rafmacalaba/data-use-mentions-extended huggingface dataset:
International migration—the movement of people across national borders—has important economic, social, and political implications. Despite the recent emergence of a dynamic literature, empirical analysis of migration flows and their impact lags behind the policy debate and the theoretical literature. The main reason is the absence of comprehensive and reliable data on international migration patterns and migrant characteristics at either the aggregate or the household level.

The objective of this article is to use data from more than one thousand national censuses and population registers to estimate a complete global origin–destination migration matrix for each decade over 1960–2000. These 226*226 matrices, comprising every country, major territory, and dependency around the world, are divided into periods corresponding to the last five completed census rounds. The gender dimension of international migration over this period is also presented.

The primary source of the raw data is the United Nations Population Division‘s Global Migration Database, created through the collaboration of the United Nations Population Division, the United Nations Statistics Division, the World Bank, and the University of Sussex (United Nations [2008]). This unique data repository comprises 3,500 individual census and population register records<sup>1</sup> for more than 230 destination countries and territories over the last five decades. The database provides information on international bilateral migrant stocks (by citizenship<sup>2</sup> or place of birth), sex, and age. There is considerable variation, however, in how destination countries collect, record, and disseminate immigration data. Meaningful comparison of destination country records over time is thus often confounded.

In constructing global bilateral migration matrices, several challenges arise. First, destination countries typically classify migrants in different ways—by place of birth, citizenship, duration of stay, or type of visa. Using different criteria for a global dataset generates discrepancies in the data. Second, many geopolitical changes occurred between 1960 and 2000, with many international borders redrawn as new countries emerged and others disappeared
al., 2002; Levine and Schmukler, 2006). On the other hand, even when barriers to financial transactions are removed, frictions (such as information asymmetries, fixed transactions costs, or tax treatments) can still cause market segmentation to persist (La Porta et al., 1997; Karolyi and Stulz, 2003; Pirinsky and Wang, 2006; Bekaert et al., 2011; Colla et al., 2013). Therefore, market specific developments can be important for firms and investors participating in those markets.

We focus on East Asia for different reasons. First, corporate issuances in East Asia account for most of the capital market activity in emerging economies as a whole. The amount of bonds raised by East Asian firms during 1990-2016 accounted for about 70 percent of the total amount raised by firms from emerging regions in domestic and international markets. Second, East Asia weighs heavily in international benchmark indexes, which international investors widely use to allocate funds across economies (The Economist, 2019). This interest by international investors might push firms to raise capital in international markets in detriment of domestic markets. Third, following the 1997-98 Asian Financial Crisis (AFC), policy makers in the region have made a conscious effort to develop domestic bond markets as an alternative to international financing. By comparing bond issuance activity in East Asia with that in other regions, we can examine how international and domestic markets have developed over time, whether they act as substitutes or complements, and how the supply and demand of capital behave when financing takes place in different markets.

To perform the analysis on issuance activity and types of issuing firms, we use transactionlevel data on corporate bonds issued in domestic and international (cross-border) markets over 19902016. The data include 81,194 bond issuances conducted by 13,084 firms from East Asian economies. The analysis focuses on the largest 10 economies in the region in terms of gross domestic product (GDP). We account for the heterogeneity in these economies by computing the main patterns in
Policy Research Working Paper 8852

# **Abstract**

This is the background paper for the productivity extension of the World Bank’s Long-Term Growth Model (LTGM). Based on an extensive literature review, the paper identifies the main determinants of economic productivity as innovation, education, market efficiency, infrastructure, and institutions. Based on underlying proxies, the paper constructs indexes representing each of the main categories of productivity determinants and, combining them through principal component analysis, obtains an overall determinant index. This is done for every year in the three decades spanning 1985–2015 and for more than 100 countries. In parallel, the paper presents a measure of total factor productivity (TFP), largely obtained from the Penn World Table, and assesses the pattern of productivity growth across regions and income groups over the same sample. The paper then examines the relationship between the measures of TFP and its determinants. The variance of productivity growth is decomposed into the share explained by each of its main determinants, and the relationship between productivity growth and the overall determinant index is identified. The variance decomposition results show that the highest contributor among the determinants to the variance in TFP growth is market efficiency for Organisation for Economic Co-operation and Development countries and education for developing countries in the most recent decade. The regression results indicate that, controlling for country- and time-specific effects, TFP growth has a positive and significant relationship with the proposed TFP determinant index and a negative relationship with initial TFP. This relationship is then used to provide a set of simulations on the potential path of TFP growth if certain improvements on TFP determinants are achieved. The paper presents and discusses some of these simulations for groups of countries by geographic region and income level. An accompanying Excel-based toolkit, linked to the LTGM, provides a larger set of simulations and scenario analysis at the country level for the next few decades.

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
1 km at the equator), this source contains two main sub-sources: population count grids for the years 1990, 1995, and 2000, and population count grid future estimates for the years 2005, 2010, 2015 and 2020 and the data are adjusted to reflect the historical, current, and future projections of the United Nations World Population estimates. Following Holder and Rascky (2014), we extracted these data at the district level and linearly interpolated them to estimate population counts for the missing years. Thus, we were able to construct a dataset with population count estimates over the period from 1990 to 2015.

The Armed Conflict Location and Event Data (ACLED)<sup>13</sup> is our fifth source, providing disaggregated spatial conflict data that identifies the locations in which conflicts have taken place around the world since 1997. We exploit the spatial nature of the ACLED data to identify places where conflicts occurred in Africa between 1997 and 2007 before pairing them (at the district

> 10 Note, however, that the key limitation of using the USGS dataset is the lack of information on the levels of mineral productions at the mining site, and opening and closure of such sites.

> 11 The data are available at: <u>http://www.met.reading.ac.uk/~tamsat/cgi-bin/data/rfe.cgi</u>

> 12 From the Socio-Economic Data and Application Centre (SEDAC) hosted by the Centre for International Earth Science Information Network (CIESIN) at Columbia University which are available at: <u>http://beta.sedac.ciesin.columbia.edu/data/collection/gpw-v4</u>

> 13 Available at: <u>www.acleddata.com/</u>

9
child|1|
|Same-sex spouse/partner|0|Tutored/foster child|1|
|Sibling/sibling-in-law|0|Tutored child|1|
|Sibling|0|Grandchild|2|
|Stepsibling|0|Grandchild or great grandchild|2|
|Sibling-in-law|0|Great grandchild|2|
|Cousin|0|Great-great grandchild|2|

Notes: Categories not classified are: Other relative, not elsewhere classified; other relative with different family name; non-relative; friend; housemate/roommate; visitor; godparent; godchild; domestic employee; relative of employee; spouse of servant; child of servant; other relative of servant; roomer/boarder/lodger/foster child; boarder; boarder or guest; lodger; employee, boarder or guest; other specified non-relative; agregado; temporary resident, guest; group quarters; group quarters, non-inmates; institutional inmates; non-relative, n.e.c.; other relative or non-relative; unknown.

Figure 1 shows the unweighted average rate of coresidence by age in the sample pooling all

> 5A similar approach is followed by Alesina et al. (2021) with Census data from Africa.

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Center: The SIGI index of discrimination in the family (Discriminatory Family) is based on laws on child marriage, household responsibilities, inheritance, and divorce; the SIGI Physical Integrity Index (Restricted Physical Integrity) includes laws on violence against women and reproductive autonomy, attitudes towards and prevalence of female genital mutilation (FGM) and domestic violence, missing women, and access to family planning.<sup>20</sup> In addition, we obtain indicators from the World Development Indicators: Literacy Rate is the adult female literacy rate; Gender Equality is a CPIA index on gender quality (1=low, to 6=high). Second, we measure a country’s individualistic culture by Individualism, which is based on World Value Survey, and it captures the extent to which the people in a society are mentally and habitually empowered to make their own choices and to pursue them in their actions. In more individualistic societies, we presume that competition is more encouraged and fiercer. Finally, we use the rule of law index from the World Bank’s Governance Indicators (Kaufman, Kraay and Mastruzzi 2004). To aid interpretation and to normalize in light of the different scales of these indicators, we transform all these indicators into dummy variables, which equal one when the value is above the median values among the countries, and zero else.

# **Incidence of female-headed firms and their basic characteristics**

Female-headed firms ideally would have women being the primary owner and running the firm, that is, acting as both owners and chief executives. Female-owned firms may not be a good indicator for entrepreneurs because family ownership is prevalent in both developed and developing countries (Anderson and Reeb, 2003), and when women inherit family firms without effective control, the firm is not in reality a female-headed firm. Furthermore, the previous literature using the firm sample of several African countries in the WBES data has documented that using the definition of women managers tends to better capture firms truly led by women than that
WEDP-x) loan amounts disbursed according to Wasasa’s administrative data. Outcomes 3-4 indicate if the entrepreneur was over 90 days late on their scheduled (WEDP-x) loan repayment according to Wasasa (0/1). Columns 1 and 3 present specifications with the continuous psychometric score measure and columns 2 and 4 present specifications with the binary (above or below the median psychometric score for the IE sample) measure. Observations represent number of individuals. Loan amounts are in 100 Ethiopian birr (100 ETB = 3.4 USD at baseline and 2.2 USD at endline). Robust standard errors are in parentheses. * _p <_ 0 _._ 05, ** _p <_ 0 _._ 01, *** _p <_ 0 _._ 001
(Provisional Measure 2001) and 50% in the Transition biome (Mato Grosso Complementary State Law No. 38/1995).<sup>9</sup> Data inconsistencies between FEMA and SEPLAN datasets present a challenge to accurate deforestation assessment in Mato Grosso. The deforestation data available to us identified areas on which forest cover was lost, but did not identify pre-existing forest cover. The SEPLAN land use/ land cover map is based primarily on 1995 satellite imagery with some additional images from 1996 and fieldwork conducted in 1997 with updates from 1999 (SEPLAN 2004). As the SEPLAN land use/land cover map integrates information from different years, there are inconsistencies between the SEPLAN map and FEMA deforestation data. The proportion of

> 9 The legal reserve requirement was revised since 1996 (Provisional Presidential Decree No. 1.511/96). Prior to 1996, RL requirement was 50% in forest areas and 20% in the Cerrado (Forest Code 1965).

17
electricity consumption across all sectors, they have a higher share of firms

> 10 Capital stock estimates are derived from past investments and deprecation by asset class using the perpetual inventory method (see online appendix C in (Feenstra, Inklaar, & Timmer, 2015)). Total employment in PW9 is sourced mainly from the ILO, WDI, and The Conference Board (TCB), as described in the PWT9 labor detail file: <u>https://www.rug.nl/ggdc/productivity/pwt/.</u>

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# **1 Introduction**

The past 30 years have seen an unprecedented push for trade liberalization with 262 regional trade agreements (RTAs) involving 188 countries entering into force over this period. While the reduction in trade barriers offers tremendous potential for economic growth and productivity gains, the impacts of trade liberalization may not be universally positive. In particular, there is mounting evidence suggesting that increased global trade may lead to environmental degradation (e.g. Abman and Lundberg, 2020; Zhang _et al._ , 2017; Leblois _et al._ , 2017) which has attracted attention from policy makers, scholars, and the public alike. Indeed, the potential for increased deforestation in Brazil has become a critical stumbling block that has all but halted the ongoing ratification process of the EU-Mercosur trade agreement. Concern over potential adverse impacts of opening trade has stimulated a debate on whether trade agreements should include specific provisions to limit the negative consequences thereof. While some see provisions in trade agreements targeting the environment as a form of thinly veiled protectionism, others perceive them as an important tool for mitigating potential harm from opening trade and a commitment device for environmental policy reform (Frankel, 2009). Despite negotiations and policy discussions around the inclusion of such provisions, there has been little in the way of careful, rigorous work exploring whether these provisions actually function as designed.

Assessing whether environmental provisions in trade agreements work is as difficult as it is important. First, until recently there was a scarcity of detailed information on the specific provisions included in trade agreements. Second, from a methodological point of view, carefully assessing the impact of environmental provisions on environmental outcomes presents a number of econometric challenges that stem from the non-random inclusion of such provisions.

In this paper, we study the effectiveness of environmental provisions in trade agreements in mitigating environmental harm. We use high-resolution, satellite-derived estimates of deforestation as a measure of environmental damage. Deforestation is one of the most pressing environmental challenges of the modern era, both in its threat to biodiversity
Povcalnet are included in Figure 15 for comparison. The projections in Povcalnet are extrapolated using the consumption distribution of NSS-2011 and applying the growth in private final consumption expenditure observed in national accounts. The method therefore assumes that inequality has remained unchanged since the NSS-2011<sup>24</sup> . We compare our approach to Newhouse and Vyas (2019) and Edochie, et al.(2022) in Section 5.2 and reflect on the potential reasons for why their estimates are lower. Gupta, Malani and Woda (2021b) use the raw CPHS data to construct headcounts for 2019 and the post-pandemic period; our reservations with this approach are documented in Section 3.

The rate of poverty reduction between 2004 and 2011 is estimated at approximately 2.5 percentage points per year. After 2011 poverty reduction has slowed down. By our estimates, poverty has declined by an average of 1.3 percentage points per year between 2011 and 2018. It should be noted that at lower levels of poverty, it would take increasingly larger rates of consumption growth and/or reductions in inequality to sustain the high rates of poverty reduction (e.g. Bourguignon, 2003).

Figure 12 in Appendix 4.1 dis-aggregates the trends in poverty by rural and urban. Three observations stand out. First, rural poverty in 2019 is 14.7 percentage points lower than in 2011 while urban poverty reduced by 7.3 points over the same period. This is consistent with a continuation of the rural-urban poverty convergence observed over the past six decades (see Datt, Ravallion and Murgai, 2019).<sup>25</sup> Second, urban India experienced a churn in poverty trends around 2016. Urban poverty rose by 2 percentage points in that year followed by a rapid rise in consumption that drove poverty down by 3.2 percentage points in the following year. Third, the fastest poverty reduction occurred in the years 2017 and 2018. Thereafter, the rate of poverty reduction stalled considerably.

Headcount poverty rates
# **3. Data and Descriptive Statistics**

## **Demographic and Health Survey**

The data on child health come from two rounds of the Demographic and Health Survey (DHS) conducted in 2003 and 2008.<sup>11</sup> The DHS are nationally representative household surveys with a large sample size that contain detailed information on demographic characteristics, fertility, contraceptive use, infant and child mortality, immunization levels, use of family planning, maternal and child health, breastfeeding practices, and nutritional status of mother and young children.<sup>12</sup> Nutritional status, especially in children, has been widely and successfully assessed by anthropometric measures in both developing and developed countries (Mei and Grummer-Strawn, 2007). Thus, following the norm in the literature we proxy the health status of children with their anthropometric measures and information on recent symptoms of illness, specifically diarrhea.<sup>13</sup> Children height and weight are converted into standardized Z-scores (height-for-age and weight-forheight), using the WHO Anthro program (WHO, 2006). For each child, depending on their sex and age, the Z-score system expresses anthropometric values in terms of standard deviations below or above the median of an international reference group.<sup>14</sup> Height-for-age Z-score (HAZ) indicates chronic malnutrition and is considered a good measure of long-term health. In contrast, weight varies in the short run due to acute starvation or severe disease. Therefore, the weight-for-height Z-score (WHZ) provides a more current indicator of health status.

The Nigeria DHS samples were selected using a stratified two-stage cluster design. The 2003 DHS consist of 7,864 households in 365 clusters or villages -with 54% of them being rural villages. In contrast, the 2008 DHS had a much larger sample size consisting of 36,800 household in 886 clusters (69% in rural areas).<sup>15</sup> We restrict the sample to those born
.027)|(0.027)|(0.027)|(0.027)|
|No. of obs|<br>37,248|<br>37,248|<br>37,248|<br>37,248|<br>37,248|<br>37,248|
|R-squared|0.287|0.287|0.287|0.287|0.287|0.287|

_Note_ **_:_** Results are from 2019-2022 panel regressions where the unit of observation is the village council and the dependent variable is the NDVI for fields planted with winter wheat in each of the relevant years. Crimea and areas that have been occupied since 2014 are excluded. GDD is cumulative growing degree days above 0°C between October 1and May 31 approximated hourly by sinusoidal distribution of minimum and maximum temperature (d’Agostino and Schlenker, 2016); and fall, winter and spring months are Oct-Nov, Dec-Feb, and Mar-May, respectively. ‘Any’ (Col. 5) is the maximum over the 8 two-week periods considered whereas ‘conflict intensity’ (col 6) accounts for the temporal dimension by taking a value of 1 for any of the 8 two-week periods when any conflict activity was observed. See appendix table 1 for full specification including weather variables. Standard errors in parentheses. * p<0.10, ** p<0.05, *** p<0.010.

35
inversion strength. Pollution data comes from the Van Donkelaar group. Inversion data comes from the MERRA re-analysis data and measures the incidents when temperature at atmospheric levels above the surface is higher than at the surface by district for each fiscal year. Inversions are measured as the share of days with an inversion. Inversion strength is the average difference in temperature when there is an inversion. All inversion data and weather controls are population weighted averages across districts. The unit of observation is the district year. In all columns we control for the first difference of the number of days in one of fifteen precipitation bins, the number of days the mean temperature is in one of twenty-five bins, a second order polynomial of relative humidity and surface pressure, four wind speed bins, ten wind direction bins, and interactions of relative humidity and the square of relative humidity with all of our temperature bins. All columns include fiscal year fixed effects. In all columns we cluster standard errors at the district level. We winsorize GDP growth at 1%. (* p _<_ .10 ** p _<_ .05 *** p _<_ .01).

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This paper also explores possible heterogeneity in agglomeration effects by the development level of countries and between countries and subregions with distinct historical and institutional backgrounds. The results show that agglomeration effects on firm innovation are prevalent across developing Asia; they are present in both upper-middle income and low and lower-middle income country groups, as well as in China, India and Southeast Asian countries, each considered separately.

Finally, using a widely cited ranking of universities and majors (QS 2019a and QS 2019b), it is possible to identify which cities are home to one or more of the top 500 ranked Asian universities as well as universities with a major in the top 500 global ranking of major fields of study. The presence of a top university is found to be positively and significantly associated with firm innovation, and having a university with a top 500 globally ranked engineering major even more so. These results indicate an important role for locally available human capital, especially that which relates to technical fields, as a channel through which agglomeration affects innovation.<sup>3</sup> It must be noted that the analysis here does not estimate the causal effects of city size on innovation. To do so would require instruments that affect current city size but are unrelated to unobserved city characteristics that impact innovation. Given the difficulties in finding plausible instruments, especially in a cross-country setting, this paper focuses on establishing important associations and exploring potential heterogeneity in the relationship between city size and firm innovation while controlling for various city and firm level characteristics.

This paper is closely related to two strands of literature. The first is on the measurement of urban agglomeration economies. Most of the empirical evidence in this strand has focused on whether city size and/or density affect firm and worker productivity.<sup>4</sup> Studies examining urban agglomeration economies associated with innovation-related activities are limited. Nevertheless, the available evidence—mostly for developed countries—suggests that innovation in a given country is heavily concentrated in a few cities, and that a city