rafmacalaba/datause-encoder-data huggingface dataset:
pattern of attendance gains for Cohort 1 Busia girls in 2001 is similar to the pattern in test score gains for that subsample, with large gains near the winning test score threshold and at the bottom of the test score distribution (Figure 7). The pre-program attendance gains might be due to anticipation of the future scholarship opportunity. Attendance impacts were not significantly different between school terms 1, 2 and 3 in 2002 (not shown), so there is no evidence that gains were largest in the period immediately preceding exams, due to cramming, for instance. Busia district boys in scholarship schools show similar effects, with larger gains in 2001 than in 2002 (Table 7, Panel B, regression 2). It is unclear why attendance gains are considerably larger in 2001 than in 2002.

Teachers in program schools were five percentage points more likely to be present at school than comparison school teachers during 2002, reducing overall teacher absenteeism by approximately onethird (Table 7, Panel C), and this large effect is robust to alternative specifications (not shown).<sup>20</sup> The 2002 Student Questionnaire collected information on educational inputs, study habits, and attitudes that may have affected school performance, to capture other dimensions of effort at least in part. Because the questionnaire was administered in mid-2002, cohort 1 girls had already competed for the scholarship when they filled out the questionnaire, while cohort 2 girls had not yet competed.

There is a significant increase in textbook use among program girls in cohort 1 (Table 8, Panel A): girls in program schools report having used textbooks at home 6 percentage points more than in comparison schools, suggesting that the program led to more intensive studying among the girls (although there is no effect for cohort 2). However, there is no impact on the likelihood that program school students sought out extra school coaching, handed in homework, were called on by the teacher in class, or did chores at home (Panel A). In the case of chores, the estimated zero impact suggests there was minimal cost of the program in terms of lost work effort at home, suggesting any increased study effort may have come out of children’s leisure time. In terms of educational inputs such as the number of new textbooks

> 19 We thank Joel Sobel for this point.

> 20 These results are for all teachers in the schools.  It is difficult to distinguish between teacher attendance in grade 6 versus other grades, since the same teacher often gives a class (i.e., mathematics) in several different grades in a given year, and the data were recorded on a teacher by teacher basis rather than by grade and subject, unfortunately.

24
_44_ _for decades, and the other day [Robert] Heller wrote in The Wall Street Journal about the possibility of a commodity standard._ _There are lots of other people around who feel that sooner or later something like that will have to be taken seriously again, as it was at various times in the past, for instance, at the first UNCTAD when Kaldor and Hart presented a detailed technical proposal, for a commodity standard._ _MR. O'BRIEN: A more concrete point then is: [supposing that] we have in the technological developments of the 1980s the seeds of faster growth in the 1990s, then is that actually good for the non-OECD countries, for the non-industrialized countries? The gap between where they are and where the industrialized countries are_ **_is_** _great enough at the moment for them_ **_to_** _work a miracle without ac.ually having to catch up on yet another advance in the OECD countries._

**_Linkage between OECD and developing_** _countries..._

_MR. STEER: On the linkage between OECD growth and developing countries, I would be interested to know whether people_ **_think_** _that the Armington and Fardoust kind of numbers make sense, because Professor Boltho said just a minute ago that in the old days all the fast-growing developi.._ :ountries really did it because we were growing in the OECD countries through trade, and now they have got to learn how to do it without the industrial countries._

**_But_** _unless you look at the numbers that are presented in this paper by Armingeon and Fardoust, in fact you_ **_see_** _that even under a rather gloomy OECD economic situation, the 2.5 percent solution,_ **_7ou_** _have got trade growing, if they are right in volume terms and you have got the terms of trade improving._

_You have got trade growing at almost exactly_ **_the_** _same rate, well, slightly lower but not much lower than in the 1965 to 1986 period, in 1990s._ _I mean, if these numbers are right, we really must nip in the bud Professor's Boltho's idea that the developing countries should not rely on OECD and trade._ _Now these numbers may be quite wrong. In the 1965 to 1987 period you have got an average 3.3 percent [per annum] GDP growth in OECD, which permitted export volumes of the developing countries to grow by 4.2 percent [per annum]; you have got a much lower OECD growth in the projected period [of about 2.8 percent per annum], leading to no lower growth in LDC's export volume._ WP/EOLTHO/SF:Ig
and experience accumulated overseas does not match demand in home labor markets, job search frictions may lead to higher unemployment among return migrants (Lucas 2005). On the other hand, past migration overseas may increase participation in economic activities at home by raising workers’ human and financial capital and thus workers’ productivity and earnings potential back home. The sign of the association between past migration overseas and labor supply in home labor markets is therefore an empirical question.

Few studies have investigated the labor market participation of return migrants compared to nonmigrants, and existing evidence on this question is mainly descriptive. Dustmann and Kirchkamp (2002) find that a sizeable share of return migrants from Germany to Turkey retire at an early age, presumably thanks to savings accumulated abroad.<sup>13</sup> Some studies evidenced that a longer duration of stay at destination can increase the likelihood to be unemployed after returning due to higher reservation wages (Piracha 2015). In the South Asian context, earlier work by Arif (1998) and Ilahi (1999) using data from Pakistan reports that return migrants from the Middle East are less likely to be employed than nonmigrants.

Table 2 and Figure 3 first display the labor market status of return migrants compared to nonmigrants by age in Bangladesh, Nepal and Pakistan. Labor market status is summarized by three indicators: (i) employment rate, defined, as the share of the working age population that is employed; (ii) labor force participation rate, defined as the share of working age population that is either employed or actively looking for employment; and (iii) unemployment rate, defined as the share of the economically active population (participating into the labor force) who is employed. First, employment rates are systematically lower among return migrants than nonmigrants in all three countries. The difference in employment rates between the two groups is statistically significant and larger in Nepal and Pakistan compared to Bangladesh (Table 2). This finding is consistent with the results of Arif (1998) and Ilahi (1999) using earlier data from Pakistan.

Figure 4, however, shows that the gap in employment rates between return migrants and nonmigrants declines with the number of years since the return. While the employment gap between returnees and non migrants is large one year after return, it diminishes with duration of stay, driven by both a decline in the unemployment rate and an increase in labor force participation.

Table 3 shows the effect of the duration of stay on return migrants back home, controlling for other individual worker characteristics: the negative association between past migration overseas and the likelihood of being employed decreases with duration of stay at home for all three countries. In Nepal, there is no statistically significant difference in the likelihood of being employed between nonmigrants and return migrants who have returned 3 years ago or

> 13The mean age of return migrants who are out of the labor force in their sample is 45 years old.

12
2002) and elsewhere. The negative effect for widows is consistent with the earlier reviewed evidence that Nigerian widows face economic and social difficulties.

The estimated coefficients on age reflect the patterns seen in Figure 1, with BMI rising with age but at a slower rate as women get older. Each year of completed schooling increases women’s nutritional status by 0.3 percent (significant at the 1% level). Unsurprisingly, pregnancy is strongly positively associated with BMI. Household structure plays a larger role than household size, as shown by the insignificant coefficient for the latter and the significant (negative) association with larger shares of children, especially young ones (the share of men 65 and older is omitted). Living in a female-headed household, being the head or the head’s spouse are all linked to higher BMI (the coefficients are large and highly significant). The head’s education has its own role, equal to a third of the effect of the woman’s own education. As one might expect, the wealth index is positively associated with BMI (significant at the 1% level). Ethnicity plays a key role, with Hausa-Fulani women having significantly lower BMI and those from the mixed religion Yoruba and Igala groups enjoying higher BMI. This correlation is found despite state fixed effects, suggesting ethnic-specific factors that affect BMI over and above location.

The positive and significant dummy for the later survey year indicates that, controlling for other factors, Nigerian women’s BMI increased between 2008 and 2013. As noted, the regression specification in column (2) obscures the possibility of catching up over time, or indeed, of any other parameter differences between the groups. Separate regressions for each religious group address this concern. Columns (3) and (4) of Table 3 present the results when all coefficients are allowed to vary by religion. Although BMI rose for both groups between 2008 and 2013, the improvement disproportionately favored rural Muslim women. Their BMI rose a dramatic 2.9 percent over the five-year period, or 0.6 percent annually. This was considerably higher than the 0.2 annual percent increase experienced by Christians (t=3.95 on the difference). These results are encouragingly suggestive of convergence. However, an important caveat is in order. As in the rest of the world, 2008 was a high food price year in Nigeria. The estimates may simply reflect a recovery rather than a trend. For example, the poorest may have struggled to feed themselves more so than the less poor, resulting in the food price crisis affecting Muslims and Christians differentially. Against that, rural food producers, more numerous among Muslims, may have been better protected and able to rely on non-traded staples such as yams more so than 18
Policy Research Working Paper 10094

# **Abstract**

This paper examines the effect of tradable services growth on non-tradable services across Indian districts. The analysis uses a shift-share “Bartik-type” instrumental variable, which relies on changes in foreign demand shocks for tradable services, weighted by the initial district employment shares in tradable services. Using multiple rounds of the Indian Economic Censuses, the findings show that an increase in tradable services employment leads to an increase in non-tradable services employment and increases the number of firms in non-tradable services. The evidence suggests that this positive impact is due to an increase in consumer demand for local non-tradable services that results from the growth in tradable services employment, and not due to sectoral linkages between tradable and non-tradable services sectors. The employment impact is much larger for female workers compared to male workers, and for the number of female-owned firms relative to male-owned firms. Further, the employment impact is only significant for small non-tradable service firms.

This paper is a product of the Office of the Chief Economist, Equitable Growth, Finance and Institutions Practice 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://www.worldbank. org/prwp. The authors may be contacted at gnayyar@worldbank.org; bavdiu@worldbank.org; rchaurey@jhu.edu; and vbk.singh@gmail.com.

_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
22

# _5.1 Specification tests_

Table 3 reports the results of the reverse causality tests. For the per capita health spending variable, and most PYLL variables, there is no evidence that the DID, random trend and differential trend models fail to account adequately for any correlation between _eit_ and _SHIit_ in eqn (1). The exceptions are the ischemic heart disease PYLL variable, where the coefficient on the lead value of _SHIit_ is significant at the 5% level in all three models, and cerebrovascular disease, where the coefficient on _SHIit_ +1 is significant at the 10% level in the random trend model and almost so in the basic DID model. In the case of the formal sector share labor market outcome, there is evidence that the three generalized differences-in-differences models fails adequately to take into account the correlation between _SHIit_ and the error term in eqn (1). This is not an unexpected result, given that SHI hinges on formal-sector contributions; it is less easy to rationalize why there appears to be reverse causality in the cases of ischemic and cerebrovascular disease but not in the cases of other diseases.

Table 4 reports the results of the parallel-trend assumption (PTA) tests for the randomtrend and differential-trend models, i.e. eqns (5) and (8). The only two relevant outcomes where the PTA is rejected are the influenza and pneumonia PYLL variable, and the pregnancy PYLL variable. In the former, the PTA is rejected (at the 10% level only) in favor of the random trend model but not in favor of the differential trend model, while in the case of the latter the PTA is rejected (at the 1% level) in favor of the differential trend model but not in favor of the random trend model.

Table 6 shows the relevant IV diagnostic test statistics for all the outcomes, though the interest lies with ischemic heart disease, cerebrovascular disease, and the formal-sector share of
of each explanatory variable is obtained by multiplying the corresponding regression coefficient (from Table 3) times the difference in the value of this explanatory variable between each Latin American and Caribbean country and the comparator country.

The importance of a particular explanatory variable would, therefore, depend on the size of its effect on informality in the cross-section of countries _and_ how far apart the two countries are with respect to the explanatory variable in question. Naturally, the sum of the contributions equals the total difference in predicted informality between each individual country and Chile. This difference is plotted in Figure 4. As expected, it shows that all the countries have larger (predicted) informality levels than Chile. Haiti, Honduras and Guatemala are predicted to be the most informal (and in general show the largest difference with respect to Chile). On the other hand, Uruguay, Argentina and Costa Rica are predicted to be the least informal among the Latin American and Caribbean countries, though they still show larger informality levels than Chile.

Figure 5 presents the decomposition of the difference of (predicted) informality between each of the 20 countries under analysis and Chile. The figure has four panels, corresponding to each of the four informality indicators. The most remarkable observations are the following. Policy and institutional variables related to the quality of the state are the most important factors explaining the differences in informality. Restricted regulatory freedom tends to contribute to larger informality in all Latin American and Caribbean countries for the Heritage index, self employment and pension coverage, while deficient law and order explains the bulk of informality for the Schneider index.

Education, measured by average years of secondary schooling, does not play a major role in explaining differences in informality with respect to Chile for any of four informality measures, even in the cases of Haiti and Honduras. Socio-demographic factors are particularly important in explaining the differences regarding _labor_ informality, and less so regarding _production_ informality. Moreover, in the case of _labor_ informality, the larger the differences in informality with respect to Chile, the larger the importance of socio-demographic factors. This is the case of, for instance, Haiti and Honduras, where all determinants of informality (excluding educational level) are about as important. On the other hand, there is not such trend regarding the two _production_ 13
**5.2 Predictions - Further Analysis of the Child Labor-Poverty/Gender Link** We now turn to predictions of the mean probability of being engaged in child labor for the full sample -- as well as the sub-samples -- and across gender for the full sample as well as each of the sub-samples in order to further identify the most vulnerable groups, as related to harmnful child labor.

The findings are presented in table 5.1. They confirm our earlier findings of rural children being more likely than urban children (10.7 and 4.1 percent, respectively) and girls more likely than boys (10.6 and 6.2 percent, respectively) and poor children more likely than non-poor (16.2 and 2.5 percent, respectively, when controlling for individual, household and community characteristics of the children. The gender gap is robust across all sub-samples, rural girls work more than rural boys, urban girls work more than urban boys, and poor and non-poor girls work more than poor and non-poor boys.<sup>8</sup>

**Table 5.1 Predictions of Child Labor Across (Sub-) Samples (Percent)**

|||All|Rural|Urban|Girls|Boys|Poor|Non-<br>Poor|
|---|---|---|---|---|---|---|---|---|
|Predicted Probability<br>(at the mean)||8.5|10.7|4.1|10.6|6.2|16.2|2.5|
|Observed Probability||12.1|15.0|5.3|13.6|10.7|20.2|5.3|
||Rural<br>Girls|Rural<br>Boys|Urban<br>Girls|Urban<br>Boys|Poor<br>Girls|Poor<br>Boys|Non-<br>poor<br>Girls|Non-<br>poor<br>Boys|
|Predicted Probability<br>(at the mean)|12.5|9.1|6.2|1.9|18.9|11.0|2.9|1.6|
|Observed<br>Probability|16.5|13.8|7.6|2.9|21.6|19.0|7.5|3.3|

_Source:_ Core Welfare Indicators Questionnaire 1997 (Ghana).

In the preliminary data analysis, we found the incidence of child labor to increase with age. In order to evaluate the estimated model, a natural next step, then, would be to predict children's activities over age. The findings from this exercise are presented in table 5.2, and confirm that the incidence of child labor increase with age, when controlling for individual, household and community characteristics of the children. However, again, this increase is most pronounced for girls, who - starting at more or less the same level as boys at age nine - increases rapidly and almost double at age fourteen,

**8** Note, however, that the model consistently under-predicts the incidence of child labor - this is a wellknown feature of the probit model, which predicts best, when the two outcomes are distributed fifty-fifty, see Greene (1997).

14
5 “experiment” from the point of view of health financing, but also are data-rich countries, having inherited and largely maintained the communist tradition of extensive data-gathering, and falling under the most data-rich regional office of the World Health Organization.<sup>12</sup> The ECA health financing experiment thus affords a valuable “laboratory” to try to answer some of the unanswered questions listed above.

To get at these issues, we use a regression-based generalization of the differences-indifferences (DD) method on panel data from the 28 ECA countries for the period 1990-2004. We explore different approaches to allowing for the possible endogeneity of SHI. The first is a simple individual-specific effects model estimated along the lines of the DD approach. This allows for the endogeneity of SHI insofar as the unobservables that are correlated with SHI adoption and with our outcomes are time-invariant. We explore two more specifications. One allows for a time-varying unobservable that can be correlated with SHI adoption and outcomes, and whose growth rate is allowed to vary from one country to the next. The other approach is Instrumental Variables (IV), implemented in the case of those outcomes for which the DD generalizations do not seem to control adequately for the possible endogeneity of SHI adoption.

The organization of the paper is as follows. Section 2 provides a brief history of the SHI reforms in the post-communist ECA region. Section 3 outlines our methods. Section 4 describes our data. The empirical results for the health and labor analyses are presented in sections 5 and 6, respectively. Section 7 discusses the results and presents our conclusions.

> 12  The European office of the World Health Organization developed and has maintained a huge database to track progress towards its Health for All initiative. In addition, it is home to the European Observatory on Health Systems and Policies, which has produced detailed overviews of the health systems of the member countries (known as Health Systems in Transition (HiT) profiles), as well as a variety of volumes that discuss health systems and health policies in the region.