rafmacalaba/datause-encoder-data huggingface dataset:
24 Time series data on emergency and non-emergency food aid are availabls only from 1979. Although this short panel does not allow the VAR estimation applied in this paper, it still allows us to examine the relationship between nonemergency food aid and commercial food imports. Indeed, the results are changed dramatically when the emergency components are netted out of total food aid. The contemporaneous coefficient of non-emergency food aid in the food import regression (that includes also lagged values of non-emergency food aid) now increases to -0.816 with a t value of 6.1. The parameter estimates of the aid variable lagged 3 periods are now all negative, but only marginally significantly different from zero. The important implication of these results is that increments to regular non-emergency food aid are replacing on the margin regular food imports almost totally. Thus the main force behind the disincentive hypothesis is eliminated: the net increase in food supply following an increase in regular food aid is relatively very small. We also estimated an equation that included 3 lagged values of the dependent variable, commercial food imports, as well as current and lagged value of regular non-emergency aid. The results did not change very much.

In another scenario the negative effect of food aid is minimized when the basket of food aid products is very different from the locally produced basket. In the short run there are more possibilities for complementarity than substitutability. The demand for domestic products may even increase. This is additional to the income effect generated from the transfer of income in kind (which leads to an increase in demand for domestic output). The available evidence suggests that most of the food items in the aid basket are different from the local produce in Sub-Sal aran Africa.
<!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Couple, 2 children Single, no child<br>income<br>% of median disposable<br>TUR HUN BGR USA SVK ROU HRV PRT ISR EST CAN CZE GRC LVA MLT SWE ESP NOR FRA OECD BEL ITA AUS CYP NLD POL SVN NZL ISL LUX CHE AUT KOR FIN DEU LTU IRL GBR DNK JPN<br><!-- End of picture text --> _Source: OECD (2023), Adequacy of minimum income benefits (indicator)._ _<u>https://data.oecd.org/benwage/adequacy-of-minimumincome-benefits.htm</u>_ _Note: The estimate for Bulgaria Includes the heating allowance._

# Heating Allowance

**The heating allowance is a non-contributory allowance granted to low-income households in Bulgaria.** The heating allowance is given to eligible individuals during the cold season (from November to March). In the 2019-20 season, the amount of the monthly allowance was determined to be sufficient to pay for 500 kWh of energy consumption per month, which could cover the cost of running a 2 kW heater for 8 hours a day for a month, enough to heat a space of approximately 24 m2. This allowance is provided to lone persons or households with low incomes. The right to a targeted heating allowance is granted to persons with income lower than the Differentiated Minimum Income for Heating (DMIH), which varies based on family demographics. The Minister of Labor and Social Policy determines the benefit amount for each heating season, lasting five months from November 1st to March 30th. For the 2018/2019 heating season, the corresponding benefit was BGN 74.83 monthly during the heating season (Vaughn & Cabrera, 2022).

**Before the reform, eligibility for the heating allowance was also linked to the GMI. Still, the minimum income thresholds to determine eligibility (Differentiated Minimum Income for Heating - DMIH) were 2 – 3 times higher than those in the MSA scheme (Differentiated Minimum Income).** More details of the eligibility conditions are in Annex 3. Therefore, the program covered about 8 percent of the population and roughly one in four poor in 2018. (World Bank, 2021, pp. 77–78). According to previous estimates from the World Bank (2020) with 2013/14 SILC data, the coverage of the heating allowance was in the range of 4.7 – 4.9 percent of the population in heating seasons 2012/13 and 2013/14, and the range of 15.4 – 15.9 percent of the poorest quintile respectively (World Bank, 2020).

**Nevertheless, the lack of a monthly electricity consumption threshold and the absence of GMI level indexing with inflation have constrained access to the heating allowance program, suggesting there is scope for policy improvements** . Energy poverty is estimated to impact the poor in Bulgaria significantly. In 2022, 42.7% of poor households (measured by the at-risk-of-poverty rate) could not afford adequate home heating, ranking second highest in the EU. Additionally, in 2021, over half of households spent more than 10% of their income on energy. Recent microsimulations on rising energy prices suggest a potential increase in energy and income <mark>poverty (Robayo-Abril and Rude, forthcoming).</mark> While Bulgaria's heating

11
## LIST OF TABLES AND ANNEXES

|Table 1:|PHN Lending for FY88 . . . .. . . . . . . . . . . . . . . .|.<br>2|
|---|---|---|
|Table 2:|PHN Lending Program, FY83-87 . . . . . . . . . . . . . . .|.<br>2|
|Table 3:|FY89 PHN Lending Program . . . . . . . . . . . . . . . . .|.<br>4|
|Table 4:|Five-Year PHN Lending Program, FY89-93 . . . . . . . . . .|.<br>5|
|Table 5:|PHN Five-Year Lending Program by Subsector . . . . . . . .|.<br>6|
|Table 6:|Distribution<br>of FY88 PHN Project Costs . . . . . . . . . .|.<br>10|
|Table 7:|Structural Adjustment Lending Program, FY88 . . . . . . . .|.<br>15|
|Table 8:|Average Supervision Staff Weeks for Agriculture,<br>Education, PHN and Urban Projects, FY85-88 . . . . . . . .|.<br>18|
|Table 9:|PHN Sector Reports Completed by Region, FY81-88 . . . . . .|.<br>19|
|Table 10:|Lending for Population by Region . . . . . . . . . . . . .|.<br>25|
|Table 11:|Higher-Level Staff in PHR Divisions by PHN Background and||
||Experience . . . . . . . . . . . . . . . . . . . . . . . .|.<br>32|

# Annex 1: FY90-93 PHN Lending Program

- Annex 2: Features of PHN Projects - FY88

- Annex 3: Family Planning/Population Characteristic of PHN Projects Approved, 1988 and Nutrition Actions in PHN Projects Approved in FY88

- Annex 4: Projects Arising from Health Financing Sector Work (Completed, Pioposed, or Initiated)

- Annex 5: PHN Projects Approved by Region and FY, Staff Weeks (SW) Expended and Calendar Months (PT) From Identification to Project Approval

- Annex 6: PHN Activities in FY88 Structural and Agriculture Sector Adjustment Loans (SALs)

- Annex 7; Features of PHN Projects, FY81-87

- Annex 8: PHN Sector Work Completed FY88

- Annex 9: Proposed Lending Operations Arising from Sector Work

- Annex 10: Research and Policy Work: Completed FY88

# Annex 11: Population Lending by Year and Region
where Λ(·) is a functional form that depends positively on GB<sup>s</sup> and the dots represent the other determinants of the demand for government bonds by domestic banks and profit earners. An increase in the supply of bonds GB<sup>s</sup> , fueled by an increase in the government deficit, would indeed raise the equilibrium bond rate, everything else equal.

Second, an increase in the stock of public debt will lead (with a oneperiod lag) to a rise in the perceived probability of default by investors, which will also tend to lead to higher interest rates on government bonds. As can be inferred from the previous equation, an increase in PDEF would indeed raise the equilibrium bond rate. The rise in interest rates would in turn worsen the overall deficit of the government–making the adoption of corrective fiscal policies inevitable. Higher interest rates therefore make an unsustainable fiscal policy more unsustainable, hastening the need for policy reforms.

To model credibility, we assume that the expected rate of inflation (which affects directly the demand for domestic currency, private investment, saving rates, and wage formation in the private formal sector), is given as a weighted average of the perceived (or explicitly announced) inflation target of the central bank, INFL<sup>TARG</sup> , and the one-period lagged inflation rate: where INFL is defined in (126) and 0 < CREDIB < 1 is our measure of credibility, defined as Credibility in our framework depends therefore only on fiscal policy; the stance or effectiveness of monetary policy (as measured, for instance, by deviations between actual and target inflation rates) plays no role.<sup>40</sup> Full credibility (CREDIB = 1) occurs only if the probability of default PDEF is zero. An increase in the probability of default lowers credibility and leads 40In Ozatay (2000) and Civcir (2002), credibility is measured by the average maturity of new domestic non-indexed public debt issues. However, maturity is treated as an exogenous variable, instead of being (inversely) related to the debt-to-GDP ratio, as one would expect. For other ways of modeling credibility involving forward-looking expectations in stochastic models, see Laxton, Ricketts, and Rose (1994), Huh and Lansing (1999), Isard, Laxton, and Eliasson (2001), and Erceg and Levin (2003). For econometric studies, see Ruge-Murcia (1995) and Agénor and Taylor (1992).

51
_Ways Out of Povertv - Dzffusinsg Best Practices and Creating Capabilities_ _20_ to allow physical assets, ideas, people, and funds to be used again in new and better ways when a firm fails. This is where the competition in markets comes in. Competitive markets select firms that pursue particular experiments or routines. They also provide incentives for market participants to improve and to adjust when they are facing failure. In some cases, incumbent firms adapt successfully. In others, new entrants drive out nonperforming firms.

> Over the last twenty years considerable work has been carried out to understand better the

> way markets with firms work. Most of this work covers advanced countries.<sup>48</sup> However,

> a significant amount of work has also been carried out in developing countries of all types. The picture is similar in developed as well as developing countries with the main

> difference that developing economies tend to have smaller firms than advanced

> economies (figure 1). 49

Figure **11: Distribution of employment by firm size** <!-- Start of picture text --> 90%<br>100% - X<br>2 80%<br>70% -<br>6 60%<br>5 50%<br>4 0%<br>30%<br>20%<br>10%<br>100-500 500-1000 1000- 2000- 5000+<br>2000 5000<br>GNP Per Capita, US$<br>oi Mcro (1-4) Srmall (5-19) 0 Mediumr(20-99) o Large (100+)<br>Source: Snodgrass and Biggs (1996).<br><!-- End of picture text -->

**Heterogeneity among firms.** Traditional economic textbooks treat markets as if all firms are efficient, driven by the incentives from the price signals in markets. When demand increases new firms would enter, and when it decreases some incumbents would fail. In reality the picture is quite different. Typically existing firms are not similarly efficient. Instead, they are quite heterogeneous with substantial dispersion in productivity among firms and among sectors. A detailed review of firm-level performance in 44 different

> 48 Caves 1998; Sutton, 1997; Tybout, 2000.

> 4 9 Audretsch, 2000; Aw, 2001; Nugent and Yhee, 2001. Also see the World Bank's RPED micro-studies for Afnca over the last decade (A list of available studies can be found at

> http://www.worldbank.org/afr/findings/frenchIrped.htm ).
<!-- Start of picture text --> Figure. .1: PROVINCIAL REVENUE AND EXPENDITURE<br>PER CAPITA, 1992<br>10 -<br>7<br>uJ<br>-2 l: - ll<br>5 5.5 6 46.7 .8 85<br>a.~~~~~~~<br>5 5.5 6 6.5 7 7.5 8 8.5<br>GOP per Capita (Log Scale)<br><!-- End of picture text --> In addition, there may of course be significant tax and expenditure spillovers across provinces. In 1992, for example, total expenditure of the central and provincial governments together accounted for almost 25 per cent of GDP. Of this, a little less than onehalf was contributed by revenues from State enterprises and turnover and profits taxes on nonState enterprises: and about 4 per cent of GDP or 16 per cent of total expenditures was financed by fiscal deficit. Taxes on enterprises (profits tax, depreciation and turnover tax) are basically collected predominantly from the two major cities (Ha Noi and Ho Chi Minh) by virtue of the location of head offices of major enterprises and the concentration of production and trading activities in these cities. In reality, however, these taxes tend to be passed on to consumers throughout the country. Similar spillovers will also occur, probably to a lesser extent, on the expenditure side. Although the inter-regional incidence of various revenues and expenditures is too complex a question to explore here, it seems reasonable to suggest that if these factors were taken into account the net transfers received by the poorer provinces would be generally lower than indicated by the gross transfer concept used in the preceding analysis.

38
Additionally, our data shows that firms facing financial constraints, on a volatile operating environment, and that experienced a growth slowdown played a special role, as they reduced their Gross Capital Formation and increased their ES at a significant faster pace. Nevertheless, it is worth emphasizing that these trends are also present in financially nonconstrained firms, in firms facing a low-volatility operating environment, and in high-growth and low-growth firms. These results hold even after controlling the financial management policy of the firms, the level of diversification to non-core activities, and the macroeconomic environment.

One of the objectives of this paper was to shed light on the nature of ES using firm-level data instead of national accounting aggregates. One of the main takeaways is that even though firms’ characteristics play a role, the phenomenon is more pervasive than initially expected. The policy message seems to be that although reducing financial constraints, reducing operating volatility, and ensuring investment opportunities for firms will certainly help, it will not solve the problem of the excess savings of the non-financial corporate sector, as firms not facing these challenges are still accumulating excess savings in their balance sheets.

12
significant, see table 3, for children born between 1980 and 1994) as well as children from mothers with at least some secondary (difference significant for all birth cohorts from 1980). Around the Khmer Rouge period, the usual advantage in terms of infant and child survival of the urban and educated was lost.

# **III.  Fertility and Marriage**

During a period of excess mortality, forced relocation and violence, fertility is expected to be low. Figure 9 displays the average number of births per women and per year for the two birth cohorts interviewed in 2000 that were already of fertile age in 1975. During the 1975-79 period, women born between 1950 and 1954 experienced a sharp decline in their fertility, although they were theoretically in their most fertile years. Similarly, the fertility of the 1955-59 birth cohort was very low, when compared with the 1950-54 birth cohort at the same age five years before. Combined with a high mortality rate among women, this very low fertility means that very few babies were born around the Khmer Rouge period.

However, very shortly after the end of the Khmer Rouge rule, Cambodia experienced a “baby boom” as illustrated on figure 9. During the early 1980s, fertility rebounded dramatically, reaching, for the two birth cohorts analyzed a probability to give birth during the year above 30 percent. Heuveline (2003) provides a detailed analysis of the Cambodian baby-boom, based on preliminary data collected by the Mekong Island Population Laboratory.

One of the main determinant of fertility is marriage. Figure 10 shows for the three birth cohorts that were of nuptial age in 1980, the probability that the first marriage occurred in any specific year. This probability was relatively low for each birth cohort during the 1975-79 period. The striking feature is however the very high marriage rates in 1979 and 1980 for the birth cohorts born in 1955-59 and 1960-64. The probability to have the first marriage peaks dramatically for both cohorts in the same year –1979 – whereas one would have expected, under normal circumstances, that the peaks for the two cohorts would have been five years apart. Similarly, the peak for the 1950-54 birth cohort is in 1970, nine years before the peak for the subsequent birth cohort. It seems therefore that the women who according to their age were most likely to marry for the first time during the Khmer Rouge period delayed their marital prospects.

Young adult males were among the most likely victims of the excess mortality during the Khmer Rouge period, as demonstrated in figure 3. In the early 1980s, there was therefore a large shortage of young males on the marriage market. However, it does not seem that this led to a large fraction of unmarried women: by 2000, not more than 9
|ShandongEnergyEfficiencyProject<br>Jonathan Edwards Sinton|
|---|
|ShandongEnergyEfficiencyProject<br>Jonathan Edwards Sinton|
|ShandongEnergyEfficiencyProject<br>Jonathan Edwards Sinton|
|ShandongEnergyEfficiencyProject<br>Jonathan Edwards Sinton|
|ShandongEnergyEfficiencyProject<br>Jonathan Edwards Sinton|
|ShandongEnergyEfficiencyProject<br>Jonathan Edwards Sinton|
|ShandongEnergyEfficiencyProject<br>Jonathan Edwards Sinton|
|ShandongEnergyEfficiencyProject<br>Jonathan Edwards Sinton|
|ShandongEnergyEfficiencyProject<br>Jonathan Edwards Sinton|
|ShandongEnergyEfficiencyProject<br>Jonathan Edwards Sinton|
|ShandongEnergyEfficiencyProject<br>Jonathan Edwards Sinton|
|ShandongEnergyEfficiencyProject<br>Jonathan Edwards Sinton|
|CM - Lom Pangar Hydropower Proj.(FY12)<br>Nicolas Jean Marie Sans|
|CM - Lom Pangar Hydropower Proj.(FY12)<br>Nicolas Jean Marie Sans|
|CM - Lom Pangar Hydropower Proj.(FY12)<br>Nicolas Jean Marie Sans|
|CM - Lom Pangar Hydropower Proj.(FY12)<br>Nicolas Jean Marie Sans|
|CM - Lom Pangar Hydropower Proj.(FY12)<br>Nicolas Jean Marie Sans|
|CM - Lom Pangar Hydropower Proj.(FY12)<br>Nicolas Jean Marie Sans|
|CM - Lom Pangar Hydropower Proj.(FY12)<br>Nicolas Jean Marie Sans|
|CM - Lom Pangar Hydropower Proj.(FY12)<br>Nicolas Jean Marie Sans|
|CM - Lom Pangar Hydropower Proj.(FY12)<br>Nicolas Jean Marie Sans|
|CM - Lom Pangar Hydropower Proj.(FY12)<br>Nicolas Jean Marie Sans|
|CM - Lom Pangar Hydropower Proj.(FY12)<br>Nicolas Jean Marie Sans|
|CM - Lom Pangar Hydropower Proj.(FY12)<br>Nicolas Jean Marie Sans|
|CM - Lom Pangar Hydropower Proj.(FY12)<br>Nicolas Jean Marie Sans|
|RechargingFees for Lamps can buyHours of Solar Light<br>Dana Rysankova|
|RechargingFees for Lamps can buyHours of Solar Light<br>Dana Rysankova|
|Lights for life in Sub-Saharan Africa<br>Dana Rysankova|
|Lights for life in Sub-Saharan Africa<br>Dana Rysankova|
|Village LightingSolutions to improve Education,Health,Safetyand PDana Rysankova|
|Village LightingSolutions to improve Education,Health,Safetyand PDana Rysankova|
|One Child one Solar Light<br>Dana Rysankova|
|One Child one Solar Light<br>Dana Rysankova|
|Power to the Poor: Off-Grid Lightingfrom Cassava Waste in NigeriaDana Rysankova|
|Power to the Poor: Off-Grid Lightingfrom Cassava Waste in NigeriaDana Rysankova|
|Lightingthe way<br>Dana Rysankova|
|Lightingthe way<br>Dana Rysankova|
|FamilyPedal Power and LightingProject East Africa<br>Dana Rysankova|
|FamilyPedal Power and LightingProject East Africa<br>Dana Rysankova|
|ProvidingAffordable and Reliable Solar Systems in Northern TanzanDana Rysankova|
|ProvidingAffordable and Reliable Solar Systems in Northern TanzanDana Rysankova|
|ProvidingAffordable and Reliable Solar Systems in Northern TanzanDana Rysankova|
|Wenchuan Earthquake Recovery<br>Paul Procee|
|Wenchuan Earthquake Recovery<br>Paul Procee|
|Wenchuan Earthquake Recovery<br>Paul Procee|
|Wenchuan Earthquake Recovery<br>Paul Procee|
|Wenchuan Earthquake Recovery<br>Paul Procee|
Africa to 199 in Japan. The average ratio of liquid liabilities to GDP among sending countries is 99 percent.

The geographic distribution of the population in both sending and receiving countries might also be an important driver of the cost of sending remittances, as a more sparsely distributed population might be harder to reach and, therefore, imply higher transaction costs. We use the share of rural population in both sending and receiving countries to proxy for the disparity in geographic distribution.<sup>14</sup> These data come from the World Bank’s World Development Indicators. Among receiving countries, the percentage of rural population varies from 13 percent in Lebanon to 87 percent in Uganda. On average, 48 percent of the population in receiving countries lives in rural areas. In contrast, on average, only 21 percent of the population in the sending countries is considered rural. This variable ranges from 0 for Singapore to 40 percent for South Africa.

To measure access to financial services more directly, in some estimations, we also control for the number of bank branches per capita in sending and receiving countries<sup>15</sup> We expect that this variable will have a negative association with the costs of sending remittances, as higher branch penetration will reduce transaction costs and increase scale. Among receiving countries, the ratio of branches per capita averages close to 6 per 100,000 inhabitants, while it averages close to 34 per 100,000 inhabitants in sending countries.

Second, we include proxies for factors that might influence the degree to which remittance service providers can determine prices. We posit that providers will be better able to influence prices if there is little competition in the remittance market and if costumers are not well informed. Because we do not have a direct measure of competition among remittance service providers, we use two different indirect measures. For each corridor, we include the number of remittance service providers in the database. We speculate that since the World Bank survey tries to cover the most important providers in a corridor, corridors where more providers

> 14 We consider the share of rural population a better proxy to capture the effect of service delivery than population density, which is an average within a country and does not take into account, which share of the population actually lives in more remote areas.  However, we also tried the population density variable, with similar findings. 15 These data come from Beck, Demirguc-Kunt and Martinez Peria (2007) and can be found at ~ <u>http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/0,,contentMDK:20652043 pagePK: 64214825~piPK:64214943~theSitePK:469382,00.html. Because these data are available for a reduced number of</u> corridors, we do not include this variable in all estimations.

8