rafmacalaba/datause-encoder-data huggingface dataset:
|P122178|2013|2016|6/23/2016|6 Closed|Turkey|
|---|---|---|---|---|---|
|P122178|2013|2017|1/3/2017|7 Closed|Turkey|
|P122178|2013|2017|6/27/2017|8 Closed|Turkey|
|P122178|2013|2018|1/9/2018|9 Closed|Turkey|
|P122178|2013|2018|6/22/2018|10 Closed|Turkey|
|P122178|2013|2019|12/27/2018|11 Closed|Turkey|
|P122178|2013|2019|6/27/2019|12 Closed|Turkey|
|P122178|2013|2020|9/30/2019|13 Closed|Turkey|
|P122181|2010|2010||0 Closed|Nicaragua|
|P122181|2010|2011||0 Closed|Nicaragua|
|P122181|2010|2012|2/13/2012|1 Closed|Nicaragua|
|P122181|2010|2013|2/13/2012|1 Closed|Nicaragua|
|P122182|2011|2011||0 Closed|Western Africa|
|P122182|2011|2012|9/25/2011|1 Closed|Western Africa|
|P122182|2011|2012|5/14/2012|2 Closed|Western Africa|
|P122182|2011|2013|5/29/2013|3 Closed|Western Africa|
|P122182|2011|2014|3/30/2014|4 Closed|Western Africa|
|P122182|2011|2015|11/29/2014|5 Closed|Western Africa|
|P122182|2011|2015|6/8/2015|6 Closed|Western Africa|
|P122182|2011|2016|12/23/2015|7 Closed|Western Africa|
|P122182|2011|2016|6/23/2016|8 Closed|Western Africa|
|P122182|2011|2017|12/28/2016|9 Closed|Western Africa|
|P122182|2011|2017|6/16/2017|10 Closed|Western Africa|
|P122187|2011|2011||0 Closed|Europe and Central As|
|P122187|2011|2012||0 Closed|Europe and Central As|
|P122187|2011|2013||0 Closed|Europe and Central As|
|P122193|2010|2011||0 Closed|India|
|P122193|2010|2012||0 Closed|India|
|P122193|2010|2013||0 Closed|India|
|P122194|2013|2013|3/20/2013|1 Closed|Peru|
|P122194|2013|2014|10/22/2013|2 Closed|Peru|
|P122194|2013|2014|6/1/2014|3 Closed|Peru|
|P122194|2013|2015|12/18/2014|4 Closed|Peru|
|P122194|2013|2015|3/5/2015|5 Closed|Peru|
|P122194|2013|2016|8/19/2015|6 Closed|Peru|
|P122194|2013|2016|12/23/2015|7 Closed|Peru|
|P122194|2013|2016|6/29/2016|8 Closed|Peru|
|P122194|2013|2017|11/22/2016|9 Closed|Peru|
|P122194|2013|2017|4/20/2017|10 Closed|Peru|
|P122194|2013|2018|11/21/2017|11 Closed|Peru|
|P122195|2012|2012|5/29/2012|1 Closed|Armenia|
|P122201|2013|2013|3/19/2013|1 Closed|Bangladesh|
|P122201|2013|2014|10/16/2013|2 Closed|Bangladesh|
|P122201|2013|2014|6/1/2014|3 Closed|Bangladesh|
|P122201|2013|2015|12/1/2014|4 Closed|Bangladesh|
|P122201|2013|2015|6/17/2015|5 Closed|Bangladesh|
|P122201|2013|2016|12/23/2015|6 Closed|Bangladesh|
density and the Morrill proxy with very high Cragg-Donald F-tests (1% level) and acceptable F-tests suggesting a strong instrument. Column 5 presents the second stage results for the complete specification with fixed effects and engineers enter again at the 1% level with a value an order of magnitude higher than the OLS estimates. We do not present all previous specifications simply because, as with the OLS specifications, both engineering and patenting retain their significance and broad magnitudes throughout.

Column 6 presents the results of a Buchinsky quantile regression model with selection into being a zero on the instrument stage. This specification is also desirable because it relaxes the assumption of joint normality implicit in the standard Heckman model. The first stage again suggests a strong instrument at the 1% level. The coefficient on engineering falls to half the 2SLS, 3.51, and maintains its strong significance. As before, the first stage is strongly significant.

In the spirit of the next section that works at the “state” level, we replicate the exercise for the 1900 5% subsample (again, which does not permit us to work at the county level), with the number of covariates reflecting the reduced degrees. This higher level of aggregation allows for more mobility of labor among unit of analysis, although, clearly, this is not preventing the emergence of very significant coefficients. Second, it allows another 20 years of impact of the Morrill program and the full consolidation of the engineering infrastructure. Third it allows introducing a measure of mining activity which is available at the state, but not country level. Finally, the aggregation also offers an alternative approach to the zeros. The OLS and instrument estimates are 1.2 and 2.1 respectively and significant, even with 48 observations (Annex Tables A2 and A3).

Taken together, the US data suggests that innovative capacity, spanned by engineering and patenting density, has a strong and independent effect on future income levels. More specifically, running specification 6 in standard deviations suggests that a one standard 20
<!-- Start of picture text --> _&<br>’<br>wey<br>aSo 4 i;<br>=woon ‘ i<br>~ ‘<br>Ses °<br>FyBo feo eeee be <i, fhe si =i<br>on La a.<br>Cc z<br>2a<br>="<br>on<br>Okm 50-100 km Som13-200distance km,rings 200-300 km 354 00km<br>——®—- small rural =— # — large rural<br>—+— smallurban = —4#— large urban<br>OLS. correlationswt. smal rural<br><!-- End of picture text --> <!-- Start of picture text --> me<br>ay :4<br>22 "<br>w %<br>= “t<br>oe ,<br>eyEar8, {Fagae Seomy aie ee ee :<br>= ee ian<br>‘<br>otc=<br>=<br>oa<br>oo<br>Okvon 5-100 km caer13200etmeki rigs 250-300 km 350-400km<br>-—f®-- smallnal ==— #*— large nal<br>— +— small urban = ——*— large urban<br>‘OLS. conelatons wit smal whan<br><!-- End of picture text --> <!-- Start of picture text --> a<br>wet<br>iolSeo nN<br>=owot \<br>= \<br>mary 5 re a<br>=Zo i igae = ag - Fog ag =<br>ota ee aS =. — a_i<br>= a<br>20<br>a<br>=o<br>akm 5-100 km Eokrh dibencorings 250-300 km 250-400km<br>—-*-- smallrual =—-— largerral<br>—+—- small urban —#— largeurban<br>OLS. sorrelstionswrt. lenge rune<br><!-- End of picture text --> <!-- Start of picture text --> =<br>= =D<br>==<br>cal a<br>Bm ,<br>icy a, = i ae — a<br>a] e * Sey 8 sy ah gerne<br>2. fe Ee ; Seg? See<br>ee i aL a ta pot ala é<br>a = We coe fas a ; ef<br>onos ge er : “<br>ae<br>[=]<br>Geo<br>Okm 50-100 km SOikm150-2distance00 km.rings 200-300 km 350-4 00km<br>-—#-- smallnal = — #— large nal<br>—+—- small urban =—*— large urban<br>OLS, conelations wurt large nus<br><!-- End of picture text -->
Chart 4 <!-- Start of picture text --> Irrigated Area by method<br>20<br>1~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .<br>56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92<br>Fiscal Year<br>|Hirrigated *Canal *T.Wolls|<br><!-- End of picture text -->

Source: Statistical Survey ofFPakistan Total cultivated area has been more or less constant for quite some time, and most expansion in irigated area has occurred through reduction of rainfed area. Since the early 1980s, irrigated area has hardly expanded at all. This indicates that limits of both cultivable area and irrigated area have been reached.

A comprehensive picture of Pakistan's soil resources is available from the Soil Survey of Pakistan, carried out as part of the National Conservation Strategy in 1993. This has surveyed more than 700,000 square ldlometers of land - about 80 percent of Pakistan's total area. Soils were then grouped into eight Land Capability Classes, according to their agricultural potential or relative suitability for sustained agricultural use (Table 5).

10
WPS3973-IE <mark>I</mark> MPACT <mark>E</mark> VALUATION <mark>S</mark> ERIES <mark>N</mark> O <mark>. 5</mark>

# **Conditional Cash Transfers, Adult Work Incentives, and Poverty**

Emmanuel Skoufias The World Bank and Vincenzo di Maro University College London

## **Abstract**

Conditional cash transfer (CCT) programs aim to alleviate poverty through monetary and inkind benefits, as well as reduce future incidence of poverty by encouraging investments in education, health and nutrition. The success of CCT programs at reducing poverty depends on whether, and the extent to which, cash transfers affect adult work incentives. In this paper we examine whether the PROGRESA program of Mexico affects adult participation in the labor market and overall adult leisure time, and we link these effects to the impact of the program on poverty. Utilizing the experimental design of PROGRESA’s evaluation sample, we find that the program does not have any significant effect on adult labor force participation and leisure time. Our findings on adult work incentives are reinforced further by the result that PROGRESA leads to a substantial reduction in poverty. The poverty reduction effects are stronger for the poverty gap and severity of poverty measures.

## _JEL classification:_ J22; O12; C21

_Keywords:_ Adult Work Incentives; Conditional Cash Transfers; Difference-in-Differences; Mexico; Poverty Measures; PROGRESA; Randomized design.

# World Bank Policy Research Working Paper 3973, August 2006

_The Impact Evaluation Series has been established in recognition of the importance of impact evaluation studies for World Bank operations and for development in general. The series serves as a vehicle for the dissemination of findings of those studies. Papers in this series are part of the Bank’s Policy Research Working Paper Series. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ _Corresponding author_ : Emmanuel Skoufias, World Bank, 1818 H Street NW, Washington, D.C. 20433-USA. Tel: (202)458-7539. Fax: (202) 522-3134. Email: <u>eskoufias@worldbank.org.</u> _Acknowledgements:_ The authors are grateful to Claudia Aburto for excellent research assistance and to Alessandro Tarozzi for useful suggestions.
questions, compared to the baseline. However, the total score (i.e. the sum of correct answers on the eight questions) increased significantly from 2.6 to 2.9 after the training, suggesting that the training improved business and financial knowledge on average.

The baseline and exit test also included a number of questions to measure financial perception and attitudes, such as risk aversion and preference for using credit vs. own funds to finance purchases. Panel A in Table 6 illustrates that financial perceptions changed significantly from baseline to the exit test. Specifically, respondents were more risk averse after the training and less likely to prefer using credit instead of own funds. Moreover, respondents had a better understand of the importance of having a good credit history. In fact, before the training only 22 percent of entrepreneurs thought that a good credit history could help them obtain larger or better loans, while 75 percent of entrepreneurs thought so after the training.

Our follow‐up survey was conducted over the phone, and therefore did not allow us to ask all of the business and financial knowledge and financial perception questions. Instead, we chose to include only the three shortest and easiest to administer business and financial knowledge questions in the follow‐up survey. As shown in Table 5, these questions test whether the respondents know VAT law, whether they know what the credit registry is, and whether they understand diversification. The results in the last column of Table 4 indicate that all training participants were significantly more likely to answer these questions correctly at follow‐up than 19
Table 10: Baseline Estimates Pooled

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|VARIABLES|ROA|logSales|logAvOutput|logAvWage|logLev|
|Connect|-0.616***|0.427***|0.152***|0.221***|-0.031|
||(0.230)|(0.028)|(0.023)|(0.013)|(0.020)|
|SOE|-3.749***|0.175***|-0.335***|0.263***|-0.045***|
||(0.135)|(0.019)|(0.016)|(0.007)|(0.014)|
|Constant|12.331***|10.236***|9.402***|7.700***|-0.932***|
||(0.062)|(0.007)|(0.006)|(0.003)|(0.005)|
|Wald|138.017|55.677|297.127|8.142|.322|
|p-value|_<_0.001|_<_0.001|_<_0.001|.004|.570|
|Observations|10,541,077|11,178,831|8,846,434|7,465,385|10,945,643|
|R-squared|0.039|0.499|0.444|0.660|0.065|
|Sector Dummies|✓|✓|✓|✓|✓|
|Size Dummies|✓|✓|✓|✓|✓|
|Year FE|✓|✓|✓|✓|✓|
|Country FE|✓|✓|✓|✓|✓|

Robust standard errors in parentheses clustered at the level of firm *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 34
families. Heads of households are female in slightly more than one-quarter of all households and while only 48 percent of household heads are literate, more than 60 percent are numerate.

Column (2) of the table shows the characteristic of the FSS households. The final column shows the t-statistic and p value for the test of comparison of means of the two samples.<sup>6</sup> Note that for variables related to location, the FSS sample seems to deviate from that of the GLSS by being more rural, having more households engaged in agriculture and being more likely to be located in the Coastal and Forest Zones of the country. We hypothesize that this resulted from using simple random sampling of enumeration areas for the FSS. This fails to take into account the greater population found in urban enumeration areas that was captured in the original probability proportional to size sample. Fortunately, the effects are not strong, but it should be remembered that the sample here somewhat over-represents the rural population.

Between the time that households were interviewed for the GLSS 5 and when reinterviews of these households were attempted for the FSS, some households had moved or dissolved and could not be re-interviewed. Others chose not to respond. Of the 2291 households re-visited, a total of 335 could not be re-interviewed.<sup>7</sup> If attrition between the two interviews is randomly distributed there is no effect on the experiment other than cost. However, systematic differences that might exist between the households that could and could not be re-interviewed could be problematic.

> 6 Due to revisions to the PSU codes in the GLSS 5 after the final FSS data set was produced it was not possible to use the GLSS5 sampling weights in the analysis.

> 7 Fifty percent of the non-response was due to vacant dwellings (either permanent or temporary) and 40 percent because the household had moved.  Actual refusals represented less than 3 percent of all nonresponse.

15
Africa, Asia, and Latin America is that they are better off liberalizing trade unilaterally rather than engaging in regional arrangements.

The UTL argument is undoubtedly powerful. But suppose that the developed and newly industrialized countries are divided into three blocs: North America, Western Europe and East Asia. Also suppose that each one of these blocs allows free internal trade but imposes high duties or voluntary export restraints on extra-bloc imports. Under these circumstances, will developing countries in Africa, Asia and Latin America benefit more from UTL or from joining one of the blocs? Common sense dictates that the latter option is likely to be welfare superior since it guarantees market access, though it involves adopting and maintaining the bloc's barriers to trade with extra-bloc countries.

Wonnacott and Wonnacott (1981) recognize that the conventional literature on FTAs fails to take into account transport costs and trade restrictions imposed by partner countries and, more importantly, by the outside world. Once these restrictions are taken into account, it is entirely possible for an FTA to be superior to UTL. In the above example of trading blocs, UTL may fail to give a country access to the markets of any of the blocs. Accession to a bloc may solve this market access problem.

This raises the question of the pros and cons of a world divided into trading blocs. In the introduction, it was suggested that such a division offers advantages and disadvantages. Advantages insofar as it helps solve the free-rider problem and difficulties that of multilateral negotiations when the number of participants is large. Disadvantages insofar as a few large trade blocs may get caught into a prisoners' dilemma problem. While it would be preferable for all to cooperate, individually, each bloc is likely to take on a selfish attitude and impose restrictions.

The implications of a non-cooperative trade strategy were frst explored by Krugman (1991), and, more recently, by Deardorff and Stern (1991) and Srinivasan (forthcoming). Krugman employs 26
- 2 -

# **I. INTRODUCTION**

Rail transport generally has the advantage for large-volume long-haul freight operations. Because of the large amount of fixed costs required for rail infrastructure development, rail operations exhibit significant economies of scale. A practical norm is that 1 million net tons a year would be needed to justify economic viability of railways (World Bank 2010). By distance, railways can achieve good financial performance with an average haulage of more than 500-600 km (e.g., World Bank 2013; Llano et al. 2017). From the environmental point of view, railways also have an important advantage to foster sustainable transportation. In theory, railways are more than 50 percent greener than road transportation (Kopp et al., 2013). According to the global statistics, the rail sector accounts for only 3.6 percent of total transport emissions, while it contributes to over 8 percent of the total movements of passengers and goods over the world (IEA and UIC, 2015).

Railway carries 6.3 percent of the global passenger transport demand and 9 percent of the total freight demand ( **Table 1** ). For passenger movements, road transport is a dominant mode in many countries, except for some countries, such as China, India, Japan and the Russian Federation, where railway accounts for about 15-30 percent of the total passenger transport ( **Figure 1** ). In Africa, railway passenger transport is minimal.

On the other hand, railway has a more vital role to play in freight transportation. With navigation excluded, rail carries the same amount of freight as road does. In non-OECD countries, the demand for rail freight transport has been increasing in recent years, while developed countries have been experiencing more stable rail freight activities ( **Figure 2** ). The rail dependency varies across countries: While rail freight accounts for more than 30 percent in India and the United States, the modal share of rail freight in Japan is less than 1 percent. In the Russian Federation, the share exceeds 85 percent.
2002)<sup>2</sup> , assets and land holdings (e.g. Fields et al. (2003), Strauss et al. (2004)), and weight (Glewwe and Nguyen, 2002).

A key condition for the validity of such instruments is that any measurement error in the instrument is uncorrelated with the measurement error in income. Glewwe and Nguyen (2002) question this assumption in the case of expenditure, where individuals may systematically underreport both income or expenditure, or interviewers may reduce the level of detailed questioning on both measures. However, even when the instrument is uncorrelated with the measurement error, consistency of the instrumental variables estimator still requires that the instrument also be uncorrelated with the non-measurement error component of the error term in the data generating process. Glewwe and Nguyen state that such an assumption is extremely unlikely to hold when variables such as education or land, which have a causal relationship with income, are used as instruments. In this paper we show further that if the instrument itself follows an AR(1) process, then the instrumental variables estimator will converge to the autocorrelation coefficient of the instrument. As a result, instrumental variables will only be consistent if the instrument is uncorrelated with the measurement error and has the same amount of mobility as earnings. This condition appears extremely unlikely to hold in practice.

The literature has devoted less attention to assessing the impact of attrition on estimates of mobility. However, the typical labor force panel in developing countries reinterviews dwelling units, rather than households, so that households that move attrit from the sample. The Mexican Urban Labor Force Survey (ENEU) used in this study is a quarterly rotating panel which follows this approach, and on average loses 35 percent of the sample due to attrition over the five periods. Thomas, Frankenberg and Smith (2001) discuss the experience of the Indonesia Family Life Survey, which explicitly tracked movers, and do find that those who move are different in terms of initial characteristics than those who stay. Although they do not examine whether changes in income or

> 2 Luttmer actually examines mobility in expenditure, rather than income, and uses income and subjective well-being as instruments for expenditure.

3
|Eberhard et<br>al. (2011)|case study|Africa|power sector<br>indicators|multi-<br>country|eliminating pricing<br>inefficiencies can<br>close the funding gap<br>in the power sector|
|---|---|---|---|---|---|
|Bhattacharya<br>(2007)|case study|South Asia|power sector<br>indicators|multi-<br>country|reform undertaken<br>produced no<br>significant results,<br>electricity prices still<br>highly subsidised|
|He et al.<br>(2011)|macro study|China|coal and<br>electricity<br>prices|CGE<br>modelling|coal price increase<br>caused a rise in the<br>costs of electric<br>power industry while<br>the influence<br>gradually descended<br>with increases in coal<br>price|
|Kennedy<br>(2003)|case study|Transition<br>economies|power sector<br>indicators|multi-<br>country|implementation of<br>reform should be<br>enhanced to improve<br>reform performance|
|Fankhauser<br>and Tepic<br>(2007)|case study|Transition<br>economies|affordability<br>indicators for<br>utilities|multi country|level of tariffs<br>needed for cost<br>recovery bear<br>important<br>affordability<br>consequences|
|Akkemik<br>(2011)|case study|Turkey|macro and<br>micro<br>variables with<br>focus on<br>energy<br>producing<br>sectors|single<br>country;<br>social<br>accounting<br>matrix|cost reflective<br>electricity tariff<br>affect consumers<br>prices slightly less<br>than producer prices|

# **_3.1.2. Impacts of reforms on quality of service and access_**

One of the principal aims in most reforming countries was to enhance the quality of energy supply (Joskow, 1998; Briceno-Garmendia et al., 2004). Reforms were expected to enhance energy production, lead to efficient utilisation of existing capacities and add new capacities by attracting investments and reduce energy losses. Studies, such as Cubbin and Stern (2004, 2006), Erdogdu (2014) and Zhang et al. (2008), find that market competition and increased 19