rafmacalaba/datause-encoder-data huggingface dataset:
Applying this idea to Vietnam in 1993 and 1998 produces the results indicated in column [b] of Table 2. The costs of reaching 2100 calories a day have been calculated to be 750 and 1287 thousand Dong respectively (current prices) (Glewwe, Gragnolati, and Zaman 2000). Column [a] for each year shows the effect of defining D(x) as the per capita food spending of the individual's household, while column [b] shows the effect of deducting a food allowance corresponding to 2100 calories but constraining ATP to be non-negative. Unsurprisingly, the second case produces a distribution of deductions that is less pro-rich than the first case (cf. the values of _CD)._ The value of S(the average deduction rate) falls in the move from full deductibility to the food allowance. The element of progressivity of payments on pre-payment income attributable to the deductions is higher for case [a] than case [b]. Unsurprisingly, because the progressivity of payments on pre-payment income remains the same, the regressiveness of payments on ATP rises. We conclude, therefore, that payments appear more regressive on ATP when the latter is defined as pre-payment income less a flat-rate food allowance than when it is defined as pre-payment income less actual food spending.

# **3.3.2. Should deductions reflect only food costs?**

With respect to deductions, there is, of course, the issue of whether D(x) should reflect food costs only or whether it should reflect other costs that might be considered to be non-discretionary. The costs of shelter (e.g. rent), clothes, heating and energy are obvious examples. But what about the costs of, say, water, garbage disposal and education? Again, there is the issue of whether one should deduct actual expenses incurred or whether one should deduct an allowance. The latter approach is less straightforward than in the case of food, where it is relatively easy to agree on a target level of food intake (say, 2100 calories a day) and then compute the cost of reaching it. The obvious alternative is to adopt the national or international poverty line as the appropriate value for _D(x)._ The difficulty with this is that it is intended to cover not just the costs of food and other key non-food items such as shelter, energy, clothing, and so on, but also the costs of health care. This is not a trivial issue in countries like Vietnam where around 5-6% of household consumption is devoted to out-of-pocket payments for health care. Clearly, one would need to adjust the national or international poverty line downwards to reflect this when coming up with a figure for _D(x)._ We have done this exercise for Vietnam for 1993 and 1998, using the national poverty lines computed by the World Bank and the Government of Vietnam (Glewwe, Gragnolati, and Zaman 2000). These were constructed by computing the annual cost of reaching 2100 calories per person per day (in current prices 750 and 1287 thousand Dong in 1993 and 1998 respectively), and then adding to this amount a sum to cover non-food _13_
_Low-Income Countries’ Access to Private Debt Markets_

# **Figure 20 External debt as a share of GNI in low-income countries and HIPCs, 2000 and 2007**

<!-- Start of picture text --> Percent<br>400 2000 2007<br>300<br>non-HIPCs HIPCs<br>200<br>100<br>0<br><!-- End of picture text --> _Source:_ World Bank Debtor Reporting System One might expect that countries with that low external debt burdens are well placed to manage the risks entailed in borrowing from private creditors on non-concessional terms. But history has shown that debt burdens alone are not a good indicator of default risk. Reinhart, Rogoff and Savastano (2003) stress that debt default has often occurred in emerging market economies with moderate debt levels. Many low-income countries are vulnerable to large external (especially terms of trade) and domestic (often policy-induced) shocks that can worsen the risk outlook significantly (Catão and Sutton 2002, Catão and Kapur 2004, Loayza and others 2007, and Raddatz 2007). The risk of debt distress therefore remains a major concern, as evident in recent developments. The spread on Pakistan’s sovereign bonds widened from below 200 basis points in mid-2007 (comparable to the EMBI Global composite index) to over 2000 basis points in October 2008, yet its external debt was only 28 percent of GDP in 2007, well below the average level for middle-income countries (47 percent). Similarly, spreads widened to over 1000 basis points in four other countries where external debt was under 30 percent of GDP in 2007. These examples serve to illustrate just how quickly investor confidence can deteriorate in countries with low external debt burdens.

Assessing debt sustainability has become complicated by the development of domestic debt markets in some countries. Governments in many developing countries have been financing a growing share of their borrowing requirements in the domestic market. Unfortunately, the quality of data on the volume and attributes of domestic public debt is poor (at best). Data for 2005 indicates that the stock of public debt outstanding in domestic markets is significant in many developing countries, averaging around 25 percent of GNI (figure 21). Non-resident purchases of domestic public debt are substantial in some cases, raising the risk that a sudden shift in investor sentiment could lead to instability in the domestic financial market.<sup>21</sup> This is of particular concern in countries where specialized investment funds have focused on making short-term returns generated by high domestic interest rates and the anticipation

> 21 Non-resident purchases of debt issued in domestic markets which should be classified as external debt from a balance of payments perspective but often goes unreported.

26
<!-- Start of picture text --> 0.8<br>® 07. [11MS yearsyear<br>&9 0.6 HB10 years<br>><br>5 0.5<br>s<br>®<br>£03<br>S<br>22<br>£-<br>n<br>0.1<br>0<br>Advanced economies EMDEs<br><!-- End of picture text --> <!-- Start of picture text --> 2:<br>8<br>Cc<br>—<br>>4.<br>0;<br>TFP Labor productivity Technology<br><!-- End of picture text --> <!-- Start of picture text --> 0.45<br>[11 year<br>0.4 155 years<br>® HB10 years<br>oC9 0.35<br>> 0.3<br>5<br>© 0.25<br>® 0.2<br>2£<br>‘6 0.15<br>2<br>£04<br>n<br>0.05<br>0<br>Advanced economies EMDEs<br><!-- End of picture text --> Advanced economies EMDEs <!-- Start of picture text --> 15<br>8 10<br>Cc<br>—<br>><br>5<br>0<br>TFP Labor productivity Technology<br><!-- End of picture text -->
- Shaw, E.S. (1973), _Financial Deepening in Economic Development._ (New York: Oxford University Press).

- Srinivasan, T.N. (1993), "Saving in the Development Process", in J.H. Gapinski, ed., _The Economics of Saing_ (Dordrecht: Kluwer).

- Stock, J. H. and M. W. Watson (1993), "A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems", _Econometrica,_ 61, 783-820.

- Taylor, L. (1983), _Structuralist Macroeconomics: Applicable Models for the Third World,_ (New York: Basic Books).

- Vaidyanathan (1993), "Consumption, Liquidity Constraints and Economic Development", _Journal ofMacroeconomics,_ 15, 591-610.

- Van Wijnbergen, S. (1982), "Stagflationary Effects of Monetary Stabilization Policies: A Quantitative Analysis of South Korea", _Journal of Development Economics,_ 10, 133-69.

- Zeldes, S.P. (1989), "Consumption and Liquidity Constraints: An Empirical Investigation", _Journal ofPolitical Economy,_ 97, 305-46.

26
-22 -

Chronic Bronchitis. Recent epidemiologic studies have related long-term exposure of air pollution to a higher prevalence of chronic respiratory disease or significant decrements in lung function. For example, Detels et al.<sup>3</sup> 9 found that residents living in the Los Angeles air basin who were exp.sed over a long period of time to relatively high levels of particulates and oxides of sulfur and nitrogen had significanly lower lung function than a cohort less exposed. Abbey et al.A' conducted a study on 6,600 Seventh Day Adventists, nonsmokers who had lived for at least 11 years in California. In this study, participants above age 25 (n=3914) were matched with 10 years of exposure to ambient pollutants based on their monthly residential location. New cases of chronic bronchitis between 1977 and 1987 were recorded. A logistic model was estimated that included adjustment for sex, past and passive smoldng and occupational exposure. A statistically significant association was reported between long-term exposure to TSP and chronic bronchitis. Using the mean incidence rate of bronchitis during the 10-year period, the functions were linearized and converted to PM10 equivalence, and annual number of cases of bronchitis can be estimated. The functions are as follows: Upper change in chronic bronchitis = 9.18 x 105 * change in annual PM10 Central change in chronic bronchitis 6.12 x 10r5* change in anmual PM1O Lower change in chronic bronchitis = 3.06 x 10YS * change in annual PMLO Other (Non-uantified) Health Effects. There is limited evidence linking long term exposure to TSP **to** increases in cancer in women.' Since TSP includes seveal materials known or believed to be carcinogenic, reductions in particulate matter will likely reduce cancer cases, as well- There also may be other acute and cromnic effects for which no empincal information is curreny available. For example, the mortality effects calculated in this study only relate to acute exposures; longer term exposures to particles may increase the likelihood of prematre mortality. Finally, no estimates are
# **Abstract**

This paper assesses corruption levels and trends among countries in the transition countries of Eastern Europe and Central Asia (ECA), based on data from several sources that are both widely used and cover most or all countries in the region. Data from firm surveys tend to show improvement in most types of administrative corruption, but little change in “state capture” in the region. Broader, subjective corruption indicators tend to show somewhat greater improvement in ECA than in non-ECA countries on average. A “primer on corruption indicators” discusses definitional and methodological differences among data sources that may account in large part for the apparently conflicting messages they often provide. This discussion concludes that depending on one’s purpose, it may be more appropriate to use data from a single source rather than a composite index, because of the loss of conceptual precision in aggregation. A second conclusion is that the gains in statistical “precision” from aggregating sources of corruption data likely are far more modest than often claimed, because of interdependence among data sources. The range of detailed corruption measures available in firm surveys are exploited to show that broad, perceptions-based corruption assessments appear to measure primarily administrative corruption, despite their stated criteria placing great weight on “state capture.” Finally, the paper emphasizes the need for scaling up data initiatives to fill significant gaps between our conceptual definitions of corruption and the operational definition embodied in the existing measures.

2
**Figure 14: Percentage of ninis who decide to start their own household and are not searching for work in LAC by gender and age, 1992-2010**

Panel A: 15-18 years Panel B: 19-24 years <!-- Start of picture text --> 25.7 46.9 47.2<br>23.5 43.5 43.5<br>41.6<br>20.4<br>18.2 17.8 16.7 34.7 33.0 32.7 35.7 35.1<br>15.4<br>13 . 6<br>12.2 12.4<br>0.3 0.3 0.8 0.8 1.1 4.0 2.4 2.5 3.5 3.3<br>1990 1995 2000 2005 2010 1990 1995 2000 2005 2010<br>Year Year<br>Total Men Total Men<br>Women Women<br>50.0<br>25.0<br>40.0<br>20.0<br>15.0 30.0<br>10.0 20.0<br>& not searching for work 5.0 & not searching for work 10.0<br>Ninis starting own household (% relevant Ninis population) Ninis starting own household (% relevant Ninis population)<br>0.0 0.0<br><!-- End of picture text -->

**Figure 15: Ninis who decide to start their own household and are not searching for work in LAC by gender and age, 1992-2010 (as % of ninis not searching for work)**

Panel A: 15-18 years Panel B: 19-24 years <!-- Start of picture text --> 29.2 58.7 58.2<br>27.4 54.1<br>21.0 21.8 23.8 19.7 20.5 50.644.5 51.946.2 46.3 50.9 49.3<br>16.6 17.5<br>15.4<br>7.6 5.8 6.2 8.7 7.9<br>0.4 0.5 1.3 1.2 1.6<br>1990 1995 2000 2005 2010 1990 1995 2000 2005 2010<br>Year Year<br>Total Men Total Men<br>Women Women<br>30.0 60.0<br>20.0 40.0<br>10.0 20.0<br>& not searching for work not searching for work) & not searching for work not searching for work)<br>Ninis starting own household (% Ninis in relevant population Ninis starting own household (% Ninis in relevant population<br>0.0 0.0<br><!-- End of picture text --> Source: authors’ own computation with data from CEDLAS and The World Bank (2015) 22
First, the crowding out of government investment by rising revenue deficits potentially hurts private investment. For example, an empirical study in RBI (2002c) concludes that public investment in infrastructure has strong positive complementarities with private investment.

Second, the desire to build up reserves while sterilizing capital inflows to keep inflation low means higher real interest rates the higher the fiscal deficit and government debt, especially in an economy with capital controls. In fact, real interest rates for borrowers rose with growing government debt and remained high between 1996/97 and 2001/02, averaging over 12 percent per annum as shown in Figure 3 based on Table 1 in Mohan (2003). Thus, it does appear that a “loose fiscal-tight money” policy mix emerged during the Ninth Plan period to help meet reserve and inflation targets in the absence of a fiscal adjustment.

<!-- Start of picture text --> Figure 3 : Real Interest Rates (1990/91-2001/02)<br>Percent<br>16.0<br>14.0<br>12.0<br>10.0<br>8.0<br>6.0<br>4.0<br>2.0<br>0.0<br>-2.0 90/91 91/92 92/93 93/94 94/95 95/96 96/97 97/98 98/99 99/00 00/01 01/02<br>-4.0 Central government Borrowers<br><!-- End of picture text --> Source: Speech by Rakesh Mohan, RBI Monthly Bulletin (Jan 2003) Third, over the last two years or so, interest rates have fallen in India in line with global trends; but there are significant barriers to translating the low marginal interest rates on government debt into low lending rates to the real sector. The Indian financial system is segmented and must conform to various minimum lending and portfolio composition requirements. For example, the prime lending rate tends to be kept high because it cannot be exceeded when lending to the priority small scale sector, which might partly be motivated by the inefficient policy of small-scale reservations. Insurance companies must hold at least 50 percent of their assets in government debt and provident funds at least 50 percent in central government debt and 25 percent in state government debt. Provident funds have also purchased debt of “special purpose vehicles” (SPVs) guaranteed by the state governments. Annual inflows into small (postal) savings, which compete with banks for deposits, are invested wholly in government debt. These have grown from an average of 7.9 percent of GDP in the Eighth Plan to nearly 10 percent currently and consume nearly the whole increase in net household financial saving. The administratively-determined interest rates for these instruments were set artificially high, at 10.513 percent until 2000 and are still 7.5-9 percent, with tax concessions. This worsens public debt dynamics while raising the cost of funds for banks and hence lending rates to the private sector. Moreover, banks are under pressure to reduce their non-performing assets, which attracts them even more toward holding government debt. And non-bank financial corporations<sup>44</sup> (NBFCs),

> 44 See Bossone and Zahir (1999)

17
here is to test certain hypotheses regarding the determinants of student learning. These hypotheses relate to the effects of school quality, particularly the teachers’ quality, training, and teaching practices. Issues regarding teachers’ incentives, supervision, facilities, and specific students' characteristics and their parents' are also included in the analysis.

Based on the EEEP from SEP, Table 1 shows the distribution of Spanish and Mathematics test scores by school quintiles. The best 20% schools in the nation have a score of 57.7 on average in Mathematics (out of 100 points) and a relatively higher score in Spanish. The standard deviation is higher in this group compared to the rest of the learning achievement quintiles. The highest grade dispersions are concentrated at the tails of the distribution.

**Table 1.** **<u>5</u>**<sup>**th**</sup> **<u>Grade Test Scores by Learning Achievement quintile</u>**

|**Quintile**|**Mathe**|**matics**|**Span**|**ish**|
|---|---|---|---|---|
||**Mean**|**SD **|**Mean**|**SD **|
|1|40.7|2.9|46.5|2.7|
|2|45.6|0.8|51.5|1.0|
|3|48.4|0.7|54.5|0.7|
|4|51.5|1.0|57.8|1.3|
|5|57.7|4.2|65.5|5.0|
|Total|48.7|6.1|54.9|6.8|

Source: Primary Education Assessment Survey, second round 1997.

Table 2 shows the distribution of test scores nation-wide by stratum. Private urban schools perform relatively better than do other types of schools. Public urban schools rank second while indigenous schools are at the bottom of the distribution. Nonetheless, the grade differences between indigenous schools and community schools are small, particularly in Spanish scores. The highest dispersion of test scores is found in the learning of Spanish scores in private urban schools.

9
# **A Cautionary Tale: An Experiment on The Stability of Business Environment Perceptions in a Firm Survey**

## **1. Introduction**

The private sector is a fundamental engine of economic growth in an economy. The ability to check the pulse of the private sector is crucial for policy makers to formulate evidence-based recommendations. However, there is a dearth of information on firms across the developing world, thereby generating the need for business surveys such as the World Bank Enterprise Surveys. Questions in the surveys generally come in two forms. The first is what is defined as experience-based questions. Here the respondent, typically the manager or owner of the firm, provides information regarding a specific event, over a defined time frame, on a particular element of the business environment. For instance, “ _Over last complete fiscal year, did this establishment experience power outages?_ ” The second type of question is perception-based. Here the respondent provides a rating, typically using a Likert scale of responses, of a business environment element which is more holistic in nature. For instance, “ _To what degree is electricity an obstacle to the current operations of this establishment?_ ” The choices, consistent across all WBES, are: “no obstacle,” “minor obstacle,” “moderate obstacle,” “major obstacle,” and “very severe obstacle.” Both types of questions address the same element of the business environment, electricity in the example above, but the experiencebased question narrows it down to a specific component of an element of the business environment -usually a key component such as power outages- whereas the perception-based question encompasses all dimensions of that element of the business environment. It is because of this all-encompassing generality that perceptions-based questions may lead to biases or misinterpretations. The aim of this study is to explore the stability of such indicators by evaluating whether the ordering of perception-based questions matters. In this study, we analyze data from a unique experiment conducted in the República Bolivariana de Venezuela in 2006 that randomized the order of perception-based questions. Due to budgetary challenges 2
Table 18: Pooled regression estimates of the effect of ICT use on the Number workers: Manufacturing

||(1)|(2)|(3)|
|---|---|---|---|
|**Dependent variable: ln(Number of **|**white-collar **|**workers)**||
|Share of labor that uses computers|0.0088***|0.0084***|0.0094***|
||(0.0003)|(0.0003)|(0.0002)|
|Share of labor that uses Internet|0.0077***|0.0073***|0.0087***|
||(0.0003)|(0.0003)|(0.0003)|
|ln(stock of computer equipment/worker)|0.1773***|0.1450***|0.1253***|
||(0.0077)|(0.0085)|(0.0076)|
|**Dependent variable: ln(Number of b**|**lue-collar **|**workers)**||
|Share of labor that uses computers|0.0045***|0.0046***|0.0056***|
||(0.0003)|(0.0003)|(0.0002)|
|Share of labor that uses Internet|0.0033***|0.0034***|0.0048***|
||(0.0003)|(0.0003)|(0.0003)|
|ln(stock of computer equipment/worker|-0.0455***<br>(0.0069)|-0.0490***<br>(0.0077)|-0.0567***<br>(0.0069)|
|**Controls**||||
|ln(capital/worker)|No|Yes|Yes|
|Age dummies|No|No|Yes|
|Size dummies|No|No|Yes|
|Sectoral efects|No|No|Yes|
|Observations|22,876|22,876|22,876|

Robust standard errors in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 Source: Authors’ calculations with data from the 2009 and 2014 Economic Censuses, INEGI.

47
differences between the health-mechanism impacts on girls and boys. Overall, the effects on the health mechanisms investigated here are in line with the reductions in infant and neonatal mortality seen in Tables 6 and 12.

Although the health-mechanism results are not perfectly comparable to the main findings, they indicate that health care delivery through immunization programs, and to some degree also simple diarrhea treatment (among poor mothers), improve after a country reaches Decision Point of the HIPC Initiative. These measures are possibly more straightforward and uncomplicated to implement, partly with assistance from debt relief partners (mainly the World Bank and IMF), compared with advancements in child delivery and prenatal care, which are more likely to take longer and be more intricate to achieve. Furthermore, the heterogeneous effects across different groups of mothers suggest that the aims to target vulnerable population groups set out in the PRSPs and Decision Point documents have been successful.

# **5 Conclusion**

In this paper I analyze if debt relief under the HIPC Initiative is associated with better child health. The initiative was introduced by the IMF and the World Bank in the late 1990s to reduce the debt burdens of poor countries and secure focus on and resources for pro-development policies. It consists of two stages, Decision Point and Completion Point. A debt-ridden and poor country may reach Decision Point if it shows a track record of reform in accordance with agreements with the IMF and the World Bank. At Decision Point, a path to debt sustainability is decided and the country benefits from interim debt relief. Further reforms are necessary to reach full debt relief at Completion Point. In December 2014, the IMF reported that the enhanced HIPC Initiative and the MDRI had led to a 2.5 percentage point increase in poverty-reducing spending between 2001 and 2013 in recipient countries (IMF, 2014).

I apply micro data on infant mortality from country-specific DHS from 56 low- and middle-income countries of which 31 are HIPCs to make use of mother- and family-specific effects. My findings suggest that when a country reaches Decision Point, the probability of infant mortality goes down with approximately 0.5 percentage point or 5 infant deaths per 1,000 live births. This represents approximately 3,000 fewer infant deaths in an average HIPC in the year of Decision Point. Results are stronger for infants born to poor mothers and mothers residing in rural areas. This indicates that Decision Point policies are favorable to vulnerable women and infants who are poor and/or reside in the countryside. There are no such effects when a country reaches Completion Point. Neonatal mortality is largely unaffected by debt relief under the HIPC Initiative, which indicates that the impact of HIPC at Decision Point on infant mortality does not originate from children dying within their first month of life. An important health 17