rafmacalaba/datause-encoder-data huggingface dataset:
in which policies of import substitution predominated. (It goes without saying that such policies are anomalous from the perspective of the World Bank’s Doing Business database and Cato Institute’s Economic Freedom indicators [World Bank, various years; Gwartney, Lawson, and Hall 2012]).

But in Chapter 7, Firpo and Pieri argue that by the late 1970s, the country had run out of room for continuous and long-term structural change, at least along broad intersectoral lines, and had to rely on within-sector enhancements—like investing in human capital and new technologies (in agriculture especially) and improving institutions. In fact, they assert that efforts aimed at reversing this natural trend (by enlarging manufacturing and contracting agriculture) failed “and the early years of the 1980s of slow growth can serve as evidence of those efforts.” So what path remains open for Brazil today? The authors argue that the Brazilian experience suggests a return to the old policies is likely to fail. They believe horizontal, across-the-board policies are more likely to spur productivity within sectors than selective policies that give priority to some sectors over others. Given where Brazil stands in term of its stage of development, it is reasonable to expect that future growth will have to rely predominantly on investment in fundamentals (institutions and human capital), and that broad patterns of structural change will play a comparatively small role. In particular, it will be difficult for Brazil to reindustrialize. But there are still strategic opportunities that could be exploited by a nimble government. If used well, the country’s deep-water oil reserves should boost not only oil exports but also a range of associated services and industries at home.

As for Botswana, its story is similar to Brazil’s, in that the share of employment in agriculture fell dramatically between 1970 and 1990. But unlike Brazil, the decline in agriculture’s share of employment was almost entirely matched by an increase in the share of the labor force in services. Moreover, numerous government efforts to industrialize never succeeded (perhaps not surprising for a small landlocked country). The authors of Chapter 3—McCaig, McMillan, Verduzco-Gallo, and Jefferis—point to two distinct periods in Botswana’s economic evolution. Between 1970 and 1989, they find that labor productivity grew at an average of 8 percent per year, with structural change playing a major role in this spectacular growth, especially in the 1970s (Figure O.8). But in the decades that followed, labor productivity slowed to 1.9 percent per year, driven entirely by within-sector productivity growth—with structural change an actual drag on growth in the 2000s.

Historically, diamonds played a significant role in fueling Botswana’s economic growth, although this has changed in recent years. Between 1968 and 2010, economic activity shifted out of agriculture, first to mining and later to 26
# **Data**

The table below lists, defines and provides the data source for key variables in our empirical analysis. Group A are variables measured at the village level; Group B at the school level; and Group C at the teacher level.

|**Variable**|**Description**|**Survey**<br>**Source**|
|---|---|---|
|_Group A: Village L_|_evel_||
|Grant per capita|Grant amount per private school going child in<br>treatment villages.<br>For _L_ villages, this is Rs<br>50,000/total private enrollment, and for_H_ villages,<br>this equals (50,000*# of private schools in vil-<br>lage)/total private enrollment. Control schools are<br>assigned a value of 0.|School|
|_Group B: School L_|_evel_||
|Closure|An indicator variable taking the value ‘1’ if a school<br>closed during the study period|School|
|Refusal|An indicator variable taking the value ‘1’ if a school<br>refused a given survey||
|Enrollment|School enrollment in all grades, verifed through<br>school registers. Coded as 0 after school closure.|School|
|Fees|Monthly tuition fees charged by the school averaged<br>across all grades.|School|
|Posted Revenues|Sum of revenues across all grades obtained by mul-<br>tiplying enrollment in each grade by the monthly<br>fee charged for that grade. Coded as 0 after school<br>closure.|School|
|Collected<br>Rev-<br>enues|Self-reported measure on total monthly fee collec-<br>tions from all enrolled students. Coded as 0 after<br>school closure unless otherwise specifed.|School|
|Test Scores|Child test scores in English, Math and Urdu, are av-<br>eraged across enrolled children to generate school-<br>level test scores in these subjects. Tests are graded<br>using item response theory (IRT), which appropri-<br>ately adjusts for the difculty of each question and<br>allows for comparison across years in standard de-<br>viation units.|Child<br>tests|
|Stayer|A stayer is a child who self-reports being at the<br>same school for at least 18 months in round 3.|Child<br>survey|
|Fixed Costs|Sum<br>of<br>spending<br>on<br>infrastructure<br>(construc-<br>tion/rental of a new building, additional classroom,<br>furniture and fxtures), educational materials, and<br>other miscellaneous items in a given year. Data is<br>collected at the item level, e.g.<br>furniture, equip-<br>ment, textbooks etc. Coded as 0 after school clo-<br>sure.|School|

_Continued on next page_ 27
# **Appendix II. Balancing tests**

# **Table A1. Balancing tests on characteristics of e-households and non-e-households**

||Mean differ|ence betwe|en the matc|hed e- and n|on-e-househ|olds|
|---|---|---|---|---|---|---|
|**Household head characteristics**|Block 2|Block 3|Block 4|Block 5|Block 6|Block 7|
|Age of HH head|-1.20|1.52|0.61|0.57|-1.32|-3.83|
|If HH head is male|-0.12|-0.03|0.03|-0.03|0.00|-0.01|
|If HH head is married|-0.08|-0.03|0.02|0.02|-0.03|0.00|
|HH head’s highest education is...|||||||
|less thanprimary|0.25|-0.31|-0.05|0.01|-0.04|0.03|
|primaryschool completed|0.08|0.24|0.04|-0.05|-0.09|0.01|
|middle school completed|-0.22|0.02|-0.05|0.02|0.09|0.10|
|high school completed|0.00|0.00|0.00|0.02|0.10|0.13|
|vocational school completed|-0.12|0.04|0.06|-0.07|-0.06|-0.18|
|college completed|0.00|0.02|0.00|0.04|0.01|0.20|
|universitycompleted|0.00|0.00|0.01|0.03|-0.01|-0.30|
|Health of HH head is...|||||||
|healthy|-0.07|0.09|0.02|-0.02|-0.02|0.00|
|sick but can work|-0.10|-0.10|-0.02|0.02|0.02|0.00|
|sick and cannot work|0.17|0.02|0.01|0.00|0.00|0.00|
|Hukou status of HH head is...|||||||
|registered in current village|0.13|-0.15|-0.06|0.05|0.06|-0.21|
|same county different village|-0.02|0.00|0.00|0.02|-0.05|-0.03|
|same province different county|-0.05|-0.04|0.06|-0.01|-0.05|-0.05|
|different province|-0.07|0.19|0.01|-0.06|0.03|0.28|
|If HH head worked in urban areas before|-0.02|0.18|0.10|-0.02|-0.04|0.05|
|If HH head learned about e-commerce while in urban|||||||
|areas|0.00|0.00|0.03|0.04|0.00|0.17|
|If HH head is self-employed|-0.12|-0.18|0.19|0.19|0.34|0.35|
|**Household characteristics**|||||||
|Household size|-0.30|-0.14|0.04|-0.26|-0.25|0.69|
|Agricultural land (Mu)|-1.17|-1.75|0.90|-0.94|0.36|0.29|
|Share of HH members age 15~35|0.08|0.05|-0.01|0.00|0.04|-0.06|
|Share of HH members age 36~49|0.13|-0.05|-0.01|-0.03|-0.04|0.03|
|Share of HH members age 50~69|-0.07|0.04|0.02|0.01|-0.01|0.00|
|Risk preference index (ranges 0-1; 0 means most risk-<br>loving and 1 means most risk-averse)|-0.08|0.00|-0.03|0.00|0.01|0.07|
|**Village characteristics**|||||||
|Population in the village|-3400.00|-114.50|-258.66|-422.36|2104.72|-387.82|
|Whether have Dibaopolicy|0.02|-0.08|0.01|-0.01|0.00|-0.02|
|Distance to the nearest train station|-1.39|-1.50|0.97|0.15|-0.48|-0.76|
|Distance to the nearest logistic center|-0.11|-0.03|-0.03|0.03|0.02|-0.05|
|<br>Whether have piped water|0.02|0.00|0.01|-0.03|0.00|0.09|
|Whether in the suburbs of a big city|-0.17|0.07|-0.01|-0.03|0.04|0.11|
|Number of observations|64|65|258|276|196|86|

. * p-value <0.1 ** p-value<0.05 *** p-value<0.01 29
# Table A1: Structured pedagogical interventions

|**Country**|**Intervention**|**Method**|**Test**|**Effect**<br>**Sizes**|**Reference**|
|---|---|---|---|---|---|
|Brazil|Decentralised schooling system, Grade 4|DID|**Portuguese proficiency**|**0.19**|Leme, 2010|
|Brazil|Decentralised schooling system, Grade 8|DID|**Portuguese proficiency**|**0.29**|Leme, 2010|
|Cambodia|The School Readiness Programme (SRP)|Multivariate analysis|**Khmer language**|**0.5**|Nonoyama‐Tarumi and<br>Bredenberg, 2009|
||||**Vocabulary**|**‐0.1**||
|Chil|The Collaborative Language and Literacy|Mltill li|Nonword reading fluency|0.11|Pllt 2013|
|e|Instruction Project (CLLIP)|ueve anayss|**Reading comprehension**|**‐0.06**|aane|
||||**Word reading**|**0.14**||
|India|Year 2, Machines and activities|RCT|**English test**|**0.06**|He, Linden and Macleod,<br>2007|
|India|The Pratham PicTalk programme: Year 2,<br>Machines only|RCT|**English test**|**0.06**|He, Linden and Macleod,<br>2007|
|India|The Pratham PicTalk programme: Year 2,<br>Activities only|RCT|**English test**|**0.06**|He, Linden and Macleod,<br>2007|
|India|The Pratham PicTalk programme: Year 1|RCT|**English test**|**0.08**|He, Linden and Macleod,<br>2007|
|Idi||RCT|**Burt reading test**|**0.48**|Dixon, Schagen and|
|na|||Schonell spelling test|0.58|Seedhouse, 2011|

28
**Table 9: Employment change (%) of 10% wage subsidy** **<u>to low-skilled and medium-skilled formal labor</u>**

||Base|Low|Medium|High|
|---|---|---|---|---|
|Low-skilled formal labor|3451.5|3.3|6.7|12.2|
|Agriculture|761.6|4.9|10.8|21.2|
|Industry|1069.7|2.1|4.1|7.1|
|Services|1620.2|3.3|6.5|11.2|
|Medium-skilled formal labor|3207.0|3.0|6.2|12.2|
|Agriculture|34.7|3.8|8.6|18.4|
|Industry|432.2|2.5|5.0|9.8|
|Services|2740.1|3.1|6.4|12.5|
|High-skilled formal labor|1300.7|0.0|0.0|0.0|
|Agriculture|16.4|0.3|1.0|2.5|
|Industry|133.4|0.2|0.3|0.4|
|Services|1150.9|0.0|0.0|-0.1|
|Informal labor|2913.4|0.0|0.0|0.0|
|Agriculture|301.8|0.0|1.8|5.0|
|Industry|357.3|0.1|0.2|0.3|
|Services|2254.3|0.0|-0.3|-0.7|
|Self-employed labor|346.3|0.0|0.0|0.0|
|Agriculture|18.2|0.3|1.4|3.8|
|Industry|43.1|0.1|0.1|0.0|
|Services|285.1|0.0|-0.1|-0.3|
|Total labor force|11218.9|1.9|3.8|7.2|

Source: CGE model simulations.

Increased employment from the wage subsidy leads to increased GDP. For a 10 percent wage subsidy, GDP increases from 0.6 percent (low substitution elasticities) to 2.4 percent (high substitution elasticities), see Table 10. **Table 10: Percent Change in Real GDP given 10% wage subsidy to low-skilled and medium-skilled formal labor**

||Base|Low<br>M|edium|High|
|---|---|---|---|---|
||(Billion R)|10%|10%|10%|
|Absorption|1231.0|0.6|1.3|2.4|
|Household Consumption|786.3|1.0|2.0|3.8|
|Fixed investment|200.3|0.0|0.0|0.0|
|Inventory|5.3|0.0|0.0|0.0|
|Government consumption|239.1|0.0|0.0|0.0|
|Exports|339.8|0.6|1.1|2.1|
|Imports|-319.4|0.6|1.2|2.3|
|GDPatmarket prices|1251.5|0.6|1.3|2.4|

Source: CGE model simulations.

The modeling results estimate that a 10 percent wage subsidy with low elasticities of substitution will cost R19.7 billion (in 2003 rand, see Table 11). However, real GDP increases as employment increases, and tax revenues will also increase, offsetting the cost of the wage subsidy. As a result, the effective cost of the wage subsidy is 75 percent of the total wage subsidy bill, in the low elasticity case. If one assumes the economy is more flexible, the effective net cost of the wage subsidy falls to 55 percent of the wage subsidy bill. The wage subsidy per job

- 18 -
# 1 Introduction

The very strong correlation between education levels and health outcomes, even after controlling for income, has been recognized as a robust empirical observation in the social sciences and economic literature (Deaton and Paxson 1999; Fuchs 1982; Lleras-Muney 2001). However, the question of the mechanisms driving this correlation remains challenging. The ability to process information is thought to be one channel through which education affects health outcomes. This paper tests this hypothesis by examining the effectiveness of an information campaign that aims at preventing the spread of the HIV/AIDS epidemic in Uganda.

The HIV/AIDS epidemic is probably the greatest challenge facing Africa. According to UNAIDS, in 2003, between 25 and 28.2 million people were infected by HIV/AIDS in Sub-Saharan Africa (this represents 65 to 70 percent of the worldwide total), between 2.2 and 2.4 million died from the virus and between 3 and 3.4 million became newly infected. In Uganda, the percentage of infected adults in 2001 was 5 percent, down from 14 percent in the early 1990s (United States Government 2000). While the epidemic is still on the rise in most of Africa, especially in southern Africa, with a prevalence rate as high as 38.8 percent (UNAIDS 2002) in the adult population of Botswana, Uganda is one of the few countries that has been successful in fi ghting the epidemic. Therefore, there is a high potential to learn important lessons from the Ugandan experience, both in order to understand behavioral responses and from a policy perspective.

Unprotected sex was not initially perceived as dangerous in Uganda. At the end of the 1980s and early in the 1990s, as the Ugandan government and several national and international organizations embarked on large-scale prevention campaigns, information about the HIV/AIDS epidemic gradually became available and revealed the risk associated with multiple partners and unprotected sex. In addition to information about the transmission modes of the disease, the campaigns also promoted ways to avoid the infection, including condom use.

This paper uses a unique individual level data set from a longitudinal survey, the General Population Cohort (GPC) of the Medical Research Council (MRC) Programme on AIDS in Uganda, which follows the general population of a cluster of villages over 12 years. This cohort study started shortly after the initiation of the information campaign and is still ongoing, which makes it useful for assessing the impact of the prevention efforts. The longitudinal nature of the data allows us not only to observe trends in HIV prevalence, but also to analyze HIV incidence, the infection rate, 2
_Formal worker population covered by the subsidy._ We use the total number of workers in September 2018 taken from the Social Security Institution and multiply it with the coverage rate of the subsidy to get the total number of workers in all treated firms. We assume that the coverage rate, i.e., the percentage of workers that are covered by the subsidy in the eligible regions, is roughly the same as the coverage rate in our dataset. 86 percent of workers in eligible regions were covered by the 6 points subsidy in our dataset, and we take this percentage throughout the cost-benefit analysis whenever needed. Note that this coverage is highly likely to be an overestimation, as our dataset includes the firms that benefit from the 5 points subsidy, which is already a subset of all firms in operation in the eligible regions. However, unfortunately, we do not have a good estimate of coverage for the 5 points subsidy in the regions eligible for the 6 points subsidy to further refine this assumption. We expect the overall bias in net benefit that this possible overestimation to cause to be limited as we use the same coverage assumption in both the cost and the benefit sides of the analysis. Furthermore, an overestimation of the coverage rate would cause an underestimation of the net benefit.

_Additional formal employment._ Since our effect size is calculated as the average effect on the treated, we first need to make a reasonable assumption on the number of workers in the treatment group, and estimate the additional formal employment generated by the subsidy using this total number. We use the formal worker population covered by the subsidy (explained above) as the total number of workers after the implementation of the subsidy in the treatment group, and calculate the total number of workers in the counterfactual (i.e., if the subsidy was not implemented) using the effect size. The total increase in formal employment as a result of the subsidy may then be calculated as follows: T T tt tt tt<sup>−TTNNTllTTNNPPTT</sup> NNTTNNAANNTTNNTTll = TTNNT llTTNNPPT (1 + TTA TTeeBBeeTT) _Total payments by the firm to the government._ Payments to the government include social security premium payments as well as income and stamp taxes. We use the payments that would be associated with the average worker wage for September 2018.

_Minimum wage._ Minimum wage is set by the government and is updated twice each year, one at the beginning of the year and a second, mid-year update. We take the minimum wage in September 2018 for the purposes of this analysis.

Table 5 provides the results of the cost-benefit analysis. Our results suggest that assuming an effect size of 6%, it costs the government around 720 TL, or around 35 percent of the minimum wage in 2018, to generate each additional formal employment using the subsidy. Benefits equalize the cost of subsidy with an effect size of around 9.5%.

Another way to identify whether benefits equalize costs is to investigate the return of investment based on each worker. Assuming that the worker benefits from the subsidy for an 19
17 difference regression of Col. (1) where we control for initial conditions suggests that urbanization correlates with both. The Beta coefficient is twice as large for FMXGDP than for AGXGDP, likely due to agricultural exports requiring rural labor. In the panel specifications (Cols. (2)-(4)), only FMXGDP survives. Likewise, lags of AGXGDP have positive but insignificant coefficients (Web Appx. Table D.3). Indeed, not all agricultural exports may be associated with urbanization. Some crops require large amounts of labor and cities may develop too slowly for the correlations to appear with panel regressions. Web Appx. Table D.2 then shows stronger correlations for MFG than for SERV, and positive correlations for SERV, and FIRE or non-FIRE services.

The results shown so far are aligned with Propositions 1-4. We further test these propositions in the next section, and in particular the fact that the mechanism of the urbanization process might also influence the employment composition of urban areas.

# **4. Resources, (De-)Industrialization, and Urban Employment**

**Model.** We rely on IPUMS census microdata for 62 sample countries accounting for almost two-thirds of the world’s total urban population, which is high considering that we ignore most developed economies. In total, we obtain 184 census samples during the period 1990-2015. For our cross-sectional country ( _c_ )-level analyses, we then select for each country the closest year to the year 2000 and estimate the following model:

EMP _c_ 00 = _α_ + _β_ MFGSERV _c_ 00+ _γ_ NRXGDP _c_ 60 _−_ 00+ _δ_ DEINDU _c_ 80 _−_ 00+ _ε_ URB _c_ 00+ _Xcκ_ + _μc_ (10) In this equation, EMP is a sector’s labor share in the urban areas of country _c_ c. 2000. Since most censuses are centered around the year 2000, we use information from 2000 for MFGSERV in country _c_ , the mean of NRXGDP in country _c_ in the period from 1960 to 2000, and DEINDU in country _c_ between 1980 and 2000 (a positive value indicates deindustrialization). To compare countries with _similar_ levels of urban economic development, we control for the urban share (URB) in 2000. We can then ignore the initial conditions in 1960 but still add the controls ( _X_ ) for area (2000), population (2000), small islands, and urban definitions. We use country populations in 2000 as weights. Finally, we examine the differences between coefficients _β_ and _γ_ , and then between _β_ and _δ_ .

**Sectors.** IPUMS has information on 16 industries from which we construct 10 sectors. As can be seen in Table 3, for a given urban share, urban areas in industrialized MFGSERV countries have more employment in tradables (MFG, FIRE and SUM = MFG + FIRE) and
Policy Research Working Paper 6160

# **Abstract**

This paper examines the relationship between firm performance and growth and the business environment in the countries of the South Asia Region—Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka—using firm-level data from the World Bank’s Enterprise Surveys. The analysis uses an approach in which the responses of firms to questions about the quality of the business environment can be interpreted as shadow prices: estimations by managers of the cost imposed on the firm by inadequacies of an aspect of the business environment—public inputs such as regulation, physical infrastructure, availability of skilled labor, macroeconomic conditions, rule of law, etc.—for the growth of their firm. The analysis finds, in line with this approach, that higher-productivity and better-performing firms in the region, and in particular firms that recently expanded their employment and created jobs, report significantly higher constraints in terms of the supply of public inputs. The authors discuss the differences across countries in the importance of various industries, how they relate to various firm characteristics, how informal and rural sector firms are constrained by public inputs, and how firms in the South Asia Region countries compare with firms in the rest of the world.

This paper is a product of the Human Development Department, South Asia Region. 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 author may be contacted at m.e.schaffer@hw.ac.uk.

_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