rafmacalaba/datause-encoder-data huggingface dataset:
16 competition and effective regulation (Figure 4).6 Despite assuming that public managers<sup>would</sup> adopt new technology and more rational procedures they also concluded that privatized firm performance was superior to the alternative of continued state ownership.

**Figure 4: Welfare Effects of Selling State-owned Enterprises**

<!-- Start of picture text --> Me=can Alin155<br>Aerornidca<br>Medoo Telmax .| c ~ .s sw B<br>Spons Toto<br>Kdang Conth rTearminaln_<br>Britsh odrlohlmmRS<br>-10 0 10 20 30 40 50 s0<br>Percent<br>N. W�Nb .gN. - p-Ndo - t -W sa les D. tt 1y bd.- p t.U.U-<br><!-- End of picture text -->

Source: Galal, Jones, Tandon and Vogelsang. 1994.

40. Perhaps due to the complexity of the approach, and the lack of reliable data and skilled analysts, there has been but a single application of the welfare methodology in low-income countries. One of the originators of the method, Jones, along with Jammal and Gokur (1998), applied it to 81 privatizations in C6te d'Ivoire, covering not just infrastructure firms but a range of firms already operating in competitive markets (in agriculture, agro-industries, tradable<sup>and</sup> non-tradable sectors). For the entire privatized sector, they concluded that there were substantial benefits: (i) the firms performed better after privatization; (ii) they performed better<sup>thanthey</sup> would have had they remained under public ownership; and (iii) the set of transactions<sup>asa</sup> whole contributed positively to economic welfare, with annual net welfare benefits equivalent<sup>to</sup> about 25 percent of pre-divestiture sales. These results stemmed from a number of effects, including increases in output, investment, labor productivity, and intermediate-input productivity.

41. All in all, the broader welfare consequences of privatization, and how "fair"<sup>a</sup> privatization transaction or program is perceived to be by the most affected segments<sup>or indeed</sup> the general population of a country, is critical to its acceptance and success. More studies of<sup>the</sup> welfare consequences type are needed.

**6** Aeromexico was sold to a buyer who made a number of unwise decisions, and lost a lot of money before the company was sold and eventually turned around. This illustrates the point that private managers<sup>makemistakes just</sup> as public ones do, but under private ownership losses are limited by bankruptcy and<sup>restructuring,whereasunder</sup> public ownership such losses are tolerated indefinitely. This point is discussed in greater detail in Jones, Jammal and Gogkur, 1998.
<!-- Start of picture text --> 250-<br>200- ;<br>150-<br>=<br>& 100-<br>&<br>50-<br>rr 3<br>ar er oe -—= - -§ -<br>FIA FID FIE GDP growth _ Inflation Military Natural res.<br>Coefficient<br>Cluster “1 -@ 2<br><!-- End of picture text --> <!-- Start of picture text --> 105-<br>102-<br>5<br>s 99-<br>=<br>96-<br>2010 2015<br>Year<br>Cluster —— 1 — 2<br><!-- End of picture text --> <!-- Start of picture text --> th f —_enePayhy ge,uN _é ay Cc ~ =<br>[x Se Ss BY a . S 7<br>% ~~ ti se oN YIN \<br>yyo edHE <¥ on ar?~\, fe oc & ss e><br>{; ya . ASEyr. eae?a fo \Meevo,<br>. nese! a Ay<br>“. ,. @ .<br>{ y<br>L Fy FF 4<br>5q Cluster: [71 Miz [_JN/A .pe Lb<br>IBRD 48198 | .<br>JUNE 2024<br><!-- End of picture text -->
two intertwined issues (UN-Habitat 2009; Gulyani and Basset 2010). When UN-Habitat developed their housing deprivations they found the region with the highest proportion of people living in slums, or suffering from one or more housing deprivations (UN-Habitat 2009). An examination of poverty indicators for the countries analyzed in this paper corroborates the descriptive relationship between poverty and housing quality. While the relationship between national income and housing conditions are examined later, poverty indicators provide insight into the extent of poverty.

**Figure 8. Poverty Indicators for Countries Examined**

_Source: WDI Poverty Indicators Database_ Note: Because poverty indicators are not consistently collected, these figures reflect the most recent year such data exist. For poverty headcount ratios at $2 a day, this is anywhere between 2001 and 2011, whereas this range is 2005 to 2012 for urban poverty headcount ratio.

Figure 8 displays the poverty headcount ratio at $2 per day as well as the poverty headcount ratio at the urban poverty line. All but five countries face poverty headcount ratios at $2 per day of more than 60 percent, with Liberia having more than a 90 percent poverty headcount ratio. Poverty headcount ratios are less extreme in urban areas, but all but five countries have urban poverty headcount ratios above 20 percent, with São Tomé and Principe facing a nearly 64 percent urban poverty headcount ratio. These figures illustrate the extent of poverty, its potential relationship with poor housing conditions across the countries examined, and the need for a better understanding of the specific housing issues that plague these countries in order to make inroads into improving livelihoods in the region.

# **Box 3. Informal housing in SSA**

Housing conditions in SSA are seen as the worst of any other region in the world in terms of the extent of slums and the severity of shelter deprivations. In 2012, an estimated 61.7 percent of SSA’s urban population lived in slums, 27 percentage points more than Southern Asia, which has the second highest incidence of slums (UN-Habitat 2013). In addition to those living in areas defined as slums, formal sector housing is largely absent from African cities. Some 11
budget actuals. The best sources for this information are the ministries of planning and finance, the parliament, and published budget laws.

- _Operator level._ Financial variables are collected from SOEs, public corporations, or parastatals, and special funds, following fiscal data template 11. The best source for this information is the public operator itself, be it a corporation or a special fund.

# Fiscal data template at the national level

## **Macroeconomic parameters for normalization (template 8)**

Template 8 is used to guide data collection at the national level. Template 8 collects basic macroeconomic data for normalization (for example, GDP, exchange rates), most of which are available through internationally validated secondary sources (the World Bank and IMF Web sites, for example). The template should be completed for the same years for which data for templates 10 and 11 have been collected (that is, the most recent five years).

## **Template 8  Macroeconomic parameters for normalization**

|**Policy**<br>**code**|**Series code**|**Variable**|**Definition**|**Unit**|
|---|---|---|---|---|
|struc<br>IN)|XXXXXXX|Gross domesticproduct||LCU per year|
|infra<br>re (N|XXXXXXX|Average exchange rate||LCU per year|
|Non<br>tu|XXXXXXX|Population||LCU per year|

_Source: Authors’ elaboration_ . _Note:_ LCU = local currency units.

# Relevant definitions: Functional and economic classifications

The following definitions and clarifications may be useful when completing all the templates related to fiscal data (see templates 9 and 10).

## **The functional classification of expenditures or COFOG**

Using the GFSM 2001 as the starting point for this exercise provides a methodological platform that is well used and known across countries and has substantive advantages. The use of the GFSM 2001 functional classification allows for examining expenditure trends over time, regardless of country-specific institutional arrangements or restructuring. Another advantage is the unambiguous definition of the sectoral scope.

The functional classification of expenditures, also known as the classification of functions of government (COFOG), is a detailed classification of the functions, or socioeconomic objectives, that 35
variation from national repatriation inflows, and ii) municipal variation on birth municipality of individuals repatriated in the period 1995-2002. Based on our 2010-2018 sample, we document that deported individuals overwhelmingly go back to their birth municipalities. Individuals who were repatriated between 1995 and 2002 came back to El Salvador before the United States drastically strengthened and changed its migration enforcement in response to the terrorist attacks of 2001. The changes in the United States’ migration policies not only included a sudden increment in the number of repatriations, they also modified the strategies of deportations.<sup>2</sup> We document that repatriation inflows have a negative impact on average wages in the formal sector. An increase of 0.04 percentage point in the inflow of returnees reduces the average monthly salary paid by formal firms by approximately 9.4 percent. These effects are predominantly driven by men’s salaries. The wage impacts are larger among formal firms in sectors that face more intense competition from informal ones. In the case of employment, while we do not find any aggregate impacts from repatriation inflows, the effects are negative and statistically significant among formal firms in sectors that are predominantly informal. When looking at other outcomes related to labor demand, such as firm entry and number of firms, we do not find any significant impacts. These findings are consistent with deportees representing a labor supply shock in the informal sector which tends to reduce wages, even among formal firms in typically informal sectors. At the same time, the reduction in formal employment among the latter is consistent with repatriation inflows inducing negative effects on formal firms and weakening their demand for labor as they face increased unfair competition from their informal peers, as documented by Rozo and Winkler (2019).

Our paper contributes to the growing literature that examines the effects of forced migration. The impacts of large international forced migration inflows in hosting countries have been largely centered around studying the impacts of forced migrants on the local workers;<sup>3</sup> with a smaller

> 2Total United States deportations are composed of voluntary returns and involuntary removals. As documented by Rozo et al. (2020), after 2002 involuntary removals from the United States spiked dramatically while voluntary returns fell sharply. As such, the initial location of voluntarily repatriated individuals should be more related to family and

> 3See Card (1990); Ruiz and Vargas-Silva (2015); Del Carpio and Wagner (2015); Ceritoglu et al. (2017); Borjas

3
programs and credit subsidies with the aim to alleviate SMEs’ financing constraints have rarely had the expected success, due to mis-targeting, rent-seeking and lack of fiscal sustainability (Khwaja and Mian, 2005; Zia, 2008).

Many countries around the world have therefore made Partial Credit Guarantee (PCG) funds a central part of their strategy to alleviate SMEs financing constraints. Multi- and bilateral donors have supported the set-up of such schemes around the developing world. These schemes seek to expand lending to SMEs, sometimes focusing on specific regions or sectors through reducing lending risk for banks or other financial institutions. Specifically, a PCG fund is a risk transfer and risk diversification mechanism; it lowers the risk to the lender by substituting part of the risk of the counterparty by that of the issuer of the PCG (the fund), which guarantees repayment of part of the loan upon a default event. A PCG fund can also help diversify risk by guaranteeing loans across different sectors or geographic areas. Furthermore, there can be informational gains if the guarantor has better information about the borrower than the lender.

Partial (and full) credit guarantee funds have existed at least since the beginning of the 20<sup>th</sup> century and have become more popular over the past decades. Beck, Demirguc-Kunt and Martinez Peria (2008) report that banks see partial credit guarantee schemes as the most common and most effective government support program for SME lending, ahead of directed credit and interest rate or regulatory subsidies. In spite of their recent growth and initial evidence suggesting success of some of these funds, there is a dearth of analysis to systematically inform the process of design of PCG funds, pricing of their guarantees, their regulation, and the implication that PCG fund characteristics have with respect to the prudential regulation of banking portfolios covered by such guarantees.

This paper is a first effort to provide evidence on the variety of partial credit guarantee funds across the world.<sup>2</sup> Specifically, based on a recent survey of PCG funds

> 2 There are a few surveys of partial credit guarantee schemes, such as Gudger (1998) and Green (2003), but none provides the same wealth of quantitative information as our database.

2
# Fans charts of probabilistic hurricane events

<!-- Start of picture text --> GDP compared to baseline Potential GDP compared to baseline<br>00<br>55<br>-10 -10<br>-15 -15<br>-20 -20<br>-25 -25<br>-30 -30<br><!-- End of picture text --> <!-- Start of picture text -->
-35<br>5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 8&5<br><!-- End of picture text -->

## Consumption compared to baseline

<!-- Start of picture text -->
-35<br>5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85<br><!-- End of picture text -->

## Debt/GDP

<!-- Start of picture text --> 0<br>-4<br>-8<br>-12<br>-16<br>-20<br>-24 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85<br><!-- End of picture text --> <!-- Start of picture text --> 160<br>140<br>120<br>100<br>80<br>60<br>40<br>20 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85<br><!-- End of picture text -->
Traditionally India has sought to achieve food security through actions to ensure that food is available and accessible. Policy has focused on an aggressive drive toward self-sufficiency in food grains, encompassing comprehensive support to farmers through the introduction of improved varieties and inputs; rapid expansion of irrigated area; subsidies of various inputs; efforts to maintain relatively stable producer prices; and management of the long-run relationship between domestic and world prices.

Protecting poor consumers through stable market prices and access to subsidized food has been another major feature of India’s food policy. Aside from these interventions, for decades the government has maintained public stocks of food grains to buffer against production shocks, assist in maintaining national food self-sufficiency, stabilize food prices, and supply the PDS. These policies are supplemented with highly interventionist trade policies that rely on a combination of tariffs and periodic quantitative restrictions.

These policies have been highly successful in stabilizing both production and prices. Irrigation expansion in particular has been a major contributor to wheat production stability, with over 90 percent of wheat cultivated now on irrigated land (Figure 2). The reduction in production variability is even more impressive considering output now is almost four times the level in 1970.<sup>4</sup>

**Figure 2: Stability of Wheat production and irrigation expansion**

<!-- Start of picture text --> 0.25 100<br>0.2 80<br>0.15 60<br>0.1 40<br>0.05 20<br>0 0<br>Output Variability (5 year rolling Std. Dev. ‐ left axis)<br>1950‐51 1953‐54 1956‐57 1959‐60 1962‐63 1965‐66 1968‐69 1971‐72 1974‐75 1977‐78 1980‐81 1983‐84 1986‐87 1989‐90 1992‐93 1995‐96 1998‐99 2001‐02 2004‐05 2007‐08 2010‐11*<br><!-- End of picture text --> _Source:_ Authors using data from Directorate of Economics and Statistics, Dept. of Agric. and Cooperation With higher and more stable output, the government is compelled to procure increasing volumes of wheat at the pre-announced MSP. It acquires wheat early in the marketing season and then uses the accumulated stocks throughout the year to supply the PDS. Since the quantities

> 4 It is important to note that while production shocks, driven by the vagaries of the monsoons, are a fact of life in India, the largest production decline since 1950-51 was in 2002-03 at 7.01 million tons in absolute terms and 10% in relative terms from the production level of the previous year.  In terms of rainfall, 1972 and 2009 had the worst monsoon years over the past 110 years, each experiencing 22% less rainfall than the 110 year average. Yet wheat production fell in 1972 by only 1.7% but grew marginally (0.02% or 0.1 million tons) in 2009.

8
denominator of the services FDI penetration - time-varying sectoral GDP. In Column 3 we use sectoral output in 1996 from the 1996 input-output table, which ignores the time variation. In Column 4, we use the time-varying economy-wide GDP. In Column 5, we drop the denominator and use simply the log of the services FDI stock. Each of these alternative services FDI penetration variables is interacted with the same plant-level weights as in our main specification. Panels A and B report, respectively, the results for output growth and TFP growth using Levinsohn and Petrin (2003). Across all specifications, we find the coefficient on the services FDI linkage to be positive and significant. The coefficients differ from those in Tables 1 and 2 since the magnitude of the services FDI penetration measures are quite different across specifications. The last two columns of Table 3 report the results from specifications where we use alternative measures of the intensity of plant-level services usage. In Column 6 we subtract from the plant-level weights the time-varying median service usage in the plant’s 3-digit industry. In Column 7, we define plant-level services usage relative to total revenues instead of total sales. The coefficient on the services FDI linkage remains positive and significant.

In Table 4 we perform additional robustness tests. First, to address further the potential reverse causality we estimate our main specification including the three-period lag of the services FDI linkage. We find positive and significant effects of the services FDI linkage using both output growth (Column 1 of Panel A) and TFP growth from Levinsohn and Petrin (2003) (Column 2 of Panel A) as the dependent variable.

Second, despite being confident in the appropriateness of the data cleaning procedures applied to the Chilean data described in the Appendix, we impose a more stringent criterion on the data to guarantee that our results are not being driven by 24
**Table 3 BROU: Balance Sheet for end of 1991**

|**ASSETS**|In US $|In Pesos|Total in LC|
|---|---|---|---|
|CASH AND BANKS<br>|1412.7<br>|393.8|3909.9|
|Cash<br>|0.0<br>|308.4<br>|308.4<br>|
|Domestic Banks & Other Dep.<br>|525.1|68.8|1375.7|
|Foreign Corresp. & Other Dep. (Net)<br>|571.6<br>|0.0<br>|1422.6|
|Reserve Requirements<br>|126.8<br>|16.6<br>|332.2<br>|
|International Reserves|189.2|0.0|470.9|
|SECURITIES<br>|45.0<br>|14.8<br>|126.7|
|Public Enterprises<br>|0.0|0.0|0.0|
|Private|45.0|14.8|126.7|
|NET CLAIMS ON GOVERNMENT|170.8|37.0|462.2|
|GROSS LOAN PORTFOLIO<br>|1025.4|861.1|3413.3|
|Public Enterprises<br>|135.2<br>|41.6<br>|378.2<br>|
|Private|890.2|819.5|3035.1|
|LOAN PROVISIONS|-266.1|-223.5|-885.7|
|NET FIXED ASSETS|0.0|384.2|384.2|
|OTHER ASSETS|252.7|275.7|904.6|
|TOTAL ASSETS|2640.5|1743.1|8418.1|
|**LIABILITIES**||||
|DEPOSITS<br>|1807.2<br>|1338.4<br>|5836.4<br>|
|Demand<br>|127.8|732.0|1050.1|
|Saving<br>|436.7|187.3|1274.2|
|Time & Other|1242.7|419.1|3512.1|
|BORROWING REQUIREMENTS<br>|560.9<br>|36.4<br>|1432.5|
|O/w Long-Term Foreign Liabilities|429.0|0.0|1067.8|
|OTHER LIABILITIES|41.1|185.0|287.4|
|TOTAL LIABILITIES|2409.2|1559.8|7556.3|
|**CAPITAL**|0.0|862.1|862.1|
|TOTAL LIABILITIES & CAPITAL|2409.2|2421.9<br>11.4|8418.4<br>11.7 10.29.0 8.1 5.9|

**Source: BROU Published Finacial Statements.**

29
27

# Regrssion 13

Dependent variable: Fraction of Years in Which Capital Controls in Place, 1960-1995 Number of obse:rvations: 139

|Mean<br>Std. dev.|of dep. var.<br> ofdep. var.|= .787387<br> =.342953|<br>LK het. test<br> <br>Durbin-Watson|=12.2811 J.0001<br> =2.03770 [<.867]|
|---|---|---|---|---|
|Sum of squa|red residuals|= 11.7237|Jarque-Bera test|=35.1819 [.000]|
|Variance|of residuals|=.090881|<br>Ramsey's RESET2|=2.65476 (.106]|
|std. error|of regression|=.301465|<br>F (zero slopes)|=5.51078 (.000)|
||R-squared|=.277703|<br>Schwarz B.I.C.|=-2.11787|
|Adjus|ted R-squared|=.227311|<br>Log likelihood|=-25.3683|
||Estimated|Standard|||
|Variable|Coefficient|Error|t-statistic|P-value|
|SSA|2.09580|.328127|6.38717|[.000]|
|ASIA|2.0)6680|.375284|5.50729|(.000]|
|ECA|2.39422|.403090|5.93967|[.000]|
|MENA|2.13384|.384726|5.54640|(.000]|
|LAC|2.0)8108|.393251|5.29200|(.000]|
|OECD|-.149049|.100129|-1.48857|(.139]|
|COMMOD|.541426E-02|.062675|.086386|[.931]|
|OIL|-.097368|.115647|-.841936|[.401]|
|LOGQAV6095|-.170939|.049805|-3.43213|t.001]|
|MICROSTATE|.025574|.080311|.318431|(.751]|
|Standard Er|rors are heter|oskedastic|-consistent (HCTY|PE=2).|
# **2. Data and Measures**

The analysis in this paper is based on the data from two rounds of the Nepal Living Standard Survey (NLSS). The NLSS is a nationally representative survey of households and communities conducted between June 1995 and June 1996 (NLSS-I) and April 2003 and April 2004 (NLSS-II) by the Nepal Central Bureau of Statistics with the assistance of the World Bank. Both rounds use similar modules to collect data on the household consumption of a wide range of food and nonfood items. The survey’s instruments also gather detailed information about the demographic composition of the interviewed households, the labor status of the household members, their health and educational achievements, and various sources of household income, including income in-kind, individual wages, and remittance and transfers received in the year preceding the survey.

The NLSS-II sample includes both cross-sectional and panel components. The cross-sectional sample was constructed using a two-stage design based on the 2001 Nepal Census sample. The survey’s sample covers 73 districts of Nepal (excluding the Rasuwa and Mustang districts). The NLSS-I sample includes information on 3,373 households in 274 PSUs, while the NLSS-II sample is based on 326 cross-sectional and 95 panel PSUs enumerating 3,912 and 1,160 households, respectively.<sup>2</sup> We use total per capita consumption expenditure as an indicator of household welfare. Our consumption aggregate includes monthly household expenditures on food and nonfood items, imputed housing expenditures, an imputed stream of services from durables goods, as well as cash expenditures and imputed expenditures for goods and services produced by the household itself. The values of home-produced items are calculated as a product of the quantity of each food item and its prevailing local market price. The poverty line for the analysis is constructed using cost-of-basic-needs approach. To assure comparability over time and across the regions, all monetary indicators (household consumption, values of remittances, wages, and so on) are deflated to 2004 all Nepal prices. The cost of the poverty basket in 2004 all Nepal prices equals NPR 7,694 per year per person––equivalent to US$107 or US$590 in PPP (World Bank 2006).

> 2 For a detailed description of the sample frame and the survey methodology see Central Bureau of Statistics (2006).

5