rafmacalaba/datause-encoder-data huggingface dataset:
- 60 -

# Legend (for both countries)

WAGE - index for average earnings. CPI **-** consumer price index. PPI m producer prices for the industry. INEMP - index for industrial employment. INPDN - index for industrial production.

Hungary

|obs|WAGE|CPI|PPI|INEMP|INPDN|
|---|---|---|---|---|---|
|1982.1|0.817021|0.781467|0.892857|1.036720|0.950820|
|1982.2|0.818440|0.799846|0.854037|1.038880|0.973588|
|1982.3|0.812766|0.823429|0.847826|1.037800|0.930783|
|1982.4|0.784397|0.828660|0.864130|1.032400|0.962659|
|1983.1|0.837589|0.847112|0.944876|1.008640|0.950820|
|1983.2|0.857447|0.853103|0.906056|1.012960|0.982696|
|1983.3|0.857447|0.866523|0.895186|1.014040|0.950820|
|1983.4|0.837589|0.873712|0.907609|1.018360|0.979053|
|1984.1|0.900709|0.921399|0.977484|1.001080|0.994535|
|1984.2|0.921986|0.931943|0.944876|1.007560|1.004550|
|1984.3|0.909929|0.944884|0.944876|1.011160|0.958106|
|1984.4|0.884397|0.939851|0.943064|1.015120|1.010930|
|1985.1|0.969503|0.987778|1.000000|0.997840|0.997268|
|1985.2|0.999291|0.993769|1.000000|1.002160|1.012750|
|1985.3|0.995745|1.006230|1.000000|1.003240|0.989071|
|1985.4|1.031210|1.012460|1.000000|1.006480|1.000910|
|1986.1|1.046100|1.045770|1.041000|0.993521|0.995446|
|1986.2|1.084400|1.041460|1.029000|0.995680|1.051000|
|1986.3|1.081560|1.055600|1.011000|0.995680|0.989071|
|1986.4|1.082980|1.068060|1.000000|0.998920|1.012750|
|1987.1|1.116310|1.105440|1.043000|0.975162|1.027320|
|1987.2|1.154610|1.126770|1.056000|0.973002|1.075590|
|1987.3|1.178720|1.163670|1.063000|0.967603|1.000000|
|1987.4|1.222700|1.182600|1.063000|C.969762|1.081970|
|1988.1|1.193620|1.302660|1.095000|0.947084|1.036430|
|1988.2|1.230500|1.299540|1.096000|0.943844|1.063750|
|1988.3|1.292200|1.338370|1.109000|0.941685|0.987249|
|1988.4|1.454540|1.361370|1.119000|0.944924|1.057380|
|1989.1|1.451000|1.489190|1.230000|0.918000|1.021000|
|1989.2<br>""|1.486000<br>m^,.<br> <br>.<br>"<br>..|1.530330<br>**mm**|1.272000<br>**mm.__**<br>**..**|0.910000<br>_.,,..._.,.|1.042000<br>**i**<br>**n._**<br>w<br>.|
other governments are planning to adopt accrual accounting. In Latin America, Chile has said that it will report government financial statistics to the IMF by 2007 and El Salvador already reports partial accrual information to the IMF.<sup>25</sup> (Such statistical reporting differs from financial reporting in that it need not be audited.) Overall, accrual accounting generates valuable information missed by traditional cash accounting. Yet it isn’t sufficient for the assessment of net worth. For one thing, accounting values can diverge too much from true values. After many years of inflation, the depreciated acquisition cost of an asset may greatly underestimate the present value of the cash flows it will generate. In addition, accounting values of assets that generate user fees and higher tax payments at best capture only the value of the user fees, since the present value of future tax revenues does not count as an asset from the conventional accounting perspective. On the other hand, durables are generally treated as assets and valued at their depreciated acquisition or replacement cost even if they generate no future cash flows from either user fees or taxes. Expenditure on a bridge to nowhere can create an accounting asset even if it generates no tolls and does nothing to increase economic output.

# **Long‐term fiscal projections**

Long‐term fiscal projections can help where accrual accounting fails. Such projections, prepared in various ways by countries such as Australia, Britain, New Zealand, and the United States, can in principle include estimates of the government’s operating and investing cash flows over the next 50 or 75 years, which can then be discounted back to the present to arrive at an estimate of the government’s net worth. Crucially, all expected cash flows under current policies can be projected, including items such as taxes and welfare expenditure.

The projections can include public investment, expenditure on operations and maintenance, and payments made to privately financed firms in public‐private partnerships. Revenues from any user fees can be included. And, if evidence

> 25 Government of Chile (2003), IMF (2004a).

21
WPS 3386

**An Evaluation of the Performance of Regression Discontinuity Design on PROGRESA**

**Hielke Buddelmeyer**

# **Emmanuel Skoufias**

**Melbourne Institute of Applied Economic and Social Research and IZA Bonn The University of Melbourne Parkville Victoria 3010 - AUSTRALIA Email:** <u>hielkeb@unimelb.edu.au</u>

**The World Bank 1818 H Street NW Washington DC 20433-USA Email:** <u>eskoufias@worldbank.org</u>

World Bank Policy Research Working Paper 3386, September 2004 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. 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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ We wish to thank Ashu Handa, David Coady and participants at the Poverty and Applied Micro Seminar Series of the World Bank and Uppsala University for helpful comments and suggestions on earlier versions of this paper.
what was observed for the mean residuals, the spatial correlations among location effects are strong and statistically significant (Figure 3). In the case of small rural, large rural and small urban areas, all correlation coefficients are above 0.4 and significantly different from zero for places within the same district. The correlation coefficients gradually decline for districts further apart, but they remain statistically significant for at least 200 km. In the case of large urban areas, the correlation coefficients follow a similar pattern for correlation with places belonging to other population size groups but are more volatile for the correlation coefficient with other large urban areas, because there are few of them.

Table 2 Benchmark regression results

||**OLS**|**WLS**|
|---|---|---|
|**_Location effects_**|||
|Place|Yes|Yes|
|**_Household characteristics_**|||
|Demographics|||
|Household size|-0.567***|-0.523***|
||(0.005)|(0.009)|
|Children under 6|-0.384***|-0.319***|
||(0.013)|(0.020)|
|Children above 6|-0.014<br>|0.008<br>|
||(0.012)|(0.019)|
|Female adults|0.068***|0.040*|
||(0.014)|(0.023)|
|Female dependents|-0.109***|-0.092***|
||(0.018)|(0.027)|
|Male dependents|0.080***|0.071***|
||(0.017)|(0.025)|
|Female household head|-0.064***|-0.055***|
||(0.005)|(0.007)|
|Skills|||
|Maximum education|0.006***|-0.003*|
||(0.001)|(0.001)|
|Maximum education squared|0.002***|0.002***|
||(0.000)|(0.000)|
|Assets|||
|Land|0.179***|0.149***|
||(0.003)|(0.005)|
|Dwelling (omitted = Own)|||
|Rent|0.107***|0.130***|
||(0.005)|(0.008)|
|Other|-0.113***|-0.094***|
||(0.009)|(0.018)|
|No|0.058|0.039|
||(0.047)|(0.052)|

Note: Estimated coefficients *significant at 0.1 level, ** significant at 0.05 level, *** significant at 0.01 level.

(Continued) 12
**<u>Table 2: child labor by gender : intrahousehold allocation</u>**

|||N|u|mber of hours|per week chi|ld between 6|and 15 years wor|ked in :||
|---|---|---|---|---|---|---|---|---|---|
||non-<br>agricultural|agriculture&<br>livestock||agriculture|livestock|chores|domestic<br>work|all econ<br>activity|all work|
|Boy|0.1725|4.225||3.264|0.929|4.559|1.444|4.488|10.489|
|Girl|0.261|1.698||0.497|1.166|3.622|3.936|2.049|9.606|
|Difference|-0.0885|2.527||2.767|-0.237|0.937|-2.492|2.439|0.883|
|P-value|0.280|0.000||0.000|0.045|0.000|0.000|0.000|0.190|

_NOTE:_ Control communities only. Results from estimations with household fixed effects. Standard errors corrected for clustering at community level. Children 6-15 years old, in households with at least 2 children in this age range. N = 1088 (from 413 households)

**<u>Table 3: difference in child labor between children assisting to school and those not assisting: intrahousehold allocation</u>**

|||N|u|mber of hours|per week chi|ld between 6|and 15 years wor|ked in :||
|---|---|---|---|---|---|---|---|---|---|
||non-<br>agricultural|agriculture&<br>livestock||agriculture|livestock|chores|domestic<br>work|all econ<br>activity|all work|
|ALL||||||||||
|Difference|0.195|-2.962||-2.892|-0.0524|-0.469|-0.502|-2.759|-3.730|
|P-value|(0.083)|(0.006)||(0.008)|(0.720)|(0.170)|(0.140)|(0.013)|(0.000)|
|BOYS||||||||||
|Difference|0.208|-5.971||-5.825|-0.110|-0.318|0.453|-5.744|-5.610|
|P-value|(0.17)|(0.002)||(0.002)|(0.55)|(0.54)|(0.14)|(0.003)|(0.002)|
|GIRLS||||||||||
|Difference|0.185|-0.566||-0.558|-0.00659|-0.588|-1.263|-0.383|-2.234|
|P-value|(0.22)|(0.35)||(0.35)|(0.98)|(0.090)|(0.009)|(0.53)|(0.000)|

_NOTE:_ Controlling for gender and age through series of age dummies by gender. Control communities only. Results from estimations with household fixed effects. Standard errors corrected for clustering at community level. Children 6-15 years old, in households with at least 2 children in this age range. N = 1088 (from 413 households) 23
# **5. REFERENCES**

Dercon, S., ed. 2004. _Insurance against Poverty._ Oxford and New York: Oxford University Press, in collaboration with UNU-WIDER.

Global Pulse. 2010. _Voices of the Vulnerable: Recovery from the Ground Up._ United Nations PDF.

- Heltberg, R.,  and N. Lund. 2009. “Shocks, Coping, and Outcomes for Pakistan’s Poor: Health Risks Predominate.” _Journal of Development Studies_ 45 (6): 889–910.

- Hossain, N. 2009. _Accounts of Crisis: Poor People’s Experiences of the Food, Fuel and Financial Crises in Five Countries. Report on a Pilot Study in Bangladesh, Indonesia, Jamaica, Kenya and Zambia, January-March 2009_ .Brighton, UK: Institute of Development Studies, UK. March 31.

- Krishna,  A.  2007. “For Reducing Poverty Faster: Target Reasons before People.” _World Development_ 35 (November): 1947–60.

- ———. 2004. “Escaping Poverty and Becoming Poor: Who Gains, Who Loses, and Why?” _World Development_ 32  (January): 121–36.

Krishna, A., and A. Shariff. 2011. “The Irrelevance of National Strategies? Rural Poverty Dynamics in States and Regions of India, 1993–2005. _World Development_ 39 ( April): 533–49.

Narayan, D. 2000. _Voices of the Poor: Volume 1: Can Anyone Hear Us?._ New York: Published by Oxford University Press for the World Bank.

Narayan, D., L. Pritchett, and S. Kapoor. 2009. _Moving Out of Poverty._ Vol. 2 of _Success from the Bottom Up._ Basingstoke, UK, and Washington, DC: Palgrave Macmillan and World Bank.

Turk, C., A. Mason, and P. Petesch. 2010. “Impacts of the Economic Crisis on Vulnerable Population Groups: Midcourse Findings from a Qualitative Monitoring Project in 12 Developing Countries.” World Bank, processed.

World Bank, Thailand. 2010. _Thailand Economic Monitor_ (November). Bangkok.

30
The empirical analysis in this paper contributes to several strands of literature. First, there is now emerging literature on trade and transport emphasizing that (i) transport prices are endogenous to trade flows, (ii) there are spillovers across routes because truckers take account of the probability of finding the next trip while planning for a trip (Brancaccio et al. 2020, Wong, 2018); and (iii) the thicker trade flows along a route allow the use of larger and more efficient trucks leading to the extent of market effect observed in our data (Allen et al.,2021; Asturias, 2020). Our empirical analysis provides suggestive evidence in favor of the presence of these externalities in the Indian setting where the trucking sector is predominantly informal and unregulated with few entry barriers. The presence of these externalities suggests that the return to the construction of a road would depend on not just the reduction in travel time but also the characteristics of the places that it is connecting.

Second, trade elasticity is a key parameter in quantitative spatial equilibrium models. We provide a causal estimate of the elasticity of trade flow with respect to trucking costs which has not been --to the best of our knowledge -- available for developing countries in the existing literature. Our estimates of elasticities imply elasticities of substitution within the range of 21-29 which are much larger in magnitude than the estimates (5-6) derived from the spatial equilibrium models estimated for India (Asturias, GarciaSantana and Ramos, 2018; van Leemput, 2021). But our estimates are very much in line with the estimates derived from direct data on freight rates by Wong (2018) (22-27). It also confirms the _a priori_ expectation that this elasticity should be higher in poorer countries where consumers tend to be more sensitive to prices (Duranton, 2014).<sup>6</sup> Third, developing countries are characterized by considerable regional inequality, where modern economies in larger cities coexist with subsistence economies in remoter regions, and improved transport connectivity is often offered as an important way to integrate and transform the isolated areas (Grover, Lall and Mal1y, 2022). Yet, literature on the impacts of transport connectivity as well as trade shocks on

> 6 The discrepancies between estimates from annual data using distance as a measure of transport cost and ne grained transaction-level data are noted already in literature (see, Wong for a discussion) and discussed in more detail in section 4.

5
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award and implementation progress, complaints procedures and other project and sector-related documents and policies.

- Participatory processes were used in the design of the local (unpaved) roads component of the project, which also involved local cost-sharing.  Local universities will also be involved in monitoring physical quality of outputs.

Combined with World Bank led fiduciary assessments, the consultation process also prompted the development of a number of mitigation measures regarding project fiduciary arrangements, including new financial management systems and improved audits, and an enhanced supervision process involving the monitoring of ‘red flags’ such as price variance in bids and procurement delays, laid out in an annex to the project documents.<sup>41</sup>

# **Partnerships to Reduce Corruption**

The BEEPS data on construction firms suggests that there is little evidence that foreignowned and domestic construction firms bribe differently in Eastern Europe and Central Asia (Tables 4 and 5). This will be in some significant part because firm decisions on corruption are largely driven by host-country (rather than home-country) conditions. In particular, perceptions of corruption in a country are likely to become a self-fulfilling prophesy. A recent survey of international firms based in Norway suggested that the most important underlying motivation for bribery was “the fear of losing contracts because someone else had bribed the decision-makers,” for example (Soreide, 2007). Multi-stakeholder partnerships can reduce the perception of the likelihood of corruption through participation in anti-corruption governance mechanisms. As such, they should play a double role in increasing the risk involved in corrupt acts and reducing the underlying incentive to commit such acts.

A 2004 initiative led by Transparency International, the World Economic Forum and nineteen engineering and construction firms created a set of business principles for countering bribery. This commits signatories to a ‘zero tolerance’ policy on bribery and implementing a program of internal practices to counter corruption. This program would prohibit all forms of bribery, control and monitor facilitation and charitable payments along with gifts and hospitality to ensure that they are not used for bribery, ensure those who refuse to pay bribes are protected from retaliation and set penalties for violating program rules, provide training and confidential channels of communication, and ensure for adequate internal auditing.<sup>42</sup> An approach based on Transparency International business principles was greed amongst piping companies in Colombia’s water sector, which resulted in significantly lower bid award prices for projects with the participating companies (Lee and. Larnemark, 2007).

> 41 Available here:

> http://web.worldbank.org/external/projects/main?pagePK=64283627&piPK=73230&theSitePK=40941&m enuPK=228424&Projectid=P082026.

> 42 http://www.weforum.org/pdf/paci/principles_short.pdf.

17
- McGuire, Joel, Caspar Kaiser, and Anders Bach-Mortensen. 2020. “The impact of cash transfers on subjective well-being and mental health in low- and middle- income countries: A systematic review and meta-analysis.” Tech. rep., SocArXiv. URL `https://osf.io/ preprints/socarxiv/ydr54/` . Type: article.

- McKelway, Madeline. 2020. “Women’s Employment in India: Intra-Household and IntraPersonal Constraints.” _Working Paper_ .

- McKenzie, David. 2012. “Beyond baseline and follow-up: The case for more T in experiments.” _Journal of Development Economics_ 99 (2):210–221. URL `https://ideas.repec. org/a/eee/deveco/v99y2012i2p210-221.html` . Publisher: Elsevier.

- Morse, Nancy C. and Robert S. Weiss. 1955. “The Function and Meaning of Work and the Job.” _American Sociological Review_ 20 (2):191–198. Publisher: [American Sociological Association, Sage Publications, Inc.].

- Mree, Himaloy Joseph and Karen Homer. 2019. “Cash-for-work programme empowers Rohingya refugee women.” URL `https://www. wvi.org/myanmar-%E2%80%93-bangladesh-refugee-crisis/article/ cash-work-programme-empowers-rohingya-refugee-women` .

- Olesen, Sarah C., Peter Butterworth, Liana S. Leach, Margaret Kelaher, and Jane Pirkis. 2013. “Mental health affects future employment as job loss affects mental health: findings from a longitudinal population study.” _BMC Psychiatry_ 13 (1):144. URL `https://doi. org/10.1186/1471-244X-13-144` .

- Patel, Vikram, Benedict Weobong, Helen A. Weiss, Arpita Anand, Bhargav Bhat, Basavraj Katti, Sona Dimidjian, Ricardo Araya, Steve D. Hollon, Michael King, Lakshmi Vijayakumar, A.-La Park, David McDaid, Terry Wilson, Richard Velleman, Betty R. Kirkwood, and Christopher G. Fairburn. 2017. “The Healthy Activity Program (HAP), a lay counsellor-delivered brief psychological treatment for severe depression, in primary care in India: a randomised controlled trial.” _The Lancet_ 389 (10065):176–185. URL `https://www.thelancet.com/journals/lancet/article/ PIIS0140-6736(16)31589-6/fulltext` . Publisher: Elsevier.

- Poll, Harris. 2018. “Blue Collar Workers: Career and Life Satisfaction High; Trust in Elected Officials Low.” URL `https://www.expresspros.com/Newsroom/America-Employed/ Blue-Collar-Workers-Career-and-Life-Satisfaction-High-Trust-in-Elected-Officials-Low. aspx` .

31
25 removing the constraints on higher-level salaries or allowing the real minimum wage to fall more than higherlevel salaries. The study found that progress was slow.

Another World Bank study of fifteen countries with Bank-supported civil service reform programs also found mixed results.<sup>27</sup> Although a primary goal of most of the programs was to reduce the aggregate wage bill, in ten of fifteen countries -- including those in which reforms had progressed furthest -- the wage bill as a share of government spending actually increased. Of the six countries with data on wage compression, half had decompression and half had further compression. There was virtually no progress on reducing or rationalizing nonwage benefits (Nunberg and Nellis 1990:17-22).

Although there was little fiscal improvement, there was some progress on employment reduction. Eight of the fifteen countries were able to implement some reductions, although three (Cameroon, Ghana, and Jamaica) had at least temporary slippage ex post. In the other seven countries, the rate of growth of employment remained more or less unchanged. All in all, net employment reductions were minimal, and not enough to generate sufficient savings to increase pay levels for remaining civil servants to compensate for previous years' salary erosion and compression (Nunberg and Nellis 1990:23).

Thus, the public sector -- measured variousi, by the wage bill or the level of employment -- continued to grow throughout the 1980s, although at a decreasing rate. In spite of the real wage trend, public sector employment continues to have great appeaL based in many countries -- particularly in Africa -- on expectations about job security and status without enforced performance standards, and on opportunities for moonlighting and rent-earning. This situation has perhaps changed in some countries in recent years -- largely due to the influence of the international development community -- but progress has been slower than anticipated.

# The political economy of reforms

Colclough (1991:222) argues that in Africa, several factors work against wage restraint and public sector decompression. First is the commonly held view -- to which the government is often publicly committed -- that more generous compensation at lower rather than higher levels is equity-enhancing. A second factor is the high public profile of the minimum wage, even outside the formal sector. When given the choice between higher wages and more employment opportunities for the unskilled, African governments tend to prefer higher wages over more jobs. A higher public sector wage for the unskilled (minimum wage) translates into higher expectations for both actual and potential job seekers. It may also bring direct benefits to rural areas in the form of increased remittances.' Thus, it is easy for governments to garner support for a "high" public sector minimum wage, and the counter-arguments about decreased income equity, increased rural-urban migration, induced unemployment and underemployment, lower output, are harder for governments to publicize