rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 7930

# **Abstract**

Disasters in Bangladesh and protests elsewhere have created an intense debate about the value, particularly to women, of apparel employment in developing countries. This paper focuses on how the forces of globalization, specifically the Multi-Fibre Arrangement (MFA), have affected women’s wages in the apparel sector in developing countries. The paper uses household and labor force surveys from Cambodia and Sri Lanka to estimate both apparel wage premiums relative to other industries and the male-female wage gap before and after the end of the MFA. The approach builds on new models that apply traditional trade theory (e.g., the Heckscher-Ohlin and Stolper-Samuelson theorems) to analyze the effect of globalization on gender-based earnings. The authors find large positive wage premiums and a closing of the male-female wage gap during the MFA period, but smaller premiums and a widening wage gap after the end of the MFA. The results suggest that the benefits of apparel exports for women in developing countries remain significant post-MFA. They also model an approach for studying the effects of globalization that differentiates males and females as separate factors. This may be a fruitful alternative to discrimination models or those that analyze the effects of globalization on women in terms of skill. Further research is necessary to identify the potential development effects of post-MFA apparel employment and to thoroughly compare the benefits documented in this paper with the costs that may come with apparel jobs.

This paper is a product of the Office of the Chief Economist, 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 authors may be contacted at gacevedo@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
The higher degree of informality in the private sector could play an important role in explaining wage differentials. A comparison between public sector and private sector needs to take into account the issue of the higher degree of informality that exists in the private sector. The analysis of ENAHO data shows that: (i) a large number of individuals working in the private sector do not have a formal contract; (ii) individuals without a formal contract earn much lower salaries. As a consequence, it is important to control for the “informality issue” in order to perform a consistent comparison between public and private sector wages. To address this issue, all the private sector employees without a formal contract have been excluded from the analysis.<sup>8</sup> Therefore, the focus of the analysis will be on the comparison between public sector and formal private sector workers (columns 2 and 4 of Table 1, respectively).<sup>9</sup> Higher levels of education might explain the higher average level of compensation in the public sector. On average, the public sector hires more educated people than the private sector. Based on the sample of ENAHO, more than 60% of public sector workers completed some kind of post-secondary education and only 5.6% of employees did not start a secondary education course. In contrast, the percentage of private sector workers with a post-secondary degree (18.2%) is lower than the share of public sector workers that completed just primary education (21.4%). Interestingly, the average education level of private sector workers improves significantly when we exclude from the sample those workers without a formal contract. A significant part of formal private sector workers (37.5%) achieved a post-secondary education level whereas only 7.5% did not start a secondary education course (see Table 1). People with higher education tend to earn higher wages; average salaries in the public sector may be higher as a result of the differential of
be written as _r_<sup>_G_</sup> (2 _N_ + _T_ ) +<sup>�</sup><sup>_M_</sup> _m_ =1<sup>_r_</sup> _m_<sup>_R_(2</sup><sup>_Nm_+</sup><sup>_T_) + �</sup> _d∈{−_ 1 _,_ 1 _}_<sup>_r_</sup> _d_<sup>_D_(</sup><sup>_N_+</sup><sup>_T_) +</sup> � _Mm_ =1 � _d∈{−_ 1 _,_ 1 _}_<sup>_r_</sup> _m,d_<sup>_RD_(</sup><sup>_Nm_+</sup><sup>_T_), where</sup><sup>_N_is total number of countries (so 2</sup><sup>_N_</sup> is the overall cross-sectional dimension of the inflow-outflow data) and _Nm_ the number of countries in group _m_ . This yields the expressions: with ˆ and _ui,t_ is the estimated residual from the factor model with _r_<sup>_G_</sup> global factors and _rm_<sup>_R, r_</sup> _d_<sup>_D, r_</sup> _m,d_<sup>_RD_group, direction, and group-direction factors.The pa-</sup> rameter _c_ in the HQ criterion was set to 2, based on the criterion’s performance in residual bootstrap-based experiments.

# **3 Data**

We assemble a balanced panel data set on annual gross inflows and outflows, drawing from the International Monetary Fund’s Balance of Payments Statistics (BoP). The panel comprises 85 countries<sup>10</sup> and spans the years 1979-2015. We further
Households visited in Wave 1 were revisited in Wave 2, including households and individuals who moved after the 2011 survey (NIS, 2016). When the entire household moved within Niger, the household was found and re-interviewed in the second wave. When individuals from the household moved, one individual per household was selected to follow. This forms a sample of approximately 3,600 households in Wave 2.

# **B.4 Nigeria**

The LSMS-ISA data from Nigeria includes three waves (NBS, 2012; NBS, 2014; NBS, 2019). The total sample consists of 5,000 panel households and is representative at the national level. Households are visited twice per wave of the Panel, both post-planting and post-harvest. The post-harvest visit is implemented jointly with a larger General Household Survey of 22,000 households (5,000 panel and 17,000 non-panel households). The sample is representative at the national level and provides reliable estimates of key socio-economic variables for the six zones in the country.

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effort to catch up will require major investments. And rural populations, which make up more than 60 percent of the population in low- and middle-income countries, have access rates about 30 percent lower than do urban populations (table 4). This discrepancy does not mean that investment requirements are significantly larger for rural areas, however, because the costs of delivering services are lower in rural areas.

**Table 3** Access of urban populations to infrastructure services, <u>by country income group</u>

||Electricity<br>(1997-01)|Water<br>(2000)|Sanitation<br>(2000)|Telecoms<br>(1999-02)|Transport<br>(1994-01)|
|---|---|---|---|---|---|
|Income<br>group|P|ercentage of urb|an population|Telephone<br>subscribers per 100<br>people|Percentage of<br>population within a<br>20 minutes walk of<br>public transport|
|Low|62.4<br>(24)|76.9<br>(56)|74.6<br>(55)|4.6<br>(65)|90<br>(7)|
|Lower middle|95.1<br>(8)|90.8<br>(40)|90.5<br>(37)|22.0<br>(52)|n.a.|
|Upper middle|n.a.|92.3<br>(23)|92.5<br>(22)|53.9<br>(36)|n.a.|

Note : Figures in parentheses indicate the number of countries for which data are available. Source: USAID Demographic and Health Surveys;
> i All adults in the household were interviewed separately and privately regarding the decision-making process in the household. The qualitative data also explored norms and behavior regarding decision making within households.  In this regard, the data enables us to study self-described roles, responsibilities, power dynamics of women and men in these households across age groups and marital status.  A limitation of the data is the respondents did not self-declare their gender identities. The household survey asked the primary respondent for the sex of each household member.  We explore the roles and responsibilities of individuals using these sex categories in the data.

> ii Within these households, 204 women and 290 men 18 years and older could not be interviewed. Table A.1 in the Appendix compares the characteristics of all men and women in households based on the household roster with the sample of men and women interviewed.  There are fewer women interviewed as a share of the women in the roster that have never been married and more women interviewed as a share of the women in the roster that are in monogamous unions. Otherwise, the samples for this analysis are similar to the full samples for both women and men. All estimations are computed using survey weights. iii An example of such a survey module can be found on the Measures for Advancing Gender Equality (MAGNET) Resource Center: https://magnet.ifpri.info/.

17
# **3.3 Identification Checks**

We describe here key tests that support a causal interpretation of the RD estimate, _γ_ , in equation (1).

**Density Test.** Incumbent village heads may be able to systematically manipulate local election results, tilting electoral outcomes in their favor on average. If this occurred, we would observe a discontinuous drop in the density of our running variable (the victory margin of the best-ranked challenger) across the threshold (McCrary, 2008). We address this concern in Figure 1 (panel b), which implements the density test from Cattaneo et al. (2018). There is no evidence of manipulation or sorting at the threshold: the p-value from this test is 0.856.

**Balance Checks.** We then report a range of balance tests to probe the validity of our RD strategy. First, Appendix Table A.1 shows balance along various predetermined village characteristics from the survey and electoral data: the number of neighborhoods or hamlets (column 1), log number of households in the village (column 2), separate dummies for the village being located in each of Indonesia’s major islands (columns 3-7), the number of registered voters (column 8), and the number of candidates competing in the most recent election (column 9). Only one of these variables (the likelihood that the village is located in NTB-Bali) is significantly correlated with turnover, at the 10% level.

Second, Appendix Table A.2 shows balance along ten predetermined village characteristics from the _Podes_ data: latitude, longitude, altitude, coastal location, forest location, a dummy indicating that agriculture is the main economic activity in the village, and four dummies indicating the dominant agricultural activity (rice, corn, rubber, or palm oil). Only one out of these ten characteristics (corn cultivation) is significantly correlated with turnover, as one would expect by chance. Appendix Table A.3 shows balance on
in panel B of Table 4. The estimated intercept is _larger_ for the farm households and the difference is statistically significant at the 10 percent level (standard errors clustered at the PSU level). The evidence in favor of a larger intercept in the farm households is stronger when we add controls to the regression; for example, the difference is significant at the 1 percent level when number of children is added to the specification (see online appendix Table A.5). This conclusion is also supported by other available evidence on India; for example, the All India Debt and Investment Survey 1991 (NSS 48th round) shows that the assets of farming households (“cultivators”) are higher than those of the nonfarming households (see P. ii, NSSO report No. 491, 1998).

The evidence in panel B of Table 4 also accords well with a substantial body of related evidence available on the nonfarm activities in India. Note that it is likely to have _Y_ 0<sup>_pn_</sup> _< Y_ 0<sup>_pf_</sup> if the low-end nonfarm occupations are primarily low productivity residual activities and provide the last resort for the poorest households. Lanjouw and Murgai (2011) use three rounds of NSS data (1983, 1993/94, and 2004/2005) and show that nonfarm employment is positively associated with rural poverty in India, consistent with the observation that nonfarm employment involves primarily low productivity economic activities (see also World Bank (2011)).

This evidence on the intercepts of the income equation provides an explanation for the higher intercept for farm households in the intergenerational mobility equation as discussed earlier.

# **(7) Evolution of Mobility: Evidence on the Younger Generation in Rural China**

The rural economy and educational policies in China went through significant changes in recent decades. The changes include gradual relaxation of Hukou restrictions, increasing returns to education as the labor market matured, accelerated structural change in favor of the nonfarm sector, and
the column 2 rates over time. Figure 1 highlights how average U.S. tariffs have fallen since the 1930s and how recent increases compare using a long-term perspective.

Currently, Column 2 tariff rates apply to U.S. imports from Cuba and the Democratic Republic of Korea, countries that do not benefit from “normal trade relations (NTR).” At different points in time, the United States also revoked the NTR status of other countries, and their exports to the United States were subject to Column 2 tariffs. For instance, under the Trade Agreements Extension Act of 1951, the United States suspended the use of MFN tariffs on

> 3See McDonald et al. (2007) and McDonald and Thierfelder (2016) for a description of the model. The model uses data from the GTAP database. See Aguiar et al. (2016) for a description of the GTAP data.

4
of using BACI rather than the underlying information from UN Comtrade is that the same trade flow, which can be reported differently by the exporter and importer, has been reconciled in order to have a single statistic on each directional bilateral relationship. BACI only reports positive trade flows and we balance the dataset along three dimensions (exporter, product, and time) by including zerovalued trade flows.

We measure changes in market access in two ways: whether the exporter-product pair is under a preferential trade agreement (discrete measure) and the magnitude of the preferences granted (continuous measure). To construct the latter, we use information on ad-valorem tariff rates applicable under each preferential scheme—GSP, EBA and GSP+ for imports into the EU and AGOA for imports into the United States—for all beneficiary countries. These data are obtained from WITS, a database maintained by the World Bank which provides access to several international measures. The original source of tariffs rates in WITS is UNCTAD TRAINS. In order to calculate the preferential tariff margin, defined as the difference between preferential and non-preferential rates, we also include the MFN tariff rate for all products. The WITS database contains an identifier for groups of countries to which a particular tariff

> 17 Original data are provided by the United Nations Statistical Division (COMTRADE database). BACI is constructed using a procedure which reconciles the declaration of importers and exporters as explained in Gaulier and Zignago (2010).

20
Yes|Yes|
|Number of Assets in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|

_Notes:_ Dependent variables: (i) Mental Issues Index is constructed using the average of the variables in columns (ii) to (vi). The Individual controls include: age, gender, employed, student status in Afghanistan, and number of friends before moving to Tajikistan. Household controls include the variables listed in Table 2.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.

68
people are distributed at the facility, sub-district, or neighborhood levels—the levels where policy interventions are typically provided. In developing countries, creating a sampling frame for surveys using representative community samples usually involves manually delineating small geographic areas (or enumeration areas) on high-resolution satellite imagery. This is a standard method employed by multiple National Statistical Offices (NSOs) but is logistically complex, and requires substantial material resources, Geographic Information System (GIS) experts, and training (Qader et al., 2020, 2021). In addition, the process is extremely timeconsuming and expensive and often leads to postponing the survey. For example, it can take 2-3 years to carry out a survey (Grosh and Glewwe, 1995). These factors demand a fast and inexpensive sampling frame and population enumeration methodologies.

In this paper, a digital national sampling frame has been created using a semi-automatic and cost-effective approach. However, background on the existing sampling frames in the country is provided before discussing the new sampling frame. Three existing datasets that can serve as a potential sampling frame for household surveys include the 2022 Census with addresses from the State Population Register (SPR), the 2011 Census with settlements (villages in rural areas, towns in non-Yerevan urban regions, and districts in Yerevan), and 2023 election data with electoral precincts.

The ArmStat conducted a Population Census in November 2022 using a combined approach based on administrative data from the State Population Register (SPR) and a sample of 25% of the addresses in the SPR (ArmStat, 2022). In this dataset, primary sampling units (PSUs) are the workload of SPR addresses for each enumerator during the census. Although these PSUs do not have identifiable boundaries like the traditional census enumeration areas (EAs), this dataset can be used as a sampling frame since they cover approximately 93% of all addresses in the country. The household listing in this dataset is as of October 5
.2<br>|2.8<br>|1.6<br>|1.2<br>|3.1|2.7|
|**Panel B: Labor for**<br>|**ce participatio**<br>|**n rate, diff**<br>|**erence over**<br>|**2015: men**<br>|**aged 25-34**<br>|**(pp)**(σ= 4)<br>|||
|Costa Rica|-2.4|-0.4|2.0|1.3|-1.3|<br>-2.4|0.4|-0.6|
|El Salvador|0.2|0.1|0.2|0.0|7.4|<br>6.5|7.5|6.5|
|Panama|-2.0|-1.4|2.5|2.1|0.7|0.9|2.4|2.1|

# **References**

**CEDLAS, and the World Bank.** “Socio-economic database for Latin America and the Caribbean _.” Centro de Estudios Distributivos, Laborales y Sociales,_ Facultad de Ciencias Economicas, Universidad Nacional de La Plata, La Plata, Argentina; _the World Bank,_ Washington DC (2019).

**Ganzeboom, Harry BG, and Donald J. Treiman.** “Internationally comparable measures of occupational status for the 1988 International Standard Classification of Occupations.” _Social Science Research_ 25, no. 3 (1996): 201-239.

**Hsieh, Chang-Tai, Erik Hurst, Charles I. Jones, and Peter J. Klenow.** “The allocation of talent and US economic growth.
their accounts to careful scrutiny.<sup>3</sup> The institutions thus represent some of the best hopes for achieving poverty reduction with profit (or at least without ongoing subsidy). Still, the average share of funding (total liabilities plus total equity) made up of subsidized resources exceeds 20 percent in this sample.

Empirical progress on understanding the trade-offs in microfinance has been held back by the lack of variation in prices and program elements necessary for identification of key parameters. Most financial institutions offer their clients a uniform set of products and they seldom change the product mix, price, or design—or institutions change policies in ways that make it difficult for researchers to disentangle patterns of product changes versus other contemporaneous changes.<sup>4</sup> The cross-country data here, however, provide substantial variation in contractual types, prices, institutional sizes and locations, and target markets. The variation provides a means to describe the nature and trade-offs of lending relationships.

The data set allows us to examine several important claims for the first time in a large comparative survey. Does raising interest rates exacerbate agency problems as detected by lower loan repayment rates and less profitability? Is there evidence of a trade-off between the depth of outreach to the poor and the pursuit of profitability? Has “mission drift” occurred—i.e., have microbanks moved away from serving their poorer clients in pursuit of commercial viability?

> 3 The data were collected as part of the _MicroBanking Bulletin_ project.  Summary statistics on the institutions have been made available in the _Bulletin_ .  This paper is based on the underlying data set, access to which has kindly been provided (with confidentiality safeguards) to the authors.  The _Bulletin_ is available at www.mixmarket.org.

> 4 New work using field experiments (e.g., Karlan and Zinman, 2005) or natural experiments (e.g., Dehejia, Montgomery, and Morduch, 2005) shows promise in ways to either exploit the variation that
For 2021, we utilize the poverty nowcasting methodology described by the World Bank (2022). In short, this approach takes the prior year’s income or consumption distribution and adjusts this distribution forward using distribution-neutral growth from national account sources.<sup>11</sup> In our case, we use the welfare distribution for 2020 from Mahler et al (2022) and project them forward to 2021 using the per capita GDP growth rate from the World Bank’s World Development Indicators. We calculate poverty in each country for 2021 using these welfare distributions along with the anchored-SPL.

To isolate the impact of the COVID-19 pandemic on poverty, we estimate counterfactual income or consumption distributions for each country in 2020 and 2021. These distributions are projected forward 8 While income declines were substantial (World Bank, 2022a), health service disruptions were also widespread and largely due to a combination of declines in spending, intentional service reductions, and fewer individuals seeking care (WHO 2022b). One review found a 37 percent reduction in the use of health care services across 20 economies over the initial pandemic period of January–May 2020 (Moynihan et al. 2021). Another, focused on maternal and child health services in eight Sub-Saharan African countries, reported disruptions in all assessed countries between March and July 2020, especially in critical services such as child vaccination and antenatal care (Shapira et al. 2021). Disruptions such as these may have increased the young child mortality rates in 2020 and 2021 by as much as 3.5% (Ahmed et al. 2022).

9 While the conditional life expectancies are reported for each year of life, the excess death information is only reported in ranges of age. We standardize the age ranges across the two sources and then assume that the age at which the excess death occurs takes place at the population-weighted average of the given range. The age ranges are the following: 0-24, 25-39, 40-49, 50-59, 60-69, 70-79, and 80+. For practical purposes
10-year Treasury Bond Yield|-0.501***|-0.576***|-0.203**|-0.275***|-0.547***|-0.532***|
||(0.0913)|(0.0954)|(0.0933)|(0.0975)|(0.100)|(0.104)|
|ICRG Index|||||||
|Trade Openness|-0.530***|-0.417**|||-0.510**|-0.352*|
||(0.180)|(0.175)|||(0.199)|(0.192)|
|Financial Openness|||-0.0899*|-0.135***|-0.0635|-0.148***|
||||(0.0488)|(0.0448)|(0.0486)|(0.0445)|
|Transparency Dummy (IMF) Subscription|4.656**|5.166**|7.351***|6.953***|5.378**|5.718**|
||(1.883)|(2.324)|(2.054)|(2.161)|(2.168)|(2.569)|
|Transparency (IMF) Subscription x ICRG|-1.561***|-1.276**|-2.113***|-1.986***|-1.643***|-1.212**|
||(0.465)|(0.509)|(0.499)|(0