rafmacalaba/data-use-mentions-extended huggingface dataset:
# _Economic growth in Egypt_

Economic growth in Egypt fluctuated between 1981 and 2015 (figure 8). It plummeted from about 7 percent in 2008 (before the global financial crisis) to about 1.8 percent in 2011, before beginning to recover thereafter. The recovery is expected to continue, as recent investment and industrial licensing laws are likely to boost investment. Security concerns and the political situation could slow growth, however. According to the government’s economic reports, the economy is expected to expand by 4.3 percent in 2018 and by 4.7 in 2019 (Egyptian Ministry of Finance 2017). In contrast to total growth, GDP per capita (measured in constant 2010 dollars) rose consistently over the last three decades, from about $1,154 in 1981 to about $2,907 in 2018 (WDI 2019).

# **Figure 8 GDP per capita in Egypt, 1981–2016**

<!-- Start of picture text --> 3,500<br>2907.3<br>3,000<br>2,500<br>2,000<br>1,500<br>1154.2<br>1,000<br>500<br>0<br>1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br><!-- End of picture text --> _Source_ : Data from IFS (2017). _Note_ : Figures are in constant 2010 dollars.

# **4. Econometric Methodology and Modeling Strategy: Auto Regressive Distributed Lag Model (ARDL)**

Economic analysis proposes that there is a long‐run relationship between variables and that the means and variances are constant and not dependent on time. In the case of time‐series variables, this stationarity of variables is not always satisfied. To account
movement trends over time by geography, and across different high-level categories of places such as workplaces, retail and recreation, groceries and pharmacies, parks, transit stations, and residential. These measures are explicitly considered proxies for social distancing and we focus on the first, workplace-related mobility, as the most relevant to economic activity and most prominent in the policy debate. The reports consist of per country downloads (with 131 countries covered initially), further broken down into regions/counties in some cases. Because location accuracy and the understanding of categorized places varies from region to region, Google does not recommend using these data to compare changes across countries or regions with different characteristics. To address this, our empirics rely only on within-area variation across time and reporting or categorization differences are absorbed in included fixed effects.

This measure is limited by the degree to which coverage of smart phones offers a representative sample of the population. As Annex 1 shows, few developing countries show coverage of smart phones above 50% and Ethiopia, Nigeria, Sudan, Bangladesh, and Pakistan hold up the bottom of the top 50 countries with rates under 20% of coverage. This said, several developing countries also have reasonable coverage when we adjust for the share of adults in the population: the United Kingdom: 100%, Sweden: 96%, the United States: 95%, Italy 67%, Japan 63%, Brazil 52%, and South Africa 50%. While clearly not representative, the differences between Italy and Japan on the one hand and Brazil and South Africa on the other are not so large as to justify throwing out the possible information on how developing countries may differ. Further, while we may miss the mobility of for instance, micro firm owners without smartphones, many of their customers will have them and the shutting down of the firm will be partially registered.

Data on restaurant reservations in the United States are taken from OpenTable.<sup>9</sup> Movie release and theater revenue data for Sweden
# **Figure 22. Commodity Importers: External Financing Items**

<!-- Start of picture text --> (a) Foreign Direct Investment as % of GDP (b) Remittances as % of GDP<br>10 12%<br>9<br>10%<br>8<br>7<br>8%<br>6<br>5 6%<br>4<br>3 4%<br>2<br>2%<br>1<br>0 0%<br>WCCEs SCCEs WCCIs SCCIs<br>SCCIs WCCIs 1990 2016<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> Note: Country groupings follow Table 2. _Source: WDI._
# **Using Remittance Transaction Data for Timely Estimation of the Foreign Worker Population in Malaysia**<sup>*****</sup>

Zainab Binti Ali Ahmad<sup>a</sup> Kenneth Simler<sup>a</sup> Soonhwa Yi<sup>b</sup> JEL codes: J61, O15, F24, C13, C46, C82 Keywords: migration, immigrant workers, remittances, data estimation a Poverty and Equity Global Practice, World Bank b Social Protection and Jobs Global Practice, World Bank

* The authors thank Achim Schmillen, Caglar Ozden, Roy van der Weide, Young Eun Kim, Amanina Abdur Rahman, and Alyssa Farha Jasmin for their comments and in-depth discussions on methodologies. We also thank Achim Fock, Firas Raad, Rinku Murgai, and Philip O’Keefe for their guidance during the preparation of this paper. The paper also benefited from data support and feedback from the Money Services Business Regulation Department of Bank Negara Malaysia, Foreign Workers Division at the Immigration Department of Malaysia, the Ministry of Home Affairs, and the Ministry of Human Resources. The findings in this paper do not necessarily represent the views of the World Bank's Board of Executive Directors or the governments they represent. Any errors or omissions are the authors' responsibility.
17 most producers (94%) applied the recommendations provided by extensionists. Of those applying the recommendations, 19% reported a 100% effectiveness, 61% reported 50 to 75% effectiveness, and 20% reported 25% effectiveness. Two more measurements of value of extension are the evaluation of INTA's technical assistance (ATPb+ATPI) and the change in producers' income as a result of that advice. Of the sampled producers, 43% and 50% ranked the service as "very helpfur' and "helpfur', respectively. Forty one percent of the sampled producers reported an increase in their income, 47% reported stable income, and 12% reported a decline in income. These indicators, although not comprehensive in nature, provide some insight on the impact of INTA's technical assistance.

Although at this stage it is still impossible to distinguish between ATPb and ATP 1 producers in the ESECA (1997a) sample, some interesting hypotheses can be stated. Table 5 shows that of the sampled producers, 25% were contacted once a week, 50% were contacted every fifteen days, and the rest (25%) were contacted between every three weeks and every three months.<sup>10</sup> Under conditions in Nicaragua, producer contacts was dependent on the level of accessibility to the producers, especially after heavy reainstorms that wash away roads. Thirty two percent and 27% of the producers were not accessible all year round by car and by motorcycle, respectively.

Table 5: Number of visits by extension agents in the ATPb and ATP1 programs <!-- Start of picture text --> Frequency of visits (days) 7 15 |21 30 45 60 901<br>Share of population (%) 25 50 3 12 1 3 1<br>Source: ESECA, 1997a<br><!-- End of picture text -->

> **10** An interesting question is what characterizes the producers that are more frequently
Only five governorates belong to the second group: Mosul, Kirkuk, Anbar, Baghdad and Salahaddin. Beginning from June 2014, there is significant or complete non‐response in Salahaddin, Kirkuk, Nineveh and Anbar. In the case of Baghdad, only 65 percent of the intended respondents completed the survey with the last quarter of the survey’s year and the rural sample being severely affected. Additionally, the number of households interviewed varies within the same governorate per month in all governorates. Furthermore, the sample frame used by C‐IHSES did not include households who were forcibly displaced across governorate boundaries during the second half of 2o14.

Despite all these limitations, a few exercises are proposed as second best options to assess the micro‐ simulation results. In order to test the Business as Usual scenario, the comparison between the first half of IHSES 2012 and C‐IHSES 2014 for the whole country seems a reasonable approximation of what would have happened if there were no crises. However, some additional constraints and challenges arise in both micro and macro inputs dimensions. From the micro standpoint, rural Baghdad was excluded from the analysis to address comparability issues between these surveys, sample weights were adjusted and poverty lines were re‐estimated based on IHSES‐2012.<sup>25</sup> From the macro perspective, macroeconomic inputs may vary differently between actual first‐half growth rates and simulated year growth rates.

# _Aggregate results in BaU scenario_

Table 10 reveals that micro‐simulation results for changes in poverty were more conservative than what might have actually happened if there were no crises for the country as a whole. For instance, while the micro‐simulation model predicted a reduction of almost 4 percentage points in the headcount between 2012 and 2014; the actual reduction in the incidence of poverty between the first half of 2012 and 2014 was about 2 percentage points higher. Inequality micro‐simulation predictions were more aligned to what Iraq experienced between 2007 and 2012 rather than what has happened in the first half of 2012
- Hill, J., V. K. Märtinson, M. Ferris, and R. Zenger Baker. (2004). “Beyond The Mommy Track: The Influence of New-Concept Part-Time Work for Professional Women on Work and Family.” _Journal of Family and Economic Issues_ , 25(1), pp. 121–136.

- Ilahi, N. (1999). “Gender and the Allocation of Adult Time: Evidence from the Peru LSMS Panel Data.” Policy Research Working Paper Series 2744, World Bank, Washington, DC.

- Ironmonger, D. (1999). “An Overview of Time Use Surveys.” International Seminar on Time Use Studies, Center for Development Alternatives, Ahmedabad, India.

Kandiyoti, D. (1988). “Bargaining and Patriarchy”. _Gender and Society_ , 2(3), pp. 274-290.

- Kongar E., and E. Memiş. (2017). “Gendered Patterns of Time Use over the Life Cycle in Turkey.” In: Connelly R., Kongar E. (eds) Gender and Time Use in a Global Context. Palgrave Macmillan, New York.

- Lihwa, F., C. Johnstone, M. Thomas, and B. Krause. (2015). “How Near is Your School? Informing NGO Programming Through Children’s Perceptions of Distance.” Manuscript.

- Lundberg, S., and R. Pollak. (1996). “Bargaining and Distribution in Marriage.” _Journal of Economic Perspectives_ , 10(4), pp. 139-158.

- Lundberg, S., and E. Rose. (1998). “The Determinants of Specialization within Marriage.” Working Paper 48, University of Washington.

- Maurer-Fazio M., Connelly R. (2017). “How Do Caregiving Responsibilities Shape the Time Use of Women and Men in Rural China.?” In: Connelly R., Kongar E. (eds) _Gender and Time Use in a
61.6%|
|No|99.5%|Yes<br>38.4%|
|Yes|0.5%||

Source: Calculations based on ENESTYC 2001.

With respect to firm size, 90 percent are micro in size, 8 percent are considered small, 1 percent medium, and almost 1 percent large, which is consistent with economic census data. Of these, the majority of manufacturing firms seem to be most concentrated in Central Mexico, accounting for 49 percent of the total, with the remainder evenly distributed between the Northern and Southern regions, and Mexico City. According to the division of activity, food, beverages and tobacco account for the largest share of firms with 36 percent; metallic products, machinery and equipment come second with 17 percent. Basic metallic industry ranks last, with only 0.1 percent. With respect to foreign openness, the percentage of firms with more than 50 percent foreign capital is only 0.5 percent and only 1 percent of firms export at least half of their output. The share of firms that invest in research and development (R&D) is 7 percent, whereas the percentage of firms that adopted new technologies is 38 percent. Only 3 percent of the firms have some quality control certification.

# **IV. FRAMEWORK AND METHODOLOGY**

We follow Hellerstein, Neumark, and Troske’s (1999) model to compare the relative marginal products and relative wages of workers. Consider a simple economy 7
added considering both direct and forward linkages, for both domestic and external markets (exports). Services are allocated to one of nine subsectors. Together with most of its comparators, Russia shows that, after considering forward linkages, the share

> 14 The data are for 2011.

> 15 There are three measures of this share: (1) The “Gross” value or as reported in the balance of payment statistics; (2) the “Direct” measure of services in terms of value added, and finally (3) the “Total” contribution of services measured in terms of value added. This last measure includes the direct contribution of services to total exports measured in terms of both their value-added content and their indirect contribution as measured by forward linkages to other export activities. This recognizes that services are inputs to other exports of goods and services, and shows which sectors contribute to the value added of final exports.

11
**imperfect information, and heterogeneity in environments of high unemployment, high nonparticipation, job instability, wage disparity, and uneven opportunities among groups** . To explain the Roma’s decisions on those margins, we are extrapolating from marriage formation and we use a unitary model for a married couple. We demonstrate that the predictions of our dynamic model can account for the multiple variables underlying spouses’ decision making on time allocation, labor supply, and wages, for couples with and without children.<sup>4</sup>

Previous Unitary Labor Household Search Models studying spouses’ labor market decisions considered only employment and nonemployment labor market status with health insurance (Dey and Flinn (2008)), and sometimes with multiple locations (Guler, Guvenen, and Violante (2012). Flabbi and Mabli’s (2018) model included the participation decision (extensive margin), on-the-job search, parttime or full-time jobs (intensive margin), and an exogenous fertility shock. Choi (2018) studied parental leave policies and labor supply. Our model is an extension of the work by Flabbi and Mabli (2018). We expand the comprehension of gender gaps and household decisions by introducing for each spouse continuous time allocation between leisure and home production, while still allowing job offers to be part time and full time.

**The results from the RRS suggest a strong gender specialization. If in a given household a spouse has a job, usually it is because the husband is working. Also, data from the time usage module reveal that females on average devote more time to home production** . For instance, wives reported spending 34.7 percent of their time on home production<sup>5</sup> when no children are present, and a slightly higher amount when children are present (39.4 percent). Husbands reported much lower shares, spending 22.4 and 22.2 percent of their time on home production, respectively. Also, in the 35.2
# Appendix C: Country Data

# _Ethnic Minorities_

|INSTITUTIONS||Bulgaria||Mexico|Morocco||Netherlands|Tanzania||Vietnam|
|---|---|---|---|---|---|---|---|---|---|---|
|The Constitution and Nati<br>Under the Constitution:<br>|onal Leg<br>|islation<br>||||||<br>|||
|Is there a general nondis-<br>crimination clause?|Yes|Constitution of the<br>Republic of Bulgaria<br>(1991), Art. 6 Para.<br>2|Yes|Constitución Política de<br>los Estados Unidos Mexi-<br>canos (1917), Art. 1|No|Yes|Constitution of the King-<br>dom of the Netherlands<br>(2008), Art. 1|Yes<br>Constitution of the<br>United Republic of Tan-<br>zania (1977), Arts. 9(g),<br>13(1)(2)|Yes|Constitution of<br>Vietnam (2013),<br>Art. 5 Para. 2|
|If yes, does it explicitly<br>mention ethnic minorities?|<br>Yes|Constitution of the<br>Republic of Bulgaria<br>(1991), Art. 6 Para.<br>2|Yes|Constitución Política de<br>los Estados Unidos Mexi-<br>canos (1917), Art. 1|N/A|Yes|Constitution of the King-<br>dom of the Netherlands<br>(2008), Art. 1|Yes<br>Constitution of the<br>United Republic of Tan-<
Table B.2 shows that our results are robust to conducting a “donut RD” in which we remove observations around the reform time to account for potential selective sorting (i.e. retiming of purchases in our case). Another potential challenge with our estimation procedure is that shorter bandwidths, which allow us to achieve a better fit of the data around the reform, require us to estimate the month fixed effects on fewer observations. Table B.3 shows that this is not a concern, as our results are almost identical when using an alternative two-step estimation procedure. This procedure is similar to the “augmented local linear” methodology suggested in Hausman and Rapson (2018). We first estimate equation 1 on the full 2010-2016 data to estimate the month-of-year fixed effects with the highest possible degree of precision. We then ˆ recover the de-seasonalized outcomes _log_ ( _Z_<sup> ̃</sup> _t_ ) = _log_ ( _Zt,m_ ) _− gm_ and estimate the regression discontinuity with a shorter data set (bandwidth) around the reform. In this second step, we estimate equation 1 without the month-of-year fixed effects _gm_ and use the de-seasonalized outcomes as dependent variable. The point estimates from this procedure are hardly distinguishable from our main estimates.

Tables B.4 and B.5 show the results when controlling for potential autorcorrelation in the outcome by including the first and second lag of the dependent variable in the estimation. With this correction, the effects are only slightly smaller than in our main estimations, suggesting that the number of card transactions increased by 40 percent and the volume of transactions increased by 20-30 percent. All point estimates continue to be highly statistically significant.<sup>19</sup> Finally, it is possible that our specification is affected by serially correlated unobservables and hence autocorrelation in the error term. We thus rerun the RD estimation using the Prais and Winsten (1954) correction for autocorrelated errors (see also Judge et al. (1985) and Davidson and MacKinnon (
schooling, and only 2 percent had any college education. The study sample is predominantly rural and participants are equally likely to be from the southern plains (Terai) as from the hills and mountains – again, similar to the distribution of migrants in the census (second panel, columns 1 and 2). Compared to the migrants in the census, the study sample is slightly more likely to be from the mid-western and far-western regions. However, this difference could reflect a change in the actual trend as migration has become more ubiquitous in 2014 than it was in 2011. Similarly, the distribution of migrants looks similar across Malaysia and the Gulf countries in both the samples (third panel, columns 1 and 2).

There are three distinct groups of potential migrants in the study sample. There are 1,411 “inexperienced” potential migrants who have not yet migrated abroad for foreign employment. Of the remainder, 1,341 are “experienced” potential migrants, those who have migrated abroad for work abroad, but do not have an existing employment contract abroad. That is, these individuals have to search for employment again. The remaining 567 potential migrants are “on leave” from their work abroad. That is, they have an existing employment contract abroad and do not have to look for work. They are back in Nepal on a holiday and must renew their passports. For the remainder of the paper, I will use this classification unless explicitly noted otherwise.

The average inexperienced potential migrant is younger and slightly more educated than the experienced one, is 6.4 years younger and has 0.7 more years of schooling (Appendix Table A.2, columns 3 and 4). The difference in schooling is likely to represent the national cohort trend in schooling more than anything else. The geographic distribution of these two groups is quite similar, except that the inexperienced are more likely to be from mid-western and far-western regions than are the experienced ones – again possibly reflecting a geographic trend as migration became more
is currently one standard deviation below steady-state income will require about 23 years to close the gap at a convergence rate of 2 percent per year (Gennaioli et al. 2014).

In this paper, we investigate whether labor productivity converged across Brazil’s states (“departments”) between 2002 and 2018. During the sample period, our findings show strong evidence of unconditional regional convergence in which states with lower initial levels of labor productivity experienced substantially faster labor productivity growth. Additionally, the convergence rate is faster for the period 2002-10 compared with the 2010-18 period. The convergence rate is particularly strong in agriculture, extractives, and manufacturing. These findings also hold when controlling state and industry fixed effects as well as states’ initial poverty rate, human capital, per capita tax collection, and infrastructure.

This paper is related to the literature on convergence, especially regional convergence. It is related to Gennaioli et al. (2014), who show that regional convergence is relatively faster than crosscountry convergence but generally slower than expected using a large sample of countries, including Brazil. We complement their findings by focusing on labor productivity (rather than per capita income) and using a matched employer-employee dataset. Unlike Azzoni and Castro (2023) who provide evidence of stronger convergence across states of Brazil only during periods of economic crisis (2008 and 2014) and show that periods of better aggregate growth are linked with increasing regional dispersion, our results generally show evidence of regional convergence between 2002 and 2018. Our results also contribute to country case studies on regional inequality that account for the sectoral dimension (e.g., Kinfemichael and Morshed 2019a; Iacovone et al. 2015).

The rest of this paper is organized as follows. Section 2 provides a brief survey of the evidence on the patterns and drivers of convergence from cross-country and country case studies. Section 3 presents the main data sources and summary statistics on labor productivity, poverty rate, and geographic concentration of different sectors. It then introduces the test used to detect if
# Fi ure 1: Shares of female ownershi b state <u>g p y</u>

<!-- Start of picture text --> A. Female ownership of unorganized manufacturing in<br>2000<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>0 10,000 20,000 30,000 40,000 50,000 60,000<br>State GDP per capita in 2000<br>Female-owned share of firms<br><!-- End of picture text --> <!-- Start of picture text --> B. Female ownership of services in 2000<br>45%<br>40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>0 10,000 20,000 30,000 40,000 50,000 60,000<br>State GDP per capita in 2000<br>Female-owned share of firms<br><!-- End of picture text --> <!-- Start of picture text --> C. Growth in female ownership share of unorganized<br>mfg., 2001-2010<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>0 10,000 20,000 30,000 40,000 50,000 60,000<br>-10%<br>-20%<br>State GDP per capita in 2000<br>ownership<br>Growth in share of female<br><!