyclical downturn in 1996. This occurs because the sharp fall in money demand that took place in 1996 has been attributed in the VECM approach to the trend component; by

**MI-Demand at Cycle and Seasonal Frequencies Inflation as Measure of Opportunity Cost** _VECM,_ **_STSM and Merged Approaches *_**

<!-- Start of picture text --> 0.4 -<br>0.3<br>0.2 -<br>0.0 !<br>-0.2<br>-0.3<br>% 0% 0% 0% 0% 0 0% 0 0<br>-- - VECM - STSM Merged VECM<br>*For VECM,, hart shows fitted values of short-run modeL For STSM and<br>merged models, chart shows sums of cycles plus seasonal components.<br><!-- End of picture text --> contrast, STSM-based trends turned out to be smoother and the sharp dip in 1996 was captured as a cyclical phenomenon (see Figure "MI-demand at Cycle and Seasonal Frequencies: Inflation as Measure of Opportunity Cost").

When log inflation ,is used as the measure of opportunity cost, the general features of short-run dynamics based on the STSM and merged approaches are similar. By contrast, the VECM-based short-run dynamics are somewhat different from the STSM and merged approaches. It is important to note, however, that the most salient characteristics of short-run dynamics are fairly robust to the change in the measure of opportunity cost from inflation to log inflation, but more so in the case of the STSM and merged approaches (see Figure "MI-demand at Cycle and Seasonal Frequencies: Log Inflation as Measure of Opportunity Cost").

Finally, when the log of the nominal exchange rate depreciation is used as the opportunity cost variable, the general patterns of short-run dynamics remain umchanged. Money Demand in Venezuela, Mario A. Cuevas 28
**Figure 4: Matching Demand with Supply of Aid for Trade**

<!-- Start of picture text --> Potential Demand for AFT, 2005<br>Controlling for GDP per Capita, Rule of Law and Trade Facilitation Index<br>MNG<br>KGZ<br>GHA MOZ MWI<br>NIC<br>VNMGEO ETHZMB<br>TZAGMB PHLMDAARMLKA MLIBIHBENBOL PRYUGA<br>MYS CHL TUN JORZAFTHA MUSCHNPANJAMSLVMEX BWABRAURYNAMTURINDARGPAKIDNHRVAZEMARCOLPERDZAUKRCRIGTMHNDKEN VENMDGKAZCMRECUZWEBGD<br>1 1.5 2 2.5 3 3.5<br>TF Index<br>Average AFT Bottom Performers<br>Top Performers<br>Source: Authors Calculations based on indexes from Wilson & Portugal (2010), World Bank and ITC<br>.03<br>.02<br>.01<br>0<br>AFT Disbursements as share of GDP -.01<br><!-- End of picture text --> Source: Martinez and Wilson (2010).

# **4. Aid for Trade: Impacts and Effectiveness**

There is an extensive literature analyzing the relationship between aid and economic growth. The analytical methods employed in these studies and the results are subject to significant debate. The literature provides a mixed picture as to whether there is a positive relationship between aid and growth.<sup>12</sup> There may be many reasons for this. It may be due to the type of aid delivered (for example, purely humanitarian vs. policy change driven) or reflect differences in absorptive capacity in developing countries.<sup>13</sup> One factor that can explain a lack of a positive relationship between aid and growth is aid-induced appreciation of real exchange rates—with aid inflows inducing Dutch disease.<sup>14</sup> A comprehensive review of this literature is beyond the scope of this paper. It is useful, however, to outline, in brief, the complexities in analyzing and understanding

> 12 See Rajan and Subramanian (2005) for a survey and new assessment; Cali and te Velde (2009) for a synthesis of the extant literature.

> 13 See Radelet, Clemens, Bhavnani (2006), OECD (2006).

> 14 The effect is well known: aid flows may be used to finance expenditures of non-tradable goods and services, leading to a rise of their relative price with respect to tradable goods and thus, to a real appreciation of the exchange rate. This reduces the competitiveness of the exporting sector, as resources are transferred from the tradable to nontradable sectors and drives up wages and other input costs. Estimates of whether aid induces a Dutch disease phenomenon can vary widely. Much depends on assumptions about the marginal productivity of additional aid and public expenditures, the complementarities between public and private capital, and the degree of flexibility of labor costs and other key resources. See e.g., Radelet, Clemens and Bhavnani (2006).

10
Policy Research Working Paper 7391

# **Abstract**

Almost all firms in developing countries have fewer than 10 workers, with the modal firm consisting of just the owner. Are there potential high-growth entrepreneurs with the ability to grow their firms beyond this size? And, if so, can public policy help alleviate the constraints that prevent these entrepreneurs from doing so? A large-scale national business plan competition in Nigeria is used to help provide evidence on these two questions. The competition was launched with much fanfare, and attracted almost 24,000 entrants. Random assignment was used to select some of the winners from a pool of semi-finalists, with US$36 million in randomly allocated grant funding providing each winner with an average of almost US$50,000. Surveys tracking applicants over three years show that winning the business plan competition leads to greater firm entry, higher survival of existing businesses, higher profits and sales, and higher employment, including increases of over 20 percentage points in the likelihood of a firm having 10 or more workers. These effects appear to occur largely through the grants enabling firms to purchase more capital and hire more labor.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at dmckenzie@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
expansion of irrigation during the decade as well -from 61 percent in 2000 to 69 percent in 2010. The standard deviation estimate (Table 1) shows that there are considerable variations in rice yields across upazilas. Among other variables, access to electricity by households improved considerably during the decade (6.5 percent annual growth rate).

# **5. Empirical Results**

In this section, the main empirical results are presented sequentially. All outcome variables as well as rainfall are expressed in logarithms. All regressions include upazila and year fixed effects. All standard errors are corrected for correlation in the error term within the upazila.

# **5.1 Rainfall, Agricultural Productivity, and Agricultural Wage**

We begin by presenting evidence on the impact of rainfall shock on agricultural productivity. Table 2 reports the results from OLS regressions where log of rice yield is regressed on deviation of rainfall after controlling for upazila and year fixed effects. Column (1) shows the estimate when no other explanatory variable is included in the regression. The specification in column (2) includes the full set of upazila level controls as discussed in the empirical strategy section (Section 3). The first panel reports results for the STE sample and the second for the rural sample. The evidence from both specifications shows statistically and numerically significant impact of rainfall shock on rice yield in the rural and STE samples. The estimated coefficients imply an _increase_ in the yield growth due to a positive shock in rainfall over its mean, with the impact marginally larger in the rural sample compared with the STEs. This result is consistent with the findings from a rich body of evidence accumulated by the agronomists and crop scientists that shows that rainfall is a major determinant of rice yield growth in Bangladesh over the last few decades (see, for example, Sarkar et. al. (2012)).

While a positive rainfall shock increases rice yield, does it increase the wage rate? We utilize HIES data on agricultural wage to examine the effects of rainfall shocks. Note that the sample size for the wage regression shown in column (3) of Table 2 is somewhat smaller due to the fact that HIES – though 21