rafmacalaba/data-use-mentions-extended huggingface dataset:
countries with annual information from 1970 to 2007.<sup>11</sup> We first test whether sudden stops are determined by the indicators that capture sound macroeconomic policy frameworks, healthy financial sectors, outward orientation of the economy and external shocks. Then, we examine whether the impact varies according to the source of sudden stops. Finally, we assess whether some particular features of the domestic economy (exchange rate regime in place, liability dollarization and openness) amplify the impact of external shocks on the likelihood of sudden stops.

# **4.1 Set-up**

<mark>Our dependent variable is the binary variable</mark> _<mark>SS</mark>_ <mark>that can only have two possible outcomes: it can take the value of 1 when there is a sudden stop (net, inflow-driven, outflow-driven or mixed) and 0, otherwise.</mark> _<mark>X</mark>_ <mark>is a vector of explanatory variables that influence the outcome</mark> _<mark>SS</mark>_ <mark>.</mark> Hence, the model takes the form: <mark>where the left hand side of the equation represents the probability</mark> _<mark>P</mark>_ <mark>of a sudden stop occurring given</mark> _<mark>X</mark>_ <mark>, and  is the</mark> _<mark>Probit</mark>_ <mark>function. Our goal is to estimate the vector  of parameters by maximum likelihood. We specify the</mark> _<mark>Probit</mark>_ <mark>model as a latent variable model and</mark> assume that there exists a random variable _SS_<sup>*</sup> such that: where  represents the error term, and _SS_<sup>*
With respect to gender and geographic area the results show that rates of return to tertiary education are higher for both urban and rural men compared to women, particularly in the upper tail of the conditional earnings distribution.

In sum, the returns to education increased in Mexico from 1988 to 1997, especially for higher levels of education and in the upper tail of the conditional earnings distribution. However, there was a reversal to this trend after 1997, especially for higher levels of education and in the middle and lower tail of the conditional earnings distribution. This may reflect a structural development, if expanding relative supplies of school-leavers are offsetting the secular tendency for rising relative demand for skills — especially at the tertiary level (see de Ferranti et al., 2004). Alternatively, it may reflect a cyclical fall in education premia in times of recession, which has also been observed in the data for Latin America (ibid). But for the present, the labor force patterns by labor force status and education are fully consistent with the equalizing patterns of income growth.

# 5. CONCLUSION

Even though the levels of educational attainment expanded very rapidly, Mexico experienced a pronounced increase in the degree of inequality over the 1980s and mid1990s. Most of the deterioration in the distribution of income happened in the middle to late 1980s (1984–89). The early 1990s displayed little change in total current income inequality except for a slight trend toward deterioration. The trends in the distribution of earnings differ from the trends in the distribution of current income in two ways. First, the gains are not limited to the richest 10 percent, as those in the seven-, eight-, and ninetenths of the distribution improved their relative earnings over the period by almost 2 percentage points. Second, the distribution of earnings clearly worsened in the 1990s until 1996, although the inequality associated with total current income was moderately stable in the 1990s, displaying an improvement after 1996. Differences in the behavior of total current income and labor earnings inequalities from 1994
_H1: Business owners with higher ‘marketing’ managerial capital will increase firm profits by implementing more growth focused policies and practices than other business owners._ In contrast, we define an efficiency focus for firms as the adoption of a different set of utilization activities, such as tracking the cost of goods, managing cash flow, and purchasing supplies more effectively; and different allocation activities, such as separating personal and business investments, using equipment at optimal periods to reduce costs, and shifting staff resources to minimize expenses. These practices are more closely related to the skills developed during finance/accounting training. Given their ‘cost and control’ emphasis, implementing these types of finance and accounting activities is likely to have a direct impact on raising profits through gains in efficiency. A focus on efficiency is also likely to encourage greater implementation of firm practices related to tracking, analyzing, and planning finances. Following this line of reasoning, we propose our next hypothesis.

_H2: Business owners with higher ‘financial’ managerial capital will increase firm profits by implementing more efficiency focused policies and practices than other business owners._

# **2.2. Who Benefits More From Business Skills Training?**

There is most likely heterogeneity in the extent to which firm owners benefit from business skills training, including individual level factors that make emphasizing a growth focus more applicable or other situations when focusing on efficiency is particularly effective. Using panel data from three countries, McKenzie and Woodruff (2016) find considerable heterogeneity in business practices based on individual and firm level outcomes such as level of human capital and firm size.

In this paper, we draw insights from the literature and theory to explicitly consider who might benefit more from marketing and/or finance training. The literature has shown that it is quite common in emerging markets for individuals to start firms because they cannot find jobs in the formal sector (Schoar 2010; Tokman 2007). Given their small, uncertain, and volatile incomes, 9
the same approach used in our past work, namely that the line should be representative of the national lines found in the poorest countries—in the spirit of the original ―$1 a day‖ line (Ravallion et al., 1991; World Bank, 1990). For this purpose, Ravallion, Chen and Sangraula (RCS) (2008) compiled a new set of national poverty lines for developing countries drawn from the World Bank’s country-specific _Poverty Assessments_ and the _Poverty Reduction Strategy Papers_ done by the governments of the countries concerned. While the Ravallion, Datt and van de Walle (1991) data set on national poverty lines was drawn from sources for the 1980s, the new and larger compilation produced by RCS are all post-1990, such that in no case do the proximate sources overlap.

5
of assets and services across four categories – human capital, physical and financial assets, social support, and state support (Foa 2013; World Bank 2013).

**The Center for Global Development’s Vulnerability to Climate Change Index** . This index provides an accounting of climate change vulnerability, by developing a Climate Drivers Index for 233 states, and quantifies vulnerability to climate change as a result of (1) weather‐related disasters, (2) sea‐level rise, and (3) reduced agricultural productivity. This Climate Drivers Index is also combined with data on governance and per capita income to incorporate measures of resilience, and with data on project effectiveness within countries. The full paper outlining the methodology by Wheeler (2011) can be found <u>HERE.</u>

**Barr et al (2010) – Climate Change Impact Rankings.** This paper provides an indicator of climate change impact in 131 countries and discusses how adaptation funding might be allocated based on this measure. The indicator includes dimensions of physical impact, adaptive capacity, and implementation capacity. On physical impact, four sub‐indicators are included, on agriculture, disasters, health, and coastal zones. On adaptive capacity, separate scores are developed which incorporate sub‐indicators such as the age dependency ratio and the Gini coefficient. For implementation capacity, data from the World Bank’s Country Policy and Institutional Assessment and Annual Review of Portfolio Performance are used. An overall vulnerability score is derived by subtracting the score for adaptive capacity from the score for impact. The full paper can be found <u>HERE.</u>

**DARA’s Climate Vulnerability Monitor.** The Climate Vulnerability Monitor provides an assessment of climate change impact in 184 countries. The Monitor provides an assessment of socioeconomic vulnerability, covering four impact areas, including (1) habitat change, (2) health impact, (3) industry stress, and (4) environmental disasters. Each of
# **5 Model**

Our deconstruction of the missing middle led us to interpret the evidence as suggestive of distortions that encourage informality and discourage formal firm growth. Entry barriers and productivitydependent idiosyncratic distortions have the potential of generating such outcomes. While the formal sector in the low-income countries under study exhibited a notable dynamism when compared with its informal counterpart, it still fell short of the dynamism of firms in advanced economies. This leaves a role to be played by productivity-dependent distortions, which discourage firm growth and misallocate resources across firms. Entry barriers, in turn, limit the incentives of firms to enter the formal sector and magnify the effect of idiosyncratic distortions.

We investigate this hypothesis formally in this section by feeding entry barriers and idiosyncratic distortions into a model of firm dynamics with endogenous informality. Our goal is to assess the extent to which these distortions can account for the prevalence of informality and the patterns of the size distribution we observed in the data and to quantify their aggregate implications. The model features a rich formal sector, with endogenous entry and exit of firms and endogenous innovation decisions, all responsive to the incentives created by the distortions. The informal sector also features endogenous entry but we assume that firm growth and the exit rate are exogenous so that the size distribution is governed by the rate of productivity growth over the firms’ life cycle vis-a-vis the exit rate. We assume that formal and informal firms produce differentiated varieties that aggregate into differentiated baskets of formal and informal goods. Consumers face a constant elasticity of substitution across varieties within a type of good and across formal and informal goods. We calibrate the model to the U.S. and estimate entry barriers and idiosyncratic distortions in Ghana, the only country in our sample for which we also count with a companion formal sector census of

## **5.1 Demand Side**

Consumers derive utility from baskets of formal and informal goods, which they combine through a Constant Elasticity of Substitution demand system. Formally: where _Q_ is the final good and _Q_
middle-income countries, only 1 percent to mainly SSA low-income countries and the reminder 3 percent to advanced economies in the Middle East and in East Asia (Figure 2).

This paper uses data from a survey of BRI-related investment that has been commissioned by the World Bank in 2018 and compiled by WIND, a Chinese consultancy firm. The WIND database covers China’s construction contracts and other investment projects in non-financial sectors in 50 countries that have been identified in the news as being BRI related. For each project, the database reports their status: completed, under construction, or planned for the years 2013 to 2018. Planned projects are all officially confirmed. For the purpose of this analysis, we only include projects that are planned or under construction from 2016 to 2018. These data are complemented with investment information compiled by the World Bank’s country economists for Tajikistan, Georgia and Djibouti. While the BRI has been announced in 2013, actual public and publicly guaranteed debt data used in this paper are available until 2016 and are therefore unlikely to include BRI-financing for non-completed projects identified from 2016 to 2018.

Figure 2. BRI Recipient Countries <!-- Start of picture text --> By number<br>Region Income Lending terms<br>Sub- High<br>Saharan income, Low<br>Africa, 2 2 income,<br>Asia, 8South & Pacific, East Asia 10 middle Upper 6 IDA, Blend, 6<br>Middle income, 14<br>East & 21<br>North Lower<br>Africa, 8 Europe & Asia, 22Central income, middle 21 IBRD, 30<br>By investment amount<br>SSA High Low<br>2% income income<br>3% 1%<br>SAR IDA Blend<br>21% Upper 16% 16%<br>EAP middle<br>MNA 34% incom<br>9% e Lower<br>46%<br>middle<
# **Table C.35: Role model effects: Main results using husbands’ data**

**Panel A: Effects on income and decision-making power**

||(1)<br>Husband’s personal<br>income(Taka)|(2)<br>Household<br>income<br>(Taka)|(3)<br>Wife’s Decision-<br>making (Index)|
|---|---|---|---|
|exposed|_−_203_._58|_−_567_._41|0_._02|
||(332_._82)|(761_._76)|(0_._11)|
|N|449|449|449|
|Non-exposed mean|10304_._84|21235_._56|0_._10|
|Non-exposed SD|5327_._06|7414_._98|1_._17|

**Panel B: Effects on assignable goods expenditures and remittances**

||Women|Girls|Men|Boys|Remittances|
|---|---|---|---|---|---|
|||(All outcomes as %|in household i|ncome)||
|exposed|0_._41|0_._16|_−_0_._69|_−_0_._32|_−_0_._93|
||(0_._89)|(0_._39)|(0_._51)|(0_._47)|(1_._25)|
|N|449|144|449|146|449|
|Non-exposed mean|6_._55|1_._82|5_._48|2_._30|9_._04|
|Non-exposed SD|6_._51|2_._22|5_._32|2_._52|12_._80|

Notes: Exposed is a dummy variable
1.33|1.25|1095|1.09|1.05|1095|
|_Panel D. Community characteristics_|||||||
|Violent crime in community|0.33|0.47|1095|0.36|0.48|1095|
|<br>Social assistance(education)available<br>Table I present summary statistics (mean an<br>These variables are available in the first thre|0.95<br>d standard<br>e rounds of|0.22<br>deviation)<br>the Peruv|1095<br>of the var<br>ian Young|0.98<br>iables used<br>Lives Surv|0.13<br>in the ana<br>ey. Sampl|1095<br>lysis.<br>e is|

Table I present summary statistics (mean and standard deviation) of the variables used in the analysis. These variables are available in the first three rounds of the Peruvian Young Lives Survey. Sample is restricted to children with available information on maternal mental health in 2002 and PPVT scores in 2006 and 2009. A detailed description of the rest of variables is presented in Appendix section.

26
**Figure 1. Health and Survival Outcomes over Time**

<!-- Start of picture text --> 100<br>90<br>87<br>90 83 84<br>80 74<br>68 69<br>70 63<br>60<br>2003/4<br>50<br>2011<br>40<br>2012<br>30<br>20<br>10 3.8 2.7<br>0<br>Prenatal Delivery Fully Infant<br>immunized mortality<br>Percentage<br><!-- End of picture text --> _Source:_ Authors’ calculations based on DHS 2003/04, ENPSF 2011, and ONDH panel 2012.

# 4.1.2 Nutrition

Children in Morocco face a number of ongoing challenges in terms of nutrition (Figure 2). There has been a decrease over time in the chances of stunting (which captures accumulated malnutrition). Almost a quarter (23 percent) of the children were stunted as of 2003/04, and although the 2011 rate was lower (15 percent), many children’s nutrition and development are threatened. More substantial improvements with respect to being underweight and wasted have been achieved. Of particular concern is the fact that as of 2006/07, only 20 percent children had access to adequately iodized salt to prevent iodine deficiency.<sup>22</sup>

> 22 Available survey data do not allow for measurement of changes in child access to iodized salt over time, because the 2006/2007 MICS survey is the only one that contains the relevant information.

9
Yu 2016), annual data on claims by banks on the private sector, provided by the IMF’s International Financial Statistics, are used as proxies for credit to the nonfinancial private sector. This broadens the sample by another 41 countries, mainly from 2000 onwards. These include Azerbaijan, Bahrain, Bangladesh, Bulgaria, Bolivia, Botswana, Colombia, Chile, Costa Rica, Cote d’Ivoire, Croatia, Egypt, Gabon, Georgia, Ghana, Guatemala, Honduras, Jamaica, Jordan, Kazakhstan, Kenya, Kuwait, Mauritius, Mongolia, Namibia, Nigeria, Oman, Pakistan, Philippines, Panama, Paraguay, Peru, Qatar, Senegal, Serbia, Tunisia, Sri Lanka, Ukraine, Uruguay, República Bolivariana de Venezuela, and Zambia.

Advanced economies (AEs) included in the sample are Australia; Austria; Belgium; Canada; Denmark; Finland; France; Germany; Greece; Hong Kong SAR, China; Ireland; Israel; Italy; Japan; Republic of Korea; Luxembourg; Netherlands; New Zealand; Norway; Portugal; Singapore; Spain; Sweden; Switzerland; United Kingdom; and United States.

23
**|86|
|Rural road network condition [3]|% in good or fair condition|61|**73**|65|
|Classified paved road traffic|AADT|1,408|**858**|2,451|
|Classified unpaved road traffic|AADT|54|**95**|107|
|Primary network overengineering|% of primary network paved with 300 AADT or less|15|**23**|18|
|Perceived transport quality [4]|% firms identifying transport as major business<br>constraint|27|**13**|18|

_Source:_ AICD Road Sector Database on 40 Sub-Saharan African countries. _Note:_

[1] Total network includes the classified and estimates of unclassified and urban networks.

[2] Main network for most countries is defined as result of adding the primary and secondary networks.

[3] Rural network is generally defined as the tertiary network and does not include the unclassified roads.

[4] _Source:_ World Bank—IFC Enterprise Surveys on 32 Sub-Saharan Africa countries. GIS = geographic information system; AADT = average annual daily traffic.

During its recent economic boom, the country has given priority to developing and maintaining surface connectivity. It has allocated substantial resources to expansion and rehabilitation of the existing network. Based on the physical configuration and condition of Botswana’s road network, it is possible to estimate the resources that would be needed to maintain the network in good condition and properly rehabilitate it as per the road life cycle. These maintenance and rehabilitation norms, when compared against existing spending patterns, give a sense of how Botswana has succeeded in securing resources for road preservation (figure 7). As the benchmarking shows, Botswana spends a significant amount over the 11
of interest _yit_ measures the proportion of a cell _i_ in year _t_ that is covered by buildings, as inferred from a high-definition satellite image of Ghana. This variable comes from the Global Human Settlement Layer (GHSL) data that have been put together by the European Commission, Joint Research Centre. A Mollweide projection was used to divide the earth’s surface into square cells of approximately 250 by 250 meters, which is rather small for an analysis of builtup area. The proportion of buildings on each cell was predicted using a machine learning algorithm trained on observations of actual buildings made on the ground (see Pesaresi et al. 2016 for details). This algorithm predicts buildup in squares of 30x30 meters. These predictions are then aggregated using GIS into 250x250m cells, thereby generating an accurate prediction of the proportion of that 5
1.848***|0.480*|0.709|
||(0.367)|(0.263)|(1.071)|
|**Afghan * Shock**|0.585|-0.130|-1.624|
||(0.549)|(0.414)|(1.515)|
|**Constant**|45.515***|-1.979***|12.082***|
||(0.173)|(0.134)|(0.499)|
|**Observations**|31158|1683|2348|

24
**Table 1:  World trade by end use, 2000**

||Number of SITC<br>Rev.3 lines|Exports in<br>Mill. USD|Total exports<br>(%)|
|---|---|---|---|
|Total|3,053|5,900,952.0|100.0%|
|Intermediate|1,873|3,397,270.5|57.6%|
|Consumption|698|1,096,182.5|18.6%|
|Capital|468|1,082,342.4|18.3%|
|Not classified|14|325,156.7|5.5%|

Source: Authors calculations based on ITCS database.

> Capital intensity, _k j_ , and skilled labor intensity,<sup>_h_</sup> _j_<sup>, of 6-digit NAICS industries in the year 2000 are taken</sup> from the U.S. NBER-CES Manufacturing Industry Database. They are measured as the natural logarithm of the total real capital stock per worker in industry _j_ , and the share of non-production workers in total employment of industry _j_ , respectively. We measure institutional intensity<sup>_q_</sup> _j_<sup>of industry</sup><sup>_j_as the share of</sup> products in the industry that are neither reference priced nor sold on an organized exchange. To construct this variable we use the Rauch (1999)’s classification, which groups goods into goods traded on an organized exchange, reference priced goods and non-reference priced goods.  The assumption is that the production of non-reference priced goods requires a relatively high level of relation-specific investments so that these goods are more subject to the hold-up problem than goods sold on an organized exchage or reference priced goods.  As suggested by Nunn (2007), the fact that a
picture text --> 15<br>10<br>5<br>0<br>-5<br>-10<br>GDP growth Employment growth<br>Real earnings growth Wagebill growth<br>Percent<br><!-- End of picture text -->

**Figure 1: The employment adjustment was delayed and gradual**

> 8 . A simple example makes this clear. China was far more affected by the financial crisis in 2009 than Morocco. But a simple comparison of post-crisis rates of growth would have given the false impression that Morocco suffered more. This is because Morocco’s GDP grew only half as fast China’s, even after the crisis, and employment growth in the two countries was nearly the same. However, in the pre-crisis period the Chinese economy had been growing more rapidly, so GDP growth slowed by 6 percentage points more in China than it did in Morocco. Employment growth also slowed by 3.5 percentage points more.

> 9 Using eight quarters of pre-crisis data rather than four may mitigate the impact of the food and fuel crisis of 2007. 10 The sample used to construct Figure 1 consists of the 24 countries that reported GDP growth, employment growth and real earnings growth in each quarter

4
funds to districts is a good instrument for actual expenditures. The instrument must meet two well-known conditions: it must be correlated with expenditures, and uncorrelated with the error term. The first condition can be tested in practice, while the second seems plausible since allocations during the years in question were developed from formulae based on the 1981 and 1993 censuses-both of which took place before the period covered in the analysis. It is therefore unlikely that allocations are correlated with unobserved changes in "tastes" for education between 1994 and 1997.13 Several potential problems with the strategies discussed above should be kept in mind when assessing our results. The 1994 LSMS is not a true baseline survey: it was conducted after FONCODES had made expenditures in 1992 and 1993. In what follows, our measure of FONCODES expenditure Fd consists of (per capita) expenditures made in 1994, 1995 and 1996. We include 1994, since lags between project approval, disbursement of funds, and actual school

> **_13_** However, it is still possible that the allocation of funds is correlated with other (unobserved) factors that affect school attendance. For example, it could be that other programs spend more or less money in districts that have been allocated more by FONCODES, or that there are macroeconomic shocks that are correlated with allocations and happen to affect attendance. As we discuss in more detail below, including controls for the amount spent by other programs on school infrastructure does not alter our results. Still, without more years of data, and more information on other programs, we cannot rule out all problems of this type.

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