rafmacalaba/datause-encoder-data huggingface dataset:
behavior (social capital) in loan recoveries involving group lending as argued by Manski (1993).<sup>3</sup> Those with the same loan-default preference are likely to join a particular group, in which case group liability essentially reflects individual liability, suggesting that a high repayment rate (e.g., the case of the Grameen Bank) is driven by unobserved individual and joint default-preference incentives. Estimating the dynamics of group liability in group lending requires controlling for this self-selection bias at both individual and group levels.

The Grameen Bank in Bangladesh, the world’s pioneer in promoting group-based lending, is an ideal case for studying the role of group, versus individual, default incentives in a group-based lending scheme. To address this central issue, this paper uses a unique data set of the weekly borrowing and repayment history of all borrowers in some 297 Grameen Bank groups over a 7-year period (since their inceptions). The data was recorded and kept by Grameen’s branch-level office but never released.<sup>4</sup> Group liability is defined as the cross-member effect of loan default rates, while individual liability is measured by an individual’s past record of loan default. Thus, when an individual loan amount or the amount repaid depends on the behavior of group members as well as the individual, the unobserved attributes of both individual and group-members are likely to influence the performance of group-based lending.

An individual fixed effects (FE) method that sweeps out group FE, as well the influence of area unobserved characteristics influencing credit demand and loan repayment, can correct for the endogeneity of certain individuals joining a group and certain groups

> 3 Karlan (2005) shows how experimental economics can be used to measure social capital and its role in loan repayment behavior. However, such an ideal experimental design is hardly possible in practice and certainly not possible within the context of Grameen Bank lending.

> 4 However, because of branch-level computerization program, detailed group level data are not anymore recorded and kept for future reference since 2004.

4
# Table 4. Beta Coefficients of Private Investment Function (Ezeluding interest rate variable)

|Variables<br>Betas|S<br>|pecifications<br>|
|---|---|---|
||(1)|(3)|
|Flow of Real Credit|0.256<br>|0.280<br>|
|Foreign Exchange<br>Reserves|0.619<br>|0.648<br>|
|Real Exchange<br>Rate|0.273<br>|0.266<br>|
|Infrastructure<br>Capital Stock|0.182|0.181<br>|
|Income Growth|--|0.092|

Source: Computed from Table 2 In relative terms, changes in lagged foreign exchange reserves or rather in the changes in restrictiveness of import access has the greatest impact on private investment. Flow of real credit to private<sup>sector and real exchange</sup> rate changes come next. Though infrastructure capital stock had the highest value for elasticity (see Table 2 and 3) and flow of real credit<sup>the lowest,</sup> the relative importance of credit as a determinant of private investment is greater that infrastructure capital stock. Lagged income growth has the weakest effect.

# (ii) Components of Private Investment

Tables 5 and 6 reports the results of both OLS and TSLS estimation for the two components of private investment. The response of each of the two major components of private investment (i,e., equipment and buildings) to changes in reserves and in the real exchange rate are similar in both size and significance to that for total private investment. The same is<sup>not true for</sup> the other determinants.

20
**Can You Work it? Evidence on the Productive Potential of Public Works from a Youth Employment Program in Sierra Leone**

Nina Rosas<sup>1</sup> and Shwetlena Sabarwal<sup>2</sup> _JEL Classification: C93, H53, I31, I38, O15, O20_ _Keywords: Public works, safety nets, social protection, impact evaluation, youth, employment, Sierra Leone_ Acknowledgments: This work has been carried out in collaboration with Sierra Leone’s National Commission for Social Action (NaCSA) and the Ministry of Finance and Economic Development’s Integrated Project Administration Unit. We thank Statistics Sierra Leone for data collection; the field coordinators, Samantha Zaldívar and Joshua McCann, for ensuring high‐quality data; and Lucine Park for excellent research assistance. The paper benefited from the support and guidance of Suleiman Namara and John Van Dyck. We thank Kathleen Beegle, Deon Filmer, Patrick Premand, and Laura Ralston for discussions and comments that improved the paper.

> 1 Social Protection Specialist, World Bank, email: nrosas@worldbank.org.

> 2 Senior Economist, World Bank, email: ssabarwal@worldbank.org.
## enterprise labor mobility and the autonomy of enterprises to recruit new workers.

Finandal reforms: Key financial reforms include the shift away from government grants as a source of investment funds. Investment financing now comes principally from the banking system, at government-determined rates of interest, and out of retained enterprise earnings. In 1989, within our sample of 226 enterprises, while only 6 percent was financed out of central or local government budgets 53 percent was financed out of retained profits, loans from other enterprises or bond sales.' Material and energy reforms: Important reforms in the materials and energy sectors include tie introduction of the dual price system, allowing for greater quantities of materials to be purchased on the market. With the sample of 226 enterprises used in the present study, by 1989 only 29 percent of material inputs were obtained from plan quotas.

2.4 In the absence of specific knowledge about the impac.t of these reforms, we expect them to have engendered greater profit-seeking behavior and market discipline. Predicted effects should include: (i) reallocations of factors among enterprises to firms capable of generating higher retuns and (ii) gains in technical efficiency as less efficient enterprises move toward the frontier isoquant of their respective industries. Together these effects should have created a tendency for factor returns to converge during the reform decade of the 1980s.

_2.5_ For an economy such as China's, administered prices and price reform can substantially affect the measurement of factor returns. We therefore acknowledge that, even in the absence of physical reallocations of factor inputs or changes in relative tchnical efficiencies, changes _ih_ relative product and input prices can affect measures of factor returns. In Section 6, we examine the impact of changing prices on measures of productive efficiency.

# 3. Studies of Productive Efficiency In Chinese Industry

31 JRZ (forthcoming) suggest that gains in allocative efficiency have been an important source of overall industrial productivity growth. Over the period 1980-88, they find a tendency

- Jefferson and Xu (1991) find in their relatively small sample that enterprises with the largest shares of self-financed investment also experienced the highest rates of growth of capital productivity over the period 1984-87. They also find that during this period, within their sample and a second sample of 120 steel plants, the most profitable enterprises also enjoyed the highest rates of capacity growth, suggesting more rational patterns of investment

_Awesaing_ **_Gains i_** _Effil odawdon_ **_Among China's_** _Industrial Enterprises,_ by _Guy_ H. Jefferson and XW Wenyi 4
Figure 8 <!-- Start of picture text --> Staff reduction by firm size 1992<br>firms under bankruptcy procedure (.)<br>other firms (.)<br>V'laff reduction June-Dec. 1992<br>10000<br>1000<br>100 +<br>+ a4..%..<br>10 ~ + . . . .<br>1 . _ .... a<br>0.1 . , 111111 tt ,, Illt I I I 11111 I LI 1 111711 1 1 1_1111<br>1 10 100 1000 10000 10000<br>Firm size August 1992<br>0.00 + 1.00<br>.MFS wave 211992. Budget and<br>LgricutiturG excluded. Log scales.<br><!-- End of picture text --> 12
occupations. These occupations require few digital skills, and the digital skills that they do require are mostly basic digital skills. Cooks, who might use computers or smartphone for purchasing or inventory management, and Early Child Development teachers, who may use digital technologies to access or provide lessons, are examples of occupations in the very low digital occupations category.<sup>26</sup>

## **3.3. Matching the occupational skills profiles with country employment data: The Southeast Asia Digital (SEAD) data set**

To analyze the skills profile of the employed population in the four countries we study, we create the Southeast Asia Digital (SEAD) data set that matches the occupation skills profiles to country employment data. We use the occupation variable to do the match with data from four Southeast Asian countries: the 2020 Cambodia Socioeconomic Survey and the 2017 labor force surveys in Malaysia, Thailand, and Vietnam. Each country data set is representative of the population at the national level, allowing us to quantify the number of people working in an occupation. As such, we have a data set of occupation skills profiles for 127 occupations and weights representing the occupations’ employment share in the four countries.

# **4. Methodology**

To explore the complementarity of digital and other skills and the levels of occupation digitalization, we use descriptive statistics, pairwise correlations, a factor analysis, and linear probability model (LPM) regressions.

## _Similarity of skills required in very low-, low-, medium-, and highly digital occupations_

After an inspection of summary statistics, we apply an LPM to estimate the probability that a skill _i_ in occupation _o_ is found in a particular digital occupation group _g_ . We use an LPM to estimate marginal effects of binary outcomes following Friedman (2012) and Bellemare (2015, 2018). The LPM is as follows: where _yog_ is a binary variable representing occupation _o_ dependent on the (digital occupation level) groups _g_ of interest (e.g. medium- vs. low-digital occupations, high- vs. low-digital occupations, etc.). _Doi_ is a dummy variable for skill _i_ of occupation _o_ taking the value of one if the skills count of a skills category of an occupation is above the mean. β _oig_ is the coefficient for the groupings _g_ quantifying the

> 26 An alternative methodology that uses a smaller set of variables to assign occupations to high, medium, and low digital categories resulted in a similar mapping. Results are available from the authors.

20
(whether they went on to graduate or not), whereas we only have information on graduates for Ecuador. Therefore, we will examine graduation outcomes only for Brazil, but will analyze the following labor market outcomes for graduates from both countries over the 12-month period following graduation: (i) whether the student was formally employed at least one month; (ii) what percent of those months she was formally employed; and average monthly wages (calculated as the average over her months of formal work, and equal to zero if she did not work formally at all). Further, we use individual-level data to construct average characteristics and previous labor market experience for peers.<sup>19</sup> Appendix I provides additional information on all variables.

# **3.4 Descriptive statistics of individual-level data**

Table 2 presents descriptive statistics of program graduates in Ecuador and Brazil, for whom we study labor market outcomes. In both countries, the average student is about 25 years old and about 60 percent of students are female (panel A). Students in Ecuador have more educated mothers than in Brazil and 27 percent of them have children of their own. The average student in Brazil has accumulated more formal labor market experience before entering the program than the average graduate in Ecuador (panel C): 64 percent of students in Brazil worked at least one month before entering the program and worked for 46 percent of the time on average, whereas only 26 percent of graduates in Ecuador worked at least one month before graduation and worked for 19 percent of the time on average. Students in Brazil, therefore, have peers with more previous labor market experience than in Ecuador. After graduating (panel D), the average graduate in Brazil is more likely than that in Ecuador to be employed formally for at least one month (70 vs. 35 percent),

> 19 For student _i_ , peer characteristics are the average characteristics of the _other_ students in her cohort (Sacerdote, 2011). Recall that a program’s cohort is set of all 2019 program graduates in Ecuador (as information on entry cohort is not available) and all 2014 program entering students in Brazil. Interviews with local experts from Ecuador confirmed that graduation and entry cohorts usually have similar characteristics.

20
# **4 Data**

In order to profile the relationship between labor market outcomes and social assistance in Turkey, we used the _Survey on Income and Living Conditions_ (SILC) that has been conducted annually by the Turkish Statistical Institute (TurkStat) since 2006. It provides information on labor market statuses and also detailed socioeconomic data on the Turkish population, covering demographics as well as household and income-related variables.

The SILC is designed in a novel fashion that allows for a specific individual to be followed for up to four years. It is conducted for four subsamples in each year, and each year the oldest subsample is replaced with a new one. Due to this rotating design, in any given year, of the individuals partaking in the survey, 75 percent can be traced back for two years, 50 percent for three years, and 25 percent for the previous four years.

Results of the SILC are published annually in both cross-section and panel formats. The samples are assigned with survey weights to be representative of the segments of the non-institutionalized Turkish population<sup>7</sup> . The panel component of the SILC is presented in a way that allows for the construction of three different balanced panel data sets from each year’s survey, namely, two-, three-, and four-year dimensional panels. Each balanced panel is assigned with corrected survey weights to represent the whole population. The two-year panel, which consists of the observations for the previous two years, includes 75 percent of all the individuals partaking in the latest year’s survey. The three- and four-year panels include 50 and 25 percent of individuals, respectively.

The SILCs are designed to produce reliable measures of social and economic inequality, poverty, and income distribution. Additionally, surveys offer a wide array of information on the labor market profiles and demographic characteristics of the individuals.

Using SILC data, we are able to classify each individual based on his/her labor market profile, such that we can identify if the individual is employed, unemployed, or inactive, and if employed, whether formally or informally. Within the scope of this paper, we divided the 14+ noninstitutionalized Turkish population into four labor market categories, namely, formal-employed, informal-employed, unemployed, and inactive. We classified the individuals as employed if they had

> 7 Non-institutionalized population comprises all the population excluding the residents of schools, dormitories, kindergartens, rest homes for elderly persons, special hospitals, military barracks and recreation quarters for officers.

11
the main beneficiary. Brazilian suppliers were able to both replace the banned suppliers in Southeast Asian countries and meet the increase in world demand. Brazilian success in the unprepared poultry industry has been driven by its large supply capacity, its status as an HPAI free country and the highly competitive price of Brazilian poultry relative to other large HPAI free suppliers. Brazilian exports of unprepared poultry rose by almost 1.5 billion between 2003 and 2006. Increases in exports, on a smaller scale, are reflected in the data from other HPAI free countries.

Table 3: Exports of unprepared poultry (million US$)

|Year|Brazil|USA|European<br>Union|China|Thailand|Others<br>HPAI<br>affected|<br>Others<br>HPAI free|Total<br>Trade|
|---|---|---|---|---|---|---|---|---|
|1995|672|1,278|982|623|411|48|224|4,238|
|1996|814|1,835|926|638|345|46|241|4,844|
|1997|847|1,991|983|567|341|41|299|5,069|
|1998|733|1,787|997|495|396|32|236|4,675|
|1999|914|1,773|878|534|431|32|390|4,953|
|2000|897|2,006|851|599|425|23|384|5,185|
|2001|1,387|2,206|856|593|572|29|417|6,060|
|2002|1,457|2,048|918|406|585|49|333|5,795|
|2003|1,741|2,005|897|416|631|45|384|6,118|
|2004|2,642|1,616|943|141|46|19|739|6,146|
|2005|3,252|1,800|990|162|15|32|885|7,136|
|2006|3,119|1,965|1,086|171|21|34|1,016|7,413|

Source: COMTRADE database.

Note: Excludes intra-EU trade. Other HPAI affected countries include: Cambodia, Hong Kong (China), Japan, Indonesia, Laos, Malaysia, South Korea, and Vietnam.

The extent of the effect of avian flu on trade flows is more evident when analyzing gains and losses of market shares at the bilateral level. International trade in unprepared poultry is quite segmented, and each import market is often dominated by one or few suppliers.

12
# **1 Introduction**

Global value chains (GVCs) have transformed trade and development since the late 1980s by fragmenting the production process across borders (Baldwin, 2013, 2016). Coming under multiple labels such as trade in tasks, international fragmentation, offshoring, and the second unbundling, GVCs have attracted enormous attention from both academics and policy makers in the developing and developed world.

Although studies on GVCs and trade have been focused mostly on the manufacturing sector, services also play a critical role in GVCs. However, the role of services in value chains is often underappreciated and poorly understood despite the dominance of services in many national economies in terms of GDP and employment (Low, 2013). One of the most startling new facts concerning services has emerged from the breakthrough that came with the development of “value added trade” concept (Koopman et al., 2014; Timmer et al., 2014; Johnson and Noguera, 2012). A joint OECDWTO project found that services contribute over 50% of total value added embodied in the exports of the US, UK, France, Germany and Italy. Even for China, traditionally viewed as a goods exporter, about one-third of its value added exports come from the service sector.

The importance of services in GVCs, however, goes beyond their large share in value added. It has been argued that a well-functioning services sector enables and facilitates fragmentation, leading to an international reallocation of production stages (Jones and Kierzkowski, 1990; Francois, 1990c; Deardorff, 2001). The recent popularization of the GVC concept has also highlighted this connection, with many authors emphasizing how services is like the glue that holds supply chains together and ensures that they function in a fluid manner (Low, 2013; Egger et al., 2015; Francois et al., 2015).

Besides the rise of GVCs, another phenomenon, not unrelated, that characterizes trade in the past three decades is the rapid surge of bilateral and plurilateral trade agreements.<sup>1</sup> This proliferation has not only been in terms of quantity but also the quality or “depth”. Recent trade agreements often include provisions on services, investment, environmental and labor standards, as well as tariff reductions (Hofmann et al., 2017).<sup>2</sup> These deep agreements have also been partly motivated by the international expansion of production networks since a harmonization in certain national policies facilitates cross-border business activities and allows GVCs to work smoothly (Lawrence, 1996). Furthermore, the actors involved in regional trade agree-

> 1Hereafter, I use the term regional trade agreements (RTA) for all bilateral and plurilateral trade agreements, also referred to as preferential trade agreements (PTA).

> 2Regarding services, half of the 302 RTAs listed in the WTO database as currently in force covers only goods and the other half covers both goods and services.

2
# **References**

- Abadie, A., and Cattaneo, M. D. (2018), “Econometric Methods for Program Evaluation,” Annual Review of Economics, 10, 465–503

- <mark>Aga</mark> _<mark>,</mark>_ <mark>Gemechu; Maemir, Hibret. 2021. COVID-19 and Africa firms: Impact and Coping Strategies. Policy Research Working Paper;No. 9642. World Bank, Washington, DC. © World Bank.</mark>

- <mark>Amin, Mohammad; Viganola,Domenico.2021. “Does Better Access to Finance Help Firms Deal with the COVID-19 Pandemic? Evidence from Firm-Level Survey Data . Policy Research working paper;no. WPS 9697;COVID-19 (Coronavirus) Washington, D.C. : World Bank Group.</mark>

- Bennedsen, M, B Larsen, I Schmutte and D Scur (2020), "Preserving job matches during the COVID-19 pandemic: Firm-level evidence on the role of government aid", Covid Economics: Vetted and Real-Time Papers 27, 9 May.

- <mark>Bruhn, Miriam (2020) Can Wage Subsidies Boost Employment in the Wake of an Economic Crisis? Evidence from Mexico, The Journal of Development Studies, 56:8, 1558-1577, DOI:</mark>

- Chetty R., J. N. Friedman, N. Hendren, M. Stepner, and T. O. I. Team. How did covid-19 and stabilization policies affect spending and employment? a new real-time economic tracker based on private sector data. Working Paper 27431, National Bureau of Economic Research, June 2020.

- <mark>Cirera Xavier, Marcio Cruz, Elwyn Davies, Arti Grover, Leonardo Iacovone, Jose Ernesto Lopez Cordova, Denis Medvedev, Franklin Okechukwu Maduko, Gaurav Nayyar, Santiago Reyes Ortega, Jesica Torres, Policies to Support Businesses through the COVID-19 Shock: A Firm Level Perspective, The World Bank Research Observer, Volume 36, Issue 1, February 2021, Pages 41–66</mark>

- Correa-Caro Carolina, Leandro Medina, Mr. Marcos Poplawski Ribeiro, and Mr. Bennett W Sutton. “Fiscal Stimulus Impact on Firms' Profitability During the Global Financial Crisis” IMF Working Papers Volume/Issue: Volume 2018: Issue 251

- <mark>Dugoff EH, Schuler M, Stuart EA. Generalizing observational study results: applying propensity score methods to complex surveys. Health Serv Res. 2014 Feb;49(1):284-303. doi: 10.1111/1475-6773.12090. Epub 2013 Jul 16. PMID: 23855598; PMCID: PMC3894255.</mark>

<mark>Enterprise Surveys, The World Bank, http://www.enterprisesurveys.org</mark>

- <mark>Garrido MM, Kelley AS, Paris J, Roza K, Meier DE, Morrison RS, Aldridge MD. Methods for constructing and assessing propensity scores. Health Serv Res. 2014 Oct;49(5):1701-20. doi: 10.1111/1475-6773.12182. Epub 2014 Apr 30. PMID: 24779867; PMCID: PMC4213057.</mark>

- <mark>Jann Ben, 2017. "</mark> **<u><mark>K</mark> MATCH: Stata module module for multivariate-distance and propensityscore matching, including entropy balancing, inverse probability weighting, (coarsened) exact matching, and regression adjustment</u>** <u>," Statistical Software Components</u> <mark>S458346, Boston College Department of Economics, revised 19 Sep 2020.</mark>

32