rafmacalaba/datause-encoder-data huggingface dataset:
country PFM systems. The DAC‟s peer reviews of its members‟ aid programs now include assessments of their consistency with Paris Declaration principles and objectives.

The DAC donors can be divided further, between the “Nordic Plus” group and others. Nordic Plus donors include Denmark, Finland, Norway, Sweden, Ireland, the Netherlands, and the United Kingdom. The group‟s purpose is to improve complementarities among its members, through division of labor based on comparative advantages (NORAD, 2006; de Renzio, 2005). By reducing the number of sectors and countries each donor operates in, transactions costs for recipients can be reduced, at the price of reduced visibility for the donors. Nordic Plus membership can thus be viewed as a proxy for low skepticism of aid‟s effectiveness among their domestic constituencies. Empirical support for this hypothesis may be interpreted as merely showing that donors committed to the harmonization elements of the PD agenda tend to be committed also to its “alignment” elements. At a minimum, however, the Nordic Plus dummy can usefully test for any significant variation among DAC bilaterals in use of country systems.

The vertical funds (sometimes called global funds) comprise a last set of donors. These donors have limited sector-specific mandates, such as the environment or specific diseases. In the PDMS, most aid from vertical funds is accounted for by the Global Fund to Fight AIDS, Tuberculosis and Malaria. It disbursed 2.2% of the aid represented in the survey - more than the Netherlands or Canada. Vertical funds, particularly in health, are often viewed as being motivated by concerns over global public goods, not integrated into national strategies, and reluctant to use country systems (Wood et al. 2011: 34-35; World Bank, 2006: 22). For these reasons vertical funds may be associated with lower use of country systems in general.

For the DAC bilateral donors, we can go beyond these donor group dummies and attempt to measure domestic constituents‟ trust in aid effectiveness in general using data from public 13
|**Table 8:** **Power analysis o**<br>|**f insignifi**|**cant outcome**<br>|**variables for**<br>|**Non-memb**<br>|**ers as well as**<br>|**POP and po**<br>|**or member**<br>|**s**<br>||
|---|---|---|---|---|---|---|---|---|---|
|**Variables**||**Non-member**|**s**|**Poorest**|**of the Poor**|**members**|**Non**|**-poor mem**|**bers**|
||Est. le|vel<br>Pro|bability is|Est. lev|el<br>Pro|bability is|Est. lev|el<br>Pro|bability is|
|||10%|5%|(level)|10%|5%|(level)|10%|5%|
|Consumption p.c.|-19|1.0%|11.7%|677|51.2%|77.6%|-143|18.0%|30.4%|
|Food (Rs/year)|-181|0.1%|1.7%|295|42.6%|66.7%|-289|11.8%|21.0%|
|Non-food (Rs/year)|163|19.7%|42.9%|382|58.9%|77.8%|146|29.0%|43.0%|
|Energy intake p.c.|-84|0.0%|1.2%||||-101|13.4%|23.9%|
|Protein intake p.c.|-1.61|0.1%|2.9%||||4.21|44.8%|62.9%|
|Non-fin. assetp.c.|231|31.3%|53.6%||||1066|82.6%|89.0%|

24
Table **I. 1**

# Small, Medium, and Large Enterprises in Korea: Manufacturing Shares ( 1963-1988)

||Class (by number<br>of workers)|||||||||
|---|---|---|---|---|---|---|---|---|---|
|Share of<br>Manufacturing<br>Establishments|Small<br>(5-19)<br>Medium<br>(20-199)|80.4<br>18.3|77.9<br>19.8|72.2<br>23.4|61.7<br>.2.4|57.9<br>35.7|59.2<br>36.2|_56.7_<br>39.2|56.9<br>39.5|
||Large<br>(200+)|1.3|2.3|4.4|5.9|6.4|4.6|4.1|3.6|
|Share of|Small<br>(5-19)|29.0|22.2|12.2|8.0|7.5|10.1|10.3|11.1|
|Manufacturing<br>Employment|Medium<br>(20-199)|37.4|32.0|27.2|29.6|30.6|36.1|39.6|40.1|
||Large<br>(200+)|33.6|45.8|60.6|62.4|61.9|53.8|50.1|48.8|
||Small<br>(5-19)|19.3|12.1|5.6|3.6|4.2|4.6|4.7|4.9|
|Share of||||||||||
|Manufacturing<br>Value-Added|Medium<br>(20-199)|33.5|23.6|21.6|20.1|22.2|24.0|26.8|30.0|
||Large<br>(200+)|47.2|64.2|72.8|76.3|73.5|7L.4|68.5|65.1|

Source: Economic Planning Board, _Report of the Mining and Manufacturing Census_ (1963, 1968, 1973, 1978, 1983) and _ReporT on Mining and Manufacturing Survey_ (1976, 1986).

45
**Figure 6: Force duration (length of accelerations and decelerations as share of total)** <u>(percent)</u>

<!-- Start of picture text --> AE - Increases<br>AE - Decreases<br>EM - Increases<br>EM - Decreases<br><!-- End of picture text -->

**Figure 7: Force magnitude (median size of force by length of accelerations and decelerations)** <u>(percent)</u>

<!-- Start of picture text --> AE - Increases<br>AE - Decreases<br>EM - Increases<br>EM - Decreases<br><!-- End of picture text --> It is also useful to examine force by the different components of expenses. Figure 8 shows the median force by component for the last six years of data. During the first years of the financial crisis we see that force was strongly positive in advanced economies, especially for social benefits. The force of social benefits continued in 2009 before being wound back in 2010 with a large negative force. Other categories that had negative force in 2009 and 2010 were compensation of employees and use of goods and service. In emerging economies we see that in 2008 there was a large positive force from subsidies, related to very high commodity prices. As the financial crises developed in 2009, the force of subsidies became very strongly negative, along with use of goods and services and wages. In 2010, wages continued to have very strongly negative force, along with social benefits.

<!-- Start of picture text --> Figure 8: Median Force by GFS component – advanced Figure 9: Median Force by GFS component – emerging<br>economies economies<br>(percentage points) (percentage points)<br>2 4<br>1 2<br>0 0<br>Wages<br>-1 Grants -2 Wages<br>Grants<br>Subsidies<br>Subsidies<br>Use of G/S<br>-2 -4 Use of G/S<br>Social Benefits<br>Social Benefits<br>Other Expenses Other Expenses<br>-3 -6<br>2006 2008 2010 2012 2006 2008 2010 2012<br><!-- End of picture text --> Note: Wages is a short notation for compensation of employees, which is a broader category of employee compensation. Source: IMF GFS data and authors’ calculation
<!-- Start of picture text --> 2 / ~~ i ° cd c \ 4 + ~<br>ae : 3 yy <a See ns ot ws :<br>7 Pre: cates<br>~ . oa. 2<br>a “a ee.<br>VISITS (MILLIONS)482.9 ~ oSoi € De7 Na: fyweg<br>0.16 \ 6,<br>ne an.)<br>IBRD 48264 |<br><!-- End of picture text -->
- 14 -

smuggling or black markets prevents full forward shifting, firms absorb tax because of monopoly profits due to import licensing or other causes, or prices of factors specialized to the industry fall, or there are broader overall shifts in commodity and factor prices, as demonstrated in general equilibrium models of shifting. Nevertheless, the generally accepted assumption is that an excise primarily results in higher prices to consumers of the commodity.

Secondly, should distribution of burden be measured in relation to consumption, or income, and if the latter is chosen, annual income or lifetime income? Since income is generally, but not universally, regarded as the best measure of tax paying ability, it is most commonly accepted as the basis for expressing the pattern of tax burden distribution. Likewise annual income is usually regarded as the appropriate measure of income, since income on a current basis is the primary source of payment of taxes. But there is no complete agreement on this issue, and in some special circumstances, expenditure may be the better basis.

Thirdly, there are related questions about exactly what items should be included in income and problems of measurement of some forms. Should gifts and welfare payments be included--though these are not significant in most developing countries. More serious is the importance of income in kind in the developing countries, products of semi subsistence agriculture, for example. These are difficult to discover and measure, and are frequently omitted in tabulations. Since this form of income concentrates in the lowest income groups, figures of tax burden as a percentage of income overstate tax for these groups.

Fourthly, data on consumer expenditure by income group are based upon consumer expenditure surveys, which are by no **meLiis** entirely accurate. The family samples selected may not be fully representative. Persons often do not remember or keep accurate records of their expenditures by commodity groups. There are definite biases on the part of many persons in reporting expenditures on various commodities, particularly alcoholic beverages; some studies
Figure 14: Composition of Commercial Banks’ Performing Loan Portfolio (2000-2005) <!-- Start of picture text --> 100%<br>8% 11% 15%<br>90% 21% 25% 31%<br>80%<br>70% 32% 30% 27% 23% 19%<br>60% 21%<br>50%<br>40%<br>30% 60% 59% 58% 56% 55%<br>48%<br>20%<br>10%<br>0%<br>2000 2001 2002 2003 2004 2005<br>Commercial Housing Consumer<br>Proportion of banks' credit portfolio<br><!-- End of picture text --> Source: BOM.

Note: Only performing loans to the non-financial private sector are included in the analysis. Performing restructured UDI-denominated mortgage loans that are off bank balance sheets (“ _fideicomisos UDIs_ ”) are also included.

Substantial progress has been achieved in ‘cleaning up’ bank loan portfolios. In particular, the NPL ratio has declined substantially since 2000, which can be primarily attributed to the resolution of the NPL overhang stemming from the 1995 crisis and the conversion of FOBAPROA notes (which were included in loan figures because they carried a downside risk for the banks) into IPAB securities (see figures 15 and 16). Quality has also improved due to enhanced loan origination standards and better risk management systems in banks, and to a greater availability of currency hedges and local currency denominated debt at longer maturities, which mitigates currency and maturity mismatches in debtor balance sheets.

Figure 15: Evolution of Past Due Loans in Commercial Bank Portfolios (2000-2005) Figure 16: Evolution of Loans Linked to Restructuring Programs (2000-2005) <!-- Start of picture text --> 120 30% 40%<br>35%<br>100 25%<br>30%<br>80 20% 25%<br>60 15% 20%<br>15%<br>40 10% 10%<br>20 5% 5%<br>0%<br>0 0%<br>2000 2001 2002 2003 2004 2005<br>Commercial Housing Consumer PDL Ratio FOBAPROA UDIS ADE<br>MXP Billion PDL Ratio (%)<br>% of total loan portfolio<br>Jan-00 Jul-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jan-05 Jul-05<br><!-- End of picture text --> Source: BOM.

Note: PDL Ratio = Past Due Loans / Total Loans to Non-Financial Private Sector. Total loan portfolio comprises of only performing loans to the non-financial private sector. Performing restructured UDIdenominated loans that are off bank balance sheets (“ _fideicomisos UDIs_ ”) are also included.

# **_Macroeconomic Performance and Financial System Reforms_**

Macroeconomic performance and financial system reforms have been the two ‘pillars’ underlying developments in financing to the private sector. In addition to segment-specific factors (described in the next section), two common drivers of credit 14
include samples of urban resident households with rural _hukou._ The 2002 CHIP survey permitted an estimate of the urban Gini coefficient both with and without migrants. The inclusion of migrants raised the Gini by 2 percentage points (Khan and Riskin 2007), but this result may be an understatement if migrants living in households have higher incomes than independent sojourners.

Nonfarm employment is important for rural household income and its distribution. Both average and, especially, marginal income are higher in local nonfarm and migrant activities than in farming. The share of wages in rural income rose sharply as rural industry burgeoned and migration accelerated. Processes of cumulative causation were initially at work as some villages industrialized and some became migration villages. Wage income contributed 21 percent of rural income inequality in 1988, 40 percent in 1995, and 41 percent in 2007.<sup>3</sup> The slowdown was due to the reduction in rural spatial income inequality as wage employment opportunities spread more widely across provinces and counties. In principle, migration can either increase or decrease inequality depending on whether poor households, which have the greatest incentive to send migrants, have the ability to do so. An analysis of the effect of migrant members on the income of rural households using the 2007 CHIP survey showed that it reduced rural poverty and, by implication, inequality (Luo and Yue 2010). Benjamin et al. (2005) analyzed rural income inequality using a Ministry of Agriculture annual survey of 100 villages. Between 1987 and 1999, the (spatially price-deflated) Gini coefficient rose from 0.29 to 0.35. Most of this increase was at the local level. Whereas household access to local 9
were channeled into local language schools with established parental network and entered into traditional parental occupations. The daughters, on the other hand, enrolled in English medium schools and were better prepared to take advantage of non-traditional jobs, especially those with a premium for English proficiency.

A straightforward implication of the above discussion is that the same set of factors is likely to be responsible for lack of improvements in educational mobility for women in rural areas. Low parental income, low returns to education, lack of skilled jobs, and stronger social norms against women‘s participation in the labor market, all combined together can result in little or no improvements in educational mobility for rural women. Also, note that among the few sociallycoveted jobs in rural areas for educated women are public sector jobs, for example, in schools and health clinics. However, hiring in public schools and health clinics was frozen or curtailed after 1991 liberalization as part of the fiscal reform, but there was no compensating private sector growth. This might have reinforced the disincentives for investing in daughter‘s education in rural areas.

# **Conclusions**

The Indian economy grew at a robust pace since its economic liberalization in 1991and achieved significant reduction in poverty. At the same time, the evidence indicates an increase in inequality (World Bank (2011), Deaton and Dreze (2002), Datt and Ravallion (2010)). This paper examines the trends in and patterns of intergenerational mobility in education among new entrants in the labor force (16-27 year olds) between 1992/93 – a year immediately following the economic liberalization – and 2006 – nearly 15 years after liberalization. To the best of our knowledge, this is the first paper in the literature to employ both intergenerational and sibling correlations to study the evolution of educational mobility in a developing country.

The empirical results indicate that educational mobility remained largely unchanged for a large proportion of Indian children after a decade and a half of high economic growth. Between 1992/93 and 2006, the only group that experienced significant improvements in educational mobility is women in urban areas and more developed states. We find that estimates of sibling and intergenerational correlations among men stayed almost the same over the reform period in urban areas and developed states, but may have increased slightly in rural and less developed regions. In contrast, the sibling and intergenerational correlations among women have declined irrespective of geographic location, but only women in urban areas and developed states have experienced a substantial decline. Interestingly, among the urban women, there are significant caste differences: 30
<!-- Start of picture text --> 5<br>4<br>3<br>2<br>1<br>0<br>ETHIOPIA_2011-12 TANZANIA_2010-11<br>MALAWI_2010-11 UGANDA_2010-11<br>Farm Enterprise, Ag Enterprise, Ind<br>Enterprise, Ser Wages, Ag Wages, Ind<br>Wages, Ser Wages, Unkn<br>5<br>4<br>3<br>2<br>1<br>0<br>ETHIOPIA_2011-12 TANZANIA_2010-11<br>MALAWI_2010-11 UGANDA_2010-11<br>Farm Wages, Ag Wages, Ind<br>Wages, Ser Wages, Unkn<br>Med. prod. ratio, output per person per year<br>Med. prod. ratio, output per hour<br><!-- End of picture text --> Figure 9: Conditional productivity gaps by activity for rural households (median). Figure 9a (top) depicts the median of intra-household productivity ratios between farming and other activities, where productivity is defined as output per worker per year. Figure 9b (bottom) depicts the median of per hour intra-household productivity ratios. This analysis is based only on households that participate in farming and another activity.

29
children between 0 and 8. We specifically use a set of variables measuring households’ attitudes towards the future that were asked following the structure of the CESD questions, a widely-used measure of depression (Radloff, 1977).<sup>9</sup>

# _Methodology_

Given the randomized assignment and the high take-up rates, we can estimate intent-to-treat estimates that are very close to average treatment effects, without concerns about potential selectivity into treatment. Specifically, we consider the impacts of the vocational training package and the productive investment package and estimate the following intent-to-treat household-level regressions: where Y are outcomes of interest, _Grant_ and _Training_ capture the random treatment assignment, Z is the set of baseline variables for which we analyze treatment heterogeneity, and X includes additional baseline controls as well as block stratification dummies.<sup>10</sup> All errors are clustered at the community level.

The outcomes of interest Y include the participation and returns to nonagricultural wage and selfemployment activities two years after the end of the intervention. As the interventions were targeted at the household level, the analysis focuses on household level outcomes. Participation in nonagricultural wage employment takes the value 1 if at least one household member had a non-agricultural wage job at some point during the last 12 months, and 0 otherwise. Participation in nonagricultural self-employment takes the value 1 if at least one household member had a nonagricultural self-employment activity at some point during the last 12 months and 0 otherwise. We use two different variables to measure returns to nonagricultural self-employment, one that measures profits of any non-agricultural self-employment activity the household was active in at the moment of the interview and a second that aggregates gross income of all nonagricultural self-employment income in the last 12 months. While the latter has the disadvantage of not accounting for costs, it provides a longer reference period. The type of nonagricultural selfemployment activities captured by these variables include activities such as food production, small manufacturing, commerce, services, or rearing of small livestock and selling of derived products. All

> 9 In addition to the quantitative data, qualitative work preceded each round of data collection. Focus groups and semi-structured interviews with a wide set of beneficiaries and other local actors in treatment and control communities, and in the main town of the municipality were conducted to explore qualitative evidence of the program’s impacts, as well as issues related to program implementation.

> 10 All regressions include the baseline value for the outcome variable (when available) and baseline controls for demographics (age of household head and main beneficiary, number of members in different age groups by gender, male household head), participation in different economic activities, distance to school and health center, land and animal holdings.

10
**Table 5: Response of Domestic Inflation to International Food Price Inflation Shocks**

||Model 1(hea<br>|dline inflation)<br>|Model 2(fo<br>|od inflation)<br>|
|---|---|---|---|---|
|Horizon|percentage<br>points|alternative<br>measure|percentage<br>points|alternative<br>measure|
|1|1.3|4.0|3.3|12.0|
|3|2.7|5.5|7.9|17.9|
|6|4.1|9.3|10.7|28.6|
|12|3.4|9.8|10.5|66.4|
|18|2.8|8.4|10.3|78.7|
|24|2.9|8.3|10.8|69.2|

_Source: Author’s estimates based on IRF derived from VAR._ It is also useful to examine the impact of domestic food inflation on core inflation, to assess whether there are second round effects of initial shocks, which could be long lasting. Indeed, the analysis of the VAR impulse response suggests that core inflation does not respond significantly to shocks to domestic food inflation (Table 6). Impulse responses of core prices indicate that the initial impact of a 1-standard deviation (initial) shock (estimated at 14.8 ppts) in domestic food prices is mostly negative, suggesting the absence of a significant spill-over effect.

**Table 6: Response of Core Inflation to Domestic Food Price Inflation Shocks**

||Core inf<br>|lation<br>|
|---|---|---|
|Horizon|percentage<br>points|alternative<br>measure|
|1|-3.8|-25.3|
|3|-2.0|-8.9|
|6|0.7|2.8|
|12|1.8|7.4|
|18|-0.5|-2.1|
|24|-1.6|-6.8|

# _Robustness Checks_

The basic results were slightly altered when the estimations were conducted using alternative measures of international supply shocks (the FAO food index). Tables 7 and 8 report these results and confirm that pass-through to CPI is low, estimated at 8% after 6 months. However, shocks from international food price inflation still have a stronger effect on domestic food inflation than on CPI inflation. However, this effect is slightly weaker than when the IMF food index is used. In fact, pass-through to food prices is estimated at 22% after 6 months. More importantly, it subsides over time. Also, the spill-over to core 17
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2 7. The present practice of transport sector taxation is described in some detail in Section IV, with reference to eight case studies prepared for this Project.A' These cases show wide variatiorns in compliance, revenue performance, administrative practice, the use of tax instruments, and rate and base structure. The final section looks at policy and administrative reform. Has the LDC experience with tax administration in general, and transport tax in particular, taught us how best to structure and administer a system of taxes and charges on motor vehicles and roadways?

8. Some caveats should be mentioned. This report discusses transport tax, but is primarily concerned with motor vehicles and road use. Airports are mentioned only in passing, and railroads and seaports are not discussed. Heavy reliance is placed on the results from a small number of case studies that may or may not be representative. Where possible, the results from other country studies are reviewed, but these are neither comparable in u.e sense of the questions asked, nor are they presented in the same level of detail. Finally, in some of the discussion the terms tax and _charge_ are loosely used. This is partly a shorthand convenience and partly because the traditional distinctions are blurred among several of the commonly used instruments of transport taxation. Where the distinction is important it is clearly made.

I/ Argentina, Bolivia, Ghana, India, hIdonesia, Nepal, Tanzania and Yugoslavia.