rafmacalaba/datause-encoder-data huggingface dataset:
_Sustainabilitv._ Data on default and arrears are problematic because BKDs only write off loans in default if the borrower moves, or if the loan is more than 5 years overdue. Table 31 ) I estimates default and

**Table 31**

**Incremental Annual Default and Arrears Rates for BKD**

|,|**19929**|**.1993**|**1994**|**1995**|**1996**|
|---|---|---|---|---|---|
|**Loan volume in default**|**6.0**|**_4.4_**|**2.0**|2 6|4**9**|
|**percent of outstanding loans**||||||
|<br>**Loan volume in arrcars**<br>**percent of outstanding loans**|22.9|17 8|15.9|17.6|**21.2**|

arrears rates for the program by simulating the program writin.g off all loans in default each year.<sup>5</sup> ' Using this approach, annual default rates ranged from 2 percent to 6 percent in the mid-1990s. Loans in default accounted for approximately 4.9 percent of loans in 1996, up from 2.6 percent in 1995. The loan volume in arrears was equal to 16 to 23 percent of outstanding credits during the mid- 1990s. The volume of loans in arrears in 1996 was 21 percent of outstanding loans, up from 16 percent in 1994.

BKDs do not adequately provision for bad debt. To partially adjust for the reporting inaccuracies that this creates, BRI reported the BKD system's profitability if the units wrote off 100 percent of their loans in arrears past the final due date each year. If calculated in this way, the system had a return on assets of 8.7 nercent and a return on equity of percent **o2.9q2i0y2.8** 11.0 percent in 1996. These rates translate into a real return on assets of 0.7 percent and a real return on equity of .8 percen. Nominalthe returns have declined slightly over

**Table 32** Adjusted **BKD Profitability** as **Reported by BRIP**

|**Profitability**<br>Measure|**1993**|**1994**|**1995**|**1996**|
|---|---|---|---|---|
|ROA(percent)|**I 1.4**|9.2|9.2|8.7|
|RealROA|**1.6**|0.6|0.2|0.7|
|(percent)<br>I|||I||
|<br> <br>ROE(percent)<br>RealaROEt4r|__<br>_<br>**14.4**<br>|<br>**11.7**|11.7|**11.0**|
|<br>(percent)|<br>4.3<br>-pe-e-|2.9|2.0|2.8<br>-|

a/ These figures simulate **the** BKD system's profitability if the units wrote **off 100** percent of their loans in arrears past final due date each y'ear.

the last three years, but real returns rebounded slightly in 1996 (Table 32). The author did not collect the data required to estimate the BKDs' unsubsidized profitability. However, the major inaccuracy in BKD reporting is the fact that the units do not adequately provision for bad debt. Thus, it is likely that the figures presented above which partially compensate for this do not greatly over-state the units' performance.

_Outreach Scope._ Active BKDs are located in 4,806 villages on Java. In Eastern Java and the island of Madura, BKD units are located in about 20 percent of all villages (Christian, 1995).

> **51** Loans in default are assumed to be loaiis that are in arrears by more than 6 months past **the** loan's final **due date.**

78
12 urban middle-income groups.<sup>7</sup> In cases where kerosene subsidies are combined with rationing, the urban poor might also not benefit as rationing schemes often require a permanent placo of residence, a requiremen **t** hat disqualifies a large portion of urban poor living in unauthorized slums.

The last two objectives are of more recent origin. The macroecononic instability experienced in the early 1980s and the need for austerity in public expenditures in these developing countries brought energy pricing polices under scrutiny. Subsidized prices have often been identified as an important source of deficits in public enterprises in the energy sector. Energy price increases are expected to improve the finmcial position of these enterprises. However, the extent of these effects also depend on the wage and employment policies of these public enterprises.

The environentall sensitive nature of energy adds another dimension to pricing policies The relationship between energy use and environmental degradation has become a subject of debate in recent years. However, how energy pricing policy can be used to attain environmental objectives may be quite ambiguous, and depend on the nature of environmental problems. In the developed countries, the cunrent focus is on the global problem ofgreen-house gases and reduction of carbon dioxide emissions originating from he conbustion offossil fuels. The energy pricing implication of this policy is an upward adjustment in energy prices, especially for fossil fuels. In the developing comtries, the current envinme focus is more often the local problem of deforestation and soil erosion. In this context, a substantia increase in domestic pnices of commerca energy mwybe considered undesirable to the extent it generates substitution of commerci energy by non-commercial energy such as firewood, thereby furdter accelerating deforestation and soil erosion. Given that deforestation contributes to global warming, the implications of enviromnental issues for energy pricing policies are quite complex.

The ieratur analyzing the impacts of energy price changes falls into two groups of stxdies. One group analyzes intenational oil price shocks during the 1970s and iheir economic impacts on both ihe OECD and developing countries, although the literature on developing countries is limited. The second group analyzes economic adjustment policies in developing coutries during the 1980s. Few of them directly address issues related to domestic energy price changes. However, to the extent ese studies 7 DU& **for Ilanieg ineate. thas t poord** 20 **pnt** of **Xth** popai **_rciVe_ lo da 10 of the kwmsui..**
Table 1: Country-pair correlation regressions

||(1)<br>GDPD|(2)<br>GDPD|
|---|---|---|
|log total|0.010<sup>_∗_</sup><br>(0.006)||
|log int-cap||0.012<sup>_∗∗_</sup><br>(0.006)|
|log fn||-0.004<br>(0.005)|
|third ex|0.004|0.009|
||(0.059)|(0.059)|
|third im|-0.289<sup>_∗∗∗_</sup>|-0.290<sup>_∗∗∗_</sup>|
||(0.071)|(0.071)|
|N|15564|15564|
|R2|0.400|0.400|
|Time windows|5|5|
|Country pairs|6202|6202|
|Time FE|YES|YES|
|CP FE|YES|YES|

Standard errors clustered by country-pair in parentheses Note: observations with non-overlapping 10-year windows. _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01 increase in trade proximity using gross trade flows is associated with an increase in the synchronization of inflation, we now find a negative association between value-added trade and inflation correlation. Column (2), however, delivers new and interesting insight which allows us to better understand the negative coefficient in column (1): inflation correlation is actually _increasing_ with the value added exported as intermediate and absorbed by the direct partner, but it is _decreasing_ with value-added trade in final goods as well as with the value-added trade in intermediate that are re-exported further. Again, when two countries trade intermediate inputs that are absorbed by one another, then such a production link is indeed associated with higher co-movement of Findings in table 2 confirm the insights from table 1, and both analysis feature a 14
chapters, though typically with less rigorous standards in certain areas. It is also noteworthy that the majority of these IP-related PTAs are legally enforceable, as designated in the World Bank data. Specifically, the database ranks the legal enforceability (LE) of PTAs on a scale of 0, 1, or 2, with 0 essentially indicating no enforcement language exists, 1 indicating weak enforcement language (e.g., “should” or “may”), and 2 identifying those with commitment language (e.g., “shall” or “must”). We use this designation (LE = 2) in selecting our treatment IPAs in the econometric analysis.

Figure 1 illustrates the substantial growth since 1990 in the number of IPAs in the World Bank data that feature strong enforceability. The red lines indicate the number of agreements and the blue line shows the number of countries in one or more of them.<sup>4</sup> As shown in panel (a), the number of IPAs rose from less than 20 in 2000 to 100 in 2015, while the number of countries involve now exceeds 100. Thus, currently around 64 percent of WTO contracting parties are members of at least one IPA, as defined here. Panel (b) shows more modest growth in IP-related PTAs involving the United States as a partner. The establishment of the CAFTA-DR agreement in 2005 markedly raised the number of countries, currently around 20, in such agreements. Panel (c) demonstrates the faster growth in EU-related and EFTA-related IPAs, with discrete jumps in country coverage in 1994 with the EEA and in 2008 with the establishment of the EUCARIFORUM agreement. Together, the EU and EFTA are party to more than 40 enforceable IPAs. Because of the high degree of IPRs harmonization within them, the EU and EFTA themselves are counted as IP-related trade agreements in our sample. Finally, panel (d) indicates that there are some 40 IPAs, involving around 60 countries, that do not involve the United States, the EU, or EFTA. Thus, there has been considerable proliferation in such agreements, both over time and across countries.

# **Figure 1: The Number of Legally Enforceable IP-Related Trade Agreements and Number of Countries with Membership in at least One such Agreement by Year, 1990-2015**

> 4 See also the DESTA database described in Dür et al. (2014), which defines IPAs as agreements with an IPRs chapter, regardless of how comprehensive or rigorous.

7
# **1 Introduction**

Corruption is a serious burden for firms in the developing world. In 2006, two out of every five firms in Africa and Latin America reported that unofficial payments were required “to get things done,” and one in six said they were expected to present informal gifts when meeting with tax inspectors. On average, informal gifts or payments “to get things done” were equivalent to 2.1 percent of firm sales, which, taken at face value, would not appear to be excessive, especially in comparison with applicable tax rates around the world. Nevertheless, the uncertainty and illegal nature associated with corruption makes it more burdensome on firms than official taxation (Shleifer and Vishny 1993; Fisman and Svensson 2007). More worrisome is that the incidence of bribes is higher precisely in the poorest countries, where development needs are most pressing. For example, whereas 9 percent of firms in Chile believe informal gifts are required to “get things done,” 87 percent of firms in Burkina Faso are of that view. Similarly, two out of every three firms in Cameroon and the Democratic Republic of Congo state that they must pay bribes when meeting with tax officials. Finally, firms in Africa report having to pay higher bribes, as a percent 2 of sales, than their counterparts in relatively-affluent Latin America, 2.7 vs. 1.4 percent, respectively.

Since growth is unlikely without a vibrant private sector, measuring and understanding how corruption affects firms is an important research area. However, efforts in that direction are thwarted by the lack of reliable information about the incidence of corruption. By its very nature, it is difficult to come by objective data on the pervasiveness of graft. Work on the subject often relies on perceptions on the extent of corruption, but there is evidence that perceptions are a poor reflection of the prevalence of 3 corrupt practices (Olken 2007; Weber 2007). In addition, existing cross-country measures of corruption are often based on surveys of a limited number of experts, a non-representative sample of firms (e.g., multinational corporations), or households, and hence may not necessarily reflect the experience of the average enterprise.

In this paper we exploit a novel dataset of nearly 10,000 firms in 33 countries in Latin America 4 and Africa to compute objective measures of the incidence of corruption. The data come from the

> 2 All figures come from the World Bank’s _Enterprise Surveys_ and are available at <u>http://www.enterprisesurveys.org</u> .

> 3 Even the figures in the opening paragraph can be criticized for reflecting firms’ views on how widespread corruption and not necessarily their own experience.

> 4 Our sample consists of firms in 18 African and 15 Latin American countries. In Africa they include: Angola, Botswana, Burkina Faso, Burundi, Cameroon, Cape Verde, Democratic Republic of Congo, Gambia, Guinea, Guinea-Bissau, Malawi, Mauritania, Namibia, Niger, Rwanda, Swaziland, Tanzania, and Uganda. In Latin America they are: Argentina, Bolivia, Chile, Colombia, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay, and Venezuela.
10 pocket spending. In other cases, the expenditure questions are in the health section. In some cases, some items are asked about in one section, e.g. the expenditure section, and other items are asked about in the other, e.g. the health section. We have used as much information as possible, and where the recall period is less than 12 months, we have annualized by multiplying the amount recorded by a timeneutral annualization factor (i.e. 12 in the case of a one-month recall, by two in the case of a six-month recall, and so on). Some surveys asked about all items of health expenditures in both the expenditure and health section. In these cases, we used whichever section had the longer recall period – usually the expenditure section – since we suspect that short recalls combined with a time-neutral annualization factor leads, on balance, to an overestimate of annual health expenditures.

We mostly relate out-of-pocket health expenditures either to total household consumption (in low- and middle-income countries) or to income (in high-income countries). Increasingly, surveys try to collect data on both, but it is still the case that income is typically not available for low- and middle-income countries. Where we can, we show how some indicators change as we move from consumption to income.

In the catastrophic expenditure analysis, we use thresholds of 10% and 25%. In the impoverishment analysis, we use the $1.90-a-day, $3.20-a-day and $5.50-a-day international poverty lines (IPLs) (Jolliffe and Prydz 2016; Ferreira and Sanchez 2017), as well as a $10.00-a-day line which may be better thought of as a global lowincome line rather than a poverty line per se (Kochhar 2015). In converting our health expenditure data to international dollars, and in deriving the IPLs, we convert values in different years to 2011 values using local CPIs and LCUs to international dollars using 2011 PPPs.

We checked the numbers underlying our estimates against published numbers. We checked our estimated consumption-per-capita estimates against the numbers in the World Bank’s PovcalNet when available and those in the World Development Indicators (WDI) when not; large discrepancies were flagged.<sup>6</sup> We checked our household health budget share against numbers from the WDI which are based on data from the WHO Global Health Expenditure Database; again, large discrepancies were flagged.<sup>7</sup> Last, we checked our $1.90-a-day poverty headcount estimates against the numbers in PovcalNet, flagging large discrepancies.<sup>8</sup> Estimates of catastrophic and impoverishing expenditure incidence were not automatically rejected when flags were raised, but our tendency was to prefer estimates where flags were not raised, and we typically only retained datapoints where the flag was only marginally up. This quality control process led to many datapoints being dropped, and some entire survey families.<sup>9</sup> In our database, many countries have series from multiple surveys or at least multiple adaptions of a survey. For any given year for any given country, we retained at most only one estimate of each measure, using the same survey for each
between poverty rates based on national poverty lines and the two SPLs, and both perform similarly. The mean deviation over all observations for the unbounded SPL is 1.7 compared to 2.3 for the lower-bound variant. The poverty rates derived from both of these lines correspond closely with the national definitions of poverty and their corresponding headcount rates.

This is in contrast to the poverty rates derived from our application of the Chen-Ravallion weakly relative poverty line and the strongly relative line set at 50 percent of median consumption (or income).<sup>32</sup> While the poverty rates derived from the Chen-Ravallion weakly relative poverty line are positively correlated with the national poverty rates, the vast majority of national poverty rates are far lower than the poverty rates suggested by the Chen-Ravallion weakly relative poverty line (Figure 5). The mean deviation between the national and relative poverty rates is 12.4 (Table 3), more than six times greater than the mean deviation for the SPL proposed in this paper. This misfit with national poverty is even more striking when examining the strongly relative poverty line. In this case, for a large majority of countries, their relative poverty rate is significantly lower than their national poverty rate (Figure 5), and the mean deviation is more than tenfold greater than that for SPL of one dollar plus 50 percent of median (Table 3). In both cases, we view the large, systematic difference between the measure of societal poverty and national poverty rates as an undesirable attribute.

Using the SPL, aggregating and lining up poverty headcounts for selected reference years following the same methods as in PovcalNet (but including high-income countries),<sup>33</sup> we

> 32 Chen and Ravallion parameterize their weakly relative poverty lines in 2005 PPP terms fixing the lower bound at $1.25. In the figure, we present the 2011 PPP equivalent, fixing the lower bound at $1.90. We adjust the intercept to be $1.90/2.

> 33 The issue is that household survey data do not exist for every country in every year, but all global poverty estimates are for a specific year. To overcome the data gaps, survey data are projected forward (and sometimes back-casted) to produce country-level poverty for each year. For an overview of the methods used, see Jolliffe et al. (2014) or Ferreira et al. (2016).

26
**Table 1**

**Descriptive Statistics By Birth Year Cohort** (Standard Deviation)

|||Birth Year Cohort||All|
|---|---|---|---|---|
||1940 to 1947|1948 to 1963|1964 to 1978|1940 to 1978|
|Years of Education|11.50|11.12|12.70|11.70|
||(3.58)|(2.78)|(2.82)|(2.96)|
|Completed Middle School|0.893|0.952|0.978|0.957|
||(0.309)|(0.212)|(0.144)|(0.202)|
|Completed High School|0.647|0.599|0.796|0.672|
||(0.478)|(0.490)|(0.402)|(0.469)|
|Completed Post-Secondary|0.202|0.106|0.310|0.184|
||(0.402)|(0.308)|(0.462)|(0.388)|
|Age|56.88|45.06|30.93|41.00|
||(2.10)|(4.31)|(4.39)|(9.04)|
|Female|0.250|0.410|0.481|0.423|
||(0.433)|(0.492)|(0.499)|(0.494)|
|Height|167.95|166.99|167.02|167.07|
||(6.87)|(7.56)|(7.45)|(7.47)|
|Father's Years of Education|4.96|5.84|9.25|6.96|
||(4.36)|(4.47)|(4.36)|(4.74)|
|Mother's Years of Education|1.86|3.48|7.43|4.74|
||(3.33)|(4.18)|(4.41)|(4.66)|
|Father's Ed Missing|0.147|0.036|0.011|0.035|
||(0.354)|(0.186)|(0.104)|(0.185)|
|Mother's Ed Missing|0.143|0.028|0.011|0.031|
||(0.351)|(0.167)|(0.104)|(0.174)|
|Number of Living Siblings|2.36|2.69|1.62|2.29|
||(1.89)|(1.57)|(1.34)|(1.60)|
|No Siblings Reported|0.290|0.110|0.321|0.198|
||(0.454)|(0.313)|(0.467)|(0.398)|
|Father was a Farmer|0.264|0.106|0.102|0.117|
||(0.441)|(0.308)|(0.303)|(0.321)|
|Father was a Worker|0.382|0.570|0.452|0.515|
||(0.486)|(0.495)|(0.497)|(0.499)|
|Father was Self-Employed|0.036|0.022|0.022|0.023|
||(0.188)|(0.147)|(0.147)|(0.150)|
|Father was a Private Businessman|0.025|0.005|0.006|0.007|
||(0.158)|(0.075)|(0.079)|(0.086)|
|Father was an Administrator|0.044|0.120|0.168|0.131|
||(0.205)|(0.325)|(0.374)|(0.337)|

**Table 1 Continued on Next Page**

32
one special case, namely when Li is strictly decreasing in Ci, as suggested by Figure 1. Measured consumption will be adjusted downward for the poorest, and upward for the richest. There will not be first-order dominance, but all standard poverty measures will increase for poverty lines up to the mean. Standard inequality measures will naturally increase; more precisely, there will be Lorenz curve dominance, with unambiguously higher inequality in the adjusted distribution. Just how much the poverty and inequality measures are affected is (of course) an empirical issue.

The implications for assessments of a workfare scheme’s impact on poverty are also ambiguous in theory. As discussed in the introduction, we expect our adjustment for the disutility of casual manual work to reveal better targeting to poor people who are more likely to do this type of work, as provided by NREGS. Against this, the effect on the welfare gains to participants of allowing for the disutility of the work provided to them will go in the opposite direction.

How should one set α? This can be recognized as a familiar normative judgment for making inter-personal comparisons of welfare. For the purpose of making consistent interpersonal comparisons of welfare, we need to impose common preferences, so we need to use one reference value of the preference parameter. In choosing that value we will draw on empirical observations of demand and supply behavior. For this purpose we construct the distribution of α as an idiosyncratic preference parameter under the assumption that each household in our sample maximizes Ci∗ = Cie−αi�Li−L<sup>R</sup> � (in which the preference parameter now varies across households) subject to a budget constraint: where Wi(> 0) is the household daily wage rate for this type of work and C0i(≥0) denotes consumption per capita at zero casual labor supply, which is taken to be exogenous.<sup>7</sup> It is readily verified that when labor supply is chosen optimally (and C0i > 0):<sup>8</sup>

> 7 Note that this problem is equivalent to maximizing ln Ci −αiLi subject to (2).

> 8 In the limiting case with  C0i = 0,  L∗i = 1/αi unless αi > 1/Li  for all Li > 0 in which case L∗i = 0.

6