rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 5192

# **Abstract**

Labor market statistics are critical for assessing and understanding economic development. In practice, widespread variation exists in how labor statistics are measured in household surveys in low-income countries. Little is known whether these differences have an effect on the labor statistics they produce. This paper analyzes these effects by implementing a survey experiment in Tanzania that varied two key dimensions: the level of detail of the questions and the type of respondent. Significant differences are observed across survey designs with respect to different labor statistics. Labor force participation rates, for example, vary by as much as 10 percentage points across the four survey assignments.

Using a short labor module without screening questions on employment generates lower female labor force participation and lower rates of wage employment for both men and women. Response by proxy rather than self-report yields lower male labor force participation, lower female working hours, and lower employment in agriculture for men. The differences between proxy and self reporting seem to come from information imperfections within the household, especially with the distance in age between respondent and subject playing an important role, while gender and educational differences seem less important.

This paper—a product of the Poverty and Inequality Team, Development Research Group—is part of a larger effort in the department to improve household survey methods. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The author may be contacted at kbeegle@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
The approach of this paper is as follows. We begin in Section 2 by examining closely the mean differences between Vietnam and the collective group of seven developing countries, termed as “Dev7” for this paper (not to be confused with the G-7 of wealthy countries). Comparing means in this context is a first pass at understanding the performance anomaly of Vietnam on empirical grounds. Do Vietnamese 15 year olds somehow enjoy better cultural, social or civic endowments to balance their economic disadvantages? An examination of mean differences will provide us with a first set of tentative hypotheses.

The insights provided by mean differences need to be explored further by a regression of the test scores on the explanatory variables. Large differences in means may not amount to much if the associated variables are not correlated with test scores. In Section 3 we adopt the regression methodology used by Fryer and Levitt to understand differences in test score results of black children in the first two years of schooling in the United States [Fryer and Levitt, 2004]. Fryer and Levitt are able to explain away all of a 0.62 standard deviation negative achievement gap for black kindergarten children. In our case, we are able to explain about half of a larger 1.28 standard deviaton positive achievement gap for Vietnam compared to Dev7 countries. The lower ability of the Fryer-Levitt method to explain the “Vietnam gap” is probably accounted for by the fact that per capita GDP lower than US $ 10,000 is the only common support across diverse economic, political and educational systems.

The Fryer-Levitt method deepens the understanding from mean comparisons, but what it does not reveal may be as interesting as what it does. Our Fryer-Levitt adaption is based on a pooled regression of eight developing countries, where we follow the fate of the magnitude of the coefficient of the dummy variable representing the Vietnamese students in the sample. However, we also need to investigate structural differences in the effects of endowments between Vietnam and Dev7 countries. In Section 4, we adopt an approach first used to explain variation in PISA performance between Germany and Finland by Andreas Ammermueller [Ammermueller, 2007]. This is an adaptation of the popular Oaxaca-Blinder decomposition of the wage earnings equation to uncover evidence of discrimination on the basis of gender [Blinder, 1973] and [Oaxaca, 1973]. In this section, we examine closely the structural differences between Vietnam and the Dev7 countries, including the contribution of differences in endowments and the coefficients to the gap in test scores.

Even a multi-variate regression approach only proves correlation with nothing more than a hint regarding causation, and so far we have only one year (2012) of PISA data for Vietnam. Even though we cannot uncover causality, there are useful policy related conclusions that we can derive from the analysis presented in this paper. There is a veritable industry of papers 4
As with other Mexico-based surveys of migration<sup>8</sup> , the ENADID will only capture data on migrants who have either returned to Mexico, or who have at least one household member remaining in Mexico. As a result it will tend to underrepresent permanent migrants (Hanson, 2006) who are likely to take their whole household. In Table 1, we use the 5% public use sample of the 2000 U.S. Census (Ruggles et al., 2004) to examine the marital status of 18-45 year old Mexican migrants who arrived in the U.S. within two years of the Census. We see that 14.4 percent of male migrants are married, with their spouse present. These individuals are likely not to be reported on in Mexico-based surveys. However, the majority of migrants are either single, or married with a spouse remaining in Mexico, and so should be reported on from Mexico. The problem is much worse with females, with 48 percent of all recent migrants in the U.S. being married with spouse present.

There are two main concerns with this undercount for our analysis. The first is that the U.S. census provides no information on the community of origin, and so no network variables are known for the unmeasured individuals. The second concern is that the education levels of migrants who are not reported on in the ENADID may differ from the education levels of migrants who are reported on. There is strong evidence to suggest that this is the case. For example, 16.0 percent of the male migrants in Table 1 who have a spouse present in the U.S. have post-high school education, compared to only 8.3 percent of those in the other marital categories. Ibarraran and Lubotsky (2006) compare the U.S. and Mexican censuses and likewise conclude that migrants that are excluded in the Mexican census are likely to be more educated.

Given these concerns, we do not look at self-selection among female migrants – with only half of all female migrants likely to be reported on in the ENADID, we consider the likely bias from doing so to be too severe. Additionally, since female migration is so closely tied to the migration of the spouse, the theory above is less directly applicable for females. For males the ENADID is likely to measure 86 percent of

> 8 The two other main Mexican surveys used to look at migrant selection are the Mexican Migration Project (MMP) and the Mexican Census. Neither of these is suitable for our study. The MMP does contain information on the age of first migration, but is not nationally representative, and in particular, samples only a small number of communities with small migration networks (see Figure 2 in McKenzie and Rapoport, 2006a). Since most communities have reasonably large networks, this makes it difficult to see how migration varies with network size starting from a low level. The Mexican Census only collects data on whether an individual has migrated within the last five years, preventing its use for looking at the determinants of first-time migration.

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Table I.2. Heterogeneous Effects on Indices by Student

||(1)<br>Prosociality<br>Index|(2)<br>Attitudes<br>Index|(3)<br>Beliefs<br>Index|
|---|---|---|---|
|Game [=1]|0.261*|0.405***|0.245*|
||(0.139)|(0.135)|(0.138)|
|Game x Student [=1]|0.009|-0.115|-0.136|
||(0.170)|(0.167)|(0.170)|
|Video [=1]|0.266*|0.426***|0.107|
||(0.150)|(0.143)|(0.146)|
|Video x Student [=1]|-0.106|-0.152|-0.147|
||(0.187)|(0.177)|(0.181)|
|Student [=1]|0.012|0.128|0.039|
||(0.130)|(0.127)|(0.124)|
|P-value: Game + Game x Student = 0|0.005|0.003|0.269|
|FDR qval: Game + Game x Student = 0|0.016|0.016|0.193|
|P-value: Video + Video x Student = 0|0.147|0.009|0.706|
|FDR qval: Video + Video x Student = 0|0.124|0.016|0.308|
|Av. Dep. Var. (Control group)|0.000|0.000|0.000|
|Observations|828|827|827|

_Notes:_ This table presents OLS estimates derived from Equation 1 with interactions between the treatment exposure indicator variables and the indicator variable for student, denoted as student [=1]. The dependent variables are constructed following the methodology outlined by (Kling, Liebman and Katz, 2007) for each group of outcome variables: (i) Prosocial Behavior (detailed in Table 2), (ii) Attitudes (detailed in Table 4), and (iii) Beliefs (detailed in Table 5). All columns control for female indicator variable. Robust standard errors are reported in parentheses. The reported p-values, such as Game + Game x Student = 0 and Video + Video x Student = 0, represent the p-value of a Wald test for each linear combination. Additionally, the False Discovery Rate (FDR) q-values for Game + Game x Student = 0 and Video + Video x Student = 0 are provided. Significance levels are denoted as<sup>_∗∗∗_</sup> at 1%,<sup>_∗∗_</sup> at 5%, and<sup>_∗_</sup> at 10%.

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**Table 3:  State of Existing Commitments and Conditional Offers on Selected BPO-Related Services**

|**Backoffice Operation Categories under F. Ot**|**UNBOUND IN THEURUGUAYSCHEDULE**<br>**her Business Services**<br>Ubd Ali Bil Chi<sup>69</sup>Idi Il EC f|**2003 INITIALGATS OFFERS FROMAUSTRALIA, **<br>**CANADA, EC, JAPAN, NEWZEALAND, NORWAY, **<br>**U.S., SWITZERLAND**|
|---|---|---|
|87203 Supply services of office support<br>personnel|noun: ustraa, raz, na, na, srae, (or a<br>majority of EC Member States), Japan, New Zealand, Norway,<br>Switzerland, etc.<br>Partial Commitment: Canada<sup>70</sup>|No improvement in commitments for these seven<br>WTO Members except for New Zealand<sup>71</sup>|
|**t. Other 8790**|||
|87903 Telephone answering services|Unbound: Australia, Brazil, China, India, Israel, EC (for all<br>Member States), Japan, New Zealand, Norway, Switzerland,<br>USA, etc.|No improvement in commitments for these seven<br>WTO Members except for Japan<sup>72</sup>|
|87909 Other business services n.e.c<br>.|Unbound: Australia, Brazil, China, EC (for all Member States),<br>Japan, New Zealand, Switzerland, USA, etc.<br>Partial Commitment: Norway|No improvement in commitments for these seven<br>WTO Members<sup>73</sup>|

Remark: This table is not an comprehensive analysis of all WTO Members. We draw from some schedules of industrialized economies (as defined by the GATS Database on Commitments of the WTO Secretariat) and a few large developing countries. The comparison of the initial offer with the Uruguay schedule is only possible where WTO Members have handed in GATS initial offers that were available to us as of July 15, 2003.

> 69  Accession schedule.

> 70  Commercial presence requirement for Ontario.

> 71  U.S. had a binding of “none” and has now specified in greater detail what sub-activities are covered by this commitment.

> 72  Canada had a binding of “none”.

> 73  Canada had a binding of “none”.
censored and can therefore be estimated by OLS (or by weighted least squares to control for potential group data heteroscedasticity).

As discussed in Olarreaga, Soloaga and Winters (1999), tariffs tend to be determined at the tariff line level and not at the industry level."<sup>7</sup> This implies that if the error is determined at the tariff line level and the number of tariff lines in each industry is not the same (as can be observed in the table in the data appendix the number of tariff lines varies from 1 in ISIC 3131 to 501 tariff lines in ISIC 3511), then the variance of the error term at the industry level will be negatively correlated with the number of lines in each industry, reducing the efficiency of our estimates. To correct for this potential heteroscedasticity, each observation at the industry level needs to be weighted by the number of tariff lines in each industry (i.e. multiplied by the square root of the number of tariff lines in each industry).

However, a problem with the above correction is that it assumes that the errors at the tariff line level are independently distributed. If observations within the same industry share a common unobserved determinant, then the above correction may reintroduce some heteroscedasticity. To test for this possibility, we follow Dickens (1990) and when necessary, we apply the correction he suggests which yields asymptotically efficient estimates.'<sup>8</sup> The data appendix describes data sources and provides a table with the data used in the estimations. The CET is the one negotiated in Ouro Preto in 1994 by Mercosur members. Tariffs or Argentina and Brazil are 1994 external official tariffs (corresponding to an FTA situation as by then more than 95 percent of their internal trade was free of tariffs). Trade data is the average of 1993, 1994 and 1995 whereas production data correspond to the industrial census of 1985 in Argentina and Brazil

> 1<sup>7</sup> If not we would not observe variation in tariff levels within industries.

> 18The test consists in verifying whether the error term of the weighted regression is correlated with the number of tariff lines in each industry. If this is the case, then one estimates consistently the variance of the common and individual error components by regressing the error of the weighted estimate on a constant and on l/nj, where **nj** is the number of tariff lines in industry j. To obtain asymptoticlly efficient estimates, one re-weighs the observations at the industry level using these variance estimates.

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The current fiscal condition of Korea is relatively sound compared to that of other advanced countries. Although, of course, multiple factors influence Korea’s fiscal situation, we conjecture that the reformed PFM system, including MTEF, may have contributed to sound fiscal management. Table 4 shows how the ratios of fiscal balance to GDP and of government debt to GDP have changed in the process of coping with the global financial crisis that began in 2008. Korea’s ratio of fiscal balance to GDP worsened from 0.4 percent in 2007 to -1.7 percent in 2010, while the average of that ratio for the G20 countries changed from -0.9 percent to -6.8 percent. Meanwhile, Korea’s ratio of government debt to GDP in increased from 30.7 percent in 2007 to 34.7 percent in 2010, while the average of that ratio for the G20 countries increased from 61.3 percent to 76.1 percent.

<Table 4> Comparisons of Korea’s Fiscal Soundness, as Ratio of Fiscal Balance or Government Debt to GDP

|Country||Fiscal<br>|Balance<br>|||Govern<br>|ment Debt<br>||
|---|---|---|---|---|---|---|---|---|
||’07|’08|’09|’10|’07|’08|’09|’10|
|US|-2.7|-6.7|-12.9|-11.1|62.1|71.1|84.3|92.7|
|UK|-2.7|-4.9|-10.3|-10.2|43.9|52.1|68.5|76.7|
|Germany|0.2|0.0|-3.1|-4.5|64.9|66.3|73.5|75.3|
|France|-2.7|-3.3|-7.6|-8.0|63.8|67.5|78.1|84.2|
|Japan|-2.4|-4.1|-10.2|-9.6|187.7|194.7|217.6|225.8|
|**KOREA**|**0.4**|-**1.5**|-**4.1**|-**1.7**|**30.7**|**30.1**|**33.8**|**34.7**|
|G20|-0.9|-2.7|-7.6|-6.8|60.8|63.8|72.6|76.1|

_Note._ % of GDP, general gov't (central + local), IMF (Nov. 2010), Korea is based on the consolidated fiscal balance, excluding social security funds Taken together, all of these data suggest that MTEF reform in Korea has achieved relative success. What can policy makers and reform agents learn from this success? A few ideas are outlined below.

# _Implications for MTEF Reform_

The Korean case provides at least three policy implications for MTEF reform. First, strong support from top decision makers is crucial in drawing attention from stakeholders, such as the central budget office and line ministries. The top decision makers should include those who deal with issues across ministries, such as the president, prime minister, and minister of the Finance and Economy, in order for MTEF to cover the entire government. In the Korean case, the support of top decision makers sustained the second attempt at PFM reform all the way through the institutionalization of MTEF in the National Assembly. By contrast, Korea’s first effort towards PFM reform, lacking such endorsement, could not last.

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3 We find very clear and robust effects of conflict on fertility. Three effects are particularly noteworthy. First, we can identify a strong replacement effect of conflict on fertility. This holds both for the short- and the long-term post-war period. Second, we find that sibling mortality, which we consider as a strong indicator of direct exposure to conflict, has a noticeable impact in the short-term only, perhaps associated with the stigma of being part of a genocide victim household. Third, we observe a clear impact of conflict on marital status and the marriage market and hence on fertility. The persistence of the institution of marriage in determining fertility is perhaps the most surprising finding of this study.

The paper advances the literature in several ways. By defining and contrasting several conflict exposure measures, our analysis disentangles the impact of conflict into the direct individual effects of conflict and cohort-specific excess mortality rates that operate through the marriage market. By pooling multiple, very detailed and large-N cross-sectional surveys we succeed in studying very differentiated impacts of mass violent conflict on fertility across time, cohorts and sub-groups. Population pressures on land and relative changes in ethnic population shares may have contributed to the genocide in Rwanda in 1994. Fertility, if not reduced sustainably, continues to be a potential driver of future tensions in Rwanda.

The paper proceeds as follows. Section 2 reviews the current knowledge on the links between violent conflict and fertility, followed by a section introducing the case study and summarizing the events of 1994 in Rwanda. The data used and the conflict proxies are discussed in Section 4 while Section 5 outlines the estimation approach. The next section presents and discusses descriptive and multivariate results. Section 7 concludes.

# **2 Review of previous research and hypotheses**

Fertility in developing countries is shown to be determined by the socio-economic status of each woman and her partner (age, ethnicity, religion, education and employment opportunities, although the latter two may be endogenous to fertility), partnership characteristics (civil status, marital duration, number of previous unions), knowledge of and access to contraception, local characteristics (including health infrastructure and child mortality), and institutional arrangements (such as social support mechanisms).<sup>1</sup> Fertility and family planning are stochastic processes in themselves; this is also true of many of the drivers 1 Examples are Ainsworth et al. (1996), Angeles et al. (2005), and Benefo and Schultz (1994).
Further, firms operating in Ethiopia and Nigeria see the market extending to the neighboring countries. One investor based in Lagos explained the importance of geography and trade traditions that led him to the location he chose: “If you are selling from Lagos, you are selling to the rest of central and western Africa.” Similarly, more than one firm in Ethiopia had eyes on the potential markets of the neighboring African and Arabic countries, even though they were not yet exporting. Several investors said that they were attracted to Ethiopia for its proximity to Europe, their traditional export market. To the textile and footwear producers, Africa offers quota advantages and other preference trade treatments vis-à-vis the American and European markets.

This finding seems to be consistent with the findings of other studies. A firm survey conducted in 2009 by CCPIT in collaboration with European Commission and United Nations Conference on Trade and Development (UNCTAD) found “[the Chinese] domestic market saturation” an important reason for the responding firms’ decision to go abroad, second only to “government policies and incentives” and ahead of “capital availability,” “labor cost” and “transportation costs.” (CCPIT 2009)

- **_Low production cost, especially “cheap labor,” is a critical factor attracting Chinese manufacturing firms to Africa.  There is no evidence that private firms depend on imported labor from China._**

A majority of the interviewed firms cited “cheap labor” a critical factor contributing to their decision to invest in Africa. This was expected as most of the firms interviewed were labor intensive manufacturers. For them, cheap labor at home in the 1980s and 1990s helped them grow into competitive producers for the world market. In the recent decade, however, these firms started to lose this competitive edge as labor cost more than doubled or tripled along the Chinese coast. For some companies, production relocation, either to inland China or to other developing countries, became a must if their businesses were to survive. Africa, a remote and unfamiliar destination for most Chinese firms until recently, became increasingly noticeable as a place offering relatively abundant and cheap labor as well as low-cost land.

Other studies support this finding. In the CCPIT survey, almost half of the surveyed firms said “increased domestic labor costs” were an important reason for them to seek relocation overseas. In addition, an internal study by Zhejiang Department of Commerce found that intensified labor shortages (due to reduced supply of migrant workers) and increased factory wages were pressing firms from that coastal province to venture out to other developing world including Africa.<sup>16</sup> Wages in Ethiopia were particularly attractive, even by African standards. Companies interviewed reported monthly wages from $30-50 for a factory worker, to $50-80 for a

> 16 “Study on Zhejiang Outbound Direct Investment” by Zhejiang Provincial Department of Commerce, 2012

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