rafmacalaba/data-use-mentions-extended huggingface dataset:
of the model is challenging due to the absence of microdata on quantities (and hence sector shares) in the BLS data on the demand side, as well as the lack of firm-specific information in the many producing countries on the supply side (for example the share of intermediates in costs, and their source). Moreover, there is also the challenge of defining a “sector” — which will determine the number of firms as well as elasticities of demand — both of which are important determinants of the firms’ pricing behavior. In all reasonable calibrations the model has difficulty matching the extreme degree of incomplete pass-through estimated in the data. However, these are common problems in the literature and my parameters are reasonably standard, though these challenges should be noted as a caveat.

There are two calibrations of the model used in the paper (parameters are listed in Table 2). The first is the baseline asymmetric calibration with 11 countries (US+10), which is the closest to the estimated data. In each sector, I keep the number of US firms the same as AB (KUS = 20), but set K = 3 firms per foreign

> 16One can also derive a related structural pass-through equation with firm market shares on the LHS. I don’t have data on firm-level market shares to test this relationship, but it is an interesting area for future research. A previous draft of this paper also investigated the relation between changes in country shares and exchange rates, with mixed results. As mentioned in the introduction, this model belongs to a class of model that has difficulty in matching both short-run and long-run responses of quantities to exchange rates.

> 17Amiti et al (2016) argue that OLS estimation of the response to competitors’ prices in structural equations like Equations 2 or 12 will be biased due to the correlation between unobserved idiosyncratic demand shocks (in the error term) and competitors’ prices via the price setting game. They provide several instruments for competitors’ prices, one of which is
<br>comparison.|
|NORMLEX|ILO|NORMLEX is a database of national labor<br>and social security laws, ratification infor-<br>mation, reporting requirements, and com-<br>ments of the ILO’s supervisory bodies.<br>Includes legislation on nondiscrimination in<br>the labor market and ratification of em-<br>ployment discrimination conventions.|<br>Covers only labor legislation and in-<br>ternational instruments and does not<br>cover issues related to institutions,<br>education, property, public goods, so-<br>cial protection, and hate crimes or<br>hate speech. Does not allow for cross-<br>country comparison.|
|General Surveys<br>(2012, 1996,<br>1986) of the<br>Committee of<br>Experts|ILO|The General Surveys are reports examin-<br>ing law and practice in a particular subject<br>area related to labor in member states that<br>have or have not ratified relevant ILO con-<br>ventions.<br>Includes legislation on nondiscrimination in<br>the labor market.|<br> <br>Covers only labor legislation and does<br>not cover issues related to institu-<br>tions, education, property, public<br>goods, social protection, and hate<br>crimes or hate speech. Does not allow<br>for cross-country comparison.|
|The OHCHR<br>antidiscrimina-<br>tion database|Office of the<br>High Com-<br>missioner for<br>Human Rights<br>(OHCHR)|<br>The database includes existing measures<br>and practices to combat address racism,<br>racial discrimination, xenophobia, and re-<br>lated intolerance at the international, re-<br>gional
**_LIBERALIZATION:_** liberalization index. It is a weighted average of three indices that respectively account for liberalization in the internal markets, liberalization in external markets and for private sector development. The index assumes values from 0 (no liberalization) to 1 (complete liberalization). Source: De Melo et al. The indices have been update for 1997 on the basis of EBRD (1997).

**_WAR:_** dummy for war torn countries.

# **INITIAL CONDITIONS:**

**_FREEDOM1993:_** initial freedom, measured by the (reversed) Freedom House index in 1993. Source: Freedom House.

**_FSU:_** dummy for countries of the Former Soviet Union.

**_INITDEVELOP:_** measure of initial overall level of development, constructed by De Melo et al. (1997). Linear combination, obtained using the method of principal components, of variables reflecting structural development factors (per capita income, urbanization, over-industrialization, etc.). Source: De Melo et al. (1997).

**_INITFREEDOM:_** initial freedom, measured by the (reversed) Freedom House index. For CEE countries initial freedom is measured in 1989, for FSU countries the reference year is 1990. Source: Freedom House.

**_INITMA CRO:_** measure of initial macroeconomic conditions, constructed by De Melo et al. (1997). Linear combination, obtained using the method of principal components, of variables reflecting the degree of initial macroeconomics distortions (trade dependence, repressed inflation and black market premium). Source: De Melo et al. (1997).

**_INITREGIME:_** dummy equal to 1 for countries where, at the start of the transition, non communist governments were in power. Source: see above.

**_LOCATION:_** dummy for countries geographically close to market economies. Source: De Melo et al. (1997).

**
were still alive had moved out to other villages, towns or cities. We use the pre-migration survey data to create a dyadic data set of all possible migrant-destination pairs, among which the migrant will have chosen one and not chosen all others. We then use census data to construct measures of the basic Todaro building blocks (expected wages and migration costs), allowing us to test the relative importance of each.

Particularly, we proxy migration costs with distance. The use of distance as a proxy for

> 1Migration costs are broadly defined here, including transport costs but also the risk associated with the move as well as setup and job search costs at the destination, and so forth. They typically increase with distance.

> 2Similarly, Lagakos et al. (2020) emphasize the importance of heterogeneous migration costs in understanding observed migration patterns and returns across destinations (and individual attributes).

2
with 175 for China, 18 for the US, and only 5 for Brazil.

Frischtak claims that these levels of expenditures in R&D and patent results are in stark contrast with the diversity, scale and potential of natural assets available in Brazil. What is needed, according to Frischtak, is a national consensus around the establishment of a clear strategy and work program that finances and creates broader science and technological and regulatory capabilities in Brazil aimed at better stimulating R&D and frontier innovation towards a pattern of growth driven by the sustainable productive use of these green assets. The detailed breakdown of green R&D expenditures data is an essential input into the debate towards achieving such a national consensus. Source: Frischtak (2011), and an average 2010 exchange rate of 1.76 Reals to the US dollar (IFS, IMF).

In contrast to the supply-push of government R&D funding, **patents** were initially devised as a decentralized demand-pull self-selection mechanism, allowing those who believe they are the most likely to succeed to risk their own resources for the ̳prize‘ of a period of exclusivity during which they can set product prices, with the quid pro quo of full disclosure of the knowledge to other researchers. In practice, significant public funding of R&D, both to public and private entities, typically accompanies the own19
more detailed questioning to better capture this work. This mechanism helps to explain the results in Figure 3 showing that the IHS4 non-proxy sample reports lower non-farm enterprise and wage employment. Recent cognitive studies conducted by the ILO across countries, for example, have found that many respondents in highly informal and agricultural contexts can have difficulty — without detailed contextual and follow-up questions — on identifying their work in an enterprise or casual wage work (Benes and Walsh, 2018b).

Table 5 aims to better understand whether this mechanism is at work for non-farm enterprise activity, where underreporting under the business-as-usual approach is more systematic across the wealth distribution and for both men and women. Specifically, Table 5 compares the enterpriserelated outcomes above that come from the labor module, with the same outcomes from a separate household enterprise module that was also part of each survey. The enterprise module in each survey creates a roster of all enterprises that household members are involved in, and for each enterprise, one person (almost always the owner/manager, discussed below) reported on roles across household members on owners, managers, as well as those in supporting roles.

We find that the IHPS individual-interview approach tends to result in lower discrepancies between the labor module and non-farm enterprise module for the overall sample (although there is a significant difference, albeit small in magnitude, for the share of women working in a supporting role). For the IHS4, on the other hand, a significantly greater number of men and women are captured by the enterprise module as owners/managers, as well as supporting workers. Within the sample of households with an enterprise, there are significant differences in both the IHPS and IHS4, where the nonfarm enterprise module captures a greater share of men and women owners/managers as compared to the labor module. The discrepancy, however, is much greater under the business-as-usual approach, and also relatively greater for women (an increase from 37 to 51 percent in women’s ownership/management moving from the labor module to the nonfarm enterprise
according to all three definitions. Informality as measured by definitions A and B is falling in domestic, personal and catering occupations. It is also falling in retailing and distribution. Both of these sectors are growing as a proportion of total employment.

# **5. Multivariate analysis of factors associated with informality**

Although we have been able to point to differences and similarities between key demographic variables and different definitions of informality, we can say nothing so far about any conditional association between these. This section presents estimates of the marginal effects of different potential influences on the likelihood of informality obtained from probit regressions. The specification of each regression includes individual demographic information, information on the geographical situation of the individual, information on the nature of the employment and information on the economic status of the remainder of the individual’s family. Included under the heading of demographic information are a quadratic function of the individual’s age and binary indicators for gender and ethnicity (reference category: white male). Indicators of illiteracy and educational attainment are also included (reference category: education to elementary level).<sup>6</sup> Two aspects of geographical situation are included. The first is a set of broad regional indicators, defined so that the heavily urbanized and industrialized region of the South East is the reference category. Given that some previous research identifies informality with urbanization, the second is a binary indicator for residence in an urban location. The urban indicator is a variable derived by the IBGE statistical agency from household address records and corresponds to residence in cities, towns and so-called isolated urban areas.

22
many developing countries.

Migration and remittances have both direct and indirect effects on the welfare of the population in the migrant sending countries.<sup>2</sup> A cross-country study of 71 developing countries found that a 10 percent increase in per capita official international remittances will lead to 3.5 percent decline in the share of people living in poverty (Adams & Page

> 2See World Bank (2005), UNDP (2009), Skeldon (2002), Kothari (2002), and De Haas (2005).

3
# **3 Data**

Our unit of analysis is an export spell defined as the length (in years) that a country _c_ exports a particular good _k_ to the United States without interruption. There can be multiple spells for a given country-product pair if a country ceases and then re-starts exporting the same product to the US destination market. All time-varying explanatory variables are measured at the beginning of a given export spell.

Table 10 in the Appendix shows summary statistics for the main variables used in our analysis. When controlling for all variables of interest, our final database consists of 252,147 export spells of 3,300 HS 6-digit products exported from 71 countries to the United States over the period 1995-2011. Online Appendix A provides the list of exporting countries in our sample.

## **3.1 Financial Vulnerability**

The standard empirical proxies capturing the different financial vulnerabilities have been developed in a series of papers that follow different industrial classifications at different levels of aggregation. Rajan and Zingales (1998) compute investment needs for 36 different sectors at the level of 3-digit and 4-digit ISIC classification, Claessens and Laeven (2003) compute asset intangibility for 20 sectors following the SIC 2-digit classification, and Raddatz (2006) computes liquidity needs for 70 sectors at the ISIC 4-digit level. Fisman and Love (2003, 2007) follow the definition of 36 ISIC sectors used by Rajan and Zingales (1998) when computing their measures of trade credit dependence and global growth opportunities.

We recompute these five measures at a significantly more disaggregated level of 192 industrial sectors (120 4-digit and 72 3-digit sectors according to the SIC classification). After merging these measures with our trade data and other control variables, the final sample comprises 174 industrial sectors (108 4-digit and 66 3-digit SIC sectors). Defining financial vulnerability across narrowly defined sectors not only allows for a more precise measurement, but it also represents a better fit with the highly disaggregated data on 11
we observe important heterogeneities due to an individual’s age at arrival in El Fasher. Comparing two IDPs of the same age, the one who was older when their household arrived is less likely to be employed, and, if both work, works fewer months on average and is more likely to work in the agriculture sector. Similarly, the gender gap in IDP employment increases with age at arrival (conditional on age today). Put differently: the more years an IDP child spends in the host community before becoming an adult (given that displacement took place), the smaller the gender gap in employment becomes between women and men IDPs.

In addition to our contribution to the cross-disciplinary literature on gendered forced displacement (as discussed above), our work is related to four literatures.

First, we contribute to the broader literature on the dynamics and impacts of violent conflict. Traditionally dominated by anthropological and macro-political perspectives, development researchers now also study the micro-level processes taking place during and after violent conflicts (Justino et al., 2013). More and better micro-level survey data from conflict-affected settings has become increasingly available (Brück et al., 2016) and has helped to better describe and understand people’s experiences, behaviors and outcomes during a conflict and beyond (Verwimp et al., 2019). Contexts and populations of situations of forced displacement have somewhat lagged behind in this regard. The data set we analyze has been part of a recent initiative to help to fill this critical gap (JIPS, 2019a; Pape and Scharma, 2019). We use this data set to produce novel and detailed insights into livelihood constraints and outcomes of individuals who flee violent conflicts, disentangling displacement experiences (such as leaving the place of origin or being exposed to violent or otherwise traumatic events) and displacement status (being an internally displaced person) and their (long-term) implications. In addition, we exploit that we can match the survey data with conflict event data to assess the role of exposure to collective violence.
organized and unorganized sectors. We winsorize this metric at its 1% and 99% values to guard against outliers. It has a mean value of 0.16, with a standard deviation of 0.69. We find even stronger patterns without winsorizing, but we prefer the more cautious approach that is often taken with Indian data.

Beyond this broad growth by state-industry in the organized sector, our next two explanatory variables are indicator variables for where in the establishment size distribution this change is most occurring. We create three categories using surveyed employment levels in

> 10 The covariance effect holds for traditional and modern sectors. The within, between, and covariance components for traditional sectors from 1989 to 2005 are -0.022, -0.057, and 0.069, respectively. These components for modern sectors are 0.052, 0.006, and 0.051.

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067)|
|Piped water|||-0.056|(0.029) *|0.086|(0.064)|
|Flush toilet|||-0.090|(0.033) ***|-0.152|(0.079) *|
|Solid roof|||0.002|(0.030)|-0.019|(0.066)|
|Mosquito net|||0.021|(0.027)|0.061|(0.060)|
|Mosquito net for kids|||0.044|(0.018) **|0.019|(0.041)|
|t=2018|0.073|(0.092)|0.255|(0.024) ***|0.151|(0.055) ***|
|constant|0.794|(0.452)*|-0.931|(0.089)***|0.246|(0.221)|
|No. of obs.|38115||||5776||
|No. of selected|5778||||||
|No. of district|||||||
|dummies|113||||||
|Wald chi|13373||||669.47||
|_ρ_|-0.366|(0.317)|||||

Note: The dependent variable is _Y_ . Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.

Since it is found that the sample selection bias is not severe, the standard probit regression can also be applied ( **Table 4** ). Not surprisingly, the results remain broadly similar
Future research could broaden this analysis to other measures of financial sector development. Here, we focus our exercise on banking sector development and specifically the extent of commercial bank branch networks. This choice reflected the theoretical and empirical support for a link between domestic and foreign banking sectors. However, other such links may exist in other parts of the financial systems and could be further explored. For example, in some regions (such as Sub-Saharan Africa), the use of mobile money and digital payments has grown rapidly in recent years (World Bank 2022; Yermack 2018). On average in EMDEs, the share of adults with a mobile money account has more than doubled from 6.5 percent of the people aged 15 years or older in 2014 to 14.8 percent only three years later, in 2017, and to 24 percent in 2021. That said, this is still well below the share of adults with an account at a financial institution (52.3 percent of the population aged 15 years or above in 2021). During our sample period of 1990-2018, data on mobile money use is only available for 2014 and 2017, too limited to yield meaningful econometric results. Future research, drawing on additional data, could explore the link between mobile money and informality further.

14
# **Employment in South Asia: A New Dataset**<sup>*****</sup>

**Robert C. M. Beyer**

## **Milagros Chocce**

**Martin Rama**

**Keywords:** employment, labor force surveys, South Asia, data harmonization

**JEL Classification:** J21, O17, C80

_____________________________________________________________________________________

* The authors thank Esha Chhabra and Zetianyu Wang for excellent research assistance and Urmila Chatterjee, Rinku Murgai, and David Newhouse, as well as participants of the _Regional Workshop on Jobless Growth in South Asia_ and of the _2nd SANEM-World Bank North America Discussion Forum,_ for comments and suggestions. This paper builds on work initiated for the Spring 2018 edition of _South Asia Economic Focus,_ entitled ‘Jobless Growth?’ (World Bank 2018a). All authors are with the office of the Chief Economist for South Asia at the World Bank. The corresponding author is Robert C. M. Beyer, at rcmbeyer@worldbank.org, 1818 H Street NW, Washington DC, USA.
of variables are presented in table A1.

The ES data are combined with weather data obtained from Matsuura and Willmott (2012a,b). The weather data set contains monthly observations of precipitation and average temperature at the 0.5-degree grid cell level. The data are transformed into average monthly temperature, and total precipitation (mm), per year, for each grid cell. Rather than analyzing the impact of rainfall averages, we look at the impact of rainfall “shocks”, i.e., unanticipated deviations from average values. To account for the non-normal distribution of rainfall, the raw rainfall data are converted to a Standardized Precipitation Index, following the methodology outlined by Neves (2015), before calculating the “shocks”; further details are provided in the next section. As illustrated in table 1, droughts (SPI<-1.5) are a more common occurrence than situations of abundant rainfall (SPI>1.5) 6
Figure 3: Share of households with zero transport expenditure <!-- Start of picture text --> Afghanistan Burkina Faso Cameroon Iraq<br>1<br>.5<br>0<br>Kyrgyz Republic Malawi Mali Mongolia<br>1<br>.5<br>0<br>Nepal Nicaragua Niger Nigeria<br>1<br>.5<br>0<br>Pakistan Sierra Leone Tanzania Uganda<br>1<br>.5<br>0<br>4 6 8 10 4 6 8 10 4 6 8 10 4 6 8 10<br>Annual expenditure per person (log scale)<br>Expenditure Household density<br>Fractions of households with null transport expenditure<br><!-- End of picture text --> Note: The graphs report the smoothed relation using the lowess function between per-capita expenditure and the share of households with zero transport expenditure using LSMS surveys.

# **3.2 Income and transport expenditures**

**Fact 2: Households spend relatively more on transport goods and services as countries get richer.** Figure 4 uses data from the Word Bank Global Consumption Database to plot the share of overall transport expenditures in total expenditures against the log of GDP per capita for most countries in the world. Shares of transport expenditures vary across countries, going from 3% in Tanzania to 22% in Honduras. The average share of transport expenditures in total household consumption is significantly correlated with GDP per capita.<sup>6</sup>

> 6

13