rafmacalaba/data-use-mentions-extended huggingface dataset:
# **V. Conclusion**

This study established a negative relationship between family management and the quality of management practices, leveraging a unique dataset using a special module spanning 41 developing and advanced economies. The results confirm what has been found in the literature, although the sample in this study is of a different nature, including many firms in regions that have not been covered elsewhere. The study extends the literature by examining how external factors could attenuate or exacerbate the negative relationship between management practices. Corruption, competition, and political connections accentuate, while stringent regulations and innovation ameliorate the relationship between family management and the quality of management practices. Furthermore, when all management is from the same family, these firms perform poorly on the management practices dimensions of the number of performance indicators monitored, knowledge of production or service provision targets, and not surprisingly, the basis of promotion. Finally, a mix of family and non-family managers may not be as detrimental to management practices as having all management be family members.

What is also established is the lack of statistical significance in certain relationships. Family ownership, firm size, exporter status, foreign ownership, manufacturing sector, and female managers do not play any significant role (neither exacerbate nor attenuate) in the relationship between family management and management practices. Furthermore, for larger firms, family managers do not exhibit any statistically significant difference compared with non-family managers with regards to how they allocate (or appear to allocate) their time to interact with other decision makers, suppliers, and employees as measured by frequency of meetings. This information is leveraged from a special set of questions on time use for large firms in the sample.

While the study makes significant strides in the literature, it is also important to note the limitations. Endogeneity concerns are difficult to alleviate given the cross-sectional nature of the data. Furthermore, the survey data do not contain information on whether the family owners are the founders, or descendants of the founders, which has been found to be important in the literature
, the share of construction is about 10% in the South and 13% in the North. On the other hand, within European regions we find substantial differences between undeclared employees and informal self-employed. In particular, the former are much more concentrated in education and health-related services, as well as in industry, whilst the latter – in agriculture, and (in Eastern and Southern Europe) also in trade, auto repair, and hospitality sector. The differences in findings between ESS and _Eurobarometer_ survey is likely to be driven by various factors. ESS does not cover secondary jobs; moreover, employed respondents are not asked about their past activities, while the _Eurobarometer_ questions refer to the last 12 months. The seasonal factor might play a role, too: most of the ESS field work has been conducted during autumn and winter months, while

31
where women travel. Unreliable and infrequent public transport systems impose a disproportionately higher burden on women than on men, affecting their access to economic opportunities and basic services (Borker, 2022). This paper examines the effect of several aspects of the public transport network on women’s labor market outcomes in three metropolitan areas: Amman in Jordan, Beirut in Lebanon and Cairo in the Arab Republic of Egypt. In particular, we investigate how the accessibility of jobs throughout each city, the availability of public transportation close to residential locations, and the safety around public transit stops affect the labor force participation of women and their likelihood of employment.<sup>6</sup> By doing so, we shed light on whether improving public transport systems is a necessary or a sufficient condition for enhancing women’s economic empowerment.

To examine these relationships, we rely on three types of data collected in 2022. First, we have mapped the transit network of each city, including timetables and frequencies to be able to construct the measures of accessibility and availability. Second, we have conducted built environment audits to assess the safety around public transportation stops. Finally, we conducted household surveys in these metropolitan areas, in which 5,912 working-age women and men were interviewed and information on their socio-economic characteristics, mobility choices and labor market outcomes were collected.<sup>7</sup> Most of the literature that assesses the impact of improving the transport system on economic outcomes in developing country contexts, relies on one type of dataset. In contrast, we triangulate three different types of datasets to assess how public transport systems influence women’s economic choices.

The transit network and built environment audits are used to construct indices for three important facets of the public transport system—accessibility, availability, and safety. Accessibility refers to the spatial accessibility of job opportunities throughout the city, it is measured by estimating the percentage of jobs reachable under 60 minutes using public transport (and walking). Availability refers to the ease with
Policy Research Working Paper 10247

# **Abstract**

While agriculture remains the mainstay for a large share of the population in Sudan, and rural poverty has seen a dramatic decrease (between 2009 and 2014/15), poverty remains relatively high among those engaged in agriculture. Households engaged in agriculture—either crop farming or raising livestock—see among the highest rates of poverty among households classified by their main livelihoods in Sudan. As these households form a major bulk of the total population, understanding why these households remain poor and identifying strategies for lifting them out of poverty is a key concern for researchers and policy makers. This concern occupies the primary motivation for this study. Using data from the 2009 National Baseline Household Survey (NBHS) and 2014/15 National Household Budget and Poverty Survey (NHBPS), this study sheds light on the rural landscape in Sudan. Though rural Sudan has fared much better than urban Sudan between survey rounds, the number of poor remains higher in rural than in urban areas. Sudan severely lags other African countries in terms of agricultural productivity. Sorghum, Sudan’s most commonly produced crop—grown by close to half the agrarian households—has seen yields increase from below 500 kg per ha in 1995 to almost 700 kg per ha in 2017. A major constraint to improving crop productivity in Sudan is the low use of productivity-enhancing inputs, particularly fertilizers and pesticides and low-yield seed varieties. Increasing input use can be achieved by investing in rural markets. Market participation of agrarian households in Sudan is low, constraining farmers’ ability to raise their income levels and escape poverty. Improving rural transportation and telecommunications networks, providing access to rural credit and financial services, and increasing the ease of doing business for input providers and output marketers can increase the geographic penetration of agrarian input and output markets. Though sorghum and millet remain the dominant crops grown in Sudan, the recent increase in the number of households growing sesame is a welcome development. Deteriorations in the irrigation infrastructure need to be reversed to
# **Table 4: Age and schooling differences between partners by birth cohort of the wife. Cambodia, Demographic and Health Survey, 2000.**

|**Birth Cohort**|**Age in**<br>**1979 (December)**|**Cohort size**|**Mean age difference**<br>**with partner**|**Percentage with**<br>**younger partner**|**Mean Schooling difference**<br>**with partner**|
|---|---|---|---|---|---|
|50-54|29-25|1119|3.93|0.164|2.212|
||||[0.212]|[0.0125]|[0.092]|
|55-59|24-20|1619|3.219|0.222|1.736|
||||[0.17]|[0.011]|[0.075]|
|60-64|19-15|2049|2.31|0.243|1.768|
||||[0.126]|[0.0100]|[0.063]|
|65-69|14-10|1994|2.137|0.234|1.516|
||||[0.1]|[0.0099]|[0.070]|
|70-74|9 to 5|1899|2.91|0.1525|1.49|
||||[0.1]|[0.0085]|[0.070]|
|75-79|4 to 0|1151|4.191|0.0911|1.75|
||||[0.127]|[0.0087]|[0
information). Doing so allows us to run regressions where we can identify the association between a firm’s ranking in a given auction and its ensuing credit growth. First, we find that winning firms experience significantly larger growth in credit relative to losing firms within the same auction. Second, we find that, again, all the effect is explained by non-collateral credit. Third, we find that the ranking itself does not seem to matter in explaining the evolution of credit. In particular, when excluding winners, we find no differential effect on credit growth between the firms that were ranked second vs. firms that were ranked third and fourth within the same auction. And fourth, we show all the previous results are not driven by the presence of pre-trends in the evolution of credit for winners vs. the rest.

**Loan applications.** Finally, in Appendix B.4, we exploit data on loan applications to see whether the acceptance probability of a credit application increases when firms get a procurement contract. In particular, we can see whether a firm has applied to a given bank and whether the loan application has been accepted or rejected throughout our sample period. We use this information to help identify an increase in firms’ borrowing capacity. To do so, we run regressions at the firmbank level and relate the probability of firms obtaining a loan to whether they have received a procurement contract. We find that the probability of receiving a bank loan conditional on having applied for it increases by approximately 2 percentage points in the quarter that a firm wins a procurement project, which is consistent with the idea that procurement represents a credit supply shock for the

# **4 The Model**

We set up a model of privately held heterogeneous firms. We build on standard models of firm dynamics with collateral constraints —as Midrigan and Xu (2014), Moll (2014), or Buera and Moll (2015)— and extend this setting to allow for (a) a public sector demanding goods
of technologies that have succeeded. The detailed studies are reported in Otsuka and Larson (2016).

# **7.1 Maize**

Recent empirical studies suggest that the adoption of technologies built on high-yielding fertilizerresponsive modern varieties (MVs) significantly increases crop revenue and household income (Bezu et al., 2014; Mathenge et al., 2014; Khonje et al., 2015), reduces poverty (Khonje et al., 2015; Mathenge et al., 2014; Zeng et al., 2015) and improves food security (Bezu et al., 2014; Khonje et al., 2015; Smale et al., 2015). Nevertheless, regional adoption rates of MV maize differ significantly, and in many places, most farmers use traditional maize technologies. For example, Smale et al. (2013) report the following MV adoption rates for 2006-2007: 33 percent of maize area in Eastern Africa; 52 percent in Southern Africa, excluding South Africa; and 60 percent in West and Central Africa. Drawing on data from 20012006, Kostandini et al. (2013) also report considerable differences in MV adoption rates among countries, ranging from 5 percent in Angola to more than 70 percent in Kenya and Zambia.

As in Asia, the full benefits of the maize technologies developed for Africa are only achieved with high-levels of soil nutrients, which in practice means using chemical fertilizers (Morris et al., 2007). And there is ample evidence that the MV technologies, once adopted and used in combination with fertilizer, perform as expected. For example, estimated marginal response rates of maize to fertilizer ranged from 12 kg maize per kg. of nitrogen application in Zambia (Xu et al., 2009) to 25 kg. in Kenya (Matsumoto and 11
. Even so, they are of great significance to communities and hence to politicians, and estimates for them have been provided much less than have estimates of the gross benefits from reform.

# _f. Effects on household and regional income disparities and poverty alleviation_

The Seattle debacle in late 1999 and the protests at numerous global economic leaders’ meetings since then make clear that there is a strong demand for empirical modellers to say something also about the impact of shocks such as trade policy reform on the distribution of incomes across households and regions within each country, and in particular on the incidence of poverty, especially in poorer countries. That requires going beyond calculating just factorial income distributional effects. It requires including utility and earnings functions for several instead of just a single household. Inputs into that specification could be household survey data for urban and rural areas, from which it may be possible to estimate the shares of different (say) quintiles or deciles of households’ incomes from different productive factors and from government transfers net of taxes, and the shares of their expenditure on different products.<sup>27</sup>

# _g. Effects of domestic policy responses to trade reform_

Having identified the gainers and any losers from a trade policy change, one could draw on our understanding of the political economy of economic policy formation to

> 27 Early examples of modelling exercises along these lines are Friedman (2000), Hertel et al. (2002) and Bhattasali, Li and Martin (2004). Hertel and Winters (2005) will report the results from a multi-country World Bank research project along similar lines, focusing on the poverty effects of a likely Doha Round outcome.

22
and among them, a little more than half were between 16 and 30 years old and almost 80 percent had not completed upper secondary education.

For young Mexicans, especially those in the northern states, the increase in violence coincided with a reduction in legal employment opportunities. The international financial crisis of 2008-2009 contracted Mexico’s GDP by more than 6 percent. After the crisis started in the United States, the transmission mechanism into the Mexican economy was driven, to a large extent, by a reduction in demand for exports. Thus, the manufacturing industry, using large volumes of relatively unskilled labor and concentrated in northern cities, was hit disproportionally. According to Bussolo et al. (2015), youth held six out of every 10 jobs destroyed in Mexico during the crisis. Thus for young men in Mexico, particularly those in the border states, the years 2008 to 2010 became a period of significant reductions in job opportunities in the legal market combined with an important increase in labor demand by organized crime.

The dramatic increase in violence, the age-crime profile, the growing number of youth, and the perceived inability to provide them with the necessary education and job opportunities have fueled a debate over Mexico’s so-called _ninis—_ the term, meaning youth who are neither working nor in school, comes from the Spanish phrase “ _ni estudia ni trabaja_ .” Almost one in five youth in Mexico—totaling more than 4.11 million people aged 15 to 24 in the second trimester of 2015 according to ENOE survey—is classified as a nini. The population’s total share of ninis, both male and female, has remained constant during the last 15 years, but the male share has increased persistently: it grew from 660,000 in 2000 to 1.12 million in 2011.

Many ninis might take part in criminal activities that do enormous harm not only to themselves but to society at large. Thus the objective of this study is to contribute to the growing literature explaining the causes and contexts of
# **C.2 Definitions of Specific Variables**

## **C.2.1 Child Care Modes**

As described in Section 2, we assume that parents can choose one of three child care modes in addition to exclusive parental care: child care centers, non-relative care, and relative care. ECLS-B defines these three modes of care in the following way. They assign to _child care providers_ children whose parents report that the main form of care on a regular basis is “child care centers” [..] “Such centers include early learning centers, nursery schools, and preschools.” They assign to _non-relative care_ children whose parents report that the main form of care on a regular basis is “someone not related to” the child. [..] “This includes home child care providers, regular sitters.” They assign to _relative care_ children whose parents parents report that the main form of care on a regular basis is “a relative other than a parent [..], for example, grandparents”. In addition to the main form of care used on a regular basis, ECLS-B also collects form of care used on an irregular basis, for example a sitter for the night. We ignore non-parental form of care used on an irregular basis.

## **C.2.2 Locations**

We assign every household to one of four locations. We define locations using 2000 Census county data on population and income. Given our focus on child care markets, we consider as the relevant population the _number of children younger than 5_ in the county. Given our focus on household decisions, we consider as relevant income the _median family income_ . We build the assignment as follows. Given each household’s county of residence, we sort households by county population and calculate their median county population (36,150 children). Households below the median are assigned to Locations 1 and 2 (small locations) and those above are assigned to Locations 3
year cash payments.<sup>8</sup> Other contemporaneous village level studies document similar features in the economic arrangements between landlords and tenants (Srinivas, 1976; Bliss and Stern, 1982; Walker and Ryan, 1990).

# **3 Data**

We digitize, compile and connect existing data to construct a district-level dataset. We draw measures of land tenancy from the National Sample Survey (NSS) Land and Livestock Survey, a repeated cross section survey of about 35,000 agricultural landholdings across India. We build on custom-made tables from the Population Census of India for measures of migration. We then add information on banking, roads, distances between districts, weather and agroclimate zones from other data sources. The details of these steps are included in the online appendix. Here, we discuss the main sources, the NSS and the Census, and highlight data challenges.

## **Tenancy: NSS Land and Livestock Survey**

To obtain district-level data on land contracts we compiled data from the National Sample Survey Land and Livestock Survey. The survey covers around 30,000 to 35,000 households from all of India (with exceptions in some rounds).<sup>9</sup> We make use of the rural sample of round 70 (conducted in 2013) and 59 (conducted in 2003). We utilize information from schedule 18.1 which lists all 8On page 49, Reddy notes that, ‘The agreements entered into by absentee landlords were more detailed than those of residents.’ On page 52, he notes that, ‘The rent in the case of absentee lessors was generally paid once per year,’ and on page 76 that, ‘Absent landlords preferred cash over grains except in times of sharp rising prices.’ This is followed up, on page 69, with, ‘The shorter the distance, the greater was the preference for receipts in kind.’ Detailed contracts, specifying the crops and often inputs, were standard; and clauses
inequality in the period between 1995 and 2002, marked by smaller growth and higher unemployment, while they find the opposite effect between 2003 and 2012. Using data from the Brazilian formal sector, Engbom and Moser (2018) and Haanwinckel (2018)<sup>19</sup> take a more structural approach and find that minimum wage increases contributed considerably to reductions in inequality between 1996 and 2012, estimating that 60% of the wage inequality reduction during the period was the direct or indirect consequence of minimum wage increases. In both models, the minimum wage can have important spillover effects, not only raising the wages of those directly affected by them, but also on percentiles as high as the 80th. In Engbom and Moser (2018), these effects on higher percentiles of the distribution happen because firms must keep up their wages’ rank with respect to the wages of competing firms. As some firms have to adjust their wages due to minimum wage increases, so must other firms if they want to attract the same type of workers. Therefore, minimum wage increases spill over to higher-paying firms.<sup>20</sup> Haanwinckel (2018) finds spillovers up to the

> 19For Haanwinckel (2018) the period of analysis starts in 1998 and only for the Brazilian state of Rio Grande do Sul.

> 20Such effect could also be the result of wages being indexed on the minimum wage. However, we do not have information on this matter. We thank Liliana Sousa for this point.

24
provided this restricted view of consumer sovereignty be accepted. Second, as expected, the before and after comparisons yield sharper results than their cross-section counterparts. Obtaining more precise estimates of the effects of providing villages with all-weather roads therefore demands a more systematic and larger panel survey design, and one of longer duration.

# **References**

- [1] Bell, C. (2011a), ‘The Benefits of India’s Rural Roads Program in the Spheres of Goods, Education and Health: Joint Estimation and Decomposition’, mimeo, University of Heidelberg, September. (To appear in the World Bank’s Working Paper Series.)

36
Consistent with earlier findings, domestic characteristics appear to matter not just for the level of domestic core inflation, but also for determining the susceptibility of core inflation to external shocks, although further research is needed to solidify this evidence.

Importantly, however, the results indicate that what sets LICs apart may not be so much that they differ from the other country groups in terms of these characteristics, but that these characteristics appear to operate differently in the LIC environment. Notably, although LICs that fix their exchange rates seem to succeed in anchoring inflation expectations about as well as other economies, those that float have had a much more difficult time in anchoring inflation expectations. This finding suggests that LICs may have in essence imported their anti-inflation credibility.

This paper presents the results of what is the first investigation reported in the literature of the effects of various inflation shocks, domestic and global, on core inflation in a large group of countries, with a specific focus on LICs. The study takes advantage of the flexibility of a heterogeneous panel SVAR framework and a large data set that includes core inflation series for 18 LICs and 61 other EMDEs. The empirical framework makes possible the analysis of the impact of global and domestic shocks on core inflation in different groups of countries in a unified framework. Moreover, it helps identify the global component of core inflation endogenously and produces a parsimonious representation of the common and idiosyncratic components of core inflation in the countries in the sample. To help identify the exogenous component of domestic agricultural supply shocks, which are typically associated with food price inflation, the study uses rainfall data as an exogenous instrument.<sup>7</sup> Finally, the paper also contributes to the literature by analyzing the country characteristics that help explain differences in core inflation responses to shocks between LICs and other country groups.

The next two sections summarize the monetary policy challenges in LICs and document the evolution of inflation over time and across countries, with special focus on LICs. The following sections introduce the heterogeneous panel SVAR model and