rafmacalaba/data-use-mentions-extended huggingface dataset:
Above, we argued that failure to account for differences in the regulatory burden on the private sector across countries could bias our main results. We sought to address the problem using a firm-level de facto measure of regulatory burden on the firms, Time Tax. We complement this control for business regulations with another one which is the freedom from business regulations taken from Fraser Institute’s Economic Freedom of the World database. Note that higher values of the variable imply less regulation of businesses.

Last, several studies have explored the relationship between corruption and inflation. <mark>Akça et al. (2012) provide a useful summary of the theoretical and empirical literature in the area.</mark> For instance, it is commonly believed by the public that inflation causes moral erosion (Paldam, 2002) and creates more opportunities for illegal and unethical behavior such as jugglery or cheating (Braun and Di Tella, 2004). Thus, higher inflation encourages greater corruption. Another argument is that inflation distorts income distribution in favor of capital owners and against those with fixed incomes including civil servants. The perceived imbalance in incomes stimulates corrupt behavior (You and Khagram, 2005). Empirical studies that find a positive impact of inflation on corruption include Braun and Di Tella (2004) Paldam (2002), Getz and Volkema (2001), Ata (2009), Tosun (2002), and <mark>Akça et al. (2012). If the level of inflation also varies systematically with the country size, our main results could be spuriously affected. We guard against this possibility by controlling for the annual rate of inflation (GDP deflator) using data from WDI.</mark> Summary statistics of all the variables used in the regressions are provided in Table 2 while the correlations between the various controls and the measures of country size are provided in Table 3.

19
***|
|(5) School and homework|220|82.0|66|114.9|
|(6) Sleeping/resting|1976|658.3|1647|657.9|
|(7) Eating/drinking|1960|75.9|1637|76.3|
|(8)Other<br>Notes:|197|196.2|150|205.7|

(1) Sample includes men and women self-reporting in the LSMS+ time diary module, aged 18 and older. All estimates weighted by household sampling weight.

(2) Significant differences in minutes spent across men and women (separately for full sample and among t>0) are indicated by asterisks; ***p<0.01 **p<0.05 *p<0.10 9
food and beverage manufacturing, fertilizer, pharmaceutical, wholesale and retail, and food services sectors. Table A.4 details the list of NAICS 6-digit codes. We defined the industry broadly given the fact that pharmaceutical companies source many of their ingredients from the agriculture sector, the growing size of the nutraceutical market, and the role of drug stores and general merchandise stores as retailers of processed food.<sup>9</sup> We eliminate a small number of links that are to be formed between the same en- reported tity and drop linkages that last for less than a day. From this sample, we create a yearly panel of active firm-to-firm connections categorized by relationship type. For the sample period from 2014 to 2022, the dataset contains about 160,000 supplier and customer links, involving more than 17,500 agriculture-related companies and approximately 26,000 nonagri partner firms. More than 4,500 of the agribusinesses are “actively covered”, that is they appear as reporting firms in the raw FactSet data.

In general, the number of firm nodes and links in the sample is increasing over time (see Figure A.9, left panel, and Figure A.10). The share of actively covered agribusinesses, non-reporting firm agribusinesses and non-agribusinesses among the set of nodes in a given year is fairly stable (see Figure A.9, right panel). More than 15,000 of the 17,500 are observed in the data. About 4000 nodes are agribusinesses during multiple years observed in all of the nine sample years, approximately 1700 of which are actively covered agribusinesses. Acknowledging the generally increasing size of our network sample, we report some results for the balanced set of firm nodes to study dynamics at the intensive margin.<sup>10</sup> While the FactSet database offers extensive coverage, it exhibits a potential sample bias towards larger, publicly listed companies due to its reliance on publicly available disclosures. Figure A.11 shows that public companies make up about
Table C.17: National Estimates of Effect of Years of Schooling on Number of Children Born - DHS - Combined Instrument

||**Years of**<br>**Schooling**<br>(First Stage)|**Number of**<br>**Children Born**<br>(Reduced Form)|**Number of**<br>**Children Born**<br>(2SLS)|
|---|---|---|---|
||(1)|(2)|(3)|
|Add’l Years of FPE or MTI|0.115|-0.037||
|w/out Script Change|(0.026)|(0.019)||
|(∆_Izy_)|[0.000]|[0.019]||
|�<br>Years of Schoolingizy|||-0.321<br>(0.156)<br>[0.040]|
|First Stage F-Statistic|19.56||19.56|
|Number of Clusters|58|58|58|
|N|24,898|24,898|24,898|

_Source_ : Author’s analysis based on data from the Demographic and Health Survey (DHS) in years 2005, 2011, and 2016. _Note_ : The dependent variable is years of schooling in column 1 and is number of births � in the other two columns. Years of Schooling _izy_ is the predicted number of years of schooling, instrumented with the combined intensity measure, ∆ _Izy_ . All samples include women in birth cohorts from 1970 to 1988. All regressions include birth year and zone fixed effects, zone-specific linear trends, and a cubic for age when multiple survey waves are included. Standard errors are clustered at the zone level and shown in parentheses; _p_ -values are shown
differently. Larger and more productive firms can more easily take advantage of the change in trade costs than smaller and less productive firms (Melitz, 2003; Melitz and Redding, 2015). As deep trade agreements can reduce trade costs among members by eliminating tariffs and by reducing other frictions due to regulatory differences and/or policy uncertainty, a natural extension of this literature is that DTAs should affect firms differently based on their characteristics, promoting the export performance of larger firms and leading to a selection effect.

This paper empirically investigates the effect of RTAs’ depth on the export performance of heterogeneous firms. The analysis combines firm-level export data for 31 developing countries from the World Bank Exporter Dynamics Database (Fernandes et al., 2016) and information on the detailed content of more than 300 RTAs from the World Bank Deep Trade Agreements database (Hofmann et al., 2017). The richness of the data allows to precisely take into account the heterogeneous consequences of deep RTAs on the exports of different firms. We use a decomposition à la Berman et al. (2012) to study the extent to which the effects of deep RTAs 1
# **Figure 2 : Trends in the Portuguease and Math test scores and in the dropout rates, for the sample  of 9th grade schools after the matching (2005-2011).**

<!-- Start of picture text --> Treated Control<br>20052007 216.8455222.1867 218.0644221.7244 Trends in Portuguese Test scores<br>2009 232.9576 234.846 240<br>2011 232.8487 233.0506<br>Mathematics grades<br>Treated Control 235 Dropout rates<br>2005 228.3743 229.4973 Treated Control<br>2007 230.0147 228.9602 2007 11.71429 11.44817<br>2009 232.6095 236.8595 230 2009 8.935165 8.80641<br>2011 236.0198 238.7859 2011 7.063919 7.08956<br>225<br>220<br>215<br>210<br>205<br>2005 2007 2009 2011<br>Treatment Control<br>Trends in Mathematics Test scores<br>245<br>240<br>235<br>230<br>225<br>220<br>215<br>210<br>205<br>2005 2007 2009 2011<br>Treatment Control<br>Trends in the dropout rates<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>2007 2009 2011<br>Treatment Control<br><!-- End of picture text -->
2022 and expedited its roll-out as a platform that would allow digital interaction between farmers and state organs and accredited users in the private sector. One objective was to establish a digital tool to overcome the informational and institutional barriers that, in the past, made it difficult for credit constrained small and medium farmers to access state support.<sup>8</sup> A second objective was to reduce leakage to intermediaries by transferring support directly into beneficiaries’ bank accounts. To allow this, all farmers, irrespective of their formal status a (i.e., a registered legal entity, a family farming business (FOP), or as individual) can use their electronic signature to sign up at the SAR website (https://www.dar.gov.ua/) without charge, thus avoiding the transaction cost and bureaucratic hurdles associated with registration as a legal entity. To assess the extent to which these objectives were achieved, MAPF, with EU support, asked the World Bank to conduct an independent evaluation of the PSG program and provided access to necessary data.

In the sign-up process, SAR gathers owner details from the State Registry of Property Rights and parcel details from the land cadaster for all land parcels to which the farmer has registered rights. This allows the government to establish, in rudimentary form, all the elements used by integrated administration and control systems (IACS) operational in EU countries.<sup>9</sup> It also helps establish the SAR as a trusted source of

> 8 It has been argued that traditional ways of transferring state support in Ukraine had little economic justification, were non-transparent, and, by _ex post_ subsidizing interest rates for farmers who have already taken out loans, have little economic justification and offer no additionality. For example, refer to <u>https://voxukraine.org/en/the-new-agricultural-support-system-in-ukraine-who-really-benefits.</u> 9 The IACS is used to manage, monitor and control all area or animal-based common agricultural policy (CAP)
12,177|11,991|12,358|9,215|12,378|14,820|11,428|12,664|

_Source:_ Authors’ calculations based on the Sri Lankan 1992–2002, 2008, 2011, and 2012 Labor Force Surveys.

_Note:_ Standard errors are in parentheses. Standard errors are clustered at the industry level. Statistically significant coefficients at the 10, 5, and 1 percent level are indicated by one, two, and three asterisks respectively.

The grand mean effects of the industries are calculated; additional controls include industry, and occupation dummies. Sri Lanka Household Income and Expenditure Survey was excluded from the analysis as hours worked were not available in this survey. Occupation dummies are according to 1-digit ISCO-08; the omitted category is managers. Industry dummies include 15 categories: Agriculture and Forestry; Mining; Food, Beverage, and Tobacco; Textile and Apparel; Wood; Other Manufacturing; Utilities; Construction; Sales; Transport; Financial, Insurance, and Real Estate; Public Administration; Social Services; and Other Services. Log wages are measured as the logarithm of real monthly individual labor income. Log wages are measured as the logarithm of real monthly individual labor income in 2005 prices.
# **1. Introduction**

The discourse on whether and how economic growth translates into poverty reduction is a topic of significant importance and enduring debate in economic development. This debate extends far beyond academic circles, touching the core of policy making. A critical challenge is the ability to offer concrete guidance on prioritizing growth-oriented policies and identifying the specific nature of growth that effectively mitigates poverty. This ongoing discourse underscores the need for rigorous analysis and evidence to inform policy choices, particularly when considering the multiple facets of poverty beyond mere income metrics to encompass a broader range of deprivations affecting individual well-being.

While a substantial body of empirical evidence exists pointing to a generally negative correlation between economic growth and poverty, it is noteworthy that the lion's share of this literature has traditionally focused on income or monetary poverty. This narrow focus on money leaves a significant gap in our understanding of the dynamics between economic growth and multidimensional poverty—a form of poverty that considers various dimensions of deprivation, including education, health, and living standards (Alkire et al., 2021). The relationship between multidimensional poverty and economic growth remains relatively underexplored, with most studies merely qualitatively tracing the trajectories of GDP growth and multidimensional poverty at the national level without engaging in formal statistical analysis (e.g., Djossou et al. (2017), Tran et al. (2015)).

A handful of studies have ventured into examining the correlation between changes in GDP and shifts in composite indices of multidimensional poverty using cross-sectional data across different time points (Alkire et al., 2017; Burchi et al., 2019; Seth & Alkire, 2021). However, rigorous assessments of the relationship between economic growth and multidimensional poverty are scarce. To our knowledge, only two attempts have been made in this direction, notably by Santos et al. (2019) and Balasubramanian et al. (2023). Both employed unbalanced panel data and a firstdifference estimator (FDE) to uncover a negative correlation, albeit focusing on different
though a person’s recall might be imperfect, our data are the first that enables to link premigration expectations to realized earnings on an individual level for temporary migrants.

Finally, the survey collects additional information on migrants’ most-recent migration episode, such as remittances sent, monthly expenses and savings, professional and personal difficulties encountered, and overall impressions. The survey includes a detailed module on demographic characteristics, enterprises and assets for the household.

# **3.2 The Household Income and Expenditure Survey (HIES)**

Because the BRMS only covers households with return migrants, we complement it with a nationally representative household survey that samples both non-migrants and current international migrants. The Household Income and Expenditure Survey (HIES, 2016-2017 wave) was designed to be representative at the national and division levels. The HIES collects detailed data on each household member, including labor market outcomes such as employment status, earnings, and sector of employment. The data are comparable to the labor market information captured by the BRMS. Information on non-migrants and current international migrants is collected through an “absentee module” answered by the household members who stayed in Bangladesh. The HIES also includes information on the debt and assets of the household, as well as detailed expenditure information that allows us to calculate household consumption and savings rates.

The Census of Population and Housing 2011 was used as the sampling frame for both the HIES and the BRMS. While the HIES covers both rural and urban areas, the BRMS sampling was designed to capture a representative sample of recently returned migrants in rural and semi-urban areas of Bangladesh. We thus restrict the HIES sample to rural and semi-urban areas in all of our comparisons. The HIES was carried out two years earlier than the BRMS.<sup>14</sup> This is an advantage in our context as current migrants in 2016 are likely to be more comparable to the return migrants captured in 2019 by the BRMS. Indeed, among the BRMS respondents, the median returned migrant had been back in Bangladesh for
.8|
|Passenger density, 1,000 passenger-<br>kms/km|||38.0|91.9|60.3|147.0|32.7|165.6|
|**Efficiency**|||||||||
|Labor productivity (1,000 traffic units per<br>employee)|121.0|722.1||502.0|3,308.1||||
|Carriage productivity (1,000 passenger-<br>km per carriage)|||1,176.5|3,285.7|||||
|Locomotive productivity (million traffic<br>units per locomotive)||||25.1|||||
|Wagon productivity (1,000 net tonne-km<br>per wagon)||||376.5|||||
|**Tariffs**|||||||||
|Average unit tariff (UT), freight, U.S.<br>cents/tonne-km|3.0||5.8|3.9|||||
|Average UT, passenger, U.S.<br>cents/passenger-km|1.0||1.0|0.8|||||

_Source:_ Bullock 2009, derived from AICD railways database downloadable at http://www.infrastructureafrica.org/aicd/tools/data Empty cells denote that data not available.

## **Challenges**

Zambian railways’ low traffic densities are well below the viability threshold of at least 2 million tons per kilometer for railways of this kind, making it difficult to capture the revenues needed to maintain assets. Also, performance for the RSZ is mixed, while TAZARA performance data is largely unavailable (table 3).

10
.44|-0.45|0.10|0.30|0.54|0.35|1|||
||(0.00)|(0.00)|(0.53)|(0.05)|(0.00)|(0.02)||||
|**Share of Top 5% of**||||||||||
|**Exporters**|0.11|0.15|-0.40|0.00|-0.21|0.14|0.22|1||
||(0.50)|(0.35)|(0.01)|(0.99)|(0.17)|(0.34)|(0.16)|||
|**Ln Total Exports**|-0.62|-0.51|0.16|0.54|0.63|0.93|0.66|0.20|1|
||(0.00)|(0.00)|(0.32)|(0.00)|(0.00)|(0.00)|(0.00)|(0.20)||

Notes: P-values in parentheses. The correlations shown in the table are based on measures in the country-year level dataset (file CY.dta) averaged across the 2006-2008 period for each country except Kuwait for which averages are across the 2009-2010 period and Portugal for which averages are across the 2003-2005 period.

**_Stylized Fact 4: The typical exporter size, total exports, and exporter survival rates at the country-sector level are not explained by country or sector characteristics while the share of_**

29
knowledge, attitude and practice (KAP) and briefly comment on explanatory variables.

Several good methodological reviews of health interview surveys in developing countries have been produced recently (for example, Kroeger 1983, Ross & Vaughan 1984, Kroeger 1985, Gray 1986, WHO et al 1986) and the UNSO is currently sponsoring the preparation of operational guidelines for collecting health and mortality information using household surveys. We therefore do not attempt a full review of the methodological issues associated with the outcome, process and explanatory variables that one might cover in the WHS. Instead, we highlight recent debates and developments which should be considered in designing a WHS.

# 4.2 Mortality - well established methods

Several comprehensive reviews of the uses, collection and analysis of data on mortality are now available (eg. Vallin et al 1984, United Nations 1984). Here we restrict the discussion to a few issues concerning the collection of mortality data in surveys focused on health. Single-round surveys that have asked straightforward questions about deaths in the preceding 12 or 24 months have frequently yielded disappointing results. Both reference-period errors and omissions appear to be problems and data on young children are often particularly poor. Moreover, it is often difficult to collect reliable information about even the basic characteristics, such as age, of those who have died. An alternative approach is the multiround survey. Reference-period errors and omissions are greatly reduced as data is collected about deaths in previously-enumerated households during the intervals between rounds of the survey. Such studies involve prolonged fieldwork operations in which households must be first traced and then reinterviewed. Moreover to collect information in one or two years on adequate numbers of deaths to measure precisely mortality levels and patterns requires a sample of at least 20,000 households and preferably far more. Multiround surveys of mortality are complex and expensive operations and it is doubtful that they are justified in the context of a World Health Survey. While mortality is of particular significance for demographic 24
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Ferracane, M.F., H. Lee‐Makiyama and E. van der Marel (2018b) “Digital Trade Restrictiveness Index”, European Centre for International Political Economy, Brussels: ECIPE.

Ferracane, M.F., J. Kren and E. van der Marel (2018a) “Do Data Policy Restrictions Impact the Productivity Performance of Firms and Industries?”, ECIPE DTE Working Paper Series No. 1, Brussels: ECIPE.

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‐ OECD (2019) “East Asia Going Digital: Connecting SMEs”, OECD, Paris, www.oecd.org/going <u>digital/East‐asia‐connecting‐SMEs.pdf.</u> <mark>van der Marel, E., H. Lee‐Makiyama, M. Bauer and B. Verschelde (2016) "A Methodology to Estimate the Costs of Data Regulation",</mark> _<mark>International Economics</mark>_ <mark
in demand for Ukraine, using data from one private portal. Our paper adds to this by offering a rigorous methodology on the aggregation of data across four portals. We also use a highly detailed skills taxonomy so that our findings lead to very clear policy recommendations on skills development and have the potential to inform education curricula. With regard to method, our paper closely relates to Deming and Kahn (2018), who apply text analysis to study heterogeneity in employer skill demand using US job platform data. We follow a similar dictionary-based approach to identify

> 3 Available data such as labor force surveys are useful to analyze overall variations in employment across sectors and age groups, but these data have limitations in reliably and timely estimating job creation and the characteristics of jobs currently created. Firm registry data can provide estimates of jobs created in the formal sector but omit an important share of the economy that is informal or dominated by small firms, which is especially important for labor markets such as Kosovo’s. Measuring skills needs quantitatively and at a granular level is challenging. An attempt at measuring skills needs quantitatively is made in some modules of the Skills Towards Employment and Productivity Surveys (“STEP surveys”) directed to employers and implemented in several countries, including in Kosovo in 2015-2016. It identifies the existence of skills constraints across sectors and type of occupations. However, the data on demand for skills are not granular enough for specific feedback for policy makers on skills needs and curricula alignments.

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