rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Vignette 3</u>**

|(1)|(2)<br>PCE +<br>household||
|---|---|---|
|PCE|characteristics||
|Coef.<br>se|Coef.|se|
|Log expenditure per capita<br>0.092***<br>0.035|0.070*|0.039|
|_Household Demographics_|||
|Female headed household<br>|0.043<br>|0.053<br>|
|Age of household head<br>|0.012<br>|0.010<br>|
|Age of household head squared|-0.000|0.000|
|Household size|-0.017|0.013|
|Number of children|0.029|0.018|
|Number of older adults|0.048|0.052|
|Number of migrants|0.021|0.029|
|_Ethnicity (Reference: Tajik)_|||
|Uzbek|0.089*|0.047|
|Russian|0.035|0.148|
|Other|-0.177|0.143|
|_Education (Reference: No Education)_<br>|||
|Primary|0.183*|0.110|
|Basic|0.126|0.107|
|General Secondary|0.191*|0.114|
|Special Secondary|0.165|0.118|
|Technical Secondary|0.128|0.114|
|Higher Education|-0.429|0.682|
|Graduate School|0.043|0.053|
|_Employment Characteristics of Household, Head’s Occupa_|_tion_||
|<br>_(Reference for Occupation: Unemployed)_|||
|Number of employed|0.036*|0.019|
|Agriculture / Fishing / Forestry|-0.068|0.066|
|Manufacture / Mining|-0.028|0.089|
|Services|0.108|0.149|
|Construction|0.055|0.084|
|Public Administration / Education / Health|0.021|-0.025|
|Sales and Services|-0.019|0.062|
|Other|-0.021|0.119|
|_Agriculture (Reference for holdings: No Land)_|||
|<br>Small Holding|-0.114*|0.066|
|Medium Holding|-0.091|0.080|
|Large Holding|0.035|0.081|
|Anylivestock, poultry, etc|0.005|0.056|
|_Geography (Reference: Dushanbe)_|||
|<br>Sogd Urban|-0.050|0.072|
|<br>Sogd Rural|-0.173**|0.082|
|Khatlon Urban|0.466***|0.098|
|<br>Khatlon Rural|0.006|<br>0.078|
|RRP Urban|-0.127|0.111|
|RRP Rural|-0.166**|0.075|
|Gbao Urban|0.372***|0.138|
|Gbao Rural|0.220***|0.083|
|Number of observations<br>4,771|4,771||
|Pseudo R2<br>0.001|0.016||

33
is equal to 1 if the respondent reports to be employed and 0 if she reports to be non-employed and actively searching for a job.

# **3.1.2 The net in-migration rate**

The 2002 census was the first census in Uganda to gather systematic information on internal mobility. Each interviewee was asked to report: (i) the number of years she had been living in the region where the census was being conducted (the answer ranges from “less than 1 year” to “more than 95 years”) and (ii) the region in which she was living before. The regional breakdown in the data set is the district, where districts are the major administrative division in the country. In 2002 Uganda was composed of 56 districts, with an average area of 4,215 km<sup>2</sup> (approximately 65 km*65 km) each. We depict these in Figure S2-3 in the appendix. In the following we refer to districts as “regions”.

We calculate a one-year-net-in-migration rate at the regional level. More precisely, for each region _j_ , we first calculate the number of migrants arriving in and the number of migrants leaving region _j_ between 2001 and 2002 as a share of the population of region _j_ in 2001. We then compute the difference between these two ratios in order to obtain the net in-migration rate in region _j_ . One should note that we focus on one-year net in-migration rates in order to minimize missing the number of migrant flows that occur within our time periods: with one-year migration flows we are simply missing intra-annual population movements.

# **3.1.3 Socioeconomic characteristics**

The census data also inform us on a set of socioeconomic characteristics of the respondent, notably her gender, age, education, and whether she lives in a rural or urban area. We define 13
of the Congo has the highest annual number of lightning strikes globally. Importantly, as Figure 5 shows, lightning intensity varies greatly within African countries, implying that this instrument can generate meaningful variation in electricity access. Data on land elevation are provided in the geo-referenced component of the DHS datasets.

Columns 2 and 5 of Table 3 confirm the significant and negative relationships between the proximity to electrification grids on the one hand, and both the elevation of the land and lightning on the other. These negative correlations are found in both the LSMS and DHS samples. In addition, the high values of Cragg-Donald Wald F-statistics and the Windmeijer F-statistics show that our instruments are strong.

Given that proximity to the road networks and the electricity grids are two potentially endogenous variables, their interaction is likely to be endogenous as well. To instrument this interaction term, we use the interaction term between proximity to our hypothetical road networks (the instrumental variable used for proximity to the road networks) and terrain elevation (one of the instrumental variables used for proximity to the electricity grids). Column 3 of Table 3 shows a positive correlation between the instrument and the outcome in both the LSMS and DHS samples. Here the high values of the Wald and Windmeijer statistics also demonstrate the strength of this instruments.

Figure 5: Lightning flash intensity <!-- Start of picture text --> Lightning density per K^2<br>0.0 - 0.003<br>0.004 - 0.0006<br>0.007- 0.012<br>0.013 - 0.017<br>0.018 - 0.024<br>0.025 - 0.030<br>0.031 - 0.0610<br>0.0611 - 0.121<br>0.122 - 0.177<br>0.178 - 0.237<br>0.238 - 0.298<br>0.299 - 0.449<br>0.450 - 0.500<br>0.501 - 0.903<br>0.904 - 1.503<br>1.504 - 1.806<br>1.807 - 2.109<br>2.110 - 5.0406<br><!-- End of picture text -->

# **4.2.3 Instrumental variable estimation strategy**

To estimate the causal effects of infrastructure on employment, we use a two-stage least squares (2SLS) model. In the first stage, we regress the three main endogenous regressors (proximity to roads, proximity to electricity, and the interaction of both variables) on our instrumental variables, controlling for a wide 19
indirect exports, is far less than the gross value of exports. This is because vertical chains of production mean the same value added is exported several times before final sale. In services, however, indirect exports (forward linkages) are far more important. Indeed, they are so important that indirect exports, on a value added basis, are consistently greater than the value of gross exports. However, as service tradability has increased since the early 1990s, for some service sectors this ratio is falling, though it is still far above the pattern observed for goods.

We have organized the paper as follows. Section 2 discusses concepts for measurement of value added intensity, as well as our data. Section 3 focuses on how services trade has evolved globally, and how this differs from goods. Section 4 then discusses data issues at a country level. This is followed by closing remarks in Section 5.

# **2 Concepts and Data**

We work with multiple versions of the GTAP database, benchmarked to 1992, 1995, 1997, 2001, 2004, and 2007. It represents a form of social accounting data – a variation on the social accounting matrix or SAM – where incomes or receipts are shown in the rows of the SAM while expenditures or outlays are shown in the columns. The basic structure of the GTAP dataset is well documented (see McDougall 2001, and McDougall et al 2005). It is produced by a consortium that includes the World Bank, US International Trade Commission, World Trade Organization, OECD, UNCTAD, UNFAO and a number of universities and research institutes. It represents a massive combined effort to produce a shared public good – a database of national intput-output tables, organized as social accounting data and linked to each other through trade and investment flows. The project itself, based as Purdue University, has been a massive undertaking. (Hertel 2011). It stands as a critical, open source input to applied policy modeling ranking from climate change to regional trade agreements and food security. Over time, the dataset has grown to include more countries and more sectors. To maintain backward compatibility, we start with the 1992 structure of regions and sectors, and carry this forward in aggregation of more recent iterations of the dataset. This structure is detailed in Table 1 below.

4
rural inequality, as measured by the Gini coefficient. However, there are no discernible changes in inequality within the urban and rural areas.

Using the $2/day poverty line, Chen and Ravallion (2004) estimate that 45.2% of the rural population in China and 4.1% of the urban population are in poverty. Applying these figures to our benchmark data for the year 2005, we obtain the poverty line of 4730 ~~Yu~~ an (1997 prices) for urban and 3580 ~~Yu~~ an (1997 prices) for rural households. By assuming a uniform distribution of the population within each of the vingtiles, we are able to estimate how the poverty headcount changes in the wake of these reforms. This information is also reported in Table 5. In the _Full-Lib_ scenario, the monetary poverty line increases by 2.4%, following the change in the CPI (Table 4). Nevertheless, higher factor earnings mean that the poverty headcount ratio declines for all household groups.

Since transfer incomes are assumed to be constant in real terms and are indexed by the consumer price index, the urban transfer specialized household group experiences only a modest decline in its poverty headcount, reflecting a decline in non-transfer income, which comprises less than 5 percent of this group’s total income. The aggregate urban poverty headcount decreases by about 2.1 percent. Rural households enjoy a 2.7 percent reduction in poverty headcount, which amounts to a 1.2 _percentage point_ reduction in rural poverty (i.e., the proportion of the entire rural population in poverty falls by 1.2 percent). Given the large population base in rural China, this translates into a rural poverty reduction of 10.6 million people. The Doha scenario shows a similar pattern of poverty reduction across households, but with lesser absolute reductions. Overall, the impoverished share of the national population falls from 31.3 percent of total population to 30.5 percent in the scenario of global free trade, and to 30.9 percent under the Doha Round scenarios.

18
In the simulations reported below, E assume that public foreign borrowing is exogenous, and that private foreign borrowing adjusts to ecq-Sribrate the balance of payments.

Figure 3 summarizes the structure of the labor market in nini-iMMPA, whereas Figure 4 captures overall linkages. Appendix A provides a complete list of equations, Appendix B varia70le definitions, and Appendix C discusses calibration and solution proceduzes, as well as parameter values.<sup>19</sup>

# **2O.L1** PrDety and1 E _]$,Z (COa_

The procedure followed in mini-IMMPA to assess the poverty a2d distributional effects of exogenous and policy shock6s is similar to the one in VMMPA, which is described at length in Ag6ncr, Lzqnierdo, and Fofack (2003) and evaluated against several alternatives by Ag6nor and Grimm (2003). This procedure assumes that initial rural and urban poverty lines are exogenously set in real terms and involves linking the "structural" component described earlier to a household income and expenditure survey, organized along the household structure described earlier. Specifically, the calculation of poverty indices-the poverty headcount index (the proportion of individuals earning less than the poverty line) and the poverty gap (the average shortfall of the income of the poor with respect to the poverty line, multiplied by the headcount index)-as well as distributional indicators (the Gini coefficient and the Theil inequality index) involves the following steps:

- Step 1. Classify the data in the household survey into the categories of households contained in the structural component of the model. Here, as noted earlier, there are .five categories of households-workers in the rural sector, those in the urban (unskilled) informal economy, urban unskilled workers in the formal sector, urban skilled wo:rkers in the formal sector, and capitalists-rentiers.

- Step 2. Following a policy or exogenous shock, generate real growth rates in per capita consumption and disposable income fo6 all 'ive cat-

19As implied by Walras' Law, one equilibrium condition may be droppe6 because it can be deducted from the other equilibrium conditions. Instead of dropping one equation, the computer program checks numerically for continuous equality between savings and investment, as given in equation (82), by ensuring that a residual variable is continuously equal to zero.

32
# **Wage Subsidy and Labor Market Flexibility In South Africa**<sup></sup>

By Delfin S. Go, _The World Bank_ Marna Kearney, _Consultant_ Vijdan Korman, _The World Bank_ Sherman Robinson, _University of Sussex, UK_ Karen Thierfelder, _US Naval Academy_

>  The framework used in the paper is based on a World Bank technical assistance project to develop a CGE-micro simulation model for the South African National Treasury in a collaboration with IDS and the US Naval Academy. The purpose of the exercise is to illustrate the potential use of the framework for analysis of policy change.  The views expressed are those of the authors and do not necessarily reflect those of their respective institutions or affiliated organizations. The authors would like to thank Rita Almeida, Rob, Davis, Shantayanan Devarajan, Lawrence Edwards, David.Faulkner, Johannes Fedderke, M. Louise Fox, Jeffrey D. Lewis, Christopher Loewald, Konstantin Makrelov, Kuben Naido, Kalie Pauw, Ritva Reinikka, Matthew Simmonds, Rogier van den Brink, and Theo van Rensburg for helpful comments and suggestions.  We also thank B. Essama-Nssah and Konstantin Makrelov in helping to formulate the CGE-micro framework.
Outcome _yis_ is a binary variable measuring whether employers respond to application _i_ of person _s_ . _Refis_ is an indicator variable for whether a reference letter was included with application _i_ . _λs_ and _μk_ capture individual and sector fixed effects, respectively. The error term _eis_ is clustered at the individual level. The coefficient of interest _β_ captures the causal of the reference letters.

# **4.1.1 Employer Responses**

Table 1 reports results from Specification 3. Column 1 to 4 report effects using the broad measure of interest as an outcome, while Column 5 to 8 report effects on interview requests. On a control mean of 4.15 percent, the reference letter significantly increases the chance of getting any employer response by 2.54 percentage points (col. 1) and on getting an interview request by 1.54 percentage points on a control mean of 2.4 percent (col. 4). Coefficient estimates are consistently positive and of similar magnitude when including sector and individual fixed effects (col. 2, 3, 6, 7).

Coefficients on the gender interaction term are statistically significant and large in magnitude: compared to the control mean, both the measure of employer interest and interview requests approximately double for women, whereas they are close to zero for men (col. 4 and 8). Overall, the results in Table 1 show that reference letters have a large positive effect on employer callbacks for women, a result that will be corroborated in Section 4.2 below using a different sample and experimental design .

Table 1: Effect of Reference Letter on Call Back

||y=E|mployer Re|sponse: **Int**|**erest**|y=E|mployer Res|ponse: **Inte**|**rview**|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Reference Letter|0.0254<sup>_∗∗_</sup>|0.0251<sup>_∗∗_</sup>|0.0244<sup>_∗∗_</sup>|-0.0019|0.0154<sup>_∗_</sup>|0.0150<sup>_∗_</sup>|0.0143|-0.0039|
||(0.0102)|(0.0102)|(0.0107)|(0.0150)|(0.0087)|(0.0088)|(0.0091)|(0.0114)|
|Female x Letter||||0.0416**||||0.0300*|
|||||(0.0206)||||(0.0171)|
|Sector F.E.|N|Y|Y|Y|N|Y|Y|Y|
|Individual F.E.|N|N|Y|Y|N|N|Y|Y|
|_R_<sup>2</sup>|0.003|0.006|0.077|0.083|0.002|0.008|0.052|0.056|
|N|2050|2050|2050|2050|2050|2050|2050|2050|
|Control mean|0.0415|0.0415|0.0415|0.0415|0.0240|0.0240|0.0240|0.0240|

Notes:<sup>_∗_</sup> _p <_ 0 _._ 10,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01. Standard errors (reported in parentheses) are clustered at the applicant level. Results report OLS estimates. Dependent variables are binary measures of employer response: interview requests (Col. 5-8) and either interview request or a different employer response expressing interest in the job applicant (Col 1-4). Sector fixed effects are included for the six sectors for which we send applications. Since we employ within-subject randomization, the reference letter varies within individual; hence the gender interaction effect can be with individual We also estimate Specification 3 including an interaction term between the reference letter 15
<!-- Start of picture text -->
1.5 80<br>1.4 75<br>1.3 70<br>1.2 65<br>1.1 60<br>1 55<br>0.9 50<br>1920 1940 1960 1980<br>Year of Birth<br>Life Expectancy at Birth<br>Index of Lifetime Earnings (1922=1)<br><!-- End of picture text -->

Note: We assume 1922 as the base year. We plot the implied present value of lifetime earnings (adjusted for the base year) from the specification II in Table 3 against the Birth Year. The lifetime earnings estimates are assumed for male residing in urban areas. The life expectancy at birth numbers are the maximum life expectancy enjoyed by a cohort across the countries, which is to capture what an average person would expect to enjoy staying on the frontier of health technology at the time of birth. While calculating the net present value of log of lifetime earnings, we try two different discount rates 2% and 5%. The period in the figure ranges from 1922 to 1987 as prior to 1922, the information on urban/rural residence is missing.

**Figure 4** : World Average Life Expectancy at Birth and Implied Lifetime Earnings Index

38
uniforms, books, and supplies. In fact, 64 percent of dropouts claim that the high cost of education is the main reason for leaving school (Fedesarrollo, 2005). Enrollment fees, uniforms, and school materials make up 90 percent of the costs for low-income individuals, and these monthly costs fluctuates between 24,000 and 50,000 pesos depending on the school and grade (US$ 13 to US$ 22), a relatively large expense considering that the poorest families in Bogota earn less than US$ 750 a year.

# **III. Research Design**

In 2005, the city of Bogota established the Conditional Subsidies for School Attendance (“Subsidios Condicionados a la Asistencia Escolar”) program in an effort to improve student retention, lower drop-out rates and reduce child labor. In an effort to improve the program over the basic conditional cash transfer model, the Secretary of Education of the City (Secretaria de Educacion del Distrito, SED) decided to implement a pilot study in two of the twelve localities in the city. The pilot was to run for a year, and then the results would be used to inform the design of the final program that would operate city-wide.

# **A. Design of Treatments**

Ultimately, three interventions were chosen for the pilot. First, operating as a reference is a basic intervention similar to that used in PROGRESA/OPORTUNIDADES. In this basic model, participants would receive 30,000 pesos (approximately US$ 15) as long as the child attended at least 80 percent of the days that month. Based on the responses to our surveys, the total annual value of the transfer (300,000 pesos) is three times more than what students report earning on average and is slightly more than the average 250,000 pesos that families report spending each year on educational expenses. The payments would be made bi-monthly through a dedicated debit card run by one of the major banks in Colombia. Students would be removed from the program if they failed to matriculate to the next grade twice, failed to reach the attendance target in two successive bi-monthly periods, or were expelled from school. Finally, all payments were based on reports provided to the Secretary of Education by the students’ principals.

- 8 -
β _g_ (St) > 1, incomes still diverge. Yet, from (8), this may be accompanied by a convergence in human capital across countries, provided that (α+γ)β _g_ (St) < 1.<sup>5</sup> To sum up, _Proposition 2_ . When the countries differ in their degree of openness, so that openness is positively correlated with income, then incomes may diverge over time. When the degree of openness is identical across countries and the aggregate level of human capital is low, both human capital and income levels converge across countries; however, when the aggregate level of human capital is high enough, incomes may diverge because of the high marginal productivity of the world technology, while human capital still converges.

Differences in total factor productivity across countries have been documented in many recent studies (see e.g., Caselli, Esquivel, and Lefort 1996; Hall and Jones 1999; Pritchett 1997, 2001). Much of the literature attributes these differences to differential access to technological opportunities, which is also found to play a potentially important role here.<sup>6</sup> The second part of the proposition is more in line with Acemoglu and Zilibotti (2001) which shows that, equal access to technologies notwithstanding, productivity differences may still arise.

The next section is devoted to the empirical testing of the above results.

# **5. Empirical Analysis**

To test the implications of the above model we run several panel cross country regressions, distinguishing between fixed and between-country effects. We first describe the data and the variables used and then go on to present the results.

# _5.1 Data Sources and Variables_

The data on the average years of schooling for the population 15 years old and older come from Barro and Lee (1993, 2001).<sup>7</sup> Gross enrollment rates as well as the data on share of public expenditure on education in GDP, and the total number of students in primary and secondary schools come from World Bank’s WDI (World Development Indicators) data base.<sup>8</sup> The GDP data come from the Penn World Tables, version 5, and are reported in 1985 constant PPP-adjusted prices; population is from the same source; openness is from the newer Penn World Tables, version 6.1 and is measured as the share of the sum total of imports and export of the GDP. Appendix tables A1.1 and A1.2 contain statistical description of the main variables of interest.

> 5 In particular, note that the lower the productivity of education spending, α, the more likely this is to hold.

> 6 Another important strand of the literature exemplified in Hall and Jones (1999) stresses institutional differences as the source of productivity differentials.

> 7 Also available from http://sima.worldbank.org/edstats/td10.asp.

> 8 WDI database was last updated in August 2003 and can be accessed at http://sima.worldbank.org/.

11