rafmacalaba/datause-encoder-data huggingface dataset:
According to industry sources, two factors are contributing to the recent slow-down. First, the negative demand shock is being felt particularly in the case of discretionary IT projects. Secondly, there has been a general mood swing against outsourcing in crisis-hit countries, particularly in the US, which is discussed in Section IV.

As far as demand contraction is concerned, there are significant differences between the IT and the ITES subsector. Indian exporters’ business is more severely affected in IT services, where about 60% of sales are of a discretionary nature, i.e. projects can be withdrawn, or not contracted at all, in times of economic hardship. ITES exports are apparently much less discretionary, with an estimated 70-80% of business characterized as non-discretionary. These latter types of services exports are likely to be more insulated from negative demand shocks because they involve activities that must be carried on even during the current crisis. For instance, in health care a system upgrading project (IT) is discretionary whereas the processing of claims (ITES) has to continue. In banking, a project to realize end-to-end automation of payments (IT) is discretionary even though it might be cost saving, whereas transaction processing is non-discretionary. Figure 7 depicts the growing importance of ITES in the sector’s overall export performance.

Figure 7: Composition of India’s IT and ITES Exports, 2000/01 – 2008/09 <!-- Start of picture text --> 50000<br>40000<br>30000<br>20000<br>10000<br>0<br>IT ITES/BPO<br>2000/01 2001/02 2002/03 2003/04 2004/05 2005/06 2006/07 2007/08 2008/09<br>USD million<br><!-- End of picture text --> Source: Reserve Bank of India, Monthly Bulletin March 2009, and National Association of Software and Service Companies (NASSCOM). Time periods are fiscal years which start in April.

Another reason for IT to be affected differently from ITES is because of differences between the two segments in the types of contractual relations. In IT, only about 25% of foreign trade takes place between captives (i.e. affiliates) and their parents, whereas in ITES this share stands at some 40-45%. Most of the large financial firms, e.g. JP Morgan, have traditionally conducted their outsourcing business via captives. As demand contracts, Indian firms are apparently also being asked by buyers to reduce prices. The ensuing margin squeeze is likely to be greater in IT services where a larger portion of projects is conducted as arms-length trade.

14
# **2.3 Leg 3: Poverty and Growth/Inequality (Poverty Regressions)**<sup>**6**</sup>

We next regressed changes in poverty headcount ratios on the initial levels of poverty, average income growth, and changes in inequality measurements with control variables of A: where Pov is a poverty index. We used poverty headcount ratios in this study.

# **2.4 Description of Explanatory Variables**

# **_Aid Variables (A)_**

Aid variables are our main interest for assessing the effectiveness of aid in reducing poverty. This study used two different sectoral data of economic aid and social aid, in addition to aggregate aid relative to GDP. It is assumed that economic aid has a positive impact on growth, as it is expected to expand supply capacity. On the other hand, social aid, which includes projects for basic human needs such as education and health, is expected to exhibit a pro-poor orientation in inequality regressions as it is more targeted to the poor. Aggregate aid is also expected to show pro-poorness, as other categories (aggregate aid except economic aid and social aid) which include commodity aid and humanitarian aid, do not have an immediate impact on growth, but assist the poorer segments of society. (Details will be given in Section 3.2.)

# **_Factors and Conditions (Z)_**<sup>**_7_**</sup>

**_Policy Variables:_** This study employed three policy variables (fiscal, monetary, and trade policies), commonly used in the empirical aid-growth literature: government consumption relative to GDP (G/GDP), the period average inflation rate, and trade (exports plus imports) relative to GDP (T/GDP).

The G/GDP was included to determine how the size of government affects growth, and whether fiscal policy affects poverty reduction. While excessively large government expenditure is negatively associated with growth, it may have a positive effect on poverty if the expenditure is targeted at the poorer people in society.

Inflation was also included as a policy variable to determine how monetary policy affects holistic growth as well as the poor. Excessive inflation tends to have a strong negative influence on household consumption. We suspect that the negative impact of inflation is much more severe on the poor, whose

> 6 We had another research question: “How does aid work for poverty reduction, all in all?” We intended to confirm the existence of poverty convergence and the elasticity of poverty reduction with respect to growth and income distribution. Furthermore, we attempted to test whether aid has additional systematic effects for poverty reduction, other than its impact through growth and changes in inequality. We found that significance of the other channels was very marginal. For brevity, the details of the results are not shown in this paper. The model is explained here in order to show our entire research framework.

> 7 The named categories for each variable are not mutually exclusive.

8
who served in a Igbo or southern minority state are more likely to date Igbos (Table 6, rows 20-21).

In contrast to the high rate of interethnic dating, only 12 percent of respondents are married to non-Yorubas.<sup>24</sup> Four percent are married to Igbos and the remaining are married to people from other ethnic groups. No one in the sample is married to an Hausa. Overall, I find no significant evidence that exposure to another ethnic region increases the likelihood of marrying someone from a different ethnicity. However, focusing on Igbos (the main group with which interethnic marriage occurs), Table 6 (row 22) reveals that participants who served in an Igbo majority state become more likely to marry an Igbo.

These results suggest that a major channel for the increase in romantic relationships is proximity to more people from the ethnic group in their host communities, and not primarily through changes in attitudes towards the group. That is, conditional on a given level of affinity towards a group, being exposed to more of them created more opportunity for romantic relationships to develop.

# **6.5 Heterogeneous Effects**

This section examines the possibility that NYSC exposure to a non-Yoruba region has differential treatment effects by participants’ age group, gender and religious match with the majority religion in the state of service. The impressionable years hypothesis in social psychology states that core attitudes, beliefs, and values are established during late adolescence and early adulthood (between 18 and 25 years) and are less open to change afterwards (Krosnick and Alwin, 1989). As such, I compare effects on participants below and above the median age (25 years) at the end of NYSC. I find no systematic evidence that younger participants are more open to change (Table A8, column 2). This could mean

> 24By the time of the survey, sixty five percent of the sample are married.

31
Table A.10: Parameter Estimates: Food Products, Beverages, and Tobacco

||VC|M|P-|R|
|---|---|---|---|---|
||coef.|s.e.|coef.|s.e.|
|Labor Share Equation: Wage|0.07<sup>_∗∗∗_</sup>|(0.01)|0.04<sup>_∗∗∗_</sup>|(0.008)|
|Labor Share Equation: Capital|-0.004|(0.009)|0.008|(0.01)|
|Labor Share Equation: Energy Price|0.0009|(0.007)|0.007|(0.006)|
|Labor Share Equation: Materials Price|0.01|(0.007)|0.01<sup>_∗_</sup>|(0.008)|
|Labor Share Equation: Output|-0.06<sup>_∗∗∗_</sup>|(0.009)|-0.05<sup>_∗∗∗_</sup>|(0.009)|
|Labor Share Equation: Time Trend|0.002<sup>_∗∗∗_</sup>|(0.0003)|0.001<sup>_∗∗∗_</sup>|(0.0003)|
|Labor Share Equation: Constant|0.2<sup>_∗∗∗_</sup>|(0.004)|0.2<sup>_∗∗∗_</sup>|(0.004)|
|Energy Share Equation: Wage|0.005|(0.004)|0.002|(0.003)|
|Energy Share Equation: Capital|-0.010<sup>_∗∗∗_</sup>|(0.004)|-0.009<sup>_∗∗_</sup>|(0.004)|
|Energy Share Equation: Energy Price|0.01<sup>_∗∗∗_</sup>|(0.003)|0.01<sup>_∗∗∗_</sup>|(0.002)|
|Energy Share Equation: Materials Price|-0.005<sup>_∗_</sup>|(0.003)|-0.005|(0.003)|
|Energy Share Equation: Output|-0.01<sup>_∗∗∗_</sup>|(0.004)|-0.01<sup>_∗∗∗_</sup>|(0.003)|
|Energy Share Equation: Time Trend|0.0005<sup>_∗∗∗_</sup>|(0.0001)|0.0003<sup>_∗∗∗_</sup>|(0.0001)|
|Energy Share Equation: Constant|0.02<sup>_∗∗∗_</sup>|(0.001)|0.02<sup>_∗∗∗_</sup>|(0.001)|
|Euler Equation for Capital: Adjustment Term|0.9<sup>_∗∗∗_</sup>|(0.2)|0.9<sup>_∗∗∗_</sup>|(0.2)|
|Euler Equation for Capital: Wage|0.4<sup>_∗∗∗_</sup>|(0.1)|-0.1|(0.08)|
|Euler Equation for Capital: Capital|0.01|(0.1)|-0.07|(0.1)|
|Euler Equation for Capital: Energy Price|-0.02|(0.07)|0.04|(0.06)|
|Euler Equation for Capital: Materials Price|0.06|(0.08)|0.2<sup>_∗∗_</sup>|(0.08)|
|Euler Equation for Capital: Output|-0.4<sup>_∗∗∗_</sup>|(0.1)|-0.2|(0.1)|
|Euler Equation for Capital: Time Trend|-0.009<sup>_∗∗∗_</sup>|(0.003)|-0.008<sup>_∗∗_</sup>|(0.003)|
|Euler Equation for Capital: Constant|-1.5<sup>_∗∗∗_</sup>|(0.04)|-1.5<sup>_∗∗∗_</sup>|(0.04)|
|Number of observations|26|3|26|3|
|Labor Share Equation: _R_<sup>2</sup>|0.9|6|0.9|6|
|Energy Share Equation: _R_<sup>2</sup>|0.7|2|0.7|1|
|Euler Equation for Capital Stock: _R_<sup>2</sup>|0.9|7|0.9|6|
|LR Test: _φl_ =_φe_ =_φm_ =_ζ_ = 0, _χ_2 (pval)||28.5 (|0.00)||
|Notes. VCM: Vintage Capital Model. P-R: Pin|dyck and R|otemberg (1|983) Model.||
|coef.: estimated coefcient. s.e.: standard error.|<sup>_∗∗∗_</sup>p_<_0.01|, <sup>_∗∗_</sup>p_<_0.05|, <sup>_∗_</sup>p_<_0.1||

45
# CONTENTS

# ACKNOWLEDGEMENTS

# LIST OF ABBREVIATIONS AND ACRONYMS USED

||INTRODUCTION<br>.<br>.<br>.|.<br>.|.|. .|.|.|.|.|5|
|---|---|---|---|---|---|---|---|---|---|
|CHAPTER I:|THE RAJASTHAN CONTEX|T .|.|. .|.|.|.|.|8|
|CHAPTER II:|BACKGROUND AND ORIGI|NS|.|. .|.|.|.|.|11|
|CHAPTERIII:|THE ORGANIZATIONAL<br>|BASE|.|. .|.|.|.|.|15|
|CHAPTER IV:|THE ROLE OF TRAINING|IN|WD|P.|.||||30|
|CHAPTER V:|THE WORKING OF WOMEN|'S|GRO|UPS|.|.|.|.|36|
|CHAPTER VI:|MONITORING AND EVALU|ATIO|N|. .|.|.|.|.|47|
|CHAPTERVII:|THE IMPACT OF WDP .|. .|.|. .|.|.|.|.|53|
|CHAPTER VIII:|PROBLEMS IN PROGRAM|DEV|ELO|PMENT||.|.|.|64|
|CHAPTERIX:|PROGRAM FUNDING . .|.|.|.|.|.||.|72|
|CHAPTER X:|MAHILk SAMAKHYA AND<br>FOREDUCATION . . . <br>C<br>T|GRO<br> .<br>|UP<br>N|FORM<br>.<br> <br>.|AT<br>.|ION<br>.|<br>.||74|
|ENDNOTES|.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.|.<br>.|.|.<br>.|.|.|.|.|83|
|REFERENCES|.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.|.<br>.|.|.<br>.|.|.|.|.|84|

# ANNEXURES

|I.|MAP OF RAJASTHAN WITH THE WDP DISTRICTS<br>.<br>.<br>.<br>.<br>.|86|
|---|---|---|
|II.|SELECTED DEMOGRAPHIC CHARACTERISI.rCS BY STATES.<br>.|87|
|III.|RAJASTHAN-<br>DISTRICT-WISE RATE OF WORKING FEMALE||
||PARTICIPATION<br>.<br>.<br>..<br>. ..<br>..<br>.<br>. . . .<br>..|88|
|IV.|RAJASTHAN-<br>LITERACY RATES AT DISTRICT LEVEL.<br>.<br>.|89|
|V.|RAJASTHAN-<br>DISTRICT-WISE<br>SEX RATIO<br>.<br>.<br>.<br>.<br>.<br>.<br>.|90|
|VI.|PLAN FORMVKS<br>.<br>.<br>a. . .<br>4<br>. . . . . . .*<br>.|91|
|VII.|CASE-STUDY OF FUNCTIONING OF WDP IN JAIPUR DISTRICT|92|
(1999) propose using the geographical and linguistic characteristics of an economy as instrumental variables for their social infrastructure. We focused on distance from the equator and the percentage of the population speaking a Western European language as their first language today.

Another problem with the IV strategy is that it limits the analysis to a cross-section as the instrumental variables are available only at a point in time. We chose the year 1995 since the data for years of schooling for 1995 are arguable measured with less error than for 2000 and 2005, where extrapolations were used. The first instrumental variable, logarithm of settler mortality rates, further limits the analysis to 59 countries that are ex-colonies. Thus, the results from the IV analysis should be interpreted with caution and considered only tentative.

# _4.2 Panel estimation results_

Table 5 shows the results from the estimation of equation (11). The columns differ in terms of the estimation technique and the variable measuring human capital per worker. For column (1) we replicate the results in World Bank (2006); that is, we just pool the observations over the years 1995, 2000, and 2005 and use OLS so that  _i_   _i_ and  _t_  0  _t_ . The indicator of human capital is simply years of schooling from Barro and Lee (2001). In column (2) we also use OLS on the pooled sample, but the indicator of human capital is given by equation (3). Both the coefficients on our indicator of institutional quality and years of schooling exhibit positive signs and are highly significant. An additional point in the rule of law index is associated with an increase in intangible wealth per capita of around $3,000. An additional year of schooling is associated with an increase in intangible wealth per capita of over $11,000 and a unit increase in human capital per worker with an increase in intangible wealth per capita of around $45,000.

The regressions in columns (1) and (2) also include income dummies relative to high income countries. All of them are negative and statistically significant. This means that countries in each income group have a lower level of intangible capital than high income countries which is consistent with the positive relationship between income and intangible capital shown in Figure 2. The coefficients are large in magnitude; around $100,000 for all income categories.

In columns (3) and (4) we introduce country fixed effects in the model. The null hypothesis of country-specific intercepts equal to zero is rejected for both regressions. Compared to columns (1) and (2) the coefficients on rule of law are reduced and, although still positive, they are no longer statistically significant. The estimated coefficients of human capital in both columns increase in size and remain highly significant. Note that of the three components of _h_ in (3)–years of schooling, returns to education and adult survival rates, returns to education does not vary over time or across countries. Thus, estimation of (11) using country fixed effects relies on the within-country variation of years of schooling and adult survival rates.

14
**Table 1. World Bank: Control of Corruption for Selected Countries 2000 and 2012**

||**2000**|**2000**|**2012**|**2012**|**Corruption Perc**|**eption Index 2012**|
|---|---|---|---|---|---|---|
|**Country**|Point Estimate|Percentile Rank|Point Estimate|Percentile Rank|Score|Ranking|
|**Denmark**|2.52|100|2.39|100|90|1|
|**New Zealand**|2.36|99|2.32|100|90|1|
|**Sweden**|2.40|99|2.31|99|88|4|
|**Norway**|2.29|97|2.24|99|85|7|
|**Finland**|2.59|100|2.22|98|90|1|
|**Switzerland**|2.21|95|2.15|98|86|6|
|**Singapore**|2.29|97|2.15|97|87|5|
|**Australia**|1.96|94|2.00|96|85|7|
|**Canada**|2.23|96|1.92|95|84|9|
|**Germany**|1.90|93|1.78|94|79|13|
|**United Kingdom**|2.24|96|1.64|92|74|17|
|**Japan**|1.23|86|1.61|92|74|17|
|**Chile**|1.54|92|1.56|91|72|20|
|**United States**|1.66|93|1.38|89|73|19|
|**Spain**|1.33|87|1.05|82|65|30|
|**Estonia**|0.65|75|0.98|80|64|32|
|**Taiwan**|0.75|78|0.72|74|61|37|
|**Poland**|0.55|72|0.59|72|58|41|
|**Korea**|0.25|65|0.47|70|56|45|
|**Lithuania**|0.19|64|0.31|66|54|48|
|**Hungary**|0.69|76|0.28|65|55|46|
|**Czech Republic**|0.08|62|0.23|64|49|54|
|**Turkey**|-0.33|47|0.17|63|49|54|
|**Latvia**|-0.29|48|0.15|63|49|54|
|**Italy**|0.72|77|-0.03|58|42|72|
|**Brazil**|0.02|60|-0.07|56|43|69|
|**South Africa**|0.61|73|-0.15|54|43|69|
|**Bulgaria**|-0.21|52|-0.24|52|41|75|
|**Zambia**|-0.85|21|-0.36|46|37|88|
|**Mexico**|-0.24|51|-0.41|43|34|105|
|**China**|-0.24|51|-0.48|39|39|80|
|**Argentina**|-0.34|47|-0.49|39|35|102|
|**Algeria**|-0.95|14|-0.54|36|34|105|
|**India**|-0.37|46|-0.57|35|36|94|
|**Egypt**|-0.39|44|-0.57|34|32|118|
|**Ethiopia**|-0.49|38|-0.60|32|33|113|
|**Indonesia**|-0.89|19|-0.66|29|32|118|
|**Iran**|-0.57|32|-0.82|24|28|133|
|**Uganda**|-0.85|21|-0.95|18|29|130|
|**Russia**|-0.92|17|-1.01|16|28|133|
|**Ukraine**|-1.07|8|-1.03|16|26|144|
|**Pakistan**|-0.82|22|-1.06|14|27|139|
|**Kenya**|-0.95|14|-1.10|12|27|139|
|**Nigeria**|-1.13|6|-1.13|11|27|139|
|**Venezuela**|-0.53|35|-1.24|7|19|165|
|**Angola**|-1.52|2|-1.29|5|22|157|
|**Congo, Dem. Rep**|-1.57|1|-1.30|4|21|160|

_Sources: World Bank Governance Indicators, Control of Corruption 2012. Control of corruption scores range from -2.5 to +2.5. Transparency International, Corruption Perception Index 2012._ 8
The basic data for the Central Bank and the domestic banking system are obtained in the form of balance sheets expressed in TL at the end of the year. In order to obtain information on the net foreign asset position of these institutions and on the foreign exchange deposits from other sectors in the economy, the original data has to be complemented with two additional sources. The Reserve Money table is used to derive all foreign asset positions of the Central Bank vis-a-vis the other sectors, while a special table (EIO) provides the same information on the domestic banking system.

A further complication arises from the existence of public banks that are included in fiscal data as well as data on the banking system. In order to avoid double counting and to group activities in meaningful sectors we decided to include the financial state enterprises in the domestic banking system. Hence, the fiscal data are adjusted in order to exclude the financial SEEs. The Turkish fiscal data explicitly distinguished the category of financial SEEs that consist only of deposit money banks and development banks.

For assets and liabilities denominated in Tl, we can readily derive the corresponding financing flows as the changes in the Tl stocks. We assume that relative prices other than the exchange rate are constant. However, for the assets and liabilities expressed in Tl but denominated in foreign currency, we face the problem of reconciliating dollar flows and TL flows. First, flows are evaluated at period average exchange rates while stocks use 17
12 Reprivatization. The Slovenian reprivatization initiative, called "denationalization," is among the most radical to date of all similar initiatives in reforming socialist countries. Although the Slovene law on privatization of social enterprises is entwined in political dispute within Parliament, a Law on Denationalization was adopted in November 1991.4' This law intends to "reprivatize" not only the land previously nationalized under the agrarian reform statutes of 1946, 1948 and 1953," but also the property and shares of businesses nationalized in 1946 and 1948,45 buildings nationalized in 1958,46 and property confiscated in 1944 and 1946 from citizens accused of collaborating with the Germans.<sup>4</sup> 7 It was estimated during preparation of the law that some 4 billion Deutsch Mark (or US$2.5 billion) worth of social property would be subject to denationalization--about 10 percent of all social property or 7 percent of all property in Slovenia. Natural persons who were Yugoslav citizens at the time of nationalization (or their close relatives or heirs) are eligible, as are religious organizations (Articles 5, 9-13). Legal entities other than religious organizations are not eligible (Article 14). If possible, the property is to be returned in-kind (Article 2). Otherwise, compensation is to be provided in substitute security interests in real property. New legislation on property' rights for foreigners is now under preparation. Although the Constitution prohibits foreigners from owning land, the new legislation is expected to permit foreigners to own buildings and hold mortgages on all types of real estate.

43 Zakon o denacionalizaciji (Law on Denationalization], Official Gazette RS, No 27 (1991).

44 The 1948 statute nationalized holdings over 25 hectares, and the 1953 statute nationalized holdings over 10 hectares. Zakon o agrarni reformi in kolonizaciji (Law on Agrarian Reform and Colonization], Official Gazette FPRY, No 64 (1945), 24 (1945), 101 (1947), 105 (1948), 21 (1956), 55 (1957), 10 (1965); and Uradni list Ljudske republike Slovenije (Official Gazette of People's Republic of Slovenia], No 62 (1945), 30 (1946), 10 (1948), 17 (1958), 17 (1959), 18 (1961), 22 (1965); Zakon o kmetijskem zemljiikem skladu splolnega ljudskega premo±enja in o dodeljevanju zemlje kmetijskim organizacijam (Law on Agricultural Land Fund and on Allocation of Land to the Agricultural Organizations], Official Gazette FPRY, No 23 (1953).

45 Zakon o nacionalizaciji zasebnih gospodarskih podjetij (Law on Nationalization of Private Economic Enterprises], Official Gazette FPRY, No 98 (1946) and No 35 (1948).

46 Zakon o nacionalizaciji najemnih zgradb in gradbenih zemljilt (Law on Nationalization of Rented Buildings and Building Land], Official Gazette PPRY, No 52 (1958).

**4'** Zakon o konfiskaciji premotenja in o izvrievanju konfiskacije (Law on Confiscation of Property and on Realization of Confiscation], Uradni list Demokratiane federativne Jugoslavije (Official Gazette of the Democratic Federation of Yugoslavia], No 2 (1945), No 40 (1945); Official Gazette FPRY, No 61 (1946), No 63 (1946), No 64 (1946), No 74 (1946).
# **_V. Conclusions and Policy Implications_**

Our analysis of data from the GDS-2 has useful implications for the design and use of surveys that measure satisfaction levels of users of public services. The analysis shows that proper interpretation of satisfaction data would require finding meaningful variation in satisfaction responses and econometric models that account for subjectivity of responses and examine the possibility of selection bias. Once the role of expectations in determining satisfaction with facilities has been factored in, the variation in satisfaction level can be highly informative for policymakers and researchers alike.

Satisfaction surveys are likely to remain a popular method for monitoring the quality of services because of the ease of administering such surveys. Thus it is useful to reflect briefly on what our analysis suggests for the design of such surveys, especially in the “second-best” scenario where collecting data from households _and_ facilities is not possible for practical reasons.

Firstly, our findings confirm that a range of factors other than those related to the quality of facilities play a key role in determining the satisfaction level of households. Thus even when an accompanying facility survey is not a practical option, there are clear benefits in having a satisfaction survey collect as much information on the characteristics of households and communities as possible, including sources of information and social status of a household. This would allow an analyst to econometrically correct for some of the subjectivity in the satisfaction data. The models estimated in this paper control for factors that proxy a household’s expectations of quality from a service, which would in turn influence the reported level of 29