rafmacalaba/data-use-mentions-extended huggingface dataset:
rural India was lost to boundaries in 1957. Those who draw attention to the potential costs of fragmentation suggest that it could account for the loss of around 3-10 per cent of net returns to farming, although farm management studies based on field observations show that adaptive farming practices significantly reduce these potential costs in practice (Heston and Kumar 1983).

There are differing views on the real significance of fragmentation. Singh (1990), for example, believes it is a serious problem that warrants serious public policy attention in its own right, while others (Vaidyanathan 1994, Thangaraj 1995) point out that the process of fragmentation has been slowing considerably since the 1960s, particularly where land markets are more active and less regulated. While the number of plots per holding tends to increase with holding size, all-India data on operational holdings show that the average number of parcels per holding has declined from 5.7 in 1961-62 to 4.0 in 1982. Given the rate of population increase over this period (see table 2), this is quite a remarkable decline. However, given that total operated area has changed little, the average size of each of these parcels of land has also declined from 0.47 ha in 1961-62 to 0.41 ha in 1982. For operational holdings, there is evidence that land-lease markets enable farmers to overcome the effects of fragmentation (Ballabh and Walker 1992, Vaidyanathan 1994).

The chief cause of fragmentation by far is the subdivision of holdings on inheritance. As with other personal laws, different laws governing inheritance apply to different religious groups, but both Hindu and Muslim law provides for equal inheritance among sons as heirs. Since their reform after independence, Hindu personal laws also provide for inheritance by women, but social norms and customs tend overwhelmingly to prevail, and in practice women's legal rights in their parental land tend to pass to their brothers (Agarwal 1995). Various institutional mechanisms have tended to counteract the driving force of population growth and partible inheritance, most notably joint family
9 million ha. This is the combined acreage of irrigated and semi-mechanized rain-fed agriculture. Due to the small scale of householdmanaged traditional rain-fed agriculture, it employs the greatest share of the population. Though such farming is found throughout the country, it is predominant in the west of the country, in the regions of Darfur and much of Kordofan. Subsistence crops such as sorghum and millet are the norm, but commercial crops, such as groundnut, and increasingly, sesame, are also grown on a small scale.

## Crops and Livestock

### Crops Grown

Sorghum is the most common crop grown by crop-producing households in Sudan, followed by millet (Figure 19 **Error! Reference source not found.** ). According to the 2014/15 survey, close to half the households engaged in crop farming grow sorghum. This has changed little since 2009, when slightly more than half of all households grew sorghum (note the list of crops in 2009 is different from those in 2014/15). Approximately one-third of crop-producing households grow millet. This proportion has increased slightly since 2009. Other common crops grown are sesame, groundnut, and tea (mainly hibiscus in Kordofan). These statistics represent households that identify a crop cultivated for their own consumption. Households that work the land with laborers and identify their income as ‘wages and salaries’ do not report which crop they grow. These statistics do not represent the proportion of crops 21
of credit, and less than one-fifth has introduced a new product or service over the past 3 years.

Second, FitchConnect provides information on banks and financial institutions. We follow Micco et al. (2007) and Panizza (2022) to assess whether a bank is state-owned. Specifically, we define a bank as stateowned if at least 10% of its assets are owned by the state.<sup>2</sup> A bank is classified as "state foreign-owned" if it is state-owned and operates in a foreign country. We construct the "Bank health index" based on the following five factors: the share of liquid to total assets, the share of core deposits to assets, the share of total deposits to assets, the ROA (net income/total assets), and the share of equity to total assets. The factors considered are based on data availability and the indicators used in CAMELS (Capital adequacy, Assets, Management capability, Earnings, Liquidity, Sensitivity) rating systems to assess a bank's overall condition.

The basic characteristics of the sample are reported in Table 1. Almost 18% of the firms with access to a line of credit are banking with a state-owned bank. Banking with a foreign bank is more common and almost 47% of firms have access to a line of credit from a foreign bank. Conversely, less than 1% of the

> 1 Firms were interviewed in 2021 in Spain; in 2020 in Armenia, the Arab Republic of Egypt, and Tunisia; in 2019 in Albania, Azerbaijan, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czech Republic, Estonia, Georgia, Hungary, Italy, Jordan, Kazakhstan, Kosovo, Kyrgyz Republic, Latvia, Lebanon, Lithuania, Malta, Moldova, Mongolia, Montenegro, Morocco, North Macedonia, Poland, Portugal, Romania, the Russian Federation, Serbia, the Slovak Republic, Slovenia, Spain, Tajikistan, Türkiye, Ukraine, Uzbekistan, and the West Bank and Gaza; in 2018
now examine factors with the greatest impact on job-finding prospects of the unemployed. For this purpose, multinomial regression analysis is applied to a sample of 1,676 respondents from the LFS 2002 who reported themselves to be unemployed one year prior to the survey. The estimated model predicts the probability of each of three outcomes (employment, unemployment or nonparticipation in 2002) based on the following explanatory variables: education level (6 categories), field of education, gender, ethnicity, age, and region of residence.  Results are presented in Appendix Table 17, Figures 16 to 18, and Appendix Figures 11 and 12.

**Education matters most.** While the importance of education for job search is hardly surprising, the magnitude of the effect is impressive. As was shown in the previous section, the mean probability of finding a job in 2001-2002 was 0.31 amongst those unemployed in 2001.  For jobseekers with higher or post-secondary education this probability was by 0.20 larger than for an unemployed with basic education of the same age, gender, ethnicity and region of residence (see Figure 16).

**Problems with vocational training in Jurmala, Rezekne and Liepaja.** The worst outcomes were experienced by a small  but statistically significant group of respondents who completed technical secondary education followed by one year of studies leading to a certificate of general secondary education (ISCED<sup>7</sup> 4A). This group represented 4.4 percent of those unemployed in the 2001 sample. A more detailed scrutiny shows, however, that it is not general education which must be blamed; rather it is a mismatch between skills provided by technical secondary (ISCED 3C) education in three of the main cities – Jurmala, Liepaja and Rezekne-and market demand. Indeed, a substantial proportion (22%) of those with ISCED 4A in the 2001 unemployed sample live in these cities. None of the ISCED 4A

> 7  International Standard Classification of Education

22
other OECD countries are insured. The coverage limit was initially HUF 1 million (approximately $3700), which was raised to HUF 3,222,222 on January 1<sup>st</sup> , 2003 and to HUF 6,555,555 on May 1<sup>st</sup> , 2004. The maximum coverage is calculated per depositor per institution.

_Sources_ : Own survey of deposit insurers, Garcia (1999), IADI Survey: Hungary (2003), Kyei (1995), and Ministry of Finance of Hungary (1993).

**Iceland.** ( _Deposit Insurance Fund for Savings Banks; Deposit Insurance Fund for Commercial Banks, Acts 86, 87/1985; Depositors’ and Investors’ Guarantee Fund 98/1999_ ) The deposit insurance system of Iceland was first established in 1985. There were separate schemes for commercial banks and savings banks which were monitored by the supervisory agency. The fund for the banks had a member of the government on its board. Even though the coverage in principle was full, the system was considered to have a co-insurance mechanism due to the fact that above the minimum coverage limit of ECU 20,000, the actual compensation of depositors were determined according to the resources of the fund, which received no public support. Act 98/1999 established the new scheme and the Fund in accordance with the EU directives since Iceland is a member of the European Economic Area (EEA). The new fund took over the assets of the previous two funds, and it is both privately established and administered. The Central Bank provides such services as accounting and bookkeeping as well as securing valuable documents. The membership to the Fund is mandatory. The coverage limit was ISK 1,700,000 which is tied to the EUR exchange rate as of January 5<sup>th</sup> , 1999 (approximately EUR 21,000) and hence, is worth ISK 2,091,000 as of 2003. Coverage is
s~~ killed increases. We see a parallel result for the high ~~-s~~ killed workers. The younger high ~~-s~~ killed workers now lose even more while the losses of the older high ~~-s~~ killed are pared down. Finally, the new scenario leads to little change in the wage effects arising from immigration or emigration, in comparison to the main scenario.

The third scenario makes the immigrants almost perfectly substitutable with natives by setting o,, = 1000. This magnifies the negative wage effects of immigration on the high ~~-~~ skilled workers, but the wage gains to the low ~~-s~~ killed remain fairly stable. The elasticity parameter we have changed has little importance in the slope of the wage responses to changes in the quantity of the native labor force, and the resulting wage effects from ageing and emigration are negligible.

# 5.2. Involuntary unemployment with redistributive benefits

Our next extension changes certain assumptions we made about labor markets and unem ~~-~~ ployment as discussed in section 2.5. Recall that we first introduce involuntary unemploy ~~-~~ ment into the model where we assume workers face exogenous job separation and matching rates. As a result workers spend a certain fraction of their time in unemployment as they search for a new match. The unemployment rates are assumed to be group ~~-s~~ pecific and we obtain these data from DIOC and the OECD shor ~~t-~~ term labour market statistics. Next, we introduce unemployment benefits that are equal to a fraction 6 < 1 of the average wage rate of the economy. We assume all workers receive the same benefits which makes the insurance program redistributive. Insurance leads to the modification of the labor supply function where the average wage incorporates the insurance payment weighted by 25
overweight gap, (vi) overweight gap squared, (vii) underweight gap, and (viii) underweight gap squared.

This study makes two important contributions to the research literature. First, while the stunting gap measure has been developed by Shekar et al. (2015), the other seven measures of malnutrition are developed for the first time in this study. Hence, in addition to the conventional headcount indicators, these proposed indicators can provide useful information about a country’s malnutrition status, especially on the depth and severity of malnutrition, which can consequently improve evidence-based decision-making. Second, we employ over 20 years of malnutrition data from 94 developing countries to calculate the new measures. Employing the new measures, we are able to identify countries that have low levels of headcount for a malnutrition measure, but comparatively high severity of malnutrition according to the gap and gap-squared measures, and vice versa. This allows us to identify cases where headcount measures may be providing an incomplete description of a certain country’s malnutrition status.

From a policy perspective, it is important to distinguish between malnutrition measures based on the headcount and measures of depth and severity in malnutrition. As countries are in particular committed to reach first those who are furthest behind in order to realize the SDGs 2030 agenda, high-quality data are needed for monitoring the progress of these individuals and providing evidence for effective policy making.

We proceed as follows. Section 2 discusses the methodology for the new measures. Section 3 describes the data used in the empirical application. Section 4 presents the results. Section 5 concludes.

**2. Methodology**

4
@@<br>()<br>1,000 10,000 100,000<br>GDP per capita, PPP (current international $), logged<br>region9 . @@ EastEuropeAsia & CentralAsia& Pacific @@ LatinMiddleAmerica East & North& CaribbeanAfrica @@ SouthNorthAmerica Asia @ Sub-Saharan Africa<br>Source: GDP per capita from the World Development Indicatrs (NY.GDP.PCAP.PP.CD). The availability of<br>Gender SDG Indicators based on authors calculations based on the UN Global SDG Database.<br><!-- End of picture text -->
_Rural-urban gradient in Latin America and the Caribbean_ _page_ 1

# **Motivation and overview**

How many rural people live in Latin American and the Caribbean (LAC)? The question is simple to pose but not well-defined. “Rurality” has many aspects and connotations. For policy purposes we need to specify precisely which aspects of rurality interest us, and define criteria for identifying these aspects. Moreover, we should not expect that any of this criteria exhibit marked break points between rural and urban areas or people. However defined, rurality is a gradient, not a discrete condition.

Here we apply two criteria for defining the rurality: population density, and remoteness from large cities. We argue that these criteria constitute important gradients along which economic behavior and appropriate development interventions might vary substantially. Where population densities are low, markets of all kinds are thin, and unit costs of delivering most social services and many types of infrastructure are high. Where large urban areas are distant, farmgate (or factory-gate) prices of outputs will be low and prices of inputs will be high, and it will be difficult to recruit skilled personnel to public service or private enterprises. Thus remoteness and low population density together define a set of rural areas that face special challenges in development.

Remoteness and population density alone, however, cannot begin to capture the heterogeneity that exists within rural areas. Many aspects of physical geography shape opportunities and constraints for rural development, and environment-development linkages. Here we focus on two summary measures of physical geography: agroclimatic suitability for cropping, and forest cover.

With rurality criteria and descriptors defined, it is possible to tabulate rural populations using one of the existing global gridded population coverages. The two main candidate datasets – Landscan, and the GPW – each have advantages and disadvantages. We chose to use the GPW 3, in part out of anticipation of a forthcoming GPW refinement which should give an improved measure of population distributions.

Using GPW 3, we tabulate the cumulative distribution of population and land area by population
, Table A6). In addition, we also account for the survey’s stratified sampling design and nonresponse rates by using the longitudinal weights provided by BNLA. Again, the results of weighted regression reaffirm our previous findings, as shown in Table A7 (Appendix A).<sup>20</sup> Fifth, while our baseline specification includes a range of covariates that are commonly employed in studies relating to labor market outcomes of refugees (e.g., Dagnelie et al., 2019; LoPalo, 2019), one may argue that language skills might be endogenous. For example, refugees who are eager to better engage in the labor market and have larger earnings might spend more effort on refining their language skills. We show that our results are robust to excluding these potential endogenous variables (Appendix A, Table A8).

Sixth, while our main interest is refugee mental illness, we also examine other (overall) health and physical health issues. In the BNLA survey, overall health is represented by an indicator denoting how refugees rated their health during the past four weeks before the interview. We recode the indicator so that it ranges from one (“excellent”) to six (“very poor”). Similarly, physical health is captured by a categorical variable with higher values representing worse physical health. We find a negative impact of overall health issue on labor outcomes, while there is little evidence of the impact of physical health on the outcome variables (Appendix A, Table A9). This lends support to our main finding that mental health issues are the main barrier affecting refugee labor outcomes.

Seventh, we construct alternative disaggregate measures of mental illness instead of the sum of the K6 items in the main analysis. Figure 1 examines each item separately to determine which dimension drives the effects of mental health. Using our preferred model specifications, we find that most of these dimensions of mental illness have a negative impact on employment status (including “nervous”, “hopeless”,
evidence puts rising urbanization and an expanding consumer base as some of the major determining factors for FDI to Africa. As a result, the large economies of the continent: South Africa, Nigeria, Kenya, Ghana, Egypt, Morocco and recently, Ethiopia and Mozambique have attracted the majority of the FDI, with South Africa leading the group.

UNCTAD data (covering both Greenfield and non-Greenfield FDI) shows that a large proportion of FDI is from Western Europe (which makes up the bulk of G7 countries) and at a 14
the labor force all throughout 2012-2018. However, in 2012, 80 percent were employed in agriculture, 4 percent in industry, and 16 percent in services.<sup>9</sup> Informal employment is quite high with the most recent available estimate in 2005 of 89.6 percent of total employment (ILO); estimates of informal output are 45.9 percent of GDP in 2012 (Medina and Schneider, 2018). However, despite the prevalence of informality, a significant number of firms are registered with the government authorities (about 14,000 firms), and thus formal according to the a legalistic definition of informality.<sup>10</sup> In 2012, only 23 percent of firms in the manufacturing and services formal sectors had female top managers and only 22 percent of the firms had a majority female ownership.

> 7 Data are not available for previous years.

> 8 The figures have hardly changed since 2012. Data are from the World Development Indicators, World Bank. 9 In 2018 these figures were:  agriculture (87 percent), industry (9 percent), services (4 percent). Source: World Development Indicators, World Bank.

> 10 The data about formal firms operating in the DRC are from a block enumeration conducted in the country prior to the 2013 Enterprise Survey. Block enumeration was needed to build the sampling frame for the data collection in absence of comprehensive and updated lists of formal firms operating in the DRC (e.g. census data or data from firm’s registrar office).

5
pattern holds for aid in all sectors (health, transport, etc.). If differences are detected across sectors, this might give some hints as to the underlying mechanisms. Irrespective of the sector considered, coefficients on the mission dummies are always positive, statistically significant at conventional levels, and large relative to the means of the dependent variables (see estimates presented in the appendix); the magnitudes are highly comparable across sectors.

A further test checks if the use of binary treatment and outcome variables is important for the results. When using the number of missions and the log number of missions plus 1 as treatment variables, results are very similar to the baseline. Another investigation is of whether the relationship between aid and missions also holds at the intensive margin; this is done by replacing the dependent variable with the number of World Bank aid projects. Estimates from OLS, Poisson, and negative binomial regressions confirm the baseline results. Finally, the correlation between aid and missions also survives the inclusion of subnational fixed effects at the ADM1 (states, governorates) or ADM2 (districts, municipalities, communes) level.

Taking stock, this section has documented a robust and sizable spatial relationship between World Bank aid and the historical presence of mission stations. Although it is hard to claim causality with observational data, the estimated relationship survives a vast array of robustness tests, including the most recent procedure suggested in the econometric literature for assessing the bias from unobservable factors (Oster, 2019).

# **3 Implications for Aid Effectiveness**

Having established that mission areas attract a disproportionate share of World Bank aid, it is natural to ask whether this has implications for aid effectiveness. Answering this question is interesting both from a policy perspective and in terms of gaining a better understanding of the mechanism at play. For example, it is possible that missionary interventions paved the ground for aid interventions later on, by providing suitable conditions for effective project implementation. These conditions might include cooperative behavior, trust in foreigners, and specific skills
(Anderson and Reeb, 2003), and in the case of women inheriting family firms but without effective control, it does not make sense to view the firm as a female entrepreneur firm. Furthermore, the previous literature using the firm sample of several African countries in the WBES data has documented carefully that using the definition of women managers tends to make more intuitive sense than that of women’s participation in firm ownership (Aterido and Hallward-Driemeier, 2011). We thus try to be conservative in defining female-entrepreneur firms and rely on two variables in the WBES data set to capture aspects of female entrepreneurship: women’s participation in ownership (“whether women have some ownership of the firm”), and whether the top manager is a woman. We define female entrepreneurship as the firm’s top manager is a woman _and_ women have ownership share in the firm. We impose the requirement of women being top managers because nonprincipal ownership by itself does not imply female entrepreneurship.

The extent of female entrepreneurship varies greatly in the world. Before presenting the statistics, we must acknowledge that the country-specific samples are not large, some industries are not covered, as mentioned earlier, and as a consequence, we must view the statistics about the extent of female entrepreneurship within a country with caution. This being said, within the sample, on average 10.9 percent of firms are headed by female entrepreneurs, 14% of firms are female-top-managed, and 31% of firms have female ownership participation (Table 3). The sample share of female-entrepreneur firms differs greatly by country (see Table 1), ranging from below 1 percent in Morocco, the Arab Republic of Egypt, Jordan, and Iraq; to 1 to 5 percent in Israel, Pakistan, India, Mauritania, and Bangladesh; to 5 to 10 percent in Sweden, Argentina, Turkey, Chile, Nepal, Ethiopia, Mexico, Tanzania, several large central Asian countries (such as Uzbekistan and Tajikstan), Senegal, Nigeria, Sri Lanka, and Peru