rafmacalaba/datause-encoder-data huggingface dataset:
47

**Figure 16: Cross-country variation in aggregate credit and share of firms with credit**

<!-- Start of picture text --> 80.00<br>60.00<br>40.00<br>20.00<br>EGY<br>0.00<br>0.00 0.50 1.00 1.50<br>Bank Credit to GDP<br>EGY<br>Other<br>Fitted values<br>Percentage of firms with loan<br><!-- End of picture text --> Source: Global Financial Development Indicators, own calculations There are several reasons to explain this discrepancy between aggregate and microdata. One reason for Egypt being such a big outlier lies in the banking sector focusing historically on mostly connected lending, based on names or connections, with the large majority of enterprises being excluded from the formal banking sector and thus puts in perspective the high value of Private Credit to GDP documented above. This is also confirmed by considering the share of lending that goes to governments and state-owned enterprises, which is substantially above its predicted value (Figure 17).

47
industrial pollution that can lead to an array of water-borne and diarrheal diseases (Field, et al., 2011). Unlike surface water sources, groundwater sources are more protected from microbial contamination because they are not exposed in the open and are naturally filtered through bedrock and sediments. Millions of groundwater tubewells were thus installed throughout Bangladesh from the 1970s onwards (Smith, et al., 2000).

However, in the early 1990s, there were emerging signs that Bangladeshis primarily from rural areas were developing signs of arsenicosis, a common effect of prolonged arsenic poisoning marked by skin discolorations and lesions, cancers, blood vessel diseases, and other disorders (Smith, et al., 2000). After there was enough evidence to document the scale of the problem, a massive testing campaign was launched in 1998 to test tubewells for the presence of arsenic in areas with high incidence of reported arsenicosis. By 2005, the testing campaign resulted in millions of tubewells that were painted red to indicate that a tubewell exceeded the Bangladeshi standard arsenic concentration level of 50 ppb (Bennear, et al., 2013). The most recent estimates indicate that about 25 percent of all tubewells across Bangladesh exceed the WHO recommended safe levels of arsenic at 20 ppb (Flanagan, et al., 2012).

Even with advancements in mitigation strategies, millions of Bangladeshis are still at risk for arsenic poisoning today from contaminated drinking water, soil, and crops (Flanagan, et al., 2012). The effect of arsenic poisoning on human health has long been established. Exposure to arsenic is associated with several dose-dependent illnesses, such as cancer of the lung and bladder, cardiovascular and pulmonary disease, and skin lesions (Smith et al., 1998; Sohel et al., 2009; Flanagan et al., 2012). Ingestion of arsenic has also been shown to contribute to poor pregnancy-related outcomes, including spontaneous abortion, low-birth weight, and other pregnancy complications (Kile, et al., 2014; Kile, et al., 2016). At an individual-level, the degree of toxicity of arsenic can depend on a person’s general health, genetics, and diet (Anawar, et al., 2002; Karim, 2000). Population-wide studies in Bangladesh have found that hyperpigmentation and keratosis are the most diagnosed health effects of chronic arsenic poisoning followed by skin cancer and other internal cancers (Karim, 2000; Yu, et al., 2003). However, it is conjectured that arsenic contributes to other dysfunctions that are more difficult to diagnose or attribute solely to arsenic poisoning (Pitt, et al., 2012).

Neurological damage is perhaps one of the more hidden consequences of arsenic poisoning; yet there is growing research to indicate that there are significant population effects on cognitive dysfunction. One of the first epidemiological studies to note correlations between arsenic exposure and neurological damage in children is by authors Calderon et al. (2001). Using the Weschler 5
# **3.2 Data**

Our analysis considers all 25 administrative regions (oblasts) in Ukraine, including Kyiv city as a special region. For the purposes of this paper, the pre-conflict period (2008-2012) is defined as the 5-year period before the Maidan protests in 2013, and the post-conflict period is between 2014 and 2019 (the last year of our data). All regional statistics for the pre-conflict period, including population and average per capita income numbers, are based on the official information gathered by the State Statistics Services of Ukraine (UkrStat). Our data also includes gross bilateral migration flows between oblasts for the same period, which were reported in UkrStat’s demographic yearbooks. Information on these flows reflects the official registrations of residence recorded by the State Migration Service of Ukraine.

Descriptive statistics show that Donetsk and Luhansk regions were among the most populous oblasts in Ukraine before the onset of the conflict in 2014 (Figure 1b). With 4.5 million and 2.3 million residents, they were ranked 1st and 7th among all regions, respectively. They also had relatively high real gross regional product per capita (2nd and 9th, respectively). Compared to other regions, however, migration flows into Donbas were relatively small. Incoming migrants constituted only 0.26 percent of local population in Donetsk, and 0.28 percent in Luhansk, putting them among the least attractive regions (24th and 25th among all) for migrant arrivals (1c). Among the most important sources of migrants were neighboring regions. Donetsk, for example received 22.8 percent of all its inflows from Kharkiv, 20.7 percent from Luhansk, and 12.3 percent from Dnipropetrovsk.

Migration outflows from Donbas were also small. While the nation-wide interregional migration outflow rate stood at 0.56 percent of population annually, it was only 0.3 and 0.4 percent in Donetsk and Luhansk— the 2nd and the 7th lowest rates among all regions, respectively. Before the conflict, migrants leaving Donbas largely concentrated in neighboring regions and in Kyiv city. For example, of all migrants from Donetsk and Luhansk oblasts, on average, 31 percent went to Kharkiv every year, and 18 percent to Kyiv city (Figure 2). More distant or economically less attractive regions received fewer migrants from Donbas.

10
rural workers. But only 6% of rural non-farm workers managed to move to urban areas – whilst almost a quarter fell back into agriculture.

Third, movements from urban non-farm employment to rural non-farm employment appear not to be associated with distress. This suggests that these better off workers may move back to rural areas voluntarily for the purpose of investment in their home areas.

# POVERTY TRANSITIONS

The income growth table shows what happened to incomes. But income growth does not necessarily translate into movements in and out of poverty. To evaluate these movements, Table 5 shows a detailed poverty transition matrix between 1993 and 2000, disaggregated by rural and urban as well as agricultural and non-agriculture. The poverty of individuals is, of course, determined by the level of welfare of their whole households. We therefore determine the poverty status of individuals who were aged between 15 and 55 in 1993 and were earning income by whether the per capita expenditure of their households fell above or below the poverty line.

Although the IFLS data are not entirely representative of the Indonesian population, the 5308 workers tracked in Table 5 do represent basic Indonesian poverty patterns reasonably faithfully. Rural poverty made up 74.9 percent of total poverty in 1993 in the unweighted IFLS data, compared with 74.0 percent in the 1993 Susenas data. Analysis of how these 5308 workers fared between 1993 and 2000 should provide very useful insights into the dynamics of poverty in Indonesia.

22
PRE Wrn2Pan2er Series

|||IAI<br>AuIb2c||Contact<br>_L_<br>_LaQ_<br>for1aprl|
|---|---|---|---|---|
|WPS702|Should Price Reform Proceed<br>Gradually or in a "Big Bang?"|Sweder van Wijnbergen|June 1991|M. Stroude<br>38831|
|WPS703|The Political Economy of Fiscal<br>Policy and Inflation in Developing<br>Countries: An Empirical Analysis|Sebastian Edwards<br>Guido Tabellini|June 1991|A. Bhalla<br>37699|
|WPS704|Costs and Finance of Higher<br>Education in Pakistan|Rosemary Bellew<br>Joseph DeStefano|June 1991|C. Cristobal<br>33640|
|WPS705|What Causes Differences in<br>Achievement in Zimbabwe's<br>Secondary Schools?|Abby Rubin Riddell<br>Levi Martin Nyagura|June 1991|C. Cristobal<br>33640|
|WPS706|Successful Nutrition Programs in<br>Africa: What Makes Them Work?|Eileen Kennedy|June 1991|0. Nadora<br>31091|
|WPS707|Population, Health, and Nutrition:<br>Fiscal 1990 Sector Review|Population, Health,<br>and Nutrition Division,<br>Population and Human<br>Resources Department|June 1991|0. Nadora<br>31091|
|WPS708|Nongovernmental<br>Organizations and<br>Health Delivery in Sub-Saharan Africa|jocelyn DeJong<br>|June 1991|0. Nadora<br>31091|
|WPS709|An Empirical Macroeconomic<br>Model<br>for Policy Design: The Case of Chile|Luis Serven<br>Andres Solimano|June 1991|S. Jonnakuty<br>39074|
|WPS710|Urban Property Tax Reform:<br>Guidelines and Recommendations|William Dillinger|June 1991|V. David<br>33734|
|WPS711|Financial Reform in Socialist<br>Economies in Transition|Millard Long<br>Silvia B. Sagari|June 1991|M. Raggambi<br>37657|
|WPS712|Foreign Direct Investment in<br>Developing Countries: Patterns,<br>Policies, and Prospects|Thomas L. Brewer|June 1^91|S. King-Watson<br>31047|
|WPS713|The Determination<br>of Wages in<br>Socialist Economies: Some<br>Microfoundations|Simon Commander<br>Karsteni Staehr|June 1991|0. Del Cid<br>39050|
— evaluation control group, who passed the psychometric cut-off _and_ the regular screening in terms of endline sales and capital stock, with negative but insignificant point estimates for most other outcomes including profits (all conditional on the psychometric score). The documentation check and business appraisal thus appears to have screened positively for firm performance. The total RCT impact evaluation sample consisted of 131 women entrepreneurs who had satisfied the test cut-off score, the documentation check and the business appraisal.<sup>7</sup> Of these women, 69 were revealed to have been computer-randomized to the control group, and 62 to the treatment group to be offered the loan.<sup>8</sup> Table 1 shows that the assignment was balanced on pre-specified observable characteristics, except for whether the respondent has children, and that the F-test for joint significant of covariates by treatment assignment is not significant. The average age of women business owners in our sample is 35, with 61% married and 59% having completed secondary school education or higher. The average business age is 6 years. The majority of firms are in the retail sector (44%), followed by cafes and restaurants (18%), and business services (12%). Over 90% of women in our RCT sample have never previously received a business loan.

The endline survey was conducted face-to-face in September-October 2021, targeting the entire baseline sample of 357 entrepreneurs.<sup>9</sup> Of these 357, a total of 314 (88%) completed the endline survey, including 124/131 (95%) of the women in the impact evaluation sample. Table 1 shows that attrition was not significantly different by treatment status, nor by prespecified covariates except for total household income, and that the F-test of joint significance

> 7Our target sample size according to initial power calculations was 600. The reduced sample size reflects the early curtailing of the project by the MFI due to the onset of the COVID-19 pandemic in March 2020. Ex post power calculations suggest reasonable power for outcomes such as firm closure; but weaker power for noisier, continuous outcomes such as profits.

> 8The pre-analysis plan mentioned a sample of 124 women, of whom 63 were in the control group and 61 were in the treatment group. This was due to an administrative error in the first version of administrative data shared by the MFI, where seven women (6 control, 1 treatment) were marked as having their applications still in process when in fact the applications had been fully processed.

> 9We also conducted a short follow-up phone survey to understand the impacts of COVID-19 in April-May 2020, targeting just the firms in the RCT impact evaluation sample. The eventual sample for the phone survey included 101 of these firms. Of the 101 who participated, 65 women’s businesses were permanently or temporarily closed due to COVID-19, and hence they did not complete parts of the survey pertaining to recent business activity. This left only 36 women with measured business outcomes during the phone survey. We hence do not rely on this phone survey as a major data source for the impact evaluation.

9
**Figure 3.7:** Mortgage Penetration and Economic Growth in SSA

<!-- Start of picture text --> 0<br>-1.5 -1 -0.5 0 0.5 1<br>-0.5<br>-1<br>-1.5<br>-2 y = 0.1383x - 1.2774<br>R2 = 0.0696<br>GDP growth (%)<br><!-- End of picture text --> <!-- Start of picture text --> R2 = 0.0696<br>-2.5<br><!-- End of picture text --> _Source_ : Authors’ calculation based on a collection of data from a survey, the World Bank and Housing Finance in Africa yearbooks 2010, 2011 and 2013. Available at: <u>www.housingfinanceafrica.org (accessed: Sept</u> 08, 2014).

_Note_ : variables are in logarithmic terms on both axes.

**Figure 3.8:** Mortgage Penetration and Gini Coefficient in SSA

<!-- Start of picture text --> 0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2 y = -0.489x + 0.4524<br>0.1 R2 = 0.0143<br>0<br>-0.02 0 0.02 0.04 0.06 0.08<br>Mortgage penetration (%)<br>Gini coeficient function of mortgage depth<br>Linear (Gini coeficient function of mortgage depth)<br>Gini coeficient (%)<br><!-- End of picture text --> _Source_ : Authors’ calculation based on a collection of data from a survey, the World Bank and Housing Finance in Africa yearbooks 2010, 2011 and 2013. Available at: <u>www.housingfinanceafrica.org</u> (accessed: Sept 08, 2014).

_Note_ : variables are in logarithmic terms on both axes.

**Figure 3.9:** Nexus between Mortgage Penetration and GDP Per Capita Growth in SSA

<!-- Start of picture text --> 0.15<br>0.1<br>0.05<br>0<br>-1 0 1 2 3<br>-0.05<br>-0.1<br>-0.15<br><!-- End of picture text -->

**Figure 3.10:** Mortgage Penetration and HDI

<!-- Start of picture text --> 0<br>-2 -1 -0.05 0 1<br>-0.1<br>-0.15<br>-0.2<br>-0.25<br>-0.3<br>-0.35<br>-0.4<br>-0.45<br>-0.5<br><!-- End of picture text --> _Source_ : Authors’ calculation based on a collection of data from a survey, the World Bank and Housing Finance in Africa yearbooks 2010, 2011 and 2013. Available at: <u>www.housingfinanceafrica.org</u> (accessed: 08/09/2014).

In order to complete this exploratory analysis, we have presented a housing finance market typology for some selected SSA countries in the appendixes. The case study focuses on three SSA countries from three different subregions. These countries are Cameroon for Central and West Africa, Tanzania for East Africa, and Zambia for Southern Africa.

11
|P075016|2002|2006|6/26/2006|9 Closed|Bangladesh|
|---|---|---|---|---|---|
|P075016|2002|2007|12/21/2006|10 Closed|Bangladesh|
|P075016|2002|2007|6/21/2007|11 Closed|Bangladesh|
|P075016|2002|2008|6/21/2007|11 Closed|Bangladesh|
|P075035|2004|2004|6/22/2004|1 Closed|China|
|P075035|2004|2005|5/20/2005|3 Closed|China|
|P075035|2004|2006|2/22/2006|4 Closed|China|
|P075035|2004|2007|8/29/2006|5 Closed|China|
|P075035|2004|2008|8/31/2007|6 Closed|China|
|P075035|2004|2009|3/11/2009|7 Closed|China|
|P075035|2004|2010|1/27/2010|8 Closed|China|
|P075035|2004|2011|6/21/2011|9 Closed|China|
|P075046|2007|2007|6/28/2007|1 Closed|Europe and Central As|
|P075046|2007|2008|6/19/2008|2 Closed|Europe and Central As|
|P075046|2007|2009|6/30/2009|3 Closed|Europe and Central As|
|P075046|2007|2010|6/30/2009|3 Closed|Europe and Central As|
|P075056|2003|2003||0 Closed|India|
|P075056|2003|2004|6/4/2004|2 Closed|India|
|P075056|2003|2005|6/13/2005|4 Closed|India|
|P075056|2003|2006|12/18/2005|5 Closed|India|
|P075056|2003|2006|3/20/2006|6 Closed|India|
|P075056|2003|2007|9/19/2006|7 Closed|India|
|P075056|2003|2007|3/13/2007|8 Closed|India|
|P075056|2003|2008|9/13/2007|9 Closed|India|
|P075056|2003|2008|3/31/2008|10 Closed|India|
|P075056|2003|2009|7/26/2008|11 Closed|India|
|P075058|2005|2005|5/7/2005|1 Closed|India|
|P075058|2005|2006|11/4/2005|2 Closed|India|
|P075058|2005|2006|5/10/2006|3 Closed|India|
|P075058|2005|2007|11/7/2006|4 Closed|India|
|P075058|2005|2007|5/3/2007|5 Closed|India|
|P075058|2005|2008|10/18/2007|6 Closed|India|
|P075058|2005|2008|4/9/2008|7 Closed|India|
|P075058|2005|2009|10/9/2008|8 Closed|India|
|P075058|2005|2009|3/23/2009|9 Closed|India|
|P075058|2005|2010|9/18/2009|10 Closed|India|
|P075058|2005|2010|4/21/2010|11 Closed|India|
|P075058|2005|2011|5/14/2011|12 Closed|India|
|P075058|2005|2011|6/6/2011|13 Closed|India|
|P075058|2005|2012|2/9/2012|14 Closed|India|
|P075058|2005|2013|9/12/2012|15 Closed|India|
|P075058|2005|2013|4/27/2013|16 Closed|India|
|P075058|2005|2014|8/17/2013|17 Closed|India|
|P075058|2005|2014|1/15/2014|18 Closed|India|
|P075058|2005|2015|7/30/2014|19 Closed|India|
|P075058|2005|2015|10/6/2014|20 Closed|India|
|P075058|2005|2015|12/15/2014|21 Closed|India|
We use this data to calculate a matrix of distances between each parish-year and each settlement open that year. We calculate distances from the boundaries of the parish polygons and refugee settlement polygons, as opposed to using their centroids, since some of these settlements are quite large. Along with distances to each settlement, we also know the population size of each settlement across the study years. Intuitively, our measure of exposure should be greater for parishes that are _closer_ to _larger_ settlements, particularly if they are near _multiple_ settlements.

We therefore operationalize _refugee presence_ in these three ways:

1. Nearest: For each parish, exposure is only based on the nearest settlement _n_ in year _t_ , log ( distancepopulationnt+1nt<sup>+ 1</sup> ), in which distance is measured in kilometers. 2. Nearest + 20: For each parish, exposure takes into account not only the nearest settlement _n_ <u>populationnt populationit</u> in year _t_ , but also all settlements _i_ within 20km of the parish, log ( distancent+1<sup>+ ∑</sup> _i∈_ rad20 _km,−n_ distanceit+1<sup>+ 1</sup> ). 3. Nearest + 50: For each parish, takes into account the nearest settlement _n_ in year _t_ and all <u>populationnt populationit</u> settlements _i_ within 50km of the parish, log ( distancent+1<sup>+ ∑</sup> _i∈_ rad50 _km,−n_ distanceit+1<sup>+ 1</sup> ).

We take the logarithm of the quotient of settlement population and distance; otherwise, this measure would be left-skewed since most parishes have low levels of exposure to refugees. We standardize all three measures to have a mean of 0 and standard deviation (SD) of 1 for ease of interpretation. For parishes within the 150km cutoff, Figure 4 displays their raw exposure measures (jittered) by year and the mean values (blue). The figure illustrates that there is a marked increase in all measures of exposure in 2016, which we would expect given the post-2014 influx of refugees. Figures S5 and S6 in the SI also show the distribution of our exposure measures. Following Maystadt and Verwimp (2014) and Maystadt and Duranton (2019), our measures of refugee presence are continuous, consider both the size of and distance to the proximate settlements, allow a locality to be affected by multiple settlements, and are more directly related to theory.

# **4.3 Outcomes: Public Goods Access and Utilization, Attitudes toward Migrants, and Insecurity**

The main outcomes of interest for public goods and development include newly constructed geocoded data on primary schools, secondary schools, health clinics and hospitals, health utilization, and road density. First, our primary school data on over 19,500 schools comes from the Uganda Education Management Information Systems, and is supplemented by about 2,700 additional schools manually collected by a Ugandan education consultant we hired to do manual checks. For each primary school, we have their geographic coordinates, founding year, and whether they 14
# **I. Introduction**

Armenia’s growth story from 2003 to 08 is that of an expatriate-financed construction boom that went bust once external financing dried up as a result of the world financial crisis. This small landlocked former Soviet republic, dependent to a large extent on Russia for energy supplies and on volatile remittances, is growing at sub-par rates, with a per-capita GDP roughly less than a third of neighboring Turkey. The pre-2009 high growth rates (and productivity) were mainly driven by resource exploitation and construction. So to move to a higher growth path, Armenia’s most critical priorities are to diversify its economy and improve firm productivity in the tradable sectors to compete internationally. As a small open economy relying a lot on remittances from Russia and export of metals and minerals to some EU countries, Armenia can still grow fast, but such an outcome is not automatically ensured. It will require efforts to boost aggregate demand by keeping investment high and expanding exports, to increase productivity by allowing the reallocation of production factors to their most efficient use, and to extend the country’s production frontier through the diversification of products and trading partners.

This paper focuses on the aggregate demand side, particularly investment, by looking at the determinants of the savings rate in Armenia. Armenia relies heavily on foreign savings to finance its investments. In 2010, about two-thirds of total investment was financed by foreign savings. Although some portion of foreign savings are channeled to Armenia as remittances and transfers, there are still major risks associated with external shocks that subject Armenia’s growth to volatility and undermine the country’s long-term growth outlook.

**Figure 1: Armenia: Domestic Saving Performance, 2002-2011**

a: Gross savings, percent of GNI b: Market capitalization, percent of GDP <!-- Start of picture text --> Average 1993-2000 Average 2001-2010 100 93.0 average 2006-2010<br>35<br>90<br>30<br>80<br>25 70<br>56.3<br>20 60<br>50<br>15 38.1<br>34.9<br>40<br>10<br>30<br>5 20 10.4 8.1<br>0 10 2.3 1.1<br>0<br>-5<br>Armenia Low Lower Upper OECD<br>Income Middle Middle<br>Income Income<br>India<br>Philippi... Indonesia Ukraine Mongolia Georgia Kyrgyzst... Armenia<br><!-- End of picture text --> _Source_ : Ministry of Finance, World Development Indicators and Staff calculations.

Armenia’s domestic saving remains dominated by private saving (household and corporate) as public saving was relatively low during 2002-2011, with an average level of 1.65 percent of GDP compared to about 18.4 percent of GDP for private saving. It improved despite an almost inexistent capital market, with its aggregate saving rate remaining higher than that of the average low income country since 2001 (Figure 1, panel a). However, its average saving rate during 2001-2010 was only at par with the average of lower middle income countries during 1993-2000. The extremely low market capitalization during 2006-2010 (Figure 1, 2