rafmacalaba/datause-encoder-data huggingface dataset:
nomic Review 95: 437-449.

Black, D., and J. Smith. (2004), “How robust is the evidence on the effects of college quality? Evidence from matching”, Journal of Econometrics. Blanden, J, P. Gregg, S. Machin (2005). Intergenerational Mobility in Europe and North America, Center For Economic Performance Report, April, 2005.

Bound, John, and Gary Solon (1999), “Double Trouble: On the Value of Twins-Based Estimation of the Return to Schooling”, _Economics of Education Review_ , 18 (1999), 169-82. Brown, Phil and Albert Park, 2002, “Education and Poverty in Rural China”, Economics of Education Review 21(6), 523-541.

Brunori, Paolo, Francisco Ferreira, and Vito Peragine. 2013. Inequality of Opportunity, Income Inequality and Economic Mobility. Some International Comparisons. Policy Research Working paper 6304. The World Bank, Washington, DC.

Busso, M., DiNardo J., McCrary, J. (forthcoming), New Evidence on the Finite Sample Properties of Propensity Score Reweighting and Matching Estimators, forthcoming in Review of Economics and Statistics.

Chen, J., and B. Fleisher, “Regional Income Inequality and Economic Growth in China, Journal of Comparative Economics 22 (1996).

Deaton, A (1997), _The analysis of household surveys: A microeconometric approach to development policy_ . Oxford University Press.

Dehejia, Rajeev, (forthcoming) “The porous dialectic: Experimental and non-experimental methods in development economics,” forthcoming in Journal of Globalization and Development.

Deng, Quheng, Gustafsson, Bjorn, & Li, Shi. 2013. Intergenerational Income Persistence in Urban China. Review of Income and Wealth.

40
between wage cuts across countries and the percentage of workers covered by collective agreements (see also Figure 1 below). This is in line with results relying on the frequency of wage cuts in industry data across countries reported by Holden and Wulfsberg (2009).

<!-- Start of picture text --> CZ<br>LT<br>PL<br>AT<br>EE<br>FR<br>HU<br>IE BE<br>NL<br>PT<br>IT<br>ES<br>0 20 40 60 80 100<br>% of employees covered by agreements<br>8<br>6<br>4<br>2<br>% of firms having cut wages<br>0<br><!-- End of picture text -->

**Figure 1: Wage cuts and coverage of union wage agreements**

# **_3.4 Reasons for Avoiding Wage Cuts_**

Having established the rarity of wage cuts in our data, we move on now to evaluate the main reasons put forward by managers for this downward nominal wage rigidity. Firm managers were asked to assess the relevance of the eight reasons listed in Section 2, and the answers were requested on a four-point scale: not relevant, of little relevance, relevant, and very relevant. Table 3 presents the percentages of firms in each country that ranked a given reason as very relevant or relevant, and Table 4 shows the overall ranking of the different reasons.

Looking first at the averages across all countries, the two most important reasons for avoiding base wage cuts are the belief that this would result in a reduction in morale or effort, and the risk that the most productive workers would leave as a response to potential wage cuts. Both of these reasons were reported as relevant or very relevant by 86% of firms. The impact on employees’ morale is an 14
cursors such as SO2 and NO,'; background pollution levels (especially of ammonia); atmospheric chemistry; geographical, climatological and meteorological characteristics; and atmospheric transport characteristics.

# **Air Quality and the** **_Programa de ContingenciasAmbientales(PCA)_**

Most air quality information comes from measurement stations across the area-the _Red Automatica de Monitereo Atmosferica_ (RAMA)-that compile time-averaged concentrations (see figure 2.1). The annual reports usually present this information in frequency tables giving the percentage of a year that a certain concentration occurred, or as a single annual average.

Day-to-day air quality data available on the Internet include daily maximum concentrations for five pollutants-PM1 O, ozone, SO 2, NO,, CO-and an ultraviolet (UV) index) for five zones<sup>2</sup> : downtown, Northwest, Northeast, Southwest and Southeast. These concentrations are expressed in IMECA points _(Indice Metropolitana del Calidad del Aire)._ Table 2.3 shows how concentrations relate to the indicator points (100 = standard).<sup>3</sup>

**Figure 2.1 Measuring PM10 and Ozone Concentrations across the ZMVM**

<!-- Start of picture text --> v Pla Stl.on.<br>Z .1nII1p.IkI.. and D.I.g.lon-<br><!-- End of picture text --> If one would consider PM2 5-a smaller mass than PM1O-emissions of SO2 and NO. become more important since these substances can be converted into particulate matter (PM1.0) in the atmosphere. Furthermore, NO. and VOCs can be attached to existing particulate matter in the atmosphere. The indications are that these small particles have disproportionately large health effects. However, given the lack of air quality information on PM2.5 (and appropriate epidemiological data) it impossible to take account of this. 2 http://sima.cor.mx/sima/df (April 2000).

3 For ozone, the IMECA indicator is proportional to ozone concentrations. For PM1 O an IMECA number follows from linear interpolation between the values indicated in the table.

4
that transparent procurement systems lead to the engagement higher quality contractors, not necessarily the improvement of existing contractors.

We find that public procurement regulation that is transparent, competitive and streamlined is associated with better infrastructure quality. The higher the score on the quality of public procurement, the better the road quality outcomes. Specific aspects of the public procurement – bid preparation, bid and contract management, and payment of suppliers - are also found to be strongly correlated with greater road quality. These findings stand after accounting for the rule of law, level of development, as well as the size of the urban population in an economy. The rest of the study is structured as follows. Section 1 explores the construction of the database, section 2 provides some basic results from the database while section 3 explores the findings for OECD vs. non-OECD economies. Section 4 concludes.

# **1. Description of the Data**

This study constructs a database that characterizes the public procurement regulation for 142 economies in 2016. The three key areas are the bidding process, the content and management of the contract, and the payment of suppliers involved in public procurement. The choice of these areas was guided by a review of previous academic literature and by consultation with world 4
33 example that there is a 90 percent probability of default and a recovery rate of only 28 percent if default occurs (see Cline and Barnes, 1997, p. 40). The persistence and continued escalation of the spreads gave an increasing sense of inevitability of default. This in turn increasingly prompted the public to protect itself by withdrawing deposits from banks to remit abroad or hoard domestically, particularly in light of the memory of the Bonex Plan freeze in bank accounts in 1990.

Figure 6 Spreads above US Treasury bonds (basis points) <!-- Start of picture text --> 3000<br>2500<br>2000<br>1500<br>1000<br>500<br> ARG<br>6/1/2000 9/1/2000 12/1/2000 3/1/2001 6/1/2001 9/1/2001 EMBI xR<br><!-- End of picture text --> As figure 6 suggests, before July 2001 the response of spreads to political shocks tended to be temporary. There had been some moderate improvement in June with the megaswap, but on July 15 the announcement of the “zero deficit” plan triggered a jump of 400 basis points in the spread, to 1600 bp. At the time, Minister of Economy Domingo Cavallo made statements that the 13 percent wage cut in the plan was needed because Argentina no longer had access to credit. Powell (2002, p. 13) argues that the seeming desperation of the zero deficit plan may have acted as a rallying signal to creditors that the situation was worse than they had realized. He notes the role of a change of this nature in the “common knowledge,” in game-theory terms, causes the players in the game (investors) to change their view of what most of the other players will do, and hence switches the equilibrium to a new (and in this case more adverse) one.

Argentina’s political system exacerbated the potential for vicious circles. There was an unfortunate tradition whereby the sitting president could be forced to resign if conditions deteriorated enough. Raul Alfonsin left office 6 months early in 1989. With the expectations of the dissident UCR faction, the Peronist opposition, apparently some media groups, and presumably many in the public that de la Rua similarly could be forced to resign, instead of a process of consensus building there was an escalation of pressures for presidential resignation by December 2001. Domingo Cavallo subsequently wrote:
the particular form of errors (Gottschalk and Huynh, 2010).<sup>34</sup> Thus much research still appears to be needed on this topic. One promising direction can be the application of synthetic panels to correct for “bad” panel data.

# _Big Data_

Various Big Data sources have been proposed as viable substitutes for missing household consumption data. For example, Jean _et al._ (2016) combine nighttime maps with high-resolution daytime satellite images to provide poverty maps for five African countries, while Blumenstock _et al._ (2015) use mobile phone records to estimate poverty. Most recently, Steele _et al._ (2017) employ cellphone data and satellite data to evaluate poverty mapping for Bangladesh. These recent advances thus offer a promising avenue where intensive machine learning techniques can be introduced to substitute for traditional econometric methods.

# _Subjective Poverty_

Subjective well-being data can be employed to provide richer analysis of poverty. In fact, if can be argued that if poverty is multi-dimensional, subjective assessment of poverty is an integral part of its measurement. It is even proposed that a “social subjective poverty line”, below which people tend to think they are poor, but above which they do not, can be a conceptual alternative to defining poverty (Ravallion, 2014). But on the other hand, some empirical evidence suggests that poverty may not necessarily overlap with unhappiness in a number of developing countries including India, Mexico, Peru, and the Russian Federation (Banerjee and Duflo (2007), Rojas

> 34 See also Jantti and Jenkins (2015) for a recent overview of other studies that investigate the impacts of measurement errors on intra-generational mobility, and Nybom and Stuhler (2016) for an analysis in the context of inter-generational mobility.

34
- 36 -

end date of Papanek's series tends to exaggerate the downward trend<sup>(as</sup> it coincided with a severe drought and unusually high<sup>rice prices).</sup> Nonetheless, his data do suggest declining real wages over the adjustment period. Collier et al. (1988) do not confirm this result in their study of 13 villages in the same provinces over a similar<sup>period.</sup> though the two studies have used different price deflators. It<sup>may also</sup> be noted that labor market adjustment will probably<sup>mitigate adverse</sup> effects on poor net consumers of tradeable goods,<sup>though that adjustment</sup> can be slow; for evidence in a possibly not dissimilar<sup>setting see</sup> Ravallion (1989a).

6. For evidence on the performance of voluntary redistributive or social insurance arrangements (the so-called "moral economy")<sup>in this setting</sup> see Ravallion and Dearden (1988).

7. For an excellent survey see Foster (1984).

8. More, formally, for a population of size n, split into m subgroups with population shares ni (i-l,...,m), the FGT class of poverty measures takes the general from m P Pa- n which is simply the population weighted mean of the<sup>subgroup poverty</sup> index (Pai), given by: PaiE E jni for a > 0 where gj = (z - yj)/z denotes the proportionate poverty gap of the jth person in subgroup i when y; is the expenditure per<sup>capita of that</sup> person's household (ranked in ascending order) and for which<sup>qi persons</sup> in subgroup i are found to have a value of yj below<sup>the poverty line,</sup>
Aschauer, D. A. (1989): “Is public expenditure productive?”.

- Atack, J., F. Bateman, M. Haines, and R. Margo (2009): “Did Railroads Induce Or Follow Economic Growth? Urbanization And Population Growth In The American Midwest, 1850-60,” 34.

- Bahia, K., P. Castells, G. Cruz, T. Masaki, X. Pedros, T. Pfutze, C. Rodriguez Castelan, and H. J. Winkler (2020): “The Welfare Effects of Mobile Broadband Internet : Evidence from Nigeria,” Policy Research Working Paper Series 9230, The World Bank.

- Bahia, K., P. Castells, G. Cruz, T. Masaki, C. Rodriguez Castelan, and V. Sanfelice (2021): “Mobile Broadband Internet, Poverty and Labor Outcomes in Tanzania,” IZA Discussion Papers 14720, Institute of Labor Economics (IZA).

- Baum-Snow, N., L. Brandt, J. V. Henderson, M. A. Turner, and Q. Zhang (2017): “Roads, railroads, and decentralization of Chinese cities,” _Review of Economics and Statistics_ , 99, 435–448.

- Bessone, P., and R. Dahis (2020): “The Impact of 3G Mobile Internet on Educational Outcomes in Brazilˆa.”

- Bhuller, M., T. Havnes, J. McCauley, and M. Mogstad (2020): “How the Internet Changed the Market for Print Media,”Memorandum 2/2020, Oslo University, Department of Economics.

- Bianchi, N., Y. Lu, and H. Song (2020): “The Effect of Computer-Assisted Learning on Students’ Long-Term Development,” NBER Working Papers 28180, National Bureau of Economic Research, Inc.

- Bonfatti, R., Y. Gu, and S. Poelhekke (2019): “Priority roads: the political economy of Africa’s interior-to-coast roads,” Tinbergen Institute Discussion Paper TI 2019-006/VIII, Amsterdam and Rotterdam.

- Buhaug, H., and J. K. Rod (2006): “Local determinants of african civil wars, 1970ˆa2001,” _Political geography_ , 315–335.

- Burgess, R., R. Jedwab, E. Miguel, A. Morjaria, and G. P. i Miquel (2015): “The value of democracy: Evidence from road building in Kenya,” _The American Economic Review_ , 105, 1817–1851.

- Canning, D., M. Mabeu, and R. Pongou (2020): “Colonial Origins and Fertility: Can the Market Overcome History?” _Unpublished Manuscript_ .

39
|Pakistan: Punjab Education Sector Project|Huma Ali Waheed|
|---|---|
|Kerala Local Government and Service Delivery|Harsh Goyal|
|Kerala Local Government and Service Delivery|Harsh Goyal|
|Kerala Local Government and Service Delivery|Harsh Goyal|
|Kerala Local Government and Service Delivery|Harsh Goyal|
|Kerala Local Government and Service Delivery|Harsh Goyal|
|Kerala Local Government and Service Delivery|Harsh Goyal|
|Kerala Local Government and Service Delivery|Harsh Goyal|
|Kerala Local Government and Service Delivery|Harsh Goyal|
|Kerala Local Government and Service Delivery|Harsh Goyal|
|Kerala Local Government and Service Delivery|Harsh Goyal|
|Kerala Local Government and Service Delivery|Harsh Goyal|
|Kerala Local Government and Service Delivery|Harsh Goyal|
|Kerala Local Government and Service Delivery|Harsh Goyal|
|Kerala Local Government and Service Delivery|Harsh Goyal|
|India - Bihar Panchayat StrengtheningProject|Farah Zahir|
|India - Bihar Panchayat StrengtheningProject|Farah Zahir|
|India - Bihar Panchayat StrengtheningProject|Farah Zahir|
|India - Bihar Panchayat StrengtheningProject|Farah Zahir|
|India - Bihar Panchayat StrengtheningProject|Farah Zahir|
|India - Bihar Panchayat StrengtheningProject|Farah Zahir|
|India - Bihar Panchayat StrengtheningProject|Farah Zahir|
|India - Bihar Panchayat StrengtheningProject|Farah Zahir|
|India - Bihar Panchayat StrengtheningProject|Farah Zahir|
|India - Bihar Panchayat StrengtheningProject|Farah Zahir|
|India - Bihar Panchayat StrengtheningProject|Farah Zahir|
|India - Bihar Panchayat StrengtheningProject|Farah Zahir|
|India - Bihar Panchayat StrengtheningProject|Farah Zahir|
|India - Bihar Panchayat StrengtheningProject|Farah Zahir|
|India - Bihar Panchayat StrengtheningProject|Farah Zahir|
|Ghana Agriculture DPL|Jan Joost Nijhoff|
|Ghana Agriculture DPL|Jan Joost Nijhoff|
|Programmatic Support for Institution BuildingIII|Birgit Hansl|
|Programmatic Support for Institution BuildingIII|Birgit Hansl|
|Programmatic Support for Institution BuildingIII|Birgit Hansl|
|StrengtheningAccountabilityfor and Audit of Disaster-Related Aid|Rajat Narula|
|StrengtheningAccountabilityfor and Audit of Disaster-Related Aid|Rajat Narula|
|StrengtheningAccountabilityfor and Audit of Disaster-Related Aid|Rajat Narula|
|StrengtheningAccountabilityfor and Audit of Disaster-Related Aid|Rajat Narula|
|National Strategyfor the Development of Statistics|Eyerusalem Fasika|
|National Strategyfor the Development of Statistics|Eyerusalem Fasika|
|Coastal Cities Pollution Control Project 2|Stjepan Gabric|
|Coastal Cities Pollution Control Project 2|Stjepan Gabric|
|Coastal Cities Pollution Control Project 2|Stjepan Gabric|
|Coastal Cities Pollution Control Project 2|Stjepan Gabric|
|Coastal Cities Pollution Control Project 2|Stjepan Gabric|
|Coastal Cities Pollution Control Project 2|Stjepan Gabric|
percent between 1987 and 2014 among GB-GD and GG staff, 7/10th of 1 percent for GE and GH level staff and 1.1 to 1.6 percent for GI-GK level staff.<sup>24</sup>

**Figure 4: Salary Trends for Staff at the WBG, 1987-2015**

<!-- Start of picture text --> 160<br>150<br>GB<br>140<br>GC<br>GD<br>GE<br>130<br>GF<br>GG<br>GH<br>120<br>GI<br>GJ<br>GK<br>110<br>100<br>1987 1990 1995 2000 2005 2010 2014<br>Average Real Salary (1987=100)<br><!-- End of picture text --> Each of these “macro-changes” over the period of our data can affect the salary gap across subgroups. For instance, the decline in GA-GD level staff, who are predominantly women in jobs with lower salaries implies that the average salary of women relative to men will rise in the institution. Similarly, differences in the profile of staff leaving the WBG will affect the salaries of those who remain. Finally, differential increases in salaries for different grades can affect both aggregate and career gaps. First, as GA-GD staff tend to be women, their lower salary growth over time will imply that the aggregate gap will also increase. Second, staff are promoted over time. If men are promoted faster to GH (for instance) relative to women and GH salaries are growing faster, this will again induce an increase in both the aggregate and career

> 24 These are salaries of all staff in a given grade, and thus include tenure effects; with zero turnover, salaries will grow with experience. A second option, in Appendix Figure 2 focuses just on entry-level salaries for different grades over this time period. Like in Figure 4, with the exception of GI+ staff, real entry salaries have increased slowly over this period with the largest increases of 15-20% for GH level staff.

15
The second problem is that capital expenditures often imply a commitment to future current expenditures (e.g.. expenditures on schools or public ttansport, or subsidies to parastatals). They therefore may build in the asymmetric expenditure response described above. Third, the social marginal efficiency of investment is likely to fall rapidly when capital expenditures rise substantially in a short period of time, due to inadequate planning and coordination. The final problem, in historical perspective, is that the overall performance of public sector investment in low-income Africa has been, with few sceptions. extremely poor (World Bank, 1981; Jaycos, et aL., 1986). This is especially true when the investment has meant expansion of the scope of public sector activities in product and resource markets (Ndulu, 1986), an observation that is corroborated by the experience of oil exporting countries in handling windfall revenues in the 1970s (Gelb, 1986).

Figure 3 gives an example of the asymmetric expenditure response phenomenon from Cote d'Ivoire. Public expenditures as a fraction of GDP rose gradually from 1970 to 1975, at a time when the terms of trade was deteriorating by 40 percent; by 1976, the public sector deficit was 7 percent of GDP. The 1975-77 coffee and cocoa boom led to an extreme short-run terms of trade improvement (these commodities represent half of Cote d'Ivoire's export earnings).17 Revenues from the boom turned the fiscal balance to surplus in 1977, but this was followed by a sharp increase in expenditures even **as** the terms of trade collapsed back to the 1975 level. The public sector deflcit rose steadily, to 10 percent of GDP in 1982, and external debt rose rapidly (the debt service ratio rose from 9.1 percent in 1975 to 33.6 17 The discussion here draws on Devarajan and de Melo (1987b).

15
**Table** **<u>3</u>** **_<u>Maquila</u>_ Performance** **<u>and National Poverty Rates, 1991–2006</u>**

|_Item_|_1991_|_1995_|_2001_|_2006_|
|---|---|---|---|---|
|Total number of workers in|||||
|_maquila_|19,400|45,327|90,016|106,501|
|Percentage of active population<br>working in_maquila_|1.3|2.6|4.3|4.4|
|Percentage of_maquila_workers|||||
|living under the poverty line|54.6|58.0|37.6|32.9|
|Percentage of nonpoor households<br>with family member working in<br>_maquila_|3.3|4.4|10.7|10.8|

_Source_ : Authors’ computation based on data from the EPHPM with the moderate poverty line set by INE.

An important component of the welfare effect brought about by an increase in the _maquila_ sector activity is given by its capacity to create new jobs. The proportion of total employment in the _maquila_ sector increased steadily beginning in 1991 (table 4). Although the change in the gender mix in the sector favored men, still in 2006 more than half of all _maquila_ workers are women. Two other important aspects highlighted by table 4 are the increase in the working-age population and the increase in underemployment. These trends suggest that the Honduran economy was unable to create the new jobs needed to satisfy the demographically driven increase in labor supply, contributing to the proliferation of part-time jobs, most of which are in the informal sector. In addition, unable to find jobs in their home country, many young Hondurans have migrated, mainly to the United States.

7