rafmacalaba/data-use-mentions-extended huggingface dataset:
For India, there are surveys from the years 2002, 2003, 2005 and 2010. Only a handful of constraints were included in the 2010 survey. However, for those that were included, a sharp increase in problems is reported. In particular, electricity emerged clearly as the most costly constraint for firms in 2010. It is interesting to note that the likelihood of power outages remained fairly constant between 2005 and 2010. This is consistent with higher reported costs from electricity as an obstacle to production if firms were improving their performance / growing faster and hence suffering larger losses from an unchanged level of unreliability of the electricity grid. Labor regulation is also reported to be markedly more problematic in 2010 than in 2005.

# _Sri Lanka_

The top five ranked constraints in Sri Lanka are electricity, government policy uncertainty, macroeconomic instability, anti-competitive practices, and labor regulations. Firms were not asked for their evaluation of the extent to which policy uncertainty and macro instability affected their business in Afghanistan, Bhutan and Nepal. Among the countries where these aspects were included, Sri Lanka is the only one where they are highly ranked as problems. Firms in Sri Lanka rated business licensing, customs, and access to land as least problematic. (A question about the obstacles posed by the courts was not included in the surveys in Sri Lanka.) Sri Lanka was surveyed in 2004 and 2010 but few aspects of the business environment were included in 2010; there were no marked changes.

# _Maldives_

Given its physical environment, it is not surprising that the top-ranked constraint in the Maldives is access to land. This is followed by corruption, the courts, and access to skilled labor. The least problematic aspects of the business environment in the Maldives are telecoms, macro instability, and business licensing. The Maldives was only surveyed once, and the survey size was small (about 140 firms were used in the calculation of most of the conditional means), so these results should also be treated cautiously, despite their plausibility.

30
6.0|16.8|27.2|1.6|15.2|17.8|9.4|
|China|8.1|10.2|7.7|12.3|24.8|9.8|2.6|30.9|-6.1|
|India|2.1|4.6|8.8|14.0|36.9|5.4|8.6|23.0|13.9|
|Korea, Rep.|2.9|6.0|10.1|12.7|23.2|4.8|7.9|13.0|10.3|
|Malaysia|15.3|24.0|24.0|11.2|13.6|6.3|4.9|13.6|0.0|
|Mexico|3.3|11.1|20.1|22.3|21.3|8.3|14.0|9.8|11.5|
|South Africa|2.2|4.6|10.4|8.8|30.7|0.8|7.9|14.2|16.5|
|Turkey|3.0|7.1|13.1|11.5|36.7|2.1|9.4|23.6|13.1|
|Total: above<br>developingco.|4.6|8.6|9.0|13.0|24.7|6.2|6.9|23.7|1.0|

Note: Data is based on two-year averages of 1991-92, 2001-02, and 2007-08.

Sources: Based on UN COMTRADE Statistics (trade data) and UNIDO database (production
(1960-2000), we project real investment back to 1930. Next, ignoring the capital stock that may have existed in that year, we accumulate the projected real investment forward into a capital stock series. The resulting level of the latter in 1960 is then taken as the initial capital stock, and the in-sample capital stock series is constructed accumulating observed investment.<sup>10</sup> Our preferred measure is the capital stock series obtained from the PWT data. Assuming, as we do for the back-casting, that the pre-sample growth rate of real investment was equal to the average of the 1960-2000 period could be misleading, in view of the severe global shocks of the 1930s and 1940s (e.g. the Great Depression and World War II), which likely had a nonnegligible reflection on the rates of growth capital stocks around the world. Nevertheless, we also report estimates using the capital stock series constructed through back-casting, In turn, the stock of human capital is proxied by the average years of secondary schooling of the population, taken from Barro and Lee (2001). Finally, the labor input is proxied by the total labor force as reported by the World Bank‘s World Development Indicators.

Measuring physical infrastructure poses a challenge. Typically, the empirical literature on the output effects of infrastructure has focused on a single infrastructure sector. Some papers do this by design,<sup>11</sup> while others take a broad view of infrastructure but still employ for their empirical analysis an indicator from a single infrastructure sector.<sup>12</sup> In reality, ̳physical infrastructure‘ is a multi-dimensional concept that refers to the combined availability of several individual ingredients – e.g., telecommunications, transport and energy. In general, none of these individual ingredients is likely to provide by itself an adequate measure of the overall availability of infrastructure. For instance, a country may have a very good telecommunications network and a very poor road system or a highly unreliable power
While the dataset we received includes the universe of firms that ever benefitted from the 6 points subsidy, for this analysis, we focus on firms that have started benefitting from the subsidy only after 2016, when firms with fewer than 10 employees also became eligible for the subsidy. More precisely, we exclude from our dataset all firms that were eligible in 2013 when the subsidy first became available, but only for larger firms. The final dataset used in this analysis is a monthly panel of firms from January 2004 to September 2018, including firm demographics, total employment, and subsidy status, as detailed below.

# 4.2. Descriptive statistics

The final dataset includes the following variables:

- **Employment:** This variable includes the total number of employees under the same firm during that month.

- **Firm Age:** We calculate firm age as the difference between the firm closure date and establishment date. If the firm is not closed, we calculate the age as the duration of months between September 2018 and the establishment date.

- **Treatment:** This variable is defined as firms that ever benefitted from the 6 additional points subsidy, irrespective of the duration of benefitting.

- **After2016:** This is a dummy variable taking on the value 1 from March 2016 onward when the minimum employee condition for benefitting from the subsidy is removed.

- **Region4, Region5, and Region6:** Dummy variables taking on the value 1 if the firm operates in Region 4, 5, or 6, respectively.

- **Nuts2:** This variable defines which of the 26 NUTS-2 regions that the firm operates in.

- **Province:** This variable defines which of the 81 provinces that the firm operates in.

- **County:** This variable defines the county within each province that the firm operates in.

- **Sector:** This variable is defined in 4-digit NACE Rev.2 and later aggregated under
socio-emotional and mental health development, food security, time use, engagement in risky behaviors, and social integration. These data allow us to examine the short to medium-term human development gaps faced by forcibly displaced minors and hosts in a comprehensive way.<sup>5</sup> It also enables the characterization of the variables that help reduce gaps between migrants and their hosts. Previous work, has mostly focused on the employment and earnings gaps between adult migrants and hosts (Borjas 1985, Abramitzky, Boustan and Eriksson 2020, Abramitzky et al. 2021) and the persistence of the economic status from first to second generations of immigrants in developed settings (Abramitzky, Boustan and Eriksson 2014). Focusing on children allows to diagnose gaps in human development that can have long-term consequences for immigrants’ economic success, including career choices and wages (Bleakley and Chin 2004, Bleakley and Chin 2010, Carlana, La Ferrara and Pinotti 2022). It can also inform the design of targeted interventions aimed at improving the life trajectories of both migrant and host populations, ultimately contributing to more informed and effective policy decisions.

Our research complements and extends the findings of Demirci, Foster and Kirdar (2022), who investigated health and nutrition disparities between native children and Syrian refugee children aged 0 to 5 years in T ̈urkiye, using data from the Demographic and Health Survey. The authors document find no significant differences in infant or child mortality rates between refugee children born in T ̈urkiye and their native counterparts, it did reveal that refugee infants have lower birth weights and age-adjusted weights and heights compared to native infants. Our work broadens the scope of analysis beyond anthropometric indicators to encompass a holistic assessment of child development. By incorporating measures of physical, cognitive, socio-emotional, and mental health development, along with factors such as food security, time use, risky behaviors, and social integration, we offer a more comprehensive understanding of the developmental challenges faced by displaced minors. Additionally, our study includes a wider age range, 5Chiovelli et
finance, _Portfolios of the Poor_ (Collins, Morduch, Rutherford and Ruthven 2009). Collins et al.’s goal was to understand “money management” of the poor; how people earning less than $2 a day in Bangladesh, South Africa and India managed their finances; juggling consumption, investments, credit payments, and emergencies. Instead of doing a conventional survey asking questions about credit, savings and assets, they conducted open-ended, narrative, interviews of 250 households in these countries. Each household was visited at least twice a month for a full year and the research team then reconstructed balance sheets and cash-flow statements from these interviews. This unusual technique revealed several patterns that were previously not understood with conventional survey methods, and they estimated that one-shot surveys were missing about half the financial activities of a household. They found that households had to cope with multiple, overlapping sources of uncertainty and risk, and that this required active financial management. A key to being able to cope with financial ups and downs was having the ability to be able to access relatively large sums of money to deal with emergencies and life events. However, respondents faced a “triple-whammy” – low incomes, irregular cash flow, and poor access to financial instruments that were unsuitable to deal with these conditions.

The key finding was that the “portfolios of poor households,” unlike those of the rich, were “managed to ensure that money can be obtained in the desired amounts at the desired times.” (page 61). The book argues this requires an entirely different set of financial instruments than those that are conventionally available in banks and micro-credit organizations. Jonathan Morduch, the economist in the Portfolios team, then teamed with a finance professional, Rachel Schneider, to use the same “financial diaries” method to examine the finances of poor Americans, also finding that they had precarious lives that were not captured in existing data (Morduch and Schneider, 2017). Both books draw on the diary technique to construct “hard
|US|JMA Management Center (Japan), Mt. Eliza Executive Education (Australia),<br>and others|
|6|IMD|Switzerland|Massachusetts Institute of Technology (US), Irish Management Institute<br>(Ireland), Tsinghua University School of Economics & Management (China),<br>and others|
|7|Babson ExecutiveEducation|US|Tecnológico deMonterrey (Mexico),HECParis (France) and others|
|8|Cranfield School of Management|UK|University of Virginia (US), Melbourne Business School (Australia),<br>Universitat St.Gallen(Switzerland), and others|
|9|University of North Carolina: Kenan-<br>Flagler|US|None Listed|
|10|Stanford Graduate School of<br>Business|US|National University of Singapore (Singapore)|

_Source_ : Financial Times Executive Education Rankings, 2013 Much like in other areas of higher education, executive education is slowly moving online. Nevertheless, for now, most programs maintain an on-site delivery approach. According to the 32
aggregate employment and wage statistics and/or small-sample surveys which might not be representative of all realities of a country, making the use of state-of-the-art methodological techniques impossible. For this reason, for developing countries, there is a greater share of evidence exploiting cross-country variation (see Belman and Wolfson, 2016, for a more in-depth methodological review of minimum wage studies in developing countries). Across the world, Sub-Saharan Africa is the region with the least evidence of the economic effects of minimum wage. Bhorat et al. (2017) argues in favor of the release of country-level earnings and employment data at regular intervals for a more substantive country-focused minimum wage research agenda.

# **4.2 Minimum Wage**

Most countries in the world have a minimum wage: statutory or negotiated minimum wages exist in 90% of the 187 ILO member countries.<sup>8</sup> But there are important differences across countries when it comes to setting it. Minimum wages can be set at the national and/ or the sub-national level (i.e., region, city or firm level); there can be different minimum wages for different workers (for example, teenagers and trainees are subject to lower minimum wage levels in some countries); and it is common that some sectors are excluded _de jure_ (as in the case of domestic workers, self-employed and unpaid family members) or _de facto_ (like informal workers).

While in high-income countries having several minimum wages could help avoid adverse economic effects (decreased employment, price rises, etc.), especially when there is heterogeneity across regions and across workers in skills and experience (Ahlfeldt et al., 2022)<sup>9</sup> ; in low-income countries more complex minimum wage systems, with differences at the subnational level, have been associated with lower levels of compliance than national regulations (Rani et al., 2013). Low compliance is an issue, particularly in developing countries where the size of the informal sector is
an estimate for the required calorie intake of a demographic population that resembles the bottom 40 percent in Brazil would be 2,268 kcal.<sup>16</sup> We round the caloric needs benchmark down to 2,250 kcal per day and re-run our CBN specifications. Table 9 shows the results. The table shows that, while the food poverty line increases proportionally with the required calorie intake (+7.1%), as expected the lower poverty line changes from R$455 to R$487.

> 16 An alternative method could also be to directly use the information from the POF 2017/18 and the consumption patterns of the consumption per capita distribution to estimate the actual kcal consumed and apply those to estimate the food poverty line. However, as has been documented in many other studies (for instance World Bank, 2019; Myanmar Ministry of Planning and Finance and WBG, 2017), the relatively poor households do not typically consume enough to cover their caloric needs. In POF 2017/18, a simple extrapolation of the average calories consumed in the bottom 40 percent is about 1,190. Thus, the definition of a food poverty line is normative and uses a benchmark instead of actual caloric consumption.

17
attributes, at least in part, because they have a higher share of large firms. However, when repeating the exercise to classify industries as “low”, “medium”, or “high” based only on the sample of large firms, the data continue to be normally distributed, i.e., around 50 percent of all observations fall within 0.67 standard deviations above or below the mean (Tables 3, 4, 5, and 6). Therefore, industries are not closely clustered together in terms of the z-values such that it remains possible to establish two unique threshold points to divide industries into three categories. And the resulting categorization of industries across the attributes under consideration remains robust to this change.

# **4. Does the Growth of Services Depend on a Manufacturing Base?**

The analysis based on firm-level data from six developing countries in the previous subsection highlighted information technology and transportation and communications as two service subsectors that are characterized as “high” across a range of characteristics indicative of learningby-doing, international trade, and innovation. Further, wholesale and retail trade was classified as “medium” with respect to the same characteristics, while also not being skill and/or capitalintensive. That the services sector is increasingly characterized by productivity-enhancing characteristics notwithstanding, there is question of whether it can develop in the absence of a manufacturing base. This concern derives from the conventional pattern of structural change from agriculture to industry, and later from industry to services (Kuznets, 1971).

Unlike some service industries – such as health, education, tourism, and community services – that are largely stand-alone, transportation and communications, wholesale and retail trade, and professional, scientific, and technical services serve consumers directly but are also linked to manufacturing activity. To the extent that final demand contributes substantially to the growth of these services subsectors, opportunities can be created independent of a country’s manufacturing base. Such opportunities might be reinforced if intermediate demand for a given services subsector derives largely from sectors
|**Column**<br>**Variable Name**|**Description**|
|---|---|
|**In AD-PER-Products :**||
|**D.**<br>**NOTES**|Comments|

## **In AD-PER-Foreign-Firms :**

**D. F_AD_MARGIN_FIRM** Final margin of dumping for a specific firm **E. NOTES** Comments

The primary concern with the Peruvian data is that it is incomplete. The INDECOPI site does have the majority of the case files as several early investigations seem to be missing, and the documentation ends in 2002.

# **3.12 Turkey (TUR)**

The Turkish antidumping process is administered by two separate bodies, the “Board of Evaluation of Unfair Competition in Importation” (the Board) and the “Department of Dumping and Subsidy Investigation” (the Department). According to the Turkish government’s website, “The Board is an independent body consisting of 8 members, representing 7 different public institutions and non-profit organisations. The Board is empowered to take decisions for the initiation of an investigation, acceptance of undertakings, termination of an investigation or imposition of anti-dumping duties. The Department is subordinated to the Prime Ministry Undersecretariat for Foreign Trade, Directorate General for Imports. The Department is entitled to make preliminary examination upon complaint, to present proposals to the Board on whether to initiate an investigation or to take measures and to carry out such investigations.” (DTM, 2005) Despite the existence of two bodies the investigations tend to follow a single track procedure as announcements on dumping and injury tend to be made simultaneously. Announcements are published in the Turkish government’s _Official Gazette_ (Resmî Gazete) publications which is available online, but only in Turkish.

31
between 2002 and 2018 using census data and following an Oaxaca-Blinder decomposition approach described in previous section. Table 1 reports the results of the Oaxaca-Blinder decomposition between 2002 and 2018 when FLFP increased 5.69 percentage points. Column (1) reports the FLFP in each year and their difference, column (2) shows the explained component of the decomposition, and column (3) presents the unexplained component. We group the variables in individual characteristics, household characteristics, variables capturing the economic situation of the household, local labor market factors, and geography variables.<sup>11</sup>

> 11 Individual characteristics include age and age squared, dummies of educational level and an indicator variable of belonging to an indigenous group. Household characteristics include a dummy of being married, number of children

11
,t_ ∗ _Electricityi,c,t_ captures the interaction of the road and electricity infrastructure, and _Paved Roadi,c,t_ ∗ _Interneti,c,t_ the interaction of the road and internet infrastructures. We add interaction effects between the dummies to better understand the complementarities between infrastructures. We do not include an interaction effect for electricity and internet as access to internet is assumed to rely on electricity access. _Controlsi,c,t_ represents the additional location-specific controls, which include initial district population, access to a main city, land size, distance to the coast, distance to the border,

> 11GHSL: Population count from the Global Human Settlement Layer. Based on population data from Gridded Population of the World v4.10 polygons, distributed across cells using the Global Human Settlement Layer global layer. Source data provided in 9 arc-second (250m) grid cells.

> 12Distance to the coast (on land only) is measured in meters. It is derived using World Vector Shorelines (Wessel and Smith, 1996).

> 13Distance to country borders is measured in meters. It is derived using the database of Global Administrative Areas (GADM) 2.8 ADM0 (Country) boundaries.

> 14Data incorporate data from Open Street Map (OSM) and the Google roads database. See Weiss et al. (2018)

> 15Yearly daytime land surface temperature are from Wan and Hook (2015).

> 16Global elevation (in meters) are from Shuttle Radar Topography Mission (SRTM) dataset (v4.1) at 500-meter resolution. See Jarvis et al. (2008).

9
standardized geo-coordinates, which were collected in the LSMS-ISA survey data.<sup>8</sup> Farm location was used as a centroid to construct several variables covering the area within 20km. The average percent tree cover within a 20km radius of each household was derived using tree cover from MODIS Vegetation Continuous Fields (MOD44B) data (DiMiceli et al., 2011). An indicator of population density based on the number of people per km<sup>2</sup> within 20km of each household was created using data from the Global Rural-Urban Mapping Population Project (Balk et al., 2006; CIESIN et al., 2011). To construct a variable on the average percentage of fertile soil within 20 km of each household, we combined information from the FAO/UNESCO Digital Soil Map of the World (FAO/IIASA/ISRIC/ISSCAS/JRC, 2012) and Nunn and Puga (2010) estimates of percentage of land surface area with fertile soil.<sup>9</sup> Finally, we used two control variables— household specific measures of annual mean temperature (C) and average annual precipitation— that were created using a standard methodology by the World Bank LSMS-ISA team and made available for all LSMS-ISA surveys (see World Bank, 2015 for details).

Finally, all models included a set of country fixed effects to account for country-level unobservables (∑���� ������. They were also rerun with district fixed effects, purging the estimated coefficients from potential unobserved district level variables (such as relative price differences across crops or market access). Table 1 summarizes the main descriptive statistics for all covariates. 3 Prevalence, Economic Contribution, and Determinants of Trees on Farms

- 3.1 One‐Third of Smallholder Farmers Cultivated Trees

With on average 33% of all rural households reporting having at least one tree on their land, trees are clearly not marginal on the smallholder farms across the African countries studied (Table 2). Prevalence
|FEPBA score in Language|70<br>488.16|18.05|487.03|18.17<br>487.86|20.02|489.66|<br>16.49|
|---|---|---|---|---|---|---|---|

Source: Author's analysis based on survey experimental data generated for this study. Note: N means number of observations in the full sample, and SD means standard deviation.

Note: N means number of observation in the full sample and Sd means standard deviation.

41