rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 10499

# **Abstract**

This paper studies the early career dynamics of employment and formality using data from the Arab Republic of Egypt. With 14 rounds of Egypt’s labor force surveys several measures of informality and underemployment are constructed to examine how the labor market conditions faced by young men when they exit school shape their future employment trajectories. Employment outcomes at different levels of potential experience are linked to cross-cohort, cross-regional and cross-schooling level variation in labor market conditions at graduation to achieve identification. The results show that cohorts of Egyptian men who enter a labor market in which employment rates are high (low) are only better (worse) off for a few years. These fast mean reversals stand in contrast to the typical finding from rich countries that scarring effects persist through the first decade of a worker’s career.

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 cjoubert@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
. Each column shows the OLS estimates of Equation (1) including dummies for each treatment arm and controlling for the baseline measure of the dependent variable (ANCOVA), camp and enumerator fixed effects, and sociodemographic controls as determined through a double-selection LASSO procedure (Belloni, Chernozhukov, and Hansen, 2014), run separately by respondent gender. Each row presents the coefficients of the specified treatment relative to the control group (which received a small weekly cash payment).

All outcomes are standardized. The ‘Household Power Index’ is an index of a series of questions around who in the household makes key decisions, with a higher household power index signaling greater decision-making power for the female head of household. The ‘Work Rights Index’ is an index of a series of questions around whether women should be permitted to work outside of the home.

Standard errors are clustered at the block level. The row labeled “Shrp. q-val Work” reflects p-values, referred to as ‘sharpened q-values,’ adjusted for multiple hypothesis testing using Anderson (2008). The adjustments are made within gender for all outcomes in this table. The row labeled “Test Male = Female” reflects the unadjusted p-value for the test of equality between the Male and Female work treatment effects based on a pooled regression (not shown) where the treatment indicators were interacted with the gender of the respondent. The row labeled “Shrp. q-val Male = Female” reflects the adjusted p-value for this test of equality across all five outcomes.

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Arai et al. (2020) : The Hidden Potential of Call Detail Records in The Gambia.

center in access in population density, access to services and structure of economic activity. Classification result is presented in Table 4.

We convert the number of residents to the ratio to the baseline to examine changes from the normal period. The average of the first two weeks of March is used as the baseline. The ratio is scaled using Indicator 3 at the national level to mitigate the impact of fluctuations in the number of active subscribers. Indicator 3 at the administrative unit level is not used as it can introduce certain urban-rural biases; Indicator 3 overestimates the number of active subscribers in urban areas where cell-tower density is higher and people are relatively mobile compared to rural areas. Though this process helps mitigate the impact of the fluctuations on Indicator 6 to a certain extent, Indicator 3 cannot sufficiently address the impact. For instance, when the significance of population decreases at the district level is greater than that of active subscribers at the national level, the value of Indicator 6 computed as the ratio to the baseline cannot not sufficiently inflated.

<mark>Table 4.</mark> Classification of 45 districts

||Urban LGAs|Rural LGAs|
|---|---|---|
|Capital districts|3|5|
|Non-capital districts|19|18|

Source: Authors’ calculations.

Figure 5 shows an increased number of people moving to non-capital districts in rural LGAs in the last weeks of March as the State of Emergency was extended. It uses the number of residents for the four groups, which is calculated as the ratio to the baseline and adjusted using indicator 3. In contrast, districts in urban LGAs and LGA-capital districts show decreasing trends. This suggests that many people in urban areas shifted to rural areas as a result of the lockdown, returning to their hometown in rural areas because of decreased job opportunities in urban areas. During Ramadan, there was a brief spike of
We aggregate the three outcomes in Table 5 into a perceived quality of care index and then instrument this index with our pooled treatment indicator to estimate its effect on reported Ebola cases. In Table 6, we report a large first-stage effect; the F-statistic (9.86) approaches the rule-of-thumb for a strong instrument. When we scale our earlier reduced-form result by this first stage, we find that a one standard deviation change in the perceived quality of care corresponds to an increase in weekly case reports of 0.43.<sup>41</sup>

**Table 6:** Perceived Quality of Care and Ebola Cases

|First-Stage|Perceived Quality of Care|
|---|---|
|Pooled (CM or NFA)|0.401<br>(0.128)<sup>_∗∗∗_</sup>|
|Two-Stage Least Squares|Total Cases|
|Perceived Quality of Care|0.430<br>(0.249)<sup>_∗_</sup>|
|F-statistic|9.86|
|Observations|5,440<br>5,440|

_Notes_ : “Perceived Quality of Care” is an equally weighted average of the variables in Table 5. Matching-triplet fixed effects included in both first- and second-stage regressions. Standard errors are clustered by section. Significance: * p < 0.10, ** p < 0.05, and *** p < 0.01.

As with the full sample, we do not find consistent positive effects for families focused on supply-side variables in the Ebola sample. Pooling the treatments, we see no significant effects on health service delivery or clinic quality (see Table E.14).<sup>42</sup>

# **4.3 Alternative Explanations**

## **4.3.1 Transmission**

We attribute the increase in total and confirmed cases in treated sections to reporting
source and the destination economies, the geographical distance between these two economies, differences in time zones, whether these economies share a common language, whether they have a common legal origin, whether they share a border, and whether there is a colonial relationship between them.<sup>5</sup> In some regression specifications, we also include bilateral trade flows between economies, which cover the 1990-2014 period for 176 source and recipient economies. The source of the trade data is the IMF’s Direction of Trade Statistics (DOTS).

We conduct the analyses in the paper for three distinct groups of economies: EAP, North, and South. The EAP region includes the following economies: American Samoa, Australia, Brunei Darussalam, Cambodia, China, Democratic People’s Republic of Korea, Federated States of Micronesia, Fiji, French Polynesia, Guam, Hong Kong SAR (China), Indonesia, Japan, Kiribati, Lao People’s Democratic Republic, Malaysia, Marshall Islands, Mongolia, Myanmar, Nauru, New Caledonia, New Zealand, Northern Mariana Islands, Papua New Guinea, Philippines, Republic of Korea, Singapore, Solomon Islands, Taiwan (China), Thailand, Timor-Leste, Tonga, Tuvalu, and Vietnam. The North comprises the following G-7 and Western European economies: Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, Netherlands, Norway, Portugal, San Marino, Spain, Sweden, Switzerland, United Kingdom, and the United States. The South includes all other economies not defined in the other two regions. We exclude from the analysis offshore financial centers as defined by the IMF.<sup>6</sup>

> 5 The source of all control variables at the bilateral level is CEPII’s GeoDist database. The only exception is the variable on economies’ common legal origin, which comes from La Porta et al. (1998). The GDP data come from the World Bank’
Friedberg, Rachel. 2000. “You Can’t Take it with You: Immigrant Assimilation and Portability of Human Capital: Evidence from Israel.” Journal of Labor Economics, 18(2): 221251.

Funkhouser, Edward and Stephen J. Trejo. 1995. “The Labor Market Skills of Recent Male Immigrants: Evidence from the Current Population Survey.” Industrial and Labor Relations Review, 48(4): 792-811.

Green, David A. 1999. “Immigrant Occupational Attainment: Assimilation and Mobility over Time.” Journal of Labor Economics, 17(1): 49-77.

Jasso, Guillermina and Mark R. Rosenzweig. 1982. “Estimating the Emigration Rates of Legal Immigrants Using Administrative and Survey Data: The 1971 Cohort of Immigrants to the United States.” Demography, 19(3): 279-290.

Jasso, Guillermina and Mark R. Rosenzweig. 1986. “What’s in a Name? Country-ofOrigin Influences on the Earnings of Immigrants in the United States.” In: Research in Human Capital and Development vol. 4, Oded Stark (ed.), Greenwich.

Jasso, Guillermina and Mark R. Rosenzweig. 1988. “How Well Do U.S. Immigrants Do? Vintage Effects, Emigration Selectivity, and Occupational Mobility.” Research in Population Economics, 6:229-253.

Jasso, Guillermina and Mark R. Rosenzweig. 1995. “Do Immigrants Screened for Skills So Better than Family Reunification Immigrants?” International Migration Review, 29(1): 85111.

Jasso, Guillermina Mark R. Rosenzweig and James P. Smith. 2000. “The Changing Skill of New Immigrants to the United States: Recent Trends and Determinants.” in G. Borjas (ed.), Issues in the Economics of Immigration. Chicago, IL: University of Chicago Press.

Heston, Alan, Robert Summers and Bettina Aten. 2006. Penn World Table Version 6.2, Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania,
individual-level data to identify individuals who are deprived in multiple dimensions (Alkire and Santos, 2014). Recently, the World Bank (WB) also proposed a global multidimensional poverty measure, which differs from the MPI in crucial ways. Most importantly, the World Bank’s approach to multidimensional poverty measurement is informed by the living standard perspective (The World Bank, 2018). Insomuch as standard monetary measures exclude critical components of welfare, supplementing them with non-monetary components gives a fuller picture of well-being. Our analysis also includes the World Bank multidimensional poverty measure for comparison. We use the MPI methodology proposed by OPHI and the World Bank as they are widely used and they incorporate dimensions and indicators that are globally comparable.

# 2.2 MPI definition

The initial step to estimating multidimensional poverty involves defining a set of indicators that belong to specific poverty dimensions, to which deprivation cutoffs are assigned in the second step. There are three equally weighted poverty dimensions considered in the global MPI analysis: education, health, and standard of living. Ten indicators are defined across these three dimensions, in line with the Millennium Development Goals (MDGs). These indicators are also equally weighted within each dimension as shown in Figure 1.<sup>4</sup>

> 4 The definitions of the indicators are provided in Table B1 in Appendix B. Appendix C explains how the weights are shifted across indicators when the necessary information is missing.

6
State bank**|26718|0.09|0.29|0|1|
|**Foreign bank**|26053|0.12|0.32|0|1|
|**Foreign state bank**|26718|0|0.01|0|1|
|**Bank health index**|7078|0.56|0.42|-2.33|1.11|
|**Bank account**|28263|0.93|0.26|0|1|
|**Ln(age)**|28113|2.68|0.76|0|5.32|
|**20-99 employees**|28444|0.16|0.37|0|1|
|**>99 employees**|28444|0.03|0.18|0|1|
|**SOE**|28056|0|0.05|0|1|
|**Foreign company?**|28037|0.02|0.15|0|1|
|**Legal origin, France**|27426|0.41|0.49|0|1|
|**Legal origin, Germany**|27426|0.59|0.49|0|1|
|**Legal origin, Scandinavia**|27426|0|0|0|1|
|**Legal origin, UK**|27426|0|0.03|0|1|

Source: authors' computations based on the World Bank Enterprise Survey Data To assess whether banking with a state-owned bank affects the probability of innovating, our baseline framework is: Innovateijt = αLine of creditijt + βLine of creditijt × Stateowned bankkjt + γLine of creditijt × Foreignowned bankkjt + + δLine of creditijt × State owned bankkjt × Foreign owned bankkjt + XijtΘ + τt +
may be self-employed (Cho et al., 2016), which means that “residence” and “workplace” may be one and the same.

In all the mobility data, April shows the biggest change from baseline. While visits to many locations increased somewhat rapidly after April, overall visits remained significantly below the baseline month well into the year. This finding appears especially true for both workplaces and retail/recreation.

# b. Search data

We present trends for six different search terms – food bank, assistance, online education, prayer, bible, and quran – in Figure 9 and Figure 10. We begin with trends by region in Figure 9. The topleft panel presents results for how often people searched for the term “food bank.” There is a large increase in just one region: North America. This could be due to the availability of food banks in that region relative to the others, rather than differences in actual needs. The top-middle panel presents results for “assistance.” Here, we see the most pronounced spike in two regions: South Asia and East Asia and the Pacific.

The top-right panel shows trends in searches for “online education.” East Asia and the Pacific and Europe and Central Asia show marked increases in searches for the term in April. Sub-Saharan Africa also shows an increase, though the magnitude of the change is much smaller than for the other two regions.

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19 positive effects from productivity spillovers among suppliers are transmitted to the wider host economy.<sup>10</sup> Another example is a study using data from the 2010 Vietnam Investor Survey by the United Nations Industrial Development Organisation (UNIDO) (Tusha, Jordaan, and Seric 2018). For a large sample of foreign and domestic firms, this data set contains information on output and inputs; the sourcing and selling behavior of the firms; the extent and intensity of inter-firm linkages; and the number, nationality, and location of buyers and suppliers. Tusha, Jordaan, and Seric (2018) find that MNC affiliates are more likely to create business relationships with other MNC affiliates located in Vietnam, thereby lowering the scope for productivity spillovers among local suppliers. At the same time, local firms that do have MNC affiliates as client firms are significantly more likely to receive support, indicating that foreign firms are a source of technology dissemination to their local suppliers. When analyzing productivity effects from supplying to foreign firms, the estimations show that these effects materialize only from having foreign client firms that are located in Vietnam and not from foreign client firms located outside the host economy. This indicates the importance of the physical presence of MNC affiliates in the host economy for meaningful technology dissemination and productivity spillovers.

Three other recent studies present findings that indicate that the impact of MNC affiliates can extend beyond productivity spillovers. Javorcik, Lo Turco, and Maggioni (2018) use firm-level data for Turkey that includes information on the products that the firms produce. Their estimations show that domestic firms that operate in industries that supply inputs to MNC affiliates experience increases in the complexity of their products, as captured by the complexity indicator by Hidalgo and Hausman (2009). This suggests that technology dissemination and support from MNC affiliates enables local suppliers to introduce new and more complex products. Using the product sophistication index by Hausman, Wang, and Rodrik (2007), Eck and Huber (2016) obtain similar evidence for India. For Romania,
and contribute to prosperity. In this section, we explore the opportunities that arise from the expanded data systems in justice. We discuss how data can help assess social prejudice and integrity in justice systems in ways that have not been possible until recently. As COVID-19 moves justice into virtual courtrooms and greater electronic processing of cases, this section assesses the opportunities that arise to evaluate and improve crucial aspects of the efficiency, quality and integrity of courts.

# **2.A The Expansion of Data Availability**

Judiciaries generate vast amounts of data on a daily basis. Historically, this data has been collected in paper form. Judicial data has rarely been analyzed quantitatively, and little information has been extracted from the overall patterns of the data. According to the literature, the reasons are twofold: first, judicial officers have historically not been concerned with performance indicators; and second, legal scholars have prioritized qualitative over quantitative analysis of the functioning of the judiciary (Dakolias, 1999). However, this trend has been changing in recent times. Judiciaries around the world are establishing e-filing and case management systems where every aspect of each case is recorded. Others have also established electronic calendars, electronic notification systems, electronic jurisprudence, virtual courts, and other technological platforms that facilitate the efficient functioning of the judiciary. These technologies, particularly case management systems, tend to generate vast amounts of data that can be utilized to evaluate the functioning of the court. Moreover, as large backlogs of cases becomes a significant problem for the efficiency of the judiciary in developing countries, interest is growing around performance metrics that will improve the functioning of the judiciary. The expansion of data availability in judiciaries around the world creates valuable opportunities to measure the overall performance of the judiciary, identify key areas for improvement and evaluate the impact of justice reforms.

The adoption of technological platforms that generate judicial data has affected both developed and developing countries. The United States, Canada, European countries, and Australia are at the
# **AN AGENDA FOR THE FUTURE**

The previous sections have provided an overview of informality in Indonesia, both in terms of existing data sources and empirical evidence. This final section summarizes the remaining gaps and outlines an agenda for the future.

# **Data gaps**

# _Informal sector_

We have identified a number of existing data sources that can be used to study informality in Indonesia. In terms of the informal sector, the IMK Survey is the most viable source of data for a number of reasons. First, its coverage of both micro and small enterprises allows it to capture more than 90 percent of all firms in Indonesia – most of which are informal in nature. Second, the sampling methodology ensures that the survey is representative of the broader MSE sector, which is critical for the generalizability of findings. Nevertheless, the survey has its own limitations. By virtue of its coverage, it excludes medium and large firms, which could also operate informally. This means that relying solely on the IMK survey will underestimate the true size of the informal sector. Moreover, medium or large informal firms may represent a different segment of the informal sector with very different motivations (e.g., rational exit) and characteristics. Finally, the survey contains a limited set of questions that can be used to understand the reasons behind firm informality, or the barriers they face to formalization. Consequently, the survey needs to be complemented with other data sources to provide a deeper and more comprehensive analysis of the informal sector.

Given the limitations of existing firm-level datasets in Indonesia, there is a case to be made for new data to be collected. Ideally, the new survey should include questions of a more qualitative nature, such as the benefits gained from operating informally, linkages with the formal sector, perceived costs or barriers to formalization, and the use of government services. Existing enterprise surveys can be used to inform the exact framing of these questions. For example, the World Bank Enterprise Survey asks firms
assume the existence of instruments _zit_ that are correlated with _xit_ and uncorrelated with _εit_ in all periods. This assumption is confirmed by the result of the Wu-Hausman F-test which rejects the null hypothesis that the dummy for diversification is exogenous.

The instrument we propose is a measure of rainfall variability computed as the average of the mean yearly precipitation during the rainy season from 2001 up to 3 years before the survey interview for each household<sup>12</sup> . In building the rainfall measure we are taking into account the different timing of the rainy season according to the different climatic zones of the country, that is: June/July to September in the North, and March/April to October in the Central and Southern areas (Mustapha and Arshad, 2014).

# **Panel IV results**

The results from the first-stage of the 2SLS estimation are reported in Table 4. The instrument we propose satisfies the relevance condition( _Cov_ ( _z, x_ ) = 0) in that it is significantly negatively correlated with the decision to diversify. Moreover, the value of the Weak Identification F-statistics of 12.45 relieve concerns regarding a weak instrument bias<sup>13</sup> . The rationale behind choosing this instrument is that a farmer’s decision to diversify taken at time _t_ it is likely to be correlated to the “rainfall memory”. Studies on farmers’ memory of precipitations’ patterns in West Africa and the Sahel confirm that it dates back to several years, especially if the event took place during the rainy season (Tschakert et al., 2010; Simelton et al., 2013). At the same time, a farmer’s per capita expenditure at time _t_ it is hardly affected by past rainfall events unless, of course, there was a big shock; in fact, according to the available data (table 2), with the notable exception of 2011, there is no sign of such an event.

Results from the second-stage of the 2SLS estimation reported