rafmacalaba/data-use-mentions-extended huggingface dataset:
It is also important to know the salient administrative/institutional details of the program. For NX evaluations, such information is key to designing a survey that collects the right data to control for the selection process. Knowledge of the program’s context and design features can also help in dealing with selection on unobservables, since it can sometimes generate plausible identifying restrictions, as discussed further in sections 6 and 8.

The data on outcomes and their determinants, including program participation, typically come from sample surveys. The observation unit could be the individual, household, geographic area or facility (school or health clinic) depending on the type of program. Clearly the data collection must span the time period over which impacts are expected. The sample design is invariably important to both the precision of the impact estimates and how much can be learnt from the survey data about the determinants of impacts. Section 9 returns to this point.

Survey data can often be supplemented with useful other data on the program (such as from the project monitoring data base) or setting (such as from geographic data bases).<sup>17</sup> Integrating multiple data sources (such by unified geographic codes) can be highly desirable.

An important concern is the comparability of the data on participants and nonparticipants. Differences in survey design can entail differences in outcome measures. Heckman et al. (1999, Section 5.33) show how differences in data sources and data processing assumptions can make large differences in the results obtained for evaluating US training programs. Diaz and Handa (2004) come to a similar conclusion with respect to Mexico’s _PROGRESA_ program; they find that differences in the survey instrument generate significant biases in a propensity-score matching estimator (discussed further in section 5), although good approximations to the experimental results are achieved using the same survey instrument.

There are concerns about how well surveys measure the outcomes typically used in evaluating anti-poverty programs. Survey-based consumption and income aggregates for nationally representative samples typically do not match the
|||||(1.75)|(0.20)|
|---|---|---|---|---|---|
|Latin Am.||||-0.811|-0.447|
|||||(3.04)***|(0.70)|
|MiddleEast||||-0.338|-0.064|
|||||(1.08)|(0.11)|
|_Barriers_*Latin<br>America|||||-0.674|
||||||(1.09)|
|_Barriers__Africa|||||-3.201|
||||||(2.34)**|
|_Barriers__High<br>Inc.|||||-0.078|
||||||(0.16)|
|_Barriers_*East<br>Asia|||||-0.118|
||||||(0.20)|
|_Barriers_*Middle<br>East|||||-0.794|
||||||(1.43)|
|_Barriers_*Eastern<br>Europe|||||1.655|
||||||(2.79)***|
|Constant|-35.753|-35.043|-35.369|-27.627|-60.129|
||(9.24)***|(9.08)***|(8.23)***|(3.71)***|(3.81)***|
|Observations|510|510|510|510|510|

Robust z statistics in parentheses

*
Policy Research Working Paper 9279

# **Abstract**

The Korean manufacturing sector has undergone active structural transformation in the past few decades. In particular, the composition of core manufacturing products has changed over time. In the 1970s, textiles, which are used to produce fabric, clothes, apparel, and shoes, were the main product. Over time, the value added shares have shifted toward electronics, ships, and cars. By analyzing plant-level microdata, this paper documents the patterns of entry, exit, job creation and destruction, and the growth of young plants during the industrial shift. This industrial shift involved active job reallocations, as well as the entry and exit of plants. The paper quantifies the extent to which such plant-level dynamics explain aggregate productivity growth. The findings show that within-plant productivity growth, which includes the effects of fast growth of young plants as well as robust growth of large continuing plants, played an important role in the productivity growth of the Korean manufacturing sector. The contribution of reallocations between continuing plants was relatively small. Moreover, productivity growth of an industry accompanied an increase of productivity dispersion, a measure commonly interpreted as the degree of misallocation.

This paper is a product of the Development Research Group, Development Economics. 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 author may be contacted at ylee@sogang.ac.kr.

_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
expected earnings**

Misinformation about earnings abroad may persist even in cases where a large share of the population is a migrant. As discussed earlier, local agents and recruitment companies have an incentive to exaggerate earnings information to induce potential migrants to go. Moreover, previous migrants may also provide biased information. They may lie about their earnings to their social network if they fear social taxation, or feel pressure to maintain any social prestige they gain from having migrated abroad (as in McKenzie, Gibson, and Stillman, 2013, Seshan and Zubrickas, 2015, and Sayad, Macey, and Bourdieu, 2004). This has fueled concern among policymakers that potential migrants may overestimate their earning potential abroad.

However, systematic evidence on the degree of such misinformation is rare. To date, there are no credible surveys of migrants in the destination countries to determine the actual earnings of Nepali migrants.<sup>20</sup> Further, the government does not have a way to track actual earnings abroad. The Department of Foreign Employment only receives reports of contractual earnings from potential migrants when they apply for permits to work abroad, and even this data is not publicly available. In this section, I use the survey data I collected to compare potential migrants’ expectations with a few benchmarks to establish that potential migrants are misinformed on their earning potential.

> 18The joint tests across all comparisons have a p-value of 0.65 for comparisons within death information treatment arms and 0.48 for comparisons within wage information treatment arms. Detailed results available on request.

> 19Results available upon request.

> 20The closest to this approach is the Nepal Migration Survey of 2009 conducted by the The World Bank (2011), which asked household members about the earnings of the foreign migrants. It also asked the returnees the actual earnings they made during their migration episode. Other than the fact that this data was collected almost six years ago, it also suffers from reporting biases of the household members, and reflects the misinformation within the household as
Figure 2: Services share of purchases in public and private markets <!-- Start of picture text --> 1995 2015<br>ROWSWETWNROUKHMVNMGRCMARNORBGRKORMYSGBRMEXARGHKGCHNHUNSGPCHERUSCANDEUBRNDNKHRVPERUSASVNCOLAUSCYPBRASAUTURTUNSVKPOLESPCHLNLDCZEFRAAUTPRTPHLLUXESTMLTBELJPNNZLZAFLTULVAIDNINDCRIISRFINIRLISLITA ROWSWETWNVNMKHMMARNORROUGRCMYSKORMEXARGBGRGBRHKGCHNHUNSGPBRNCHERUSCANDNKDEUHRVSVNUSACOLPERBRASAUAUSCYPTUNTURSVKPOLESPCHLNLDCZEAUTFRAPRTPHLLUXESTMLTBELJPNNZLZAFLTULVACRIINDIDNISRFINIRLISLITA<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Public Private<br><!-- End of picture text --> Note: Raw data are sourced from the TiVA database. Public procurement flows are computed using the “typical” definition (see the main text for details). The vertical solid line indicates the average service share in public procurement across countries. The dashed line reports the same average for private markets.

We then turn to import penetration ratios defined as the value of imports divided by total expenditures. While purely descriptive, the measure has been used extensively to assess openness to trade, including in government procurement markets (Messerlin and Mirodout, 2012). We compute the import penetration ratios by country and type of purchasing entities (public or private). Figure 3 reports the ratio of public to private import penetration ratios for goods and services. A value greater than one suggests that public markets are more open than private ones. Public markets are less open than private ones in services for all countries, while for goods the picture is more nuanced. Between 1995 and 2015, relative openness of government procurement in services increases, whereas it slightly decreases on average for goods.<sup>23</sup>

> 23When using the “narrow” definition of government procurement in Figure 2 in the Appendix, we find similar results: if anything, the relative government import penetration is lower especially in services.

18
of poverty and vulnerability) over time.

In addition to the above variables, the LiTS survey also provides data on a number of beliefs, including beliefs about the importance of effort and luck for success, preferences for the market economy and the respondent’s degree of risk aversion. The summary statistics for the variables employed in the empirical analysis in section 4 are presented in Table 3.

# **4. Empirical strategy**

In order to investigate whether perceived inequality of opportunity dampens expectations of future mobility, the following relationship is estimated: where _Mobilityi_ is the expected future mobility, as represented by the difference between the expected future position of the socio-economic ladder and the current position of the same ladder, _Ci_ is a dummy that equals 1 if connections are deemed vital and zero otherwise, _Ai_ is a dummy that equals 1 if connections are not available and zero otherwise, _Xi_ is a vector of control variables described in detail below, and errors _εi_ are allowed to be correlated within primary sampling units.

Recall that the question this study proposed to examine was whether perceptions of unequal access to opportunities dampen one’s expectations of future mobility. An important caveat, and one that informs the empirical setup described here, is that the LiTS data are cross-sectional, such as there are no readily-available sources of exogenous variation to identify a causal effect of (perceived) inequality of opportunity on expected future mobility. In this sense, the results presented in Sections 5-7 of this paper should be treated as correlational, rather than strictly causal. However, there are several ways to address the omitted variable bias. First, note that it may be problematic, in trying to examine the link between perceived inequality of opportunity and perceived future mobility, to compare mobility expectations of those who perceive connections to be vital with those of the group who perceive connections 11
On education and health outcomes, Branisa et al. (2013) report a positive association between women’s property rights and their educational attainment and fertility, using a data set of 124 countries. This is also confirmed by causal evidence from India, where Deininger et al. (2013, 2018) and Roy (2015) estimate significant positive effects of legislating equal inheritance rights for women on their educational achievements. Mishra and Sam (2016) find that increase in women’s land ownership in Nepal boosted their empowerment through greater household decision-making in health care and household purchases. Similar causal findings have been obtained for Vietnam (Menon et al. 2014), India (Mookerjee 2017) and Kenya (Harari 2018).

Despite overall positive findings, Roy (2015) shows that legislation to provide equal inheritance rights to women in India resulted in some parents circumventing the law in order to disinherit their daughters from their rightful share. Bhalotra, Brule and Roy (2018) and Rosenblum (2015) find that the reform also increased female foeticide and female infant mortality, while Anderson and Genicot (2015) show that it increased marital conflict leading to greater suicides. These negative findings alert us to the possibility of unintended consequences of gender progressive legislation in developing economies, especially in the presence of multiple constraints that interact to perpetuate the status quo of gender inequality. For example, in the case of India, _patrilocality_<sup>7</sup> is a key social norm that perpetuates preference for sons over daughters, as it ensures provision of old-age care for parents in the absence of a viable social security system. Thus, focusing on alleviating one constraint (access to property) without addressing other related ones (informal insurance) might be counterproductive, and suggests that greater attention should be paid to tackling gender discrimination using a holistic approach.

In terms of legal rights to administer marital property, unilateral divorce laws (by affecting spouses’ outside options) have also been shown to impact labor market decisions of married women.
export decisions have been suggested. Causality may run in the opposite direction, where firms overcome financial constraints by diversifying their sales markets and choose to become exporters to overcome liquidity constraints.<sup>1</sup> Trade policies may also play a role.<sup>2</sup> In parallel, a limited but growing literature on the role of demand shocks on trade flows is developing. When faced with demand shocks, firms may seek to spread their sales between domestic and foreign markets to smooth their cash flows. Indeed, Campa and Shaver (2002) suggest that Spanish manufacturing firms may decide to export to foreign markets to smooth their cash flow and thus exploit the imperfect correlation between the destination country and the Spanish business cycles. Behrens, Corcos, and Mion (2013) use micro data to identify the fall in the demand for tradable goods as the main driver for trade decline in Belgium. More attenuated effects are documented by Eaton, Kortum, Neiman, and Romalis (2011) whose counterfactual analysis shows that non-durables demand shocks lead to 18% fall in world trade. Claessens, Tong, and Wei (2012) take a different approach and study the impact of the crisis on cross-country balance sheets of non-financial firms. They find that the crisis hampered more firms with greater sensitivity to the business cycle and trade. Conversely, financing openness (measured as total international assets plus liabilities over GDP) played a more limited role. The recent crisis has highlighted the significant role of real sector shocks on trade dynamics, as suggested by Eaton, Kortum, Neiman, and Romalis (2011).

The paper proceeds in three sections. We discuss the multiple data sources used in Section 2. We then outline the empirical strategy and present the results in Subsection 3.1 and 3.2. We conclude in Section 4.

# **2 Data**

We base our analysis on information from three main data sources. First, the Business Environment and Enterprise Performance Surveys (BEEPSs) provide information
restrictions on internal movements; and international travel controls. The stringency index is calculated as the mean score of the nine metrics, each taking a value between 0 and 100. A higher score indicates a stricter regulatory regime. To match these daily data to each round of our data, we take the average daily score during each survey period. Figure 1 displays the average government stringency index in each country over time.

# **2.2 Sample Selection and Surveys**

To examine the relationship between livelihood diversification and welfare outcomes, we use panel data from high-frequency phone surveys (HFPS) in Ethiopia, Malawi, and Nigeria (WB, 2022; NSO, 2022; NBS, 2022). In each country, interviewers conducted these surveys on a monthly basis with households for a period of at least 12 months following the outbreak of COVID-19.<sup>2</sup> The sample for the HFPS is drawn from households that had been interviewed during the most recent (2019) round of the national longitudinal household survey implemented by the respective national statistical office, with assistance from the World Bank (CSA, 2019; NSO, 2020; NBS, 2019). These pre-COVID-19 Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-

> 2The following agencies implement the monthly surveys with support from the World Bank Living Standards Measurement Study (LSMS): Laterite Ethiopia, the Malawi National Statistical Office, and the Nigeria Bureau of Statistics.

5
internet surveys in the share of the respondents that are female and the average age of respondents. But the respondents to the internet survey do tend to be even more skewed towards

> 15Despite the onset of a forced displacement crisis beginning in March 2015, official figures suggest that approximately 10 percent of the population was currently displaced at the time of the surveys analyzed here, there likely has been limited migration outside the governorate where households lived prior to the escalation of the conflict, and there has also been a high share of displaced households that did not move outside their district (e.g., TFPM 2017; OCHA 2021; D’Souza et al. 2022a; D’Souza et al. 2022b).

13
performance is about 45% larger for parts in components than for final goods. In addition, trade in the Asia-Pacific region—which is where international production networks are most developed—is particularly sensitive to logistics performance. We also find evidence that South-South trade is more sensitive than average to logistics performance.

At least two important policy implications flow from our results. First, development of the logistics sector can obviously play a key role in promoting greater integration in international production networks. There is much that policymakers can do to assist that process. Building logistics competence is a many-faceted process, however, involving issues such as regulation of transport and related sectors, border procedures, infrastructure, and private sector development. Diagnostics exercises using the World Bank’s LPI database are a logical place to start for policymakers interested in addressing the key bottlenecks in national logistics performance (Arvis et al. 2010).

Second, developing country policymakers should pay particular note to the importance of logistics performance in the context of South-South trade flows, particularly in parts and components. With some degree of global rebalancing possible in the future, South-South trade is likely to assume increased prominence as a driver of worldwide demand (Haddad and Shepherd, 2011). Barriers to further SouthSouth economic integration—including those related to logistics performance—should therefore be a priority for policymakers going forward.

In terms of future research, there is considerable scope for confirming and extending our results. First, estimation in a true panel data framework would make it possible to control for an additional array of country-specific factors using fixed effects. Such an approach is not currently feasible, however. The LPI is now available for three years—2007, 2010, and 2012—but the lag with which trade data become available, especially for developing countries, means that a true panel data approach will need to be left for later versions of the LPI.

19
exposure to development policy lending, we use two measures—the number of development policy loans and the number of prior actions issued by the organization—that are categorized by the World Bank “policy cluster.” We then follow the same cross-walk procedure (see Table A1 in the Appendix) that we used to construct _n_ _~~e~~ sw_ _~~m 1~~ p_ to measure these variables at the country-policy-cluster-year level. These variables— _dpl_ _~~p~~ roj_ _~~n m 1~~ p_ and _pa_ _~~m 1~~ p_ —measure whether a respondent worked in a specific country and policy cluster that received one or more DPLs or PAs, respectively, from the World Bank during a year in which he or she evaluated the policy influence of the World Bank.

We also rely on measures of annual per capita disbursements through development policy loans ( _lnaid_ _~~d~~ pl_ _~~d~~ isb_ _~~p~~ c_ _~~0 m 1~~ p_ ) and annual per capita disbursements through investment loans ( _lnaid_ _~~i~~ nv_ _~~d~~ isb_ _~~p~~ c_ _~~0 m 1~~ p_ ). However, the World Bank does not categorize its financial disbursement data by policy cluster, so these variables are measured at the country-year level. Also, given that _lnaid_ _~~d~~ pl_ _~~d~~ isb_ _~~p~~ c_ _~~0 m 1~~ p_ , _dpl_ _~~p~~ roj_ _~~n m 1~~ p_ , and _pa_ _~~m 1~~ p_ are strongly correlated, we never introduce these variables simultaneously to minimize the risk of multicollinearity.

For ease of exposition, we refer to these five measures of exposure to World Bank assistance as “treatment” variables (see Table A2).
We also include the dummy variable indicating whether a student has a job on the side or not in this regression model. The behavior of students in terms of jobs on the side is potentially important for the formation of human capital, as labor time competes with study time (cf. Section 2). Students with jobs on the side may therefore need more time-to-graduate, fail exams more often, obtain lower grade points, and drop out from university more frequently.<sup>16</sup> For example, Stinebrickner and Stinebrickner (2003) look at student performance at a liberal arts institution in the USA, providing education to those who “have great promise, but limited financial resources”. Students receive full-tuition scholarships, and part of the cost of schooling is defrayed through a mandatory work-study program. Their empirical results suggest that working on the side has a harmful effect on student performance. In our estimation we find a negative effect from work on a student’s GPA, but the estimation is imprecise (the coefficient does not differ significantly from zero).

Comparison of the results from the SOFES database and the survey data brings us to the conclusion that students with a SOFES loan show better performance than students without a loan. This is not necessarily a purely causal impact of the loan program, but could also capture (self-)selection of students.

The second column of the table presents the results for the student’s failure rate, i.e. the number of courses that a student had to redo as a fraction of the total number of courses the student has attended. For other factors than the treatment variable, we find that (i) the student’s age again improves performance, repetion drops 0.3%-points; (ii) students in Engineering and Natural Sciences repeat at a higher frequency, 1%-point; and (iii) the student’s employment situation again shows up with an insignificant coefficient. For the treatment effect of student loans,