rafmacalaba/datause-encoder-data huggingface dataset:
Credit institutions still need to do a fair amount of reaching out to potential SME clients, primarily by relying on public databases and leveraging cross-selling opportunities. A majority of interviewed banks actively seek out SME clients (Figure 5.4) by consulting and cross-referencing information obtained from public databases such as those of DANE, _Superintendencia de Supersociedades_ , Confecámaras and credit bureaus. Such information serves as a first filter to target potential SME clients based on parameters set by each bank and to allocate resources and responsibilities to branches and sales personnel (Figure 5.5). A few banks also try to leverage their depositor base in order to cross-sell credit products to existing SME clients. The sequencing of product offerings to a new SME client varies across banks depending on their business strategy and perceived comparative advantage (Figure 5.6). With few exceptions (e.g. focus on family-owned SMEs or on suppliers of large multinational firms), most Colombian banks have yet to develop sophisticated targeting strategies (Figure 5.7).

Figure 5.5: Sources Used by Banks to Identify New Clients Figure 5.6: Sequencing in Product Offerings to Target New Clients <!-- Start of picture text --> Use information from existing firm 71% The bank first offers deposits andcash management products 38%<br>databases<br>The bank first offers loan<br>Focus o n attracting SM Es that are 25%<br>14% products<br>clients/suppliers of your existing clients<br>There is no particular sequencing<br>Rely on existing deposit clients 14% between deposits and loan 25%<br>products<br><!-- End of picture text --> _Source: Authors’ analysis of bank questionnaires (simple averages across all banks). Note: Figure 5.6 does not add up to 100 percent because some credit institutions did not provide a response._ Figure 5.7: Criteria to Target New SME Clients <!-- Start of picture text --> Company size 23%25%<br>Firm databases/Public information 19% 23%<br>Credit quality 15% 19%<br>Family businesses 6% 8%<br>Exposure size 0% 6%<br>Expected profitability 6% 8%<br>Product needs 0% 6%<br>Industry sector to which firm belongs 6% 15%<br>Geographic area where firm operates 6% 8%<br>Small Enterprise M edium Enterprise<br><!-- End of picture text --> _Source: Authors’ analysis of bank questionnaires (simple averages across all banks). Note: Figure 5.7 does not add up to 100 percent because some credit institutions provided more than one response._ 30
index as a function of distance from the treatment threshold. The plots show residuals from controls and fixed effects, along with linear estimations on each side of the threshold and 95% confidence intervals. The graphs corroborate the tables, showing significant treatment effects for transportation and labor exit from agriculture, but little clear impact on the firms, agricultural production and consumption indices. These results broadly summarize the findings of this paper: rural roads lead to increases in transportation services and reallocation of labor out of agriculture, but no major changes to village firms, agricultural production and consumption. The rest of this section examines the components of each of these indices to explain the impacts of roads in more detail, and presents results on heterogeneity.

Table 4 shows regression discontinuity estimates of the impact of a new road on an indicator variable for the regular availability at the village level of the five motorized transportation services that are recorded in the 2011 Population Census. A new road causes a statistically significant 12.8 percentage point increase in the availability of public bus services, more than doubling the control group mean of 11.9 percent. The impact on private buses is nearly as large but measured with less precision. Taxis and vans, which are more expensive forms of transportation, do not experience significant growth. Availability of auto-rickshaws, the least expensive private form of motorized transport, increases as well. Given that we are unable to observe transportation costs directly, we interpret these results as evidence that the new roads studied in this paper do meaningfully affect connections between treated villages and outside markets.<sup>14</sup> Table 5 presents impacts of new roads on occupational choice, the one domain where roads appear to substantially change economic behavior. As 92% of workers in sample villages report their occupation to be either in agriculture or in manual labor, we focus our investigation on these categories. The first two columns show the impact of new roads on the

> 14This finding is not a given; Raballand et al. (2011) argue that in remote areas of Malawi, willingness to pay for transportation services may be so low that roads may not appreciably improve transportation options.

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report being affected by the crisis more than others, those at the top of the ladder (measured either by income or by subjective position) report the highest falls in their relative standing.<sup>14</sup>

**Figure 8. Perceptions of changes in social position**

<!-- Start of picture text --> Average reported change in social position<br>by reported position four years before<br>Western Europe New EU member States* South-eastern Europe<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Belarus-Moldova-Ukraine South Caucasus Turkey<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Russia Central Asia Transition Average<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>2 2 2<br>0 0 0<br>-2 -2 -2<br>-4 -4 -4<br>-6 -6 -6<br>2 2 2<br>0 0 0<br>-2 -2 -2<br>-4 -4 -4<br>-6 -6 -6<br>2 2 2<br>0 0 0<br>-2 -2 -2<br>-4 -4 -4<br>-6 -6 -6<br><!-- End of picture text --> Source: LiTS II (2010).

Note: * Includes the countries of Central Europe and the Baltics, Bulgaria, and Romania. x-axis is household reported position on the 10-step ladder four years ago (relative income). y-axis is the difference between the household reported position today and its reported position four years ago.

Looking towards the future, households are moderately optimistic about the next four years and most (those who feel they rose and those who feel they fell in the social ladder) expect an improvement in their social position. Large shocks, both negative and positive, seem to be viewed as transitory, as those who believe that they fell furthest down the ladder expect a rebound and those who gained most in relative terms expect a slight fall. Such expectations of social mobility may affect preferences regarding the role of the state in insuring and redistributing incomes as well as life satisfaction.<sup>15</sup>

> 14 This is both because households who report they were at the top of the ladder four years before are more likely to report a fall and because the perceived fall is larger for those who considered themselves to be at the top.

> 15 For example, the “prospect of upward mobility” may affect preferences for redistribution, as shown by Benabou and Ok (2001) .

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An example of sliding-scale regulation is the system governing the New York Telephone Company established in 1986, which operated according to the following rules.

**Table 3.2: An example of sliding-scale regulation**

|**Rate of return, R**(%)|**Adjustment to revenues**|
|---|---|
|Over 15|Revenues adjusted down by**1⁄2/2(R**<br>- 15)|
|Between 13 and 15|No adjustment|
|Under 13|Revenues adjusted up by '2(13 - _R)_|

_Source:_ Laffont and Tirole (1993), A _Theory of Incentives in Procurement and Regulation,_ MIT Press, p. 16.

Under this scheme, full cost-efficiency incentives are retained in the intermediate band of 13-15% as any profit changes are passed through to the company in full. In the higher and lower bands, incentives are weakened as 50% of any gain or loss accrues to customers: this is the cost of avoiding excessive profits on the upside and excessive risk to the company on the downside. Incentives are stronger than under US-style rate-of-return regulation but far weaker than under the price-cap system.

# **3.5.3 Discretionary systems**

In many countries, particularly in continental Europe, utility companies are controlled without the kind of explicit regulatory framework that exists in the UK and United States. In Austria, for example, gas prices are set by a committee composed of a wide spectrum of interests, with representatives from government ministries, industry, agriculture and labour.

Under a discretionary system, it is unclear what criteria are used in setting prices and the regime cannot even be approximated to rate-of-return or price-cap 14
where MPK and MPL are the aggregate marginal products of capital and labour, i.e. the derivatives of total production with respect to K and L. The third term in Equation (17) is the contribution of within-sector TFP growth to aggregate TFP growth. The first two terms are the contributions of reallocations of factors across sectors to aggregate TFP growth. These expressions are very intuitive. If the growth rate of capital (labour) input is higher than average in a sector where the marginal product of capital (labour) is higher than average, this will contribute positively to higher aggregate productivity growth.

We first use this framework to provide estimates of the contribution of intersectoral factor reallocation to growth in China between 1984 and 1999, following the calculations in Kraay (1996) and World Bank (1996). We separate the Chinese economy into agriculture, industry, and services, and within industry and services distinguish between state-owned firms, collectively-owned firms, and other ownership firms consisting primarily of foreign investment, and private firms. Using published Chinese data and a number of assumptions detailed in Appendix 1 of World Bank (1996) we are able to construct estimates of the distribution of real value added and the distribution of employment across these seven sectors. Assuming a Cobb Douglas production function and using output elasticities of capital and labour as described in World Bank (1996), we are able to calculate the second line of Equation (17) which captures the contribution of labour reallocation to aggregate productivity growth. The results of these updated calculations through 1999 are reported in Table 3.<sup>9</sup> Factor reallocation has contributed about 1.3 percentage points of growth between 1984 and 1999, and the pace of this factor reallocation has remained fairly stable. What has

> 9 Unfortunately we are not able to update these calculations past 1999 due to a break in the series on the distribution of industrial production by ownership.  Up to 1999, the China Statistical Yearbooks report this distribution for all firms, but after 1999 they report this information only for large firms above a certain size threshold, and these large firms are disproportionately stateowned.

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It might be conjectured that the signs of positive welfare impacts using SDD2 reflect some broader concept of ―welfare‖ than captured by consumption. Or one might argue that welfare recall uses different implicit weights, possibly reflecting missing or imperfect markets. Looking at the SDD2 results in Table 2, there is some sign of an impact on ―health‖ that may account for the implied gain in overall standard of living. Amongst the consumption goods, clothing shows the strongest positive impact using SDD2. However, there is less sign of impacts on any of the many other dimensions of welfare for which the recall questions were used. Nor is it clear why these effects would only emerge when one uses the SDD2 method.

As an additional test for differences between project and comparison villages in ―nonincome‖ factors in subjective welfare I exploited the fact that the recall module included questions on perceived current living conditions (for the same items in Table 2). I examined the relationship between the answers for overall standard of living and consumption per person in the 2004/05 survey data, to see if there are any signs that the relationship is different between SWP and non-SWP villages, as might arise from impacts of SWP on ―non-income‖ dimensions of welfare captured in the subjective assessments. The test entailed regressing each subjective measure of the level of welfare on log consumption per capita in 2004/05, a dummy variable for SWP villages and the interaction effect between these two variables. There were no significant differences between SWP and non-SWP villages for all except one of the categories in Table 2. The one exception was for roads (―are you satisfied with village road conditions?‖); households with higher consumption in the SWP villages tended to rate road quality higher, but there was no such gradient in non-SWP villages. One might take this to suggest that the SWP enhanced perceived road quality for better-off households, although one cannot dismiss the possibility that one will get at least one significant result in 30 tests purely by chance.

These observations are hardly conclusive, but they don’t leave one confident that SDD2 has revealed some genuine impacts that were somehow missing from the DD and SDD1 estimates. As the next section will show, a further insight into the SDD estimates can be obtained by looking more closely at the relationship between the recall of changes in household living standards since the project began and the measured changes in consumption.

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the entry of Wal-Mart into Mexico facilitated the modernization of the retail sector and stimulated fundamental changes in the relationship between retailers and suppliers of soaps, detergents, and surfactants. The entry of Wal-Mart pushed highcost suppliers out of business, benefited surviving producers by providing access to a larger market and prompted suppliers to introduce more innovations. Survey evidence from Romania confirms that firms that supplied foreign supermarket chains were more likely to innovate, diversify their production and improve the quality of packaging than firms that did not serve foreign retailers (figure 2). An econometric analysis based on Romanian firm-level data also finds that the expansion of global retail chains led to a significant increase in the total factor productivity in the supplying industries. A 10 percent increase in the number of foreign chains’ outlets was associated with a 2.4 to 2.6 percent boost to the TFP in the supplying industries. The decomposition of the aggregate productivity in the supplying industries suggests that the boost to performance was driven by both within-firm improvements and between-firm reallocation (Javorcik and Li 2013).

# **Conclusions and Policy Implications**

The evidence reviewed in this study is consistent with the view that jobs created by FDI are good jobs, both from the worker’s and the country’s perspective. From the worker’s perspective, this is because such jobs are likely to pay higher 25
between October 2018 and February 2019 (World Bank, 2019). Uganda now hosts about 1.4 million refugees, from 17 different countries (UNHCR, 2020). In this study, the refugee data set captures a cumulative total of 3,391,194 refugees in the years 2000 to 2016. Arguably, Uganda currently hosts the most refugees in Sub-Saharan Africa and is the third largest refugee hosting nation globally after Turkey and Pakistan (REACH Initiative, 2018; WHO, 2018; World Bank, 2019; UNHCR, 2020). The highest cumulative total number of refugees received in Uganda between the years 2000 and 2016 are from Sudan, South Sudan and the Democratic Republic of Congo (DRC) which represent about 78% of the cumulative total number of refugees received in the country (Figure 1).<sup>2</sup> <!-- Start of picture text -->
1.5e+06<br>1.0e+06<br>500000<br>0<br>Total No. of Refugees<br>Sudan South Sudan Dem. Rep. of the Congo Rwanda Somalia Burundi Kenya Eritrea Ethiopia Central African Rep. Liberia Nigeria Congo Malawi United Rep. of Tanzania Sierra Leone Iran (Islamic Rep. of)<br><!-- End of picture text -->

Figure 1: Total number of refugees by country of origin ( _Source: Authors’ illustration using study dataset_ ) Refugees in Uganda are mostly settled in the western flank of the country (Figure 2). Of the 127 districts of Uganda, the refugees are found in Adjumani, Arua, Koboko, Moyo and Yumbe in the West Nile region; Lamwo/Kitgum in the Northern region; Kiryandongo, Hoima, Kyegegwa, Kamwenge and Isingiro in the (South) Western region and; Kampala district in the central region of the country (World Bank, 2019). Therefore, from 2000 to 2016, the refugees have been distributed over 14 districts with Adjumani district hosting the highest number of refugees, about 28% of the cumulative total of the refugees. Arua, Kampala and Isingiro districts follow with about 13% each of the cumulative total number of refugees in Uganda between 2000 and 2016. Kisoro district has hosted the lowest number of refugees with less than 1% of the cumulative total of refugees in Uganda over that period (Figure 3).<sup>3</sup>

> 2 The absolute figures are in Appendix 1, Table A1.

> 3 The percentages are in Appendix 1, Table A2 and the averages are in Appendix 1, Table A3. Figure 4 also shows that the distribution of the female and male refugees is quite balanced across the districts with some

5
# **I. INTRODUCTION**

Some progress has been made towards developing an understanding of the causes and consequences of child malnutrition in the development process of poorer countries, but substantial gaps remain in our knowledge about the size and distribution of health and nutrition problems, the determninants of health and nutrient status, the impact of health and nutrition on socioeconomic development, and appropriate nutritional policy design. This paper focuses on the first two issues, namely the extent and the determinants of poor child health and nutritional status as reflected by attained height, in the context of a socially, economically, ethnically, and geographically diverse developing country such as Guatemala.

Special attention is paid to the impact of community factors, which, in recent studies, have been found to affect a number of demographic behaviors and outcomes such as contraceptive use (Entwisle, Casterline and Sayed 1989; Entwisle _et al._ 1996), poverty (Tienda 1991), the use of modem health services (Pebley, Goldman and Rodriguez 1996), and child survival (Sastry 1996). We also investigate empirical questions that have been ignored in previous anthropometric research, such as the distribution of child stunting across communities and the magnitude of intra-family correlation of height-for-age outcomes, before and after controlling for observed covariates.

Guatemala is among the poorest countries in Latin America. According to the National SocioDemographic Survey, 65.6% of Guatemalan people lived below the poverty line in 1989. Among these, 38.1% were below the extreme poverty line. These two figures are higher for the indigenous subpopulation: 86.6% and 61.0% respectively (Steele 1993). The poverty of Guatemalans is revealed indirectly by the anthropometric outcomes of their children. The WHO Global Database on Child Growth, based on nationally representative cross-sectional data gathered between 1980 and 1992, covers 87% of the total population under age five in developing countries (de Onis _et al._ 1993). It reveals that the prevalence of stunting (low height-for-age) among Guatemalan children below age three in 1987 (57.9%) was the highest in Latin America. Moreover, among all 79 countries for which there is reliable information available, only Bangladesh, E]thiopia, and India have a higher prevalence of stunting: 64.6%, 64.2%, and 62.1% respectively. In addition, the prevalence of underweight (low weight-for-age) children in Guatemala was 33.5%, the second highest in Latin America after Haiti. The prevalence of wasting (low weight-for-height), however, was very low (1.4%).

The paper is structured as follows. Section 2 outlines the issues involved in using anthropometric outcomes as a measure of child health status. Section 3 describes the main features of the _Encuesta Guatemalteca de Salud Familiar_ (EGSF), which provided the data for our empirical work. In Section 4, we present the results of a descriptive analysis of the EGSF data. Section 5 presents the conceptual framework, the analytical strategy and the statistical methods that we have adopted to model the process 1
due to remote violence. Fatalities are slightly higher immediately before and after displacement, but these changes relative to six months before displacement are not precisely estimated. Furthermore, the magnitudes are smaller than those reported for ground battles.

For other forms of violence, we find that fatalities were highest prior to displacement, and for many months after displacement, there was actually a declining trend in other forms of violence. This pre-displacement peak explains why the post-displacement change estimated in Table 3 is actually negative.

Combined, these estimates demonstrate that there is a great deal of heterogeneity in whether and how certain types of violence cause displacement, and this is one of the reasons that the baseline estimates in Tables 1 and 2 were not larger. Ground battles are particularly important to the decision to become displaced and differ from other forms of violence in important ways- ground battles are often associated with the capture of territory and a change in governance, likely indicate a longer period of violence as many of the front lines have remained relatively stable over much of the conflict, and are more deadly than other types of violence in the country (e.g., OCHA 2019a).<sup>17</sup> Each of these factors could be particularly important to the decision of whether and when to become displaced.

But in addition to investigating heterogeneity in the types of violence that tend to force displacement, we also investigate heterogeneity in the amount of violence experienced before households become displaced.<sup>18</sup> Figure 5 reports the share of displaced households by the amount of total violence in the six months leading up to and during the month of displacement. Strikingly, 47 percent of the sample became displaced with zero fatalities

> 17The importance of ground battles to displacement has been corroborated by news sources over the conflict as well. For example, when Houthi forces captured Al Jawf in 2019, the increase in fatalities from ground battles and the capture of territory led to a significant increase in displacement (e.g., BBC 2020).

> 18We focus on households that flee districts with no violence in the months before displacement. However, we could similarly focus on households that chose not to become displaced in very violent districts. However, given the relatively large size of districts and data limitations, we are unable to infer how close households actually are to the violence that occurs in their district. Alternatively, we focus on households that become displaced with zero violent incidents precisely because, short of measurement error, can infer that there was no violence in close proximity.

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