rafmacalaba/data-use-mentions-extended huggingface dataset:
12 maximum maturity difference for the Mexican Brady bonds is slightly more than 5 years (with the last Brady, Ecuador) over a period of 24 years. The effect of a different maturity is important for short maturities and it is a much less relevant factor over such a long time horizon.

# **4. Mexico and Poland**

It is our opinion that the case of Mexico and Poland is the most suitable for the analysis of spillover effects in the Brady bond market: Mexico at the time of the crisis was a relatively stable country; Poland was stabilizing and had a relatively short record of economic reforms. The December 94 shock was substantial, but truly originating from the Mexican economy and in this sense it was exogenous to the Poland economy. This can be thought as a good approximation of an experiment: the Mexican fundamentals were dramatically changed while Poland's were not.

We will concentrate on the discount bonds: discount bonds are 30 years maturity floating market interest rate, issued at a discount to the original face value of the previously rescheduled loans. Because they are floating market interest rate, discount bonds do not involve market risk (the risk of a change in the interest rate), so that the measure of country risk given by the stripped spread on discount bonds is more accurate than the one given by the stripped spread on the Par bonds.

# 4.1 **Univariate time series description**

In this section we report the time series analysis on the variable MEXDIS (the stripped spread on the Mexican discount bonds (series A,B,C,D)), and on the variable POLDIS (the stripped spread on the Poland discount bonds). The goal is to have a preliminary understanding of the behavior of the stripped spread, that is the measure that traders use for the risk premium on a sovereign country bond. The main finding is that we cannot reject the null hypothesis of unit root for both variables, even if also a different (and stationary)
DIALOGIC READING AND STORYBOOK EVALUATION IN KENYA 21 the past week (CESD-10; Kilburn et al., 2018). We constructed a measure of household wealth based on housing quality (crowding, roofing and floor materials, access to electricity, sanitation facilities) and ownership of durable assets such as vehicles and livestock (Filmer & Pritchett, 2001).

To address the third study objective, we assessed caregiver literacy by asking caregivers to read one simple sentence (second grade level) in each of the three study languages – Luo, English and Swahili. Caregivers who either stated that they could not read or who were unable to correctly read more than one word in any of the three languages were categorized as illiterate.

# **Balance and Attrition**

Most baseline child characteristics (e.g., age, sex, height-for-age z-score, school enrollment, and receptive vocabulary) did not vary across study arms, though we did observe some variation in caregiver and household characteristics (Supplementary Tables A.1 and A.2). Given the large number of treatments, some statistically significant imbalances are to be expected. We included controls for variables that showed statistically significant levels of baseline imbalance in our analysis.

Households were followed-up five to six weeks after baseline, with 48 caregivers and 68 children lost to follow-up. Attrition was not correlated with either randomly-assigned treatments or baseline characteristics (Supplementary Tables A.3 and A.4), and thus is unlikely to bias the estimates of treatment effects.

# **Analytic Approach**
# **_5.2. Descriptive statistics_**

Table 2 shows statistics comparing IDP and non-IDP women interviewed in the three rounds of DHS. On average, IDP women are younger and less educated than their non-IDP counterparts. They are less likely to be married but more they likely to be widowed, or not be in union. IDP households have one more member than non-IDP households; they have slightly more children and adult members, but fewer elderly. The average time in displacement is 2.5 years.

<u>Table 2. Descriptive statistics</u>

||**Observations**|**Displaced**|**Non-displaced**|**Diff**|
|---|---|---|---|---|
|**Individual characteristics**|||||
|Age|110,992|28.26|29.86|-1.61***|
|||(0.23)|(0.03)|(0.26)|
|Years of education|110,772|7.46|9.02|-1.57***|
|||(0.08)|(0.01)|(0.09)|
|_Marital status_|||||
|Never married|110,977|0.33|0.39|-0.06***|
|||(0.01)|(0.00)|(0.01)|
|Married|110,977|0.14|0.19|-0.05***|
|||(0.01)|(0.00)|(0.01)|
|Cohabiting|110,977|0.36|0.29|0.07***|
|||(0.
students make choices within the of this environment.

Real-world education systems are diverse, and in particular the dynamics of the public and private sector — and the type of private sector offerings — are different across countries. In some contexts, private schools target affluent households, and jobs in private schools are often seen as more attractive than public sector jobs, typically providing some form of performance-based compensation. In other contexts, there has been a growth of ‘low-cost private schools’ that deliberately cater for the lower end of the income distribution and, in these settings, jobs in the public sector typically confer significant rents relative to the private sector.

In view of this diversity, we focus our model and empirical test on one particular regional system: Latin America. We choose Latin America because its education systems are reasonably homogeneous across countries in terms of the character of public and private schools. Specifically, the private school system caters to the middle (and upper) classes and accounts for about one-fifth of high school students. Private schools tend to be better funded (via costly school fees) and in turn pay higher teacher salaries and offer better facilities. Public schools, on the other hand, are often poorly funded and operate in highly centralized environments. Teacher pay is set on a rigid scale dictated by strong unions. Focusing on a region that has such systems makes the applied theory exercise substantially less complex, and the prevalence of large-scale national surveys in the region opens many possibilities for future empirical work.

Table 2 reports the correlation exercise in Columns (1) to (3) of Table 1 for Latin American countries only, and confirms that the relationship between management and student performance is strong in the region. Further, the coefficient on private schools indicates that students in private schools achieve higher scores, by about 55 points, than students in public schools. This affords a suitable empirical environment to study the channels that we are interested in this section.

# **3.1 Overview of the theoretical framework**

The
|
|Albania|3|5|5|6|7|
|BiH|4|4|5|6|8|
|Croatia|5|5|5|7|8|
|FYROM|3|4|5|6|7|
|Kosovo|4|5|5|5|6|
|Montenegro|4|5|5|7|7|
|Serbia|3|5|5|7|8|
|CIS||||||
|Armenia|3|4|5|5|7|
|Azerbaijan|4|4|5|5|6|
|Belarus|5|4|5|6|7|
|Georgia|2|3|4|5|6|
|Kazakhstan|3|4|5|6|7|
|Kyrgyzstan|4|4|4|5|5|
|Moldova|4|4|5|6|6|
|Russia|3|4|5|6|8|
|Tajikistan|2|4|5|6|7|
|Ukraine|3|4|5|6|6|
|Uzbekistan|4|4|5|6|7|

_Source: LiTS II, 2010._ For the region as a whole there is little support for the view that the mapping across the two scales is similar. For any verbal life satisfaction indicator, the average life satisfaction score on the 1-10 scale is higher in Sweden than in Tajikistan for example. One possibility is that respondents use a _global_ standard when answering satisfaction with life questions<sup>20</sup> , i.e.

20 Although the LITS Satisfaction with Life Questions do not explicitly invite respondents to think about “the best possible life” (as in Deaton, 2008; see Graham et al., 2009 for a discussion).

14
Policy Research Working Paper 5444

# **Abstract**

The authors use panel data on the number of new firm registrations in 95 countries to study the impact of the business environment and 2008 financial crisis on new firm registration. The data show that more dynamic formal business creation occurs in countries that provide entrepreneurs with a stable legal and regulatory regime, fast and inexpensive business registration process, more flexible employment regulations, and low corporate taxes. The data also show that nearly all countries experienced a sharp drop in business entry during the crisis. This drop is more pronounced in countries with higher levels of financial development and countries more affected by the crisis.

This paper—a product of the Finance and Private Sector Development Team, Development Research Group—is part of a larger effort in the department to study Entrepreneurship and Growth. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at lklapper@worldbank.org and ilove@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
gave to the incipient Chilean industry, a markedly foreign air.”(p 94)<sup>7</sup> Collier & Sater (1996) document that thirty-six of the forty-six dressmakers counted in 1854 were French, Americans installed the flour mills and Americans and British built the railroads. The port of Valparaiso became a major center of commerce dominated by foreigners. In 1860, of the 909 establishments surveyed by the government, 674 belonged to foreigners. For the country as a whole, the list of officers and members of the executive committee of SOFOFA, the principal organization of industrialists for the country, showed that that only three Spanish surnames accompanied those of the other 18 members of the directorate (Loveman, 1979, p. 193). Further, by 1920, as with mining more generally in Mexico, the vast copper enterprises were entirely foreign.

Two of Colombia’s subregions also show very strong immigrant dominance: In the dynamic port of Barranquilla 64% of establishments were foreign owned, 3 times what would be predicted by the male immigrant share, and tax records suggest that, as in Mexico, they were largest ones. Further, as in Chile, foreigners introduced steam technology for manufacturing and transport, revolutionizing navigation from the port to the interior of the country. As Becerra & Restrepo (1987) note, “the entrepreneurs of foreign origin occupied an indisputable place as pioneers across the 19th century, in the emergence of Barranquilla as the primary port of the country.”(p.35) High foreign participation is also notable in Santander where 50% of the most notable entrepreneurs were foreigners, roughly 8 times their representation in the population.

In Mexico, the industrial census shows that 50% of businesses were owned by immigrants or 26 times their share of the male population. As Annex 1 shows, the share of investment and production by foreigners is even greater, especially in mining, suggesting that our census data may be understating foreign influence in all countries. This is in line
- 26 -

Lug. Becauso commerce generates consistently the highest share of African rural nonfarm income, the Sierra Leone data likely seriously underestimate total rural nonfarm earnings. For present purposes, one advantage of thio omisoson is that it reduces the coverage of the nonfarm multiplier to principally production linkages. Supporting the notions advanced previously, this interpretation suggests that rural productlon linkages from agriculture are in fact very low, at least in the predominantly hand hoe agriculture of Sierra Leone.

# B. A Model of Regional Agricultural Multipliers

In view of the limited opportunities for direct estimation of growth multipliers, much of what we can learn must come from modeling. '-azell (1984) has developed a particularly useful model that allows estimtion of regional growth multipliers based on three basic parameters, two production and one consumption. Values for each can be obtained from ferm management, rural nonfarm enterprise, and consumption surveys. Although small, the model allows us to assess three important features of farm-nonfarm linkages: (a) differences in African and Asian growth multipliers; (b) the implications of alternative types of agricultural growth on the ensuing nonfarm linkages; and (c) breakdown of the total multiplier into its production- and consumption-based components.

Described in detail in Hazell (1984) and in Haggblade, Hazell and Brown (1987), the model characterizes the supply structure of rural regions as follows. ~/ The supply of their major tradable output--foodgrains or export crops--is typically fixed by technology and, in Asia by land, in Africa by labor. In contrast, output of nontradables--primarily rural services, but including some income-elastic agricultural products such as fruits, vegetables and meat, and some rural manufactures--is elastic. Assuming the prices of all traded commodities are fixed outside the region, Hazoll derives a model which estimates the increase in regional value added
>40</sup>

**Devadas and Kim (2020)** use the World Bank Long Term Growth Model (as in Section 5) to calculate the effects on growth of closing gender gaps in labor force participation.<sup>41</sup> In addition to the standard neoclassical

37 The Appendix to MGI describes this group as the G20 plus Bangladesh, Morocco, Malaysia, the Netherlands, and Sweden. Of these, none are low income, four are lower-middle income (Bangladesh, India and Indonesia, Morocco) and ten are uppermiddle income (Malaysia, Argentina, China, Mexico, the Russian Federation, Mexico, Argentina, South Africa, Turkey, Brazil). 38 In this type of model, the more talented the manager is, the more productive are the labor and capital in his or her firm (and more talented managers run larger firms). Gender gaps in entrepreneurship result in GDP losses because many talented female managers are not able to become managers, resulting in losses in total factor productivity. 39 In part this due to 20 countries with no gender participation gaps in their sample ( in their notation), though it could also be due to a decreasing returns to scale production function. 40 As the full GEGI is fairly similar to the basic GEGI, so is the relationship with the MGI and CT2016 as discussed above. Appendix As the full GEGI is fairly similar to the basic GEGI, so is the relationship with the MGI and CT2016 as discussed above. Appendix λλ= 1 40 As the full GEGI is fairly similar to the basic GEGI, so is the relationship with the MGI and CT2016 as discussed above. Appendix Figure 7 compares the MGI measures with the full GEGI, with the most important difference being that the full GEGI is more similar to the MGI (LFP+hours+Sectors measure) for MENA and South Asia (ex-India). The full GEGI is plotted against the measures from Cuberes and Teignier (2016) in Appendix Figure 8, and
of international price shocks following trade liberalization (e.g. Hertel et al., 2004; Hertel and Winters, 2006; Hertel et al., 2009) and in recent analyses of climate change and poverty (Ahmed et al., 2009, 2011).

Tanzania is a country where grain production variability may increase due to changing climate volatility (Ahmed et al., 2011). In this paper, we examine the sensitivity of prices and factor incomes in Tanzania due to climate volatility. Of particular interest are the potential inter-annual trading opportunities created by heterogeneous climate shocks, as well as the potential for trade to modulate the effects of climate-induced shocks on Tanzanian poverty.

In order to examine these potential trade effects, we first estimate the historical covariance of maize productivity shocks in Tanzania and her key trading partners. We then use an augmented version of the GTAP global trade model and associated database to quantitatively examine the interaction between trade policy and the climate-induced maize production volatility, with emphasis on the

> 5 Data from RATIN (2009) indicate that there is substantial informal cross-border trade.

3
/sup> . The Manufacturing Module contains questions about capacity (use of production capacity and hours of operation).

The sampling methodology of the World Bank‘s Enterprise Survey generates sample sizes appropriate for achieving two main objectives: (1) a sample representative of the whole economy that substantiates assertions about the whole economy, not only about the manufacturing sector. In addition to selected manufacturing industries, the overall sample should include services industries and other relevant sectors of the economy, and (2) a sample that is large enough in size for selected industries to conduct statistically robust analyses with levels of precision at a minimum 7.5% for 90% confidence intervals about<sup>18</sup> : (a) estimates of population proportions (percentages), at the industry level, and (b) estimates of the average mean of log of sales at the industry level.

> 16 These sections deals with the characteristics of the business and the investment climate in which it operates including: Control Information, General information (ownership, start-up), Infrastructure and Services (power, water, transport, and communication technologies), Sales and Supplies (imports, exports, supply and demand conditions), Degree of Competition (price and supply changes, competitors), Land (land ownership, land access issues), Crime (extent and losses due to crime), Business-Government Relations (quality of public services, consistency of policy, regulatory compliance costs), and Investment Climate Constraints (evaluation of general obstacles).

> 17 More specifically, these sections contain questions on production costs, investment flows, balance sheet information and workforce statistics. These sections include: Finance (sources of finance, terms of finance, financial services), Labor (worker skills training, skill availability, employment, education levels of workers) and Productivity (Numbers and figures needed to estimate productivity).

> 18 A 7.5% precision of an estimate in a 90% confidence interval means that we can guarantee that the
counteracted by other factors. By excluding alternative explanations, and in line with an emergent literature, we speculate that the widening of migrants’ horizon (i.e., additional information or value change) may be the main driver of the reduction in petty corruption.

Incorporating school-level budget data in our analysis, we show that there is no strong correlation between public school funding and petty corruption. Thus, the most socially accepted justification for informal payments to teachers—the need for school maintenance and wage supplements for motivation—is not a good predictor of differences between schools. Within schools, additional analysis suggests that reductions in payments to teachers quickly spill over to non-migrants. This is in line with qualitative evidence according to which per capita payments to teachers cease once a few parents in a class refuse to pay due to only weak enforcement devices in the hands of teachers or other paying parents. Our results fit with novel research that shows how participation in corruption often depends on people perceiving it as widespread. This is a prevalent phenomenon in low- and middle-income countries. In such a setting, simply increasing teachers’ salaries and school resources might decrease the perceived legitimacy of informal payments. If budget constraints made this impossible and these payments continued to exist, structures such as teacher-parent committees should formalize them as donations. The available funds should then be focused on making teachers wages sufficient while stepping up enforcement of laws against individual corruption. This way, transparency and accountability would be improved while providing solutions for underfunding that do not distort incentives. Both the opportunity to siphon off part of the payments for private use and the necessity to do so would thus decrease. For bribes which are used to get one’s own child ahead of the competition, other measures are likely to be more effective. In a bold move, the Moldovan government recently introduced videotaping of the most important high school exam to put an end to teachers, motivated by informal payments, telling answers to their classes or, worse, individual students. As such laudable
With the new data set we also contribute to a recent wave of studies that use occupational statistics to describe structural change. For example, Newfarmer, Page and Tarp (2018) study various industries in agriculture and services that share important characteristics of manufacturing industries such as tradability, scalability and productivity growth, and name these “industries without smokestacks”. Baccini et al. (2023) stress important variation in activities within the services sector from provision of informal personal services to much more productive formal business services and transport services that are used also for exporting. Duernecker and Herrendorf (2022) show how the share of service occupations within the good sectors increases in later stages of development. Using occupational statistics, Timmer et al. (2019) documented specialization of high-income countries in activities that are knowledge intensive. The _Jobs of the World Project_ is another prominent example of the compilation of a large-scale data set to compare changes in occupational structures across countries and over time. Using this data, Bandiera et al. (2022) show how the nature of jobs vary across countries by stages of development, and within countries by household wealth and gender. In this study we focus specifically on the characteristics of jobs in exports. We show how adding an industry dimension enhances the ability of occupational statistics to capture different types of worker activities. We extend the database of Timmer et al. (2019) increasing the country coverage to 52 countries at all levels of development and adding new data on activities at a higher level of detail. More precisely, activities are identified in the data by cross-classifying 13 occupational classes and 35 industries of work, resulting in 455 distinct activities. This fine-grained dataset allows us to describe for the first time overall development patterns of export specialization from an activity perspective.<sup>2</sup> As a final note we wish to emphasize that the nature of this study is explorative. Our findings are suggestive of important complementarities between various export activities, but we stay agnostic about their precise nature. Co-occurrence of