rafmacalaba/data-use-mentions-extended huggingface dataset:
Epprecht, M., D. Müller and N. Minot, 2011, How Remote are Vietnam’s Ethnic Minorities? An Analysis of Spatial Patterns of Poverty and Inequality. _The Annals of Regional Science_ , **46** (2), pp. 349-368.

Fafchamps, M. and F. Shilpi, 2013, <mark>Determinants of the Choice of Migration Destination.</mark> _<mark>Oxford Bulletin of Economics and Statistics</mark>_ <mark>,</mark> **<mark>75</mark>** <mark>(3), pp. 388-409.</mark> Fukase, E., 2013a, Foreign Wage Premium, Gender and Education: Insights from Vietnam Household Surveys. Forthcoming in _The World Economy_ .

Fukase, E., 2013b, Export Liberalization, Job Creation and the Skill Premium: Evidence from the U.S.-Vietnam Bilateral Trade Agreement (BTA), _World Development_ , **41** (1), pp. 317-337.

Fukase, E. and W. Martin, 2000, The Effects of the United States Granting MFN Status to Vietnam. _Weltwirtschaftliches Archiv_ , **136** (3), pp. 539–559.

Glewwe, P., M. Gragnolati and H. Zaman, 2002, Who Gained from Vietnam’s Boom in the 1990s? _Economic Development and Cultural Change_ , **50** (4), pp. 773-792.

Glick, P. and F. Roubaud, 2006, Export Processing Zone Expansion in Madagascar: What are the Labour Market and Gender Impacts? _Journal of African Economies_ , **15** (4), pp. 722-756.

Greene, W., 2003, _Econometric Analysis_ . Fifth Edition, Pearson Education.

GSO, 2011, _Migration and Urbanization in Vietnam: Patterns, Trends and Differentials_ .

GSO, 2010, _Statistical Yearbook of Vietnam 2010_ . Hanoi: Statistical Publishing House.
# _APPENDIX 3. ADJUSTMENTS TO THE DATA_

This appendix describes the adjustments made to the data for the former Soviet Union and Germany.

# _Former Soviet Union_

Censuses for the Soviet Union for 1959, 1970, 1979, and 1989 were collected to address the data issues created by the dissolution of the Soviet Union. These censuses all use ethnicity to identify migrants. Crucially, for 1989, comparable country of birth data exist for all 15 republics. The censuses based on ethnicity document intra-Soviet migrants (Uzbeks in Turkmenistan, for example) and external nationalities (such as Afghans). In addition, there are miscellaneous Soviet nationalities (such as the Chuvash, Tatars, and Uyghurs), many of whose homelands span several Soviet republics/countries and who should therefore not be counted as international migrants since they were born on one side of the border or the other as opposed to moving across it.

First, people of these miscellaneous nationalities were broadly aggregated to one or more of the 15 former Soviet republics on the basis of country by country research and a close inspection of the numbers over time. Similarly, external nationalities were assigned, with particular attention to determining whether these people were actually migrants. For example, people recorded as Germans will likely be ethnic Germans who migrated long before the census period examined in this study. Those recorded as Poles, however, are more likely to have been forcibly deported. Once the aggregations were completed, the ratios of foreign-born migrants to migrants defined by ethnicity in 1989 were calculated for people who were both born in one of the 15 former Soviet republics and resided there. These ratios were then applied to these republics/countries in every census period before adding the ―external‖ migrants. These corrections captured a large proportion of the most important migrants to and between the Soviet republics. This process adds many millions of migrants to the totals in the early decades and avoids the problem of a very large artificial jump in international migration between 1980 and 1990, after the dissolution of the Soviet Union.
months worked correspond to the worker not having a formal job but we have no information as to whether the worker is unemployed, self-employed, working in an informal job, or out of the labor force, a caveat of the RAIS database also noted by Dix-Carneiro & Kovak (2019).

The RAIS database includes for each worker and job spell information on detailed occupation categories. Following Gerard, Naritomi, & Silva (2021) we use the correspondence table developed by Helpman et al. (2017) to convert the Brazilian classification of occupations at the 4-digit level into the following five occupation categories: Professional and Managerial, Skilled White Collar, Skilled Blue Collar, Unskilled White Collar, and Unskilled Blue Collar. Where necessary, we group the first three categories as skilled workers and the last two categories as unskilled workers.

To better capture worker unemployment in Brazil, we use unique data from the Brazilian Unemployment Insurance (UI) registry (Seguro de Desemprego) for the 2004-2017 period. This is a longitudinal individual-level database covering all Brazilian workers that have received unemployment insurance during this period. A worker is eligible to receive unemployment insurance if he/she is laid off from a private formal job and has at least six months of job tenure at layoff. The UI registry includes information on the amount paid, separation date, paid date, and the number of months each worker receives unemployment benefits. We link the RAIS database to the UI registry through the individual unique worker identifier which is included in all years for all workers. This data allows us to measure the duration of unemployment for each worker and the amount of UI received.<sup>10</sup> To further assess compensation mechanisms for workers in Brazil, we link our worker panel database to longitudinal administrative data over the 2007-2012 period from the national registry of low-income households (Cadastro Unico), linked to all the payment records of the Bolsa Familia cash transfer program (PBF) (Folhas de Pagamento) through the individual unique identifier as in RAIS. This
from the HFPS to look at the pandemic’s socioeconomic and distributional impacts in the developing world. Kugler et al. (2021) and Khamis et al. (2021) have shown that these surveys are a rapid, efficient, and reliable source of data despite potential shortcomings. Kugler et al. (2021) as well as Bundervoet et al. (2021) both show that women, youth, and less-educated workers were significantly more likely to lose their job and experience reductions in income. Agrawal et al. (2021) use HFPS data from 17 developing countries to look at the distributional impacts of the pandemic, particularly how inequality has played a role in their economic recovery. They find that the lower-skilled, youth, and women were more likely to be impacted initially and that the recovery in employment was not sufficient to reduce the initial disparities in losses.

Our main contribution is threefold. First, we examine the differential impact of the pandemic on households across the welfare distribution using recent pre-existing or imputed data of welfare status before the onset of the pandemic, thus building on previous studies which mainly focus on impacts by gender, location, and education. We are able to obtain pre-COVID-19, household-level welfare data for all the high-frequency surveys administered in the EAP, allowing us to analyze the impacts the pandemic has had across welfare quintiles on household-level outcomes such as income, coping mechanisms, food insecurity, access to services, in addition to individual employment. Second, we take advantage of multiple rounds of data in each country, examining the evolution of impacts and coping methods over time: from periods of stringent mobility restrictions and lockdown to reopening and (limited) recovery. Third, unlike the global studies mentioned above, we aim to capture the heterogeneity of experiences across these seven EAP economies while highlighting emerging common patterns.

Our study suggests the following. First, at the extensive margin (work stoppages), employment impacts during the pandemic have been substantial among middle-income EAP countries, but particularly during periods of tighter mobility restrictions
data collection. Moreover, given the nature of this phone survey, we were unable to capture the quality of ECE among children that were attending. Furthermore, given the periodic COVID-19related partial and full school closures in the years preceding the study (Nagesh et al., 2022), rates of ECE enrollment do not necessarily reflect consistent attendance (particularly as only about 7% of those attending had access to virtual ECE services). We used a proxy measure for income, which was average monthly mobile expenditure; however, this proxy may not accurately reflect income levels, loss of income in the recent past, or socioeconomic status. Similarly, the proxy measure for household size may not accurately map onto nuclear versus extended family structure or represent older-sibling effects. The study did not allow us to parse the roles of birth order, siblings and family structure directly, an area befitting future research, especially for effects on girls. Future research may also examine how attitudes towards child gender influence caregiving behaviors and child outcomes. Additionally, we did not have district- or village-level data to examine potential COVID-19 shocks at a more granular level. Statistical approaches allowed for associational inference only rather than causal conclusions, particularly in light of the above limitations regarding omitted variables and measurement and design constraints. Lastly, this analysis is crosssectional and therefore unable to explore how early investments and relationships may differentially be associated with future outcomes by child gender. Future studies could apply longitudinal designs to better understand the dynamic nature of these relationships.

# **Conclusion**

Our study examined the moderating role of child gender in the associations between parental distress, ECE enrollment, and psychosocial stimulation and ECD across two age cohorts. We found that higher parental distress had a negative association with ECD outcomes for very young children, with girls experiencing worse ECD outcomes than boys in the context of high levels of parental distress—although both boys and girls were sensitive to these distress levels. We also found that higher psychosocial stimulation had a strong, positive association with ECD across
**Figure 2. Deficit, debt and debt service indicators**

<!-- Start of picture text --> 5 35<br>30<br>4<br>25<br>3 20<br>2 15<br>10<br>1<br>5<br>0 0<br>GDF/GDP RD/GDP Debt/GDP IP/RR<br>Debt/GDP and IP/RR<br>GDF/GDP and RD/GDP<br><!-- End of picture text --> Source **:** RBI Handbook of Statistics on State Finances, various issues. Note: GFD: Gross fiscal deficit, IP: Interest payments, RR: Revenue Receipts, GDP: Gross domestic product.

Until the mid-1990s, states‘ finances were relatively stable, characterized by low current and fiscal deficits, averaging below 1 percent and 3 percent of GDP. Debt levels remained moderate at about 20 percent of GDP, and the interest burden hovered close to 15 percent of revenue. The turning point came during 1998–99, with a significant deterioration in the current account, which became a key driving force for the declining fiscal health of the states, with increased spending on administrative services and interest payments.

The next phase, 1998–99 to 2003–04, saw the steep rise in the fiscal deficit as a ratio of GDP—from 2.8 percent to 4.2 percent; the revenue deficit more than doubled from 1.1 percent of GDP to 2.5 percent. As a result, the states‘ outstanding debt to GDP grew from 21.7 percent during 1997–98 to its peak of 32.8 percent during 2003–04. Interest payments as a share of revenue receipts (repayment burden) rose from 17.9 percent to 26.0 percent over the same period, and the primary deficit grew from 0.9 percent to 1.5 percent
age group dummy variables, marital status, household size, language skills (listening, speaking, reading, and writing), residence area, and home ownership. Mental health scores are the sum of the 6-item Kessler Psychological Distress Scale (K6), with a higher score indicating worse mental health. The scores are standardized to have a mean of zero and standard deviation of one. The critical value of the F-test from Stock and Yogo (2005) is 16.38.

39
et al., 2022), we assume informal firms sell no output to the formal sector and source a smaller proportion of their inputs from formal firms compared to small formal firms. We find that this further reduces spatial inequality in outlinks and the prominence of urban hubs relative to the formal network. Moreover, we continue to underestimate the impact of domestic shocks while overestimating the effects of trade shocks.

Our paper contributes to the literature on macroeconomic development, informality, firm networks, and spatial inequality.

First, we contribute to a growing body of research at the intersection of trade and macroeconomic development that integrates granular administrative data such as employer-employee records and data from credit registries, with broader data sources like population censuses to achieve a more accurate assessment of aggregate economic outcomes. To date, this literature has primarily focused on employment outcomes, sector shares (see e.g. Albert et al., 2021), and consumption (see e.g. Fan et al., 2023), where informal activity is somewhat more observable. However, informal activity along supply chains remains particularly elusive (B ̈ohme and Thiele, 2014; Atkin and Khandelwal, 2020).<sup>8</sup> Our results highlight the implications of the non-random selection of firms into administrative records. This is particularly important, given the growing reliance on such data in the literature (Donaldson, 2025).

Our approach to employ a structural model to bridge gaps in our understanding of informal firm dynamics also aligns with the recent literature in this field (see e.g. Ulyssea, 2018; DixCarneiro et al., 2024). Unlike related studies that focus on firm and worker-level dynamics, we do not model the endogenous response of firms and workers to simulated shocks. Crucially, however, our research design allows us to examine the role of informality for Kenya’s regionlevel input-output matrix. This is particularly relevant for research that seeks to complement predictions about aggregate national welfare with welfare estimates at the regional level to
s Calculations based on data from SEDAPAL Annual Reports<br><!-- End of picture text -->

Only 39 percent of connections were metered by 1996, and only 15 percent of connections had meters that were read (see statistical appendix table 3); an unknown but even smaller number were actually charged according to their meters.<sup>7</sup> Metering varied by type of user, with a greater proportion of industrial users charged according to a

> 4 Webb and Associates estimated the medical cost income costs of water borne diseases at US$13 per capita a year in 1992. Average family size is five persons and household income is US$240.

> 5 The connection charge in 1998 for water was US$224 and for sewerage, US$296.

> **6** The residential rates were US$0.18 per M<sup>3</sup> compared to industrial tariffs of USS0.49 in 1989; by 1996 the rates were US$0.33 and US$0.85 respectively.

> **'** In 1992 only 8 percent were charged according to a meter (LMG Consultants). These data are not strictly comparable, however, because SEDAPAL has contracted out metering and meter reading.
EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE Among road corridors serving landlocked countries, there are significant cost differences. Based on the three main intraregional arteries in East Africa, the cost of importing goods from a landlocked country lies in the range of $170 to $370 per tonnekilometer. Overall, travel to landlocked Burundi, Rwanda, and Uganda is cheaper via the northern corridor than the central corridor. For Burundi, the northern corridor has a competitive edge over the central corridor. This is striking because Bujumbura is closer to Dar es Salaam than to Mombasa.

# Even though

**Figure 2.3 . Time required to import goods by road through alternative gateways**

## a. In hours

<!-- Start of picture text --> 1000<br>900<br>800<br>700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>Djibouti-Addis Dar es Salaam- Dar es Salaam-Kigali Dar es Salaam-<br>Bujumbura Kampala<br>Customs Border Transport Ports<br>Total time required to import goods<br><!-- End of picture text -->

## b. By step (% of total time)

<!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Djibouti-Addis Dar es Salaam- Dar es Salaam-Kigali Dar es Salaam-<br>Bujumbura Kampala<br>Customs Border Transport Ports<br>Source: Source: Data collected from ―Trading Across Borders‖; Nathan Associates 2010; and AICD ports<br>database.<br>Share of time required to import goods<br><!-- End of picture text --> both Dar es Salaam and Mombasa have significantly exceeded their handling thresholds, Mombasa is slightly cheaper and
whose harvest can be easily mechanized, (b) where combines were present to facilitate the hiring of these combines in place of manual labor, and (c) where, as I show in the model described in Appendix 4, farms tended to be larger.

This expected variation in the impact of MNREGA provides an opportunity to test the hypothesis that it was increases in mechanization that linked MNREGA to increases in agricultural fires. To do so, I construct an index of mechanization for each district. The construction of this index is driven by the features of my model and the relationship between fire use and mechanization in Indian agriculture as described in the existing literature (e.g. among others Jain et al. (2014) and Bhuvaneshwari et al. (2019)). I follow Asher and Novosad (2018) and use an index rather than testing multiple measures of mechanization to limit concerns of multiple hypothesis testing.<sup>23</sup> To construct the index, I consider the district averages of the following variables in the years prior to 2006: the share of agricultural land in both marginal and medium or large holdings (less than 1 HA or more than 4 HA), the number of combines in 2006, and the production areas of wheat, rice, and sugarcane (the ability to mechanize varies across crops in India (Solomon, 2016)).<sup>24</sup> To make the index, I turn these averages into Z scores. Each district receives a Z score for each individual measure. I then add them together to determine an index measuring the likelihood of mechanization in a given district. Higher index scores indicate a higher predicted level of mechanization. In the appendix, I show that this score is positively correlated with the best available measure of post-MNREGA mechanization – the number of combines in a district in 2011. I then divide districts into quartiles based on their mechanization index score and run my difference-in-differences regression from equation 1 on districts in each quartile.<
- COVID-19 High-Frequency Phone Survey of Households 2020. Public Use Dataset. Ref: ETH_2020_HFPS_v08_M. Dataset downloaded from `https: //doi.org/10.48529/m9pb-0d05` .

- Wossen, T., T. Berger, M. G. Haile, and C. Troost (2018, June). Impacts of climate variability and food price volatility on household income and food security of farm households in East and West Africa. _Agricultural Systems Perspectives on Global Food Security 163_ , 7–15.

23
reflects the perception of quality of infrastructure in the country (transport, telephony, energy). Also, an infrastructure index is used, built as weighted average of energy and internet use.

b Results are robust to the use of International Internet bandwidth (kb/s) per Internet user (Source: WEF), and to the use of infrastructure indexes (see previous Note).

c Results are robust to the use of other variables, such as average years of schooling (not shown). d Results are robust to the use of average tax rate (not shown).

> e The use of the regulatory index (component of Pillar 7) within the same model with government effectiveness (Pillar 1) and cost of doing business (Pillar 11) may lead to biased results due to collinearity. Alternative specifications are used to test the results, incl. the use of the extent of market dominance (WEF). f Results are robust to the use of other variables related to trade facilitation. For example, the Fraser regulatory trade barriers index is used (see Annex 4)

g Results are robust to the use of an indicator of credit market regulation (Fraser, see Annex 3). h See note e.

The classification encompasses determinants of competitiveness in terms of the institutions, policies, and factors that affect a country’s productivity, prosperity, and potential growth.<sup>4</sup> It brings together factors of production that build and boost the structural side (human capital, innovation adoption, energy, and Internet adoption); means of growth (financial intermediation, openness to trade); and reforms that affect institutional quality (government effectiveness, competition and regulatory quality).

The first competitiveness area—the first four pillars—deals with the enabling environment for business. Pillar 1 recognizes the importance of institutional quality for sustainable and strong economic growth. In this study it is proxied by the government effectiveness indicator (WGI). Pillars 2 and 3 refer to extensive
evidence from the United States suggests that young high-growth firms represent a significant chunk of new jobs created in the economy over time (Haltiwanger et. al., 2013, 2017). Despite the relevance of HGFs, there is lack of knowledge about what are the key characteristics of these firms and their growth dynamics, particularly in developing countries.

To analyze the dynamics of HGF in Tunisia, we use the Tunisian Statistical Business Register, namely the _Répertoire National des Entreprises_ (RNE), collected by the National Institute of Statistics in Tunisia ( _Institut National de la Statistique_ ). The RNE results from merging various administrative data using a unique identifier at the firm level. It provides information on number of employees, age of the firm, foreign ownership, _offshore_ tax incentive status, and trade status for all registered firms in Tunisia. The data cover the 1996-2015 period, with almost 1.2 million firmyear observations, which are reflected in a firm-level panel data set with an average of 60,000 firms per year.

The analysis suggests that HGFs represent between 9% to 10.5% of firms in Tunisia, on average, in the last 17 years, depending on the definition used. We show that the share of HGFs differs across definitions, but it is remarkably stable over time and weakly correlated with GDP growth. High-growth status is more prevalent among firms in the following sectors: computer and

> 5 Although the age coefficient is negative, the age square coefficient is positive.

> 6 For example, if 1,000 small firms, with 10 employees each, manages to grow 20% per year over 10 years, their number of jobs would increase from 10,000 to almost 62,000 over this period.

2
Regularity of Supply|3.4|3.2|0.19|0.23|0.415|
|**Household Cost of Service Index**||||||
|HH Opinion on Contribution|2.1|2.2|-0.10|0.14|0.489|
|HH Opinion on Tariffs|2.0|1.9|0.11|0.07|0.108|
|**Operation & Maintenance Index**||||||
|Number of Breakdowns Last Year|3.471|5.9|-2.4***|0.72|0.001|
|Scheme Facing No Water Shortage|0.532|0.462|0.07|0.14|0.601|
|**Institutional Sustainability Index**||||||
|Proportion of Female in BG Executive<br>Committee|0.322|0.251|0.07+|0.04|0.098|
|Maintenance of Records (Yes/No)|0.163|0.006|0.16**|0.05|0.004|
|CurrentBalanceinthe SavingsAccount|15,967|3,096|12,871***|2,568|0.000|

� p�0.1, * p<0.05, ** p<0.01, *** p<0.001 The ATT estimates of the aggregate performance indexes indicate that Jalanidhi schemes perform better than the KWA-BM schemes in Availability and Reliability, Household Satisfaction, O&M and Overall Performance (Table 13). The ATT estimate for availability and reliability Index is around 1.0 which means that that the respective z-score for Jalanidhi scheme are on an average higher than the z-scores of comparable KWA schemes by 1. Since the difference between z-scores is not