rafmacalaba/datause-encoder-data huggingface dataset:
recommendations of the World Bank. However, it is not possible to establish with our experiment that the detailed self-report questionnaire (or any other alternative for that matter) is the “gold standard,” or the “best” approach to collect child labor statistics. Instead, we will be able to document variations across survey design and identify the most important dimensions along which variations occur.

In each of the four designs, in addition to the labor module, the questionnaire also includes five other modules: a household roster, and sections on household assets, dwelling characteristics, land, and consumption expenditures. The questions in these sections follow the same sequence and phrasing, and refer to the same recall periods in the detailed and short questionnaires. The labor module was administered before the consumption module, but after the land module in the questionnaire.

Before analyzing the child labor statistics, we first compare household and individual level variables across the assignments to ensure that characteristics are not statistically different on average. From an analytical perspective, we have organized the analysis to address two types of questions: (i) the effects of the change in survey design on child labor statistics, and (ii) whether survey design affects the relationship between child labor and the variables of interest that are typically documented in the empirical literature as being important covariates of child labor. Regardless of whether variations in child employment are found on average, it is possible that the survey design affects reporting on child labor in a non-random way with respect to those characteristics that are generally found to explain (or be correlated with) child labor.

To address the first question, we follow two steps. We first estimate differences in mean child labor statistics across assignments.<sup>4</sup> We compare the mean outcomes in children’s labor force participation, occupation, daily hours worked, and weekly earnings across the four groups for the child’s main job. Since the survey assignments

> 4 In the parlance of randomized control design, to estimate the average treatment effect, we ideally want to estimate Δ = Yt1-Yt0 which is the difference of the outcome variable of interest at time t between two treatments denoted by the superscripts 1 and 0. However, since Δ is unobservable to the econometrician because a household does not receive two treatments simultaneously, one estimates the 1 0 treatment effect given the observable data, i.e. TE = E (Yt | T=1) - E (Yt | T=0). Since in a properly implemented randomized design, the treatment and control groups have identical characteristics on average because the groups were composed of randomly allocated households, differing only with 0 0 respect to the treatment received, the selection bias, E (Yt | T=1) - E (Yt | T=0), equals zero and the estimate of the treatment effect is unbiased.

8
most important service import for high-income countries in 1994; as of 2004 other business services had become the largest category. Imports of financial, insurance, computer, and communication services, and royalties and license fees all increased substantially, although with the exception of financial services they represent only a small fraction of total service imports.

**Table 1: Service imports as a share of GDP, 1994 and 2004**

||**Total services**<br> <br>|
|---|---|
||**1994**<br>**2004**|
|All countries (n=178)|11.0<br>12.0|
|High income|8.0<br>12.7|
|Upper middle income|11.4<br>12.8|
|Middle income|10.3<br>11.6|
|Lower middle income|13.6<br>11.4|
|Low income|11.7<br>11.2|
||**Other services(non-transport, non-travel)**|
||**1994 **<br>**2004 **|
|All countries (n=178)|4.6<br>5.1|
|High income|3.3<br>6.7|
|Upper middle income|4.5<br>5.3|
|Middle income|4.2<br>4.6|
|Lower middle income|6.5<br>4.9|
|Low income|4.4<br>3.8|

_Source_ : IMF BOP Statistics For developing countries, transport services are traditionally the most important single service import category. Between 1994 and 2004, this share has declined somewhat. In contrast to high-income countries, a strong increase in producer related services imports is observed in only a few developing countries. However, financial services imports have generally become more important, with the median observation for all developing countries rising from around 0.1% of GDP to roughly 1% of GDP (Francois, Hoekman and Wörz, 2007).

> 11 Our discussion of global trends in cross-border services trade uses import data as these are presumably of better quality than export data. In principle aggregate imports for the world should equal aggregate exports.

8
# **Table 2. Summary Statistics for Key Financial Ratios, 1995-2000**

||1995|1996|1997|1998|1999|2000|t-test|
|---|---|---|---|---|---|---|---|
|Number of Firms:|562|549|522|611|605|496||
|**Growth and Performance**||||||||
|Nominal Sales growth (Median)||6.900|7.550|4.263|1.771|-2.72|(***)|
|Real Sales growth (Median)||-0.279|2.820|0.061|-1.194|-5.27|(***)|
|Net Income/TA (Median)|0.041|0.045|0.043|0.037|0.039|0.029|(***)|
|Operating Income/ TA (Median)|0.051|0.048|0.048|0.050|0.048|0.037|(<sup>a</sup>)|
|Retained Earnings /TA (Mean)|0.024|0.019|0.017|0.009|0.024|0.013||
|(Median)|0.040|0.039|0.034|0.030|0.029|0.037|(<sup>a</sup>)|
|Tobin's Q (Mean)|||3.35|4.01|2.12|1.45|(***)|
|(Median)|||1.064|1.028|0.993|0.97|(***)|
|**Leverage**||||||||
|Total Debt /Total Equity (Median)|0.605|0.646|0.550|0.478|0.475|0.471|(**)|
|Total Liabilities / TA (Median)|0.563|0.563|0.553|0.523|0.505|0.485|(***)|
|Current Liabilities / Total Liabilities (Mean)|0.856|0.833|0.832|0.838|0.852|0.837|( )|
|(Median)|1.00|1.00|0.99|1.00|1.00|0.99|( )|
|Interest Coverage (Median)|2.470|2.601|2.570|2.903|2.793|2.288|( )|
|Paid-in-Capital/TA|0.215|0.213|0.217|0.262|0.256|0.303|(***)|
|**Asset Composition**||||||||
|Gross PPE / TA (Median)|0.331|0.318|0.337|0.356|0.395|0.421|(***)|
|Net PPE / TA (Median)|0.220|0.215|0.201|0.203|0.207|0.215|( )|
|Cash / TA (Mean)|0.142|0.129|0.128|0.125|0.128|0.107|(**)|
|(Median)|0.060|0.056|0.053|0.049|0.057|0.039|(<sup>a</sup>)|

Source: CMA data (own calculations). TA stands for total assets. Tobin’s Q is defined as market value of equity + book value of liabilities over total assets. Real Sales Growth is sales growth adjusted for inflation (obtained from WDI). Interest coverage is defined as earnings before interest and taxes over interest payments. Gross PPE is property plan and equipment (i.e., fixed assets) at purchase cost, net PPE excludes accumulated depreciation. The t-test indicates significance for equality of the medians (or means) between years 1996 and 2000. ***,**,*, and<sup>a</sup> indicate significance at 1 percent,5 percent, 10 percent, and 15 percent, respectively.

17
# **Table 2: PSI and power supply reliability in Maharashtra districts**

DV is PSI measured in each district-year, 2009–13

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|**ln(SAIFI)**|**0.348****|**0.224'**|**0.369****|**0.239'**|
||**(0.114)**|**(0.125)**|**(0.125)**|**(0.139)**|
|ln(population)||-0.124||-0.122|
|||(0.199)||(0.202)|
|ln(area)||0.189||0.187|
|||(0.190)||(0.193)|
|% population illiterate||0.009'||0.009'|
|||(0.005)||(0.005)|
|% workers in agriculture||-0.004||-0.004|
|||(0.005)||(0.005)|
|% area forest||-0.001||-0.001|
|||(0.003)||(0.003)|
|Year FE|No|No|Yes|Yes|
|intercept|0.133*|0.340|0.082|0.271|
||(0.061)|(1.706)|(0.066)|(1.735)|
|_N_|163|163|163|163|

Robust standard errors clustered on district in parentheses. ' p < .1; * p < .05; ** p < .01.

# **B. Validation against IHDS Survey Data**

The second source of ground-truth data is the Indian Human Development Survey (IHDS), which was jointly carried out by University of Maryland and the National Council of Applied Economic Research (NCAER) in New Delhi. The survey covers all of India’s key states and union territories except Andaman and Nicobar Islands and Lakshadweep. The first round of the survey was carried out mostly in 2005 and collected information on 41,554 households. The second one, conducted mostly in 2012, interviewed a total of 42,152 households, including 83 percent of the original households. The survey asks a wide range of questions related to households’ energy consumption patterns including electrification status and daily average duration of electricity outages. While the survey includes both urban and rural households, we focus on the rural sample for reasons described in the previous section.

We calculate district-level average PSI and district-level average duration of power outages reported by rural households. We also obtain data on total population, total area, % of agriculture employment and forest area all at the district level from the 2011 Indian Census because these variables could also affect PSI values extracted from satellite images. Table 3 summarizes regression results using district-level PSI 15
Table 6: Effect of NREGS on labor and cost intensity

||Labor i|ntensity|Cost int|ensity|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|NREGS introduced in district|0.011<sup>_∗_</sup>||1727.235<sup>_∗∗∗_</sup>||
||(0.004)||(382.798)||
|NREGS registered (2007)||0.003||743.686|
|||(0.005)||(498.628)|
|Observations|1010|1010|1010|1010|
|_R_<sup>2</sup>|0.03|0.03|0.03|0.02|

Notes: Estimation in FGLS. Dependent variable in cols. (1) & (2) is labor intensity of crop portfolio, in cols. (3) & (4) cost intensity of crop portfolio. Additional controls are area cultivated (log), share of area under irrigation, fertilizer application, HYV seeds application, labor sharing, if household benefits from credit/training program, rainfall (deviation), rainfall (deviation, lag), self-reported shocks, and time trend. Variable definitions and sources are described in appendix B. Standard errors (clustered at the district in cols. (1) and (3), and clustered at the sub-district in cols. (2) and (4)) in parentheses. + _p <_ 0 _._ 10, _∗ p <_ 0 _._ 05, _∗∗ p <_ 0 _._ 01, _∗∗∗ p <_ 0 _._ 001.

41
addition, multidimensionally poor individuals tend to be renters and are located in rural areas,<sup>48</sup> live in overcrowded houses and in families with unmet medical needs (except in Croatia). Regarding family structure, AROPE individuals are significantly more prone to live in families with three or more children in all countries analyzed, with older persons (+64 years old) in Bulgaria and Croatia and with fewer adults (18-64 years old) in Bulgaria, Croatia, and Poland. In addition, this analysis indicates that individuals living in female-headed households, with an unemployed person, with several children, as well as in families receiving social aid and single-elderly households, are more prone to be AROPE in all countries.

**What are the key correlates of multidimensional poverty and its different components?** We run several Probit models on the probability of an individual being income-poor, materially and socially deprived, living in a low-work intensity household, and being multidimensionally poor, conditioning by several factors (see Annex 4 for further methodological details and Table A4.1 to Table A4.4 for regression results for the survey year 2022).<sup>49</sup>

**In all countries under analysis, the probability of being monetary poor is significantly lower for men, people living with a household head with secondary or tertiary education who is currently employed, and individuals in families composed of many working-age adults.** For instance, the probability of being AROP for women is between 0.3 (in Poland) and 2.6 percentage points (in Croatia) larger than for males. Living in towns or suburbs and in rural areas, compared to people living in densely populated areas, is significantly associated with a higher likelihood of being monetary poor in all countries, but especially in Poland and Romania. In addition, individuals living in a household with unmet medical needs are significantly more prone to be at risk of poverty in all countries, reaching a larger probability in Bulgaria and Romania (of 4.2 and 5.4 percentage points). Individuals living in households with unemployed members, with three or more children, with social aid, and single-elderly are significantly more likely to be monetary poor in all countries.

> 48 Only reporting statistically significant differences in means between groups (AROPE vs non-AROPE), with lower p- values indicating greater confidence in the observed differences (Table 6). 49 Regression results are robust to using the 2021 survey year. Even though some coefficients differ in sign it does not affect out main results and conclusions (see Table A4.5 to Table A4.8).

21
data. Let β **1** , β **2** , and β **3** be the regression coefficients. The following parameters characterize the general quadratic Lorenz function.

1 _e_ = − ( β 1 + β 2 + β 1 + 1); _m_ = ( β 22 − 4 β 1); _n_ = (2 β 2 _e_ − 4 β 3 ); _r_ = ( _n_ 2 − 4 _me_ 2 ) 2 (2.31) Table 2.3 Size Distribution of Income within the Socioeconomic Groups in the SAM

|Group|Mean|Poorest|2nd|3rd|4th|5th|6th|7th|8th|9th|10th|
|---|---|---|---|---|---|---|---|---|---|---|---|
|||Decile||||||||||
|National|1.00|0.01|0.03|0.04|0.06|0.07|0.09|0.11|0.14|0.18|0.28|
|Rural|0.66|0.02|0.03|0.05|0.07|0.08|0.10|0.12|0.14|0.17|0.21|
|Urban|1.50|0.00|0.04|0.06|0.07|0.09|0.10|0.12|0.14|0.16|0.23|

Source: Author’s calculations The unit record data underlying table 2.3 were generated randomly from the beta distribution. The probability density function is defined by the following expression (Johnson, Kotz and Balakrishnan 1995): where B(q,r) stands for the beta integral (also known as the Euler integral of the second kind) defined as:

> Setting _x_ = ( _y_ − _a_ ) /( _b_ − _a_ ) leads to the following standard form of the beta density function:

The corresponding probability integral up to **x** is given by the incomplete beta ratio or _incomplete beta integral_ : 12
# **1. Introduction**

After stagnating for much of its post-colonial history, economic performance in Sub-Saharan Africa (Africa) has markedly improved. Since 1995, average economic growth has been close to 5 percent per a year. Countries with at least 4 percent GDP growth now constitute about 70 percent of the region’s total population and 80 percent of its GDP. Per capita income grew by 1.6 percent a year in the late 1990s and by 2 to 3 percent in each year since 2000.

Recent popular and academic writing has suggested that Africa may be at a turning point in its long economic decline (see for example Commission for Africa 2005; Ndulu et al. 2007). But, predictions of Africa’s imminent economic recovery or demise have proved wrong on numerous occasions in the past 40 years. Growth in Africa since 1975 has been lower and more volatile than in any other region of the world – developed or developing (table 1). And, unlike East and (more recently) South Asia it has had few regional “champions” to serve as models of successful, rapidly growing economies.<sup>2</sup>

**<u>Table 1:  GDP per capita and  growth by region (weighted data)</u>**

|**Region**|**1975-80**|**1981-85**|**1986-90**|**1991-95**|**1996-00**|**2001-05**|
|---|---|---|---|---|---|---|
||||**GDP pe**|**r capita**|||
|Sub-Saharan Africa|1,928|1,844|1,782|1,648|1,668|1,768|
|EastAsia &Pacific|905|1,227|1,686|2,407|3,399|4,595|
|Latin America & Caribbean|6,020|6,295|6,315|6,450|6,978|7,205|
|MiddleEast & North Africa|4,179|4,180|4,055|4,326|4,651|5,197|
|South Asia|1,132|1,268|1,505|1,745|2,110|2,530|
|Low &middleincome|2,278|2,560|2,881|3,045|3,513|4,219|
||||**Gro**|**wth**|||
|Sub-Saharan Africa|-0.06|-1.60|-0.21|-1.64|0.79|1.79|
|EastAsia &Pacific|5.26|6.12|5.76|9.10|5.63|7.06|
|Latin America & Caribbean|3.31|-0.95|-0.43|1.61|1.53|1.21|
|MiddleEast & North Africa|-0.20|2.41|-1.20|1.18|1.91|2.78|
|South Asia|1.03|3.14|3.89|3.01|3.59|4.65|
|Low &middleincome|2.79|1.99|1.93|1.56|3.23|4.58|

Note: All Sub-Saharan African countries are included in calculations.

Using the most recent purchasing power parity (PPP) data for 44 sub-Saharan African countries, this paper examines country-level dynamics of long-run growth in Africa between 1975 and 2005. The next section describes our data and the characteristics of Africa’s long-run growth. Here we confirm previous findings that the

> 2 Botswana and Mauritius are notable exceptions to this statement, a theme to which we shall return below.

2
the remaining analysis, however, we will maintain the focus on German influence given the greater incidence of German FDI with respect to other major partner countries.

## [Figure 2 about here.]

The positive effect of German ownership on employment is also confirmed when using our second approach (given by equation (2)). This approach reflects the actual timing of acquisitions and, therefore, allows us to compare the effects of early and late acquisitions. The results are reported in Table 2, while Figure A.7 shows the event study associated to the double robust estimation and confirms the absence of pre-trends in employment. This approach provides more conservative estimates, yet the effects remain large and indicate that the effect of German acquisition on firm-level employment is between 7.5% and 11%. The estimates by treatment year show that the effects are larger for later acquisitions, which are associated with an increase of more than 14% in employment from 2010 onwards. Overall, both set of results point towards substantial positive effects of German acquisition on firm size.

## [Table 2 about here.]

# **5.2 Differential effects of EU accession on German acquisitions across sectors**

Figure 3 shows the results of the event study, which are obtained by estimating equation (3). The results confirm our descriptive evidence and show an increase in foreign acquisitions by German shareholders in the post-accession period. Overall difference in differences results are shown in Table A.5 and are consistent across different measures of German influence. They indicate that a 10% increase in Germany’s export share is associated with a 8% increase in German ownership (0.04 percentage points over the average ownership of 0.5%). We do not find evidence of border regions receiving more FDI from Germany (Figure A.8 of the Appendix). As a falsification experiment, we employ the same specification on similar data for Romanian firms, also from the Orbis database. Since Romania acceded the EU in 2007, we would not expect to observe a significant effect in this case. Results in Figure A.9 of the Appendix show indeed no effect of our measure of pre-determined German specialization on the German acquisition of Romanian firms. The estimates show only a negligible increase in the probability of being acquired by a German shareholder after 2007, which is also not statistically significance. Hence, these estimates argue against the possibility of Germany investing abroad more 12
Policy ReseaRch WoRking PaPeR 4814

# **Abstract**

This paper examines the differentiated outcomes of vocational and general secondary academic education, particularly in terms of employment opportunities, labor market earnings, and access to tertiary education in Indonesia. With data from a panel of two waves of the Indonesia Family Life Survey in 1997 and 2000, the paper tracks a cohort of high school students in 1997 to examine their schooling and employment status in 2000. The findings demonstrate that: (1) attendance at vocational secondary schools results in neither market advantage nor disadvantage in terms of employment opportunities and/or earnings premium; (2) attendance at vocational schools leads to significantly lower academic achievement as measured by national test scores; and (3) There is no stigma attached to attendance at vocational schools that results in a disadvantage in access to tertiary education; rather, it is the lower academic achievement associated with attendance at vocational school that lowers the likelihood of entering college.

The empirical approach of this paper addresses two limitations of the existing literature in this area. First, it takes into account the observation censoring issue due to college entry when evaluating labor market outcomes of secondary school graduates. Second, using an instrumental variable approach, the paper also treats endogeneity of household choice of vocational versus academic track of secondary education, teasing out the net effect of secondary school choice on labor market and schooling outcomes.

This paper—a product of the Human Development Sector Department, East Asia and Pacific Region—is part of a larger effort in the department to strengthen evidence-based human development policy advisory and dialogue. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dchen1@ 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
# **Figure 10 Total fiscal risks from early termination of public-private partnership portfolios of South Asian countries, as percentage of annual government revenue**

<!-- Start of picture text --> 3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Afghanistan Bangladesh Bhutan India Nepal Pakistan Sri Lanka<br>Low Medium less low High less medium<br>Percent<br><!-- End of picture text --> _Source:_ Authors’ calculations.

_Note:_ Total fiscal risks are the maximum expected loss at the 99 percent confidence over the entire contract period and are expressed as percentage of government revenue of a single year.

The probability that an entire PPP portfolio is terminated in a single year is very low, hence a more realistic analysis is to compare the fiscal risks over a period of time with an estimate of the government revenues over the same period of time. The fiscal risks from early termination of PPPs as percentage of government revenues tend to decrease over the 2020–2024 period. Figure 11 presents the fiscal risks for different periods, all starting at the end of 2019, as the ratio of government revenues of that period.<sup>20</sup> The fiscal risks decline for most countries because active projects get older and some reach the end of their contract periods.

> 20 The analysis assumes that the annual government revenue during the period 2020–2024 is the same as in 2019.

24