rafmacalaba/data-use-mentions-extended huggingface dataset:
about three and a half times larger than the push factor.<sup>14</sup> Table A2 estimates a specification using the bilateral difference (between _o_ and _d_ ) in corruption levels. The coefficients show a positive relationship between the gap in corruption and the bilateral flow that occurs from _o_ to _d_ .

As omitted variable bias may be a cause for concern, we include additional factors that might have a simultaneous effect on both corruption levels and migration flows. These include: GDP per capita, inflation rates, the importance of agriculture and services in the economy, and also whether citizens of country _o_ have the right to work in country _d_ . Reassuringly, the sign and the significance of the estimated coefficients on the four corruption indicators remain unchanged. However, as shown in Table 4, both push and pull factors have smaller magnitudes following the inclusion of these additional controls: the former is estimated to be between 3% and 24%, while the latter is between 8% and 13%.<sup>15</sup> Averaging the results across the four columns: a 1-unit increase in the corruption level in the origin country is associated with an 11% increase in out-migration. The same 1-unit increase in the destination country is associated with a 10% decline in in-migration.<sup>16</sup> Lastly, to control for differences in legal frameworks and regulations between countries, we include indicators on the employment legislation, product market regulation, and social expenditure. Results are presented in Table 5. All the estimates on the corruption indicators remain broadly unchanged: an increase in the corruption level in the origin country _o_ (resp., the destination country _d_ ) is associated with a rise (resp., fall) in migration flows of between 18% and 33% (resp., 27% and 53%).

# **4 Conclusions**

Policies to shape migration flows have become more important in a growing number of countries, especially those facing a brain
more sensitive to seasonality; in this case, the MFI may be forced to manage the portfolio in such a manner that is not profitable. That is, the test of seasonality of financial intermediation for the MFIs ultimately rests on whether _β2_ is statistically different from zero.

Estimation of equation (2) involves empirical issues such as the joint distribution of lending and other indicators of MFI portfolio investment. That is, measurement errors in loan disbursement, for example, are likely to be correlated with measurement errors in loan outstanding, inducing an attenuation factor that biases the coefficients toward zero. We are using a lagged dependent variable (LDV) method to reduce such bias. We also use some exogenous agroclimate data such as monthly rainfall and temperature as additional controls to reduce attenuation bias (Table 3). As this is an aggregate-level (MFI-level) data analysis, it is difficult to control such bias fully. Yet, as the estimates are subject to downward bias, we would observe some seasonality in financial intermediation if we find that _β2_ is statistically significant even at the 10 percent level.

Table 5 presents the summary statistics of all essential variables for our econometric analysis across the projects. The average monthly disbursement is the highest for microenterprise loans followed by seasonal loans. This means that the monthly volumes of loans outstanding, loans recovered and savings mobilized are the highest for microenterprise than other activities. The loan recovery rate is about 99 percent for all categories of lending except for rural loans (98 percent). It seems that the MFI performance is slightly better in the urban areas than in rural areas. It is no wonder that in urban areas loan volume is higher, so is recovery, outstanding, and savings mobilized compared to those in rural loans. Women members in MFIs are six times as numerous as male members. Note that not all members are necessarily borrowers at a given time.

Tables 6-9 present the regression results of equation (2) showing the impacts of seasonality of disbursement on loans outstanding, loans recovered, savings mobilized
_Data on informal workers_ _Labour Force Survey (SAKERNAS)_ SAKERNAS is a nationally representative household survey designed to collect information on the labor market activities of working-age individuals in selected households. Since its inception in 1976, the survey has undergone several changes to its frequency (Vaccaro et al., 2022). Since 2015, however, SAKERNAS has been conducted every February and August of each year. The February round providing provincial-level estimates now covers approximately 75,000 households, while the August round providing district-level estimates now covers 300,000 households (Vaccaro et al., 2022). Since 2011, SAKERNAS has used a rotating panel design whereby 75 percent of households are retained for two consecutive survey rounds. Households in the sampling frame can be interviewed a maximum of 4 times (Vaccaro et al., 2022).

The survey collects a range of information from respondents, including their educational qualifications, labor market activities, and the characteristics of the respondents’ main and secondary job, if any. Over the years, Statistics Indonesia has added new questions to capture emerging topics relevant to the labor market. In 2018, for example, questions related to the Internet and the digital economy were added and questions to identify “dependent contractors” (e.g., gig workers).<sup>7</sup> Likewise, the August 2020 and February 2021 waves of the SAKERNAS asked respondents about the impact of COVID-19 on their employment status, working hours, and earnings (Vaccaro et al., 2022).

Most relevant to informality are the specific questions used to identify informal workers. Consistent with the Proxy 1 and Proxy 2 definitions of informal employment, SAKERNAS regularly collects information on workers’ occupation and employment status. With the introduction of the

> 7 BPS defines dependent contractors as “someone who is bound to an economic unit (whether a business or company, institution, government, or other unit) to produce goods or services that can provide benefits to the economic unit, but is not a
# 2. **The questionnaire**

This section gives an overview of the main focus of the questionnaire. The first subsection describes how the questionnaire was developed, and the second subsection discusses 4 the structure of the questionnaire .

# Development of the questionnaire

The survey instrument was developed in several steps during the last four years. It started with a large number of interviews of private entrepreneurs in different Latin American countries that resulted in a short multiple choice questionnaire. This questionnaire asked whether entrepreneurs feared large and unpredictable swings in lawmaking. This questionnaire was then sent out to a very small number of finns in 28 LDCs. No stratification was done in this survey. Given the small number of responses per country, no strong conclusions could be drawn. Nevertheless, these results coupled with growth and investment data proved promising as the political credibility indicator was significantly related to economic performance of the 28 countries (see Brunetti and Weder 1995). Based on the results of this pretest the survey instrument was refined and expanded. In preparation for the WDR 1997 survey the expanded questionnaire was discussed with a number of country experts at the World Bank and at IFC. After these discussions the questionnaire was revised and finalized and resulted in the survey presented in this paper.

# Structure of the questionnaire

The questionnaire first asks for general characteristics of the firm. These questions aim to capture a brief but detailed picture of the firm that is answering in the questionnaire. Five different dimensions are considered. First the firm is asked to define its size-less than 50 employees, between 50 and 200 employees and more than 200 employees. Second the nature of the firm's business is asked (manufacturing, services and agriculture). Third the location of management is inquired (capital city, large city or small city/countryside). The last two questions of this section ask for the internationalization of the firm in the two dimensions foreign participation (yes-no) and exports (yes-no). Section 4 will provide an overview of the actual distribution of all responding
# **Table 3: Predictability, Responses and Availability of Rules and Regulations**

_(Percentage of respondent ratings under different categories)_

||agree||agree|disagree||disagree|1999|
|---|---|---|---|---|---|---|---|
|Score|6|_5_|4|3|2|1||
|Predictability of<br>government rules and<br>regulations|2|7|46|29|12|4|3.46|
|Predictability of policy<br>changes in the annual<br>central budget|2|3|45|33|9|8|3.32|
|Advance information<br>to fimns about the<br>changes affecting them|0|18|11|35|25|11|3.00|

_Source:_ World Bank - CII survey of 210 private sector firms, 1999 33
9 This paper uses the central bank reaction function as framework for the interest rate-exchange rate relationship in emerging market economies. The literature reviewed above suggests that the exchange rate has a role in determining the policy variable, although in varying degrees across emerging markets.<sup>1</sup> The literature also indicates a wide range of topics underlying the interest rateexchange rate nexus within IT. Our empirical research approach is close to Aizenman, Hutchison, and Noy (2011) who estimate a Taylor rule reaction function for 16 emerging-market countries for the period 1989Q1 to 2006Q4 employing fixed-effects panel data methods. Our analysis differs from them in at least two important ways. First, we employ dynamic panel methods, which are better at controlling for potential endogeneity problems that emerge from having inflation and exchange rate variations on the right-hand side. With this, we allow for effects from interest rates (the policy variable) to currency, which is consistent with the UIP literature. It is also reasonable to assume that increases in domestic rates will affect the output and inflation gaps: a contractionary monetary policy that increases interest rates is expected to reduce any positive output and inflation gaps. Second, we expand the sample of countries and consider the effects of the recent financial crisis on the way that central banks respond to exchange rate developments but also to the rest of the variables in their reaction function. These considerations lead us to propose alternative empirical strategies to the fixedeffects modeling by Aizenman, Hutchison, and Noy (2011).

# **3. Empirical approach**

> 1 Exchange rates may, of course, lead to inflationary effects, which forms an empirical matter of its own. Nogueira and León-Ledesma (2009), for example, use monthly data from 1995M1 to 2007M12 for Brazil and estimate inflation as a function of its past, real output growth, changes in nominal exchange rates, and changes in the price of foreign imports. They find that, after the policy change and adoption of IT in 1999M1 long-run exchange rate pass-through declined
binding when compared to richer economies. In low income countries, 10.2% of all jobs are telecommutable. However, only 3.8% of those jobs can be effectively performed from home. The DN2020 measure thus overestimates the number of telecommutable jobs by a factor of almost 3. In contrast, internet access constraints in high income countries only prevent 1 of every 12 telecommutable jobs (3.3 percent of 38.8 percent) from being performed from home. Upper and lower-middle income countries are intermediate cases where internet access limitations reduce the number of telecommutable jobs by around 22 and 41 percent, respectively.

Accounting for residential internet access limitations thus leads to the largest reductions in the share of jobs that can feasibly be performed from home in countries where telecommutable jobs are relatively scarce to start with. As a result, the correlation between GDP per capita and the feasibility of home-based work strengthens when internet connectivity is taken into consideration. This in turn implies that conventional measures of labor market exposure to COVID-19 may underestimate its impact on inequalities in job vulnerability across countries, a theme we will return to below.

# **4 Robustness tests and extensions**

The construction of the home-based work indices based on O*NET surveys and the use of the Gallup World Poll data to determine internet access requires certain assumptions which might bias our results. The goal of this section is to use alternative assumptions and assess the robustness of our main findings as represented in Figures 1a and 1b. Figure 2 displays the share of jobs amenable to telework using alternative assumptions. As was the case in Figure 1b, countries are grouped according to their average income levels.

The DN2020 index is, in essence, a weighted average of different types of tasks and embodies ICT requirement via inclusion of email dependence in the calculation. We first remove this condition to expand the set of jobs that can be feasibly performed from home without internet usage and recalculate the DN2020 index. This modification guarantees that our results are not an artifact of constraining the
12 Scholar. Modifying the harvesting tool to aggregate across these variants would be very hard, because one needs to know which records are indeed variants of a single publication and which are simply similarly named publications.

Finally, we should note that our citation data are discipline-blind. We do not—and could not—impose the requirement that the citing author be an economist. Doing so would necessitate working manually through each document citing each of the 33,000 records in or database and establishing the discipline of the citing author. In any case, it is not clear whether such an exercise make sense; we are, after all, interested in the overall impact of each item in our database. We should also point out that we do not eliminate self-citations. Doing so would necessitate another manual check all documents citing each of our 33,000 publications. This would have the advantage of eliminating gratuitous self-citation. But insofar as an author is citing his or her previous work in taking the idea forward in a new publication, a self-citation is no different from a citation by anyone else. So even if it were feasible to eliminate self-citations in our citation data, it is not clear we would want to.

# **Topic classification**

Beyond influence measured through citations we are also interested in the topics covered by the more influential of our 33,000 publications, and how the topic mix has varied over time and varies across the more influential institutions.

The JEL system used by EconLit does have subcodes, but as the article by Rubin and Chang (2003) makes clear, it is not a very informative one. Alternative classifications exist that we could have used to apply a topic code to the EconLit records. The first was developed by Cooper and Culyer (1973). At that time, the subject was relatively undeveloped and the literature sparse. They used four categories: ―economic efficiency‖, ―improving decisions‖, ―hospitals‖, and ―the value of human lives‖, revealing a British
in 2000 and 8 countries in 1990. Survey data were used for 13 European countries. These data give the immigrants’ origin for all source countries, not only for the top 5 or 10 countries, thus preventing an under-representation of small countries, as was the case for the CD dataset. In addition, for 24 out of 30 receiving countries, the data collected also give the exact education structure of immigration, while CD had to extrapolate this from the US data, with the US representing only about 44.1% total immigration to the OECD. On the whole, the estimates are based on accurate information on the immigration stock per country of origin and education level (primary, secondary, and tertiary schooling) for 88.8% of adult immigrants in 1990 and 92.7% of adult immigrants in 2000. Hence, Docquier and Marfouk (2004) provide ”reliable” estimates (i.e., based on precise information for at least 80% of the total stock analyzed) for 175 countries in 2000 and 160 countries in 1990.<sup>4</sup> Aggregating over countries, the total number of adult immigrants living in the OECD area and aged 25 or more is estimated at 58.5 million in 2000 and 39.8 million in 1990. Emigration rates by education levels are obtained by comparing the number

> 3Their estimates are based primarily on national census and register data. Survey data were also used when census statistics were not available or not su¢ciently detailed. In a limited number of case where data by country of birth are not available, immigrants are de...ned as residents with foreign citizenship (e.g., Germany, Japan, Italy or Korea).

> 4At the same reliability rate, Carrington and Detragiache (1998) provided reliable estimates for about 25 countries.

6
Policy Research Working Paper 9958

# **Abstract**

This paper identifies the impact of childcare responsibilities on adult labor supply in the context of COVID-19-related school closures in Kenya. It compares changes in parents’ labor participation after schools partly reopened in October 2020 for households with children in a grade eligible to return against households with children in adjacent grades. Using nationally-representative panel data from World Bank phone surveys in 2020–21, the findings show that the partial reopening increases affected adults’ weekly labor hours by 22 percent, with increases concentrated in household agriculture. The results suggest that school closures account for over 30 percent of the fall in average work hours in the first few months after COVID-19 cases were detected. The effects are driven by changes in household childcare burdens and child agricultural labor when a student returns to school. The impacts are not significantly different by sex of the adult. Although both women and men increased hours spent on childcare during the pandemic, women benefited more than men from reductions in childcare needs, but took on more of the childcare burden when the returning student was a net childcare provider. The results highlight the importance of siblings in household childcare and suggest that policies that increase childcare availability and affordability could increase adult labor supply in Kenya.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at upape@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
Policy Research Working Paper 9367

# **Abstract**

This paper explores heterogeneity in soil quality, lack of knowledge, and autonomy as explanations for the low adoption of improved agricultural practices. The paper uses data from a randomized field experiment that combined localized soil analyses, tailored input recommendations, extension services, and an in-kind grant. The analysis finds that while neither the degree of recommendation specificity (plot versus cluster level) nor the extent of autonomy (defined as the freedom of choice in spending the in-kind grant) had any effect on adoption during the intervention, farmers with autonomy had substantially higher adoption of improved practices two years after the intervention ended.

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 authors may be contacted at xgine@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
crosswalk for employment from KBJI 2002 to KBJI 2014. Such a crosswalk contains several many-to-many matches, which had to be split equally across matches. As a result, employment estimates could be inaccurate for these occupations. Nine out of the 35 green occupations are subject

> 57 BPS (2014).

> 58 The team is currently working with BPS to have more recent data including KBJI 2014 classification, and hopefully available by the time of the publication of this note.

27
# **3 The Data**

## **3.1 The ELMPS Survey**

For the empirical analysis, we use data from the Egypt Labor Market Panel Survey (ELMPS).<sup>5</sup> The ELMPS is a rich wide-ranging nationally representative survey carried out by the Economic Research Forum (ERF) in cooperation with Egypt’s Central Agency for Public Mobilization and Statistics (CAPMAS) since 1998. In this paper, we focus particularly on the 2012 round (ELMPS 2012). As in a typical labor force survey, the ELMPS covers topics such as employment, unemployment, job dynamics, and earnings but also provides very rich information on education, migration, and socio-economic characteristics. In 2012, the ELMPS interviewed 12,060 households, which yields to a total sample size of 49,186 individuals. The ELMPS 2012 includes a refresher sample of 2,000 households that was selected from an additional 200 Primary Sampling Units (PSUs) randomly selected from a new master sample prepared by CAPMAS. By design, it over-sampled areas with high migration rates, and is nationally representative once weights are applied (Assaad & Krafft, 2013).<sup>6</sup> We rely on the return migration module that surveys all individuals aged between 15 and 59 years old. In 2012, about 10% of the households had a returnee. In the ELMPS, a returnee is defined as an individual who has worked abroad for more than six months, but was back in Egypt at the time of the survey. This module features return migrants’ characteristics, incidences of migration, reasons for migration, financial situation before migration, year and country of first migration episode, year of final return, earnings and savings abroad, remittances, as well as documents obtained to travel and work abroad. In addition, the ELMPS 2012 collects rich retrospective information on previous employment characteristics and their location. At the same time, households are asked about current members of the household who