rafmacalaba/datause-encoder-data huggingface dataset:
Table 8: New household formation and migration.

||(1)<br>New HH|(2)<br>New HH|(3)<br>Migrated|(4)<br>Migrated|(5)<br>Metropolis|(6)<br>Metropolis|
|---|---|---|---|---|---|---|
|Treatment|-0.009|0.015|0.022|-0.033|0.005|-0.004|
||(0.0216)|(0.0415)|(0.0234)|(0.0398)|(0.0308)|(0.0577)|
|T x (0-5)||-0.122***||0.026||0.030|
|||(0.0403)||(0.0358)||(0.0543)|
|T x (6-10)||0.062||0.095**||0.004|
|||(0.0443)||(0.0402)||(0.0572)|
|T x (11-16)||0.117**||0.114***||-0.031|
|||(0.0454)||(0.0418)||(0.0600)|
|Constant|0.351***|0.379***|-0.001|0.019|-0.061|-0.060|
||(0.0642)|(0.0657)|(0.0608)|(0.0619)|(0.105)|(0.106)|
|Observations|3,351|3,351|3,351|3,351|969|969|
|R-squared|0.017|0.041|0.011|0.016|0.044|0.046|
|Mean C|0.573||0.273||0.127||
|Mean C (0-5)||0.261||0.261||0.133|
|Mean C 6-10||0.603||0.272||0.104|
|Mean C 11-16||0.788||0.370||0.176|
|Mean C 16+||0.731||0.172||0.000|

This table consists of LPMs where the purpose is to identify if the treatment might have induced new household formation or migration out of the original household. The unit of observation are individuals in relevant age groups, as described in section IV. The outcomes are dummies that correspond to: (i) _New Household_ , which identifies if the individual inhabits in a new household (as opposed to an original household); (ii) _Migrated_ , which identifies if the individual migrated to a different locality from which the original household was located; and (iii) _Metropolis_ indicates if such migration had as destination either Mexico City, Guadalajara or Monterrey, which are the main urban Mexican cities. Controls include (from ENCASEH 1997): HH head age, education, speaking a dialect, HH size, % members under 15, dummies for home, productive land and draft animals ownership, and locality marginalization index. Standard errors clustered at the locality level. Asterisks indicate significance at the ***1%, **5%, and *10% level.

25
supposed to capture the need of some—though certainly not all—social considerations from the political point of view.<sup>7</sup> High unemployment is likely to induce the authorities to adopt multiple criteria to take various social aspects into account. Our sample includes eight countries in Latin America: Argentina, Bolivia, Brazil, Chile, Colombia, Mexico, Peru and Uruguay. Note that Figure 6 reflects the average unemployment rates from 1990 to 2000. However, in our following regression analysis the unemployment variable is time-variant. Because there are annual unemployment data available, it has a variation among transactions in each country.<sup>8</sup> Bolivia, Brazil, Chile and Mexico had relatively low unemployment rates; thus, these countries are supposed to have less incentive to employ the multidimensional auction format, holding other conditions constant.

Figure 6. Average Unemployment Rates in the 1990s <!-- Start of picture text --> 14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>Argentina Bolivia Brazil Chile Colombia Mexico Peru Uruguay<br>Source: World Development Indicators.<br><!-- End of picture text --> Three governance indices are borrowed from the Worldwide Governance Indicators (WGI): government effectiveness, regulatory quality, and control of corruption. These measures are deemed particularly relevant to the infrastructure concession contracts. First, to select a set of award criteria and evaluate them effectively,

> 7 One might think that it is too much simplified to make the unemployment rate represent all socio-economic issues behind the government’ multidimensionality choice. Alternatively, the Gini coefficient may represent the degree of inequality in the economy, and the poverty headcount ratio may indicate the extent to which the governments should account for social impacts for the poor in proceeding with the PPP infrastructure transactions. However, these variables are not available for every year; thus they can only capture their country-level effect that is identical over time.

> 8 From the econometric point of view, the advantage of using this time-variant variable is that we can identify the unemployment impact separately from other country-specific characteristics, such as governance indices, or even country-specific unobservables. A disadvantage may be the government decision on whether or not to use the multidimensional rule might not be so sensitive to annual changes in unemployment rates. However, suppose that a government desires to contract out a rail freight operation when the unemployment rate is 12 percent. The government would be more concerned about the employment reduction plans of prospect private operators than the case where it could auction the transaction under the unemployment of 1-2 percent, holding other conditions constant.

17
Yoshida, Nobuo, A.S. Munoz, C.K. Lee, M. Brataj, and D. Sharma. 2015. “SWIFT Data Collection Guidelines version 2”. <u>http://documents1.worldbank.org/curated/en/591711545170814297/pdf/97499-WPP149557-OUO9-Box391480B-ACS.pdf</u> Yoshida, Nobuo, X. Chen, S. Takamatsu, K. Yoshimura, S. Malgioglio and S. Shivakumaran 2020. “The Concept and Empirical Evidence of SWIFT Methodology” (Mimeo).

World Bank. "Poverty and shared prosperity 2018: Piecing together the poverty puzzle." (2018).

World Bank. Poverty and shared prosperity 2020: Reversals of fortune. The World Bank, 2020.

28
Policy Research Working Paper 7511

# **Abstract**

Donors increasingly fund interventions to counteract inequality in developing countries, where they fear it can foment instability and undermine nation-building efforts. To succeed, aid relies on the principle of upward accountability to donors. But federalism shifts the accountability of subnational officials downward to regional and local voters. What happens when aid agencies fund anti-inequality programs in federal countries? Does federalism undermine aid? Does aid undermine federalism? Or can the political and fiscal relations that define a federal system resolve the contradiction internally? This study explores this paradox via the Promotion of Basic Services program in Ethiopia, the largest donor-financed investment program in the world. Using an original panel database comprising the universe of Ethiopian woredas (districts), the study finds that horizontal (geographic) inequality decreased substantially. Donor-financed block grants to woredas increased the availability of primary education and health care services in the bottom 20 percent of woredas. Weaker evidence from household surveys suggests that vertical inequality across wealth groups (within woredas) also declined, implying that individuals from the poorest households benefit disproportionately from increasing access to and utilization of such services. The evidence suggests that by combining strong upward accountability over public investment with extensive citizen engagement on local issues, Ethiopia’s federal system resolves the instrumental dissonance posed by aid-funded programs to combat inequality in a federation.

This paper is a product of the Social Protection and Labor Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at: qkhan@worldbank.org, J.P.faguet@lse.ac.uk, aambel@worldbank.org.

_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 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._ Produced by the Research Support Team
# **5.2 Comparison of Predicted Poverty Rates**

In this subsection, we measure whether data collected by two different interview modes show differences in predicted poverty rates. To do this, we used two different poverty lines: the official poverty line and 150,000 RSD in a 2009 price. The second line is significantly higher than the official poverty line to see the results are robust to different levels of poverty lines. We conducted the t-tests to check whether the differences of the predicted poverty rates by the two modes are statistically significant or not. All poverty rates were estimated using PovMap2 software, which follows Elbers, Lanjouw and Lanjouw’s (2003) methodology.

The predicted poverty rates using the two modes are very close and the difference is statistically insignificant. Table 8 shows the prediction of poverty rates using the data from the F2F and CATI modes in urban and rural data. With the official poverty line, the predicted poverty rates are 4.5 percent by F2F and 3.3 by CATI in urban areas and 7.4 by F2F and 8.2 by CATI in rural areas. In both cases, the differences in the predicted poverty rates by the interview modes are very small and are statistically insignificant. Therefore, we can conclude that the two survey modes do not make a significant difference in terms of the predicted poverty rates.

Indifference in prediction between the two survey modes is not due to a low level of poverty rate with the national poverty line since the difference is still small and statistically insignificant with the second higher poverty line. As shown in the table, the predicted poverty rates are 18.6 and 18.2 percent by the F2F and CATI in urban areas and 28.7 and 31.5 in rural areas, respectively. The percentage point differences between the two survey modes remain very small relative to the level of poverty rates and are statistically insignificant. Thus, the difference in poverty estimates by the F2F and CATI surveys cannot be observed even with the higher poverty rate.

To see the results are not specific to the select poverty lines, we compare poverty rates measured at different levels of poverty lines (figure 1). It is clear that poverty rates estimated from the F2F data are close to those of the CATI data for both urban and rural areas at any level of poverty line.

In summary, even if differences in some variables between the F2F and CATI surveys are statistically significant, poverty rates estimated from both datasets are very close at any level of poverty line. Therefore, we can conclude that interview modes did not cause any bias in poverty estimation based on an S2S method.

**Table 8: Imputed Poverty based on survey modes**

||Officialp<br>F2F|overtyline=<br>Phone|96,264 RSD in<br>Diff<br>(% point)|Poverty <br>F2F|line= 150,00<br>Phone|0RSD in 2009<br>Diff<br>(% point)|
|---|---|---|---|---|---|---|
|Urban|4.5|3.3|1.2|18.6|18.2|0.5|
||(1.3)|(1.3)|(1.8)|(2.1)|(2.3)|(3.1)|
|Rural|7.4|8.2|-0.8|28.7|31.5|-2.9|
||(1.6)|(1.7)|(2.3)|(2.5)|(2.7)|(3.6)|
|Pooled|6.1|6.0|0.0|24.1|25.6|-1.6|
||(1.0)|(1.1)|(1.5)|(1.6)|(1.8)|(2.4)|

Source: The authors’ estimations using PovMap 2 software.
quality‘ inventor countries, accounting for 64 percent of the world‘s total high-quality green inventions. China, in tenth place, is the only emerging economy represented among the top ten high-quality innovating countries.<sup>3</sup>

**<u>Figure 1: Increasing but Small Fraction of All Green Patents</u>**

A. Number of Green Patents Granted, Developing vs. High Income countries <!-- Start of picture text --> High Income<br>2006-2010<br>Developing 2001-2005<br>1996-2000<br>0 1000 2000 3000 4000 5000 6000<br>B. Number of Green Patents Granted, by C. Green Patents Granted as a Percentage of All<br>Developing Region Patent Grants, by Region<br>1.2%<br>60 1.0%<br>1996-2000<br>0.8%<br>45 2001-2005<br>0.6%<br>2006-2010 1998<br>30<br>0.4%<br>2004<br>15 0.2% 2010<br>0.0%<br>0<br>ECA MENA SSA LAC S_Asia EAP<br>Total USPTO grants in OECD Green Technology Areas. Ratio of 3-yr moving averages of USPTO grants in OECD<br>Source: USPTO patents granted in PATSTAT Green Technology Areas to all USPTO grants (%). Source:<br>USPTO grants in PATSTAT<br><!-- End of picture text --> Other than in China, there are few frontier green inventions in the developing world (Figure 1, Panel A).<sup>4</sup> Over the five year period spanning 2006-2010, countries in the LAC (Latin America and Caribbean), SSA (Sub-Saharan Africa) and MENA (Middle East and North Africa) regions were granted a total of 8, 6 and 3 green US patents, respectively. The EAP (East Asia and Pacific) region, and to a lesser extent S Asia (South Asia) and ECA (Europe and Central Asia) regions have a more sizable output, with 49, 17 and 13

> 3 Based on patent applications in 13 GHG-mitigation technology fields filed in 76 countries, with  ̳high-quality‘ restricted to patents filed in more than one country ( ̳claimed priorities‘ rather than  ̳singulars‘) by Dechezleprêtre et al. 2011. See the data annex for a description of these 13 technology fields, as we use the same OECD categorization in our patent analysis. Other options for measuring knowledge creation are discussed in Popp (2011), including R&D expenditures as input into the innovation process, scientific publications to measure research at earlier stages of technology development than patents, and surveys (which are particularly useful for certain process innovations). 4 Details of our estimation are given in the annex, including an explanation for why we focus on US patent grants.

4
<u>Table A4 (Cont.)</u>

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|_Local labor market variables_||||||
|Male/Female employment ratio in the municipality|-0.0157<br>[0.00370]***|-0.0142<br>[0.00249]***|-0.0138<br>[0.00252]***|-0.0154<br>[0.00377]***|-0.0143<br>[0.00243]***|
|Log of municipality GDP per capita|0.0468|0.0338|0.0411|0.0433|0.0374|
||[0.0247]*|[0.0125]**|[0.0180]**|[0.0248]*|[0.0182]*|
|Crime rate|-2.125|-2.183|-2.227|-1.627|-2.332|
||[1.776]|[1.503]|[1.560]|[1.786]|[1.533]|
|Share of male employment in primary (municipality)|0.00398|0.0213|0.0163|0.00838|0.0275|
||[0.0546]|[0.0455]|[0.0497]|[0.0559]|[0.0490]|
|Share of male employment in construction (municipality)|-0.0993|-0.0908|-0.105|-0.0964|-0.0952|
||[0.0786]|[0.0942]|[0.0982]|[0.0798]|[0.0947]|
|Share of male employment in white services (municipality)|0.0127|0.0667|0.0561|0.0168|0.0711|
||[0.0833]|[0.0890]|[0.0911]|[0.0828]|[0.0914]|
|Share of male employment in other services (municipality)|0.00105|0.0286|0.0262|0.0108|0.0193|
||[0.0635]|[0.0651]|[0.0684]|[0.0630]|[0.0640]|
|Constant|-0.815|-0.494|-0.781|-0.806|-0.608|
||[0.234]***|[0.264]*|[0.174]***|[0.234]***|[0.256]**|
|Observations|1,696,260|1,696,260|1,696,260|1,696,260|1,696,260|
|R-squared|0.256|0.255|0.255|0.256|0.255|
|Department fixed effects|Yes|No|No|Yes|No|

Sources: 2018 National Census of Guatemala, 2017 and 2018 municipality statistics (FUNDESA and INE), 2017 social spending data (ICEFI).

Notes: Women ages 25 to 49 who either have spouses that are household heads or that are household heads themselves. Robust standard errors clustered at the municipality level in columns 1 and 4 and at the department level in columns 2, 3 and 5 between brackets. *** p<0.01, ** p<0.05, * p<0.1.

35
between influence variables and employment levels<sup>48</sup> . Nonetheless, the risk factors identified in our model remain valid even if they are not perfect predictors.

Examining job losses by sector in the CMMHH using its distinct sector classification, we see a higher degree of cross-sector disparity in PFPS job losses than using the WBES categories. As shown in Table 5-10, the percentage of “PFPS” workers which were permanently laid off or suspended was highest for _manufacturing_ (39 percent), _and accommodation and food services_ (39.0), but were essentially nil in _construction and utilities_ and _ICT_ sectors

|_Table 5-9 Jordan: Model-Predicted versus S_<br>_Sector-Level PFPS Job Losses, Winter 2020_|_urvey-Measu_<br>_/ Wave 2_<br>Model-<br>predicted|_red Projections of_<br>Survey<br>Measured|
|---|---|---|
|1. Chemicals|13.2|17.3|
|2. Food, drink, and tobacco|20.8|8.0|
|3. Garments|23.8|37.4|
|4. Hotel, restaurant, and transportation|25.8|27.2|
|5. Machinery, electronics, and construction|34.9|39.2|
|6. Metals and non-metallic minerals|13.9|6.8|
|7. Wholesale, retail, and other services|26.6|29.5|
|8. Wood, paper, publishing, and printing|27.3|7.3|
|Total<br> Source: CFUWBES2. See notes to Table 5-8.|25.2|25.5|

(which are not categories in the WBES.) As shown, when individuals were asked about impacts in the past 60 days, they reported a high level of temporary or permanent layoffs relative to the total shown in column 1, confirming that job losses mounted in Jordan over the course of the pandemic. There were also significant temporary layoffs and suspensions without pay in manufacturing, ICT, and transportation and storage over that 60-day timeframe. In construction, working hours were reduced and wage payments were delayed in lieu of laying off or suspending workers. In every sector shown, a large share of PFPS workers experienced adverse employment impacts in the prior 60 days.

Although impacts were widespread, the sector- level disparity in impacts underscores the importance of assessing which sectors will experience the greatest downturns, given the specific nature of a crisis, when assessing jobs at risk. Clearly, sector-specific shocks have been key to the pattern of job losses in this crisis and are likely to remain so in future ones. Changing demand patterns, supply chain disruptions, and public health matters vary appreciably by sector. Therefore, the more detailed are the sectors captured in data sources the more accurately one can identify jobs (or workers) at risk.

# **5.4.2 Georgia**

For Georgia, as with Jordan, the projected rates of job loss vary substantially by sector. As shown in Table 5-11, our method projects that by the summer (wave 1), PFPS workers in _Garments_ had experienced the greatest job losses (33.4 percent), followed by those in _Hotel, restaurant, and transportation_ (27.4 percent of workers), and _Wood, paper, and publishing_ (26.8 percent). Workers in the chemicals sector are projected to

> 48 The mean absolute deviation / total rate of job loss is higher for wave 2.

33
**Figure 44: Selected Commodity Prices**

<!-- Start of picture text --> Aluminum<br>4000<br>3000<br>2000<br>1000<br>2000M01 2005M01 2010M01<br>Iron ore<br>200<br>100<br>0<br>2000M01 2005M01 2010M01<br>Crude Oil, West Texas Intermediate<br>150<br>100<br>50<br>0<br>2000M01 2005M01 2010M01<br>Wheat, Hard Red Winter<br>500<br>400<br>300<br>200<br>100<br>2000M01 2005M01 2010M01<br>Soybean Oil<br>1600<br>1250<br>900<br>550<br>200<br>2000M01 2005M01 2010M01<br>$/mt<br>¢/mt<br>$/bbl<br>$/mt<br>$/mt<br><!-- End of picture text --> <!-- Start of picture text --> Copper<br>10000<br>5000<br>0<br>2000M01 2005M01 2010M01<br>Barley<br>350<br>250<br>150<br>50<br>2000M01 2005M01 2010M01<br>Lead<br>400<br>300<br>200<br>100<br>0<br>2000M01 2005M01 2010M01<br>$/mt<br>$/mt<br>¢/kg<br><!-- End of picture text --> <!-- Start of picture text --> Diammonium Phosphate Fertilizer<br><!-- End of picture text --> <!-- Start of picture text --> 1500<br>1000<br>500<br>0<br>2000M01 2005M01 2010M01<br>Zinc<br>500<br>400<br>300<br>200<br>100<br>0<br>2000M01 2005M01 2010M01<br>$/mt<br>¢/kg<br><!-- End of picture text --> Source: World Bank, Prospects Group.

51
<!-- Start of picture text --> oo| Polley. Research, and External Affairs |<br>!<br>a<br>WORKING PAPERS<br>Macroeconomic Adjustment |<br>and Growth<br>Country Economics Department<br>The Worid Bank<br>March 1991<br>WPS 609<br><!-- End of picture text -->

# A Sensitivity Analysis of Cross-Cou ntry Growth Regressions

## Ross Levine and David Renelt

A vast literature uses cross-country regressions to find empirical links between policy indicators and long-run average growth rates. But conclusions from those studies are fragile if there are small changes in the independent variables.

The Policy, Research, and Extemal Affairs Complex distnbutes PRE Working Papers to disseminate the findings of work in progress and to encourage the exchange of ideas among Bank staff and all others interested in devclopment issues. These papers carry the names of the authors, reflect only their views, and should be used and ciled accordingly The findings, interpretations, and canclusions are the authors’ own, They should not he attnbuted to the World Bank, ts Board of Directors, its management, or any of its member countnes.
**A2. Performance Predictors for Public Sector Management Projects**

**Table A.7 Probit Regression Estimates with Binary IEG Outcome Ratings: PSM Projects**

|||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|---|
||Variable|IEG Outcome Rating<br>[binary]|IEG Outcome<br>Rating [binary]|IEG Outcome<br>Rating [binary]|IEG Outcome<br>Rating [binary]|IEG Outcome<br>Rating [binary]|
||DPI Share of Programmatic Parties|0.194***|0.199***|0.177**||0.132*|
|||(0.0676)|(0.0684)|(0.0699)||(0.0732)|
||Nonfree Countries [dummy]|-0.0604|-0.0560|-0.0778||-0.169*|
|||(0.0734)|(0.0739)|(0.0796)||(0.0865)|
||Free Countries [dummy]|0.0267|0.0230|-0.00857||-0.0224|
|||(0.0504)|(0.0498)|(0.0544)||(0.0537)|
||ICRG Bureaucratic Quality Rating [0-6]|0.00780|0.00685|0.00864||0.0218|
|||(0.0298)|(0.0299)|(0.0291)||(0.0294)|
|es|Net ODA Received (% of GNI)|0.00501**|0.00541***|0.00650***||0.00420|
|ariat||(0.00195)|(0.00205)|(0.00219)||(0.00264)|
|Cov|IBRD Loans and IDA Credits (DOD, % of GDP)|-0.135|-0.136|-0.0325||0.00236|
|text||(0.226)|(0.221)|(0.266)||(0.236)|
|y Con|External Debt Stocks, Public and Publicly Guaranteed (PPG)<br>(DOD, % of GDP)|-0.0548|-0.0516|-0.0857||-0.0458|
|untr||(0.0647)|(0.0675)|(0.0764)||(0.0732)|
|Co|GDP per Capita, PPP (constant 2005 international $)|5.57e-07|6.32e-07|6.64e-07||2.94e-06|
|||(1.03e-05)|(1.04e-05)|(1.14e-05)||(1.18e-05)|
||GDP per Capita Growth (annual %)|0.00687|0.00665|0.00615||0.00660|
|||(0.00447)|(0.00463)|(0.00504)||(0.00569)|
||Inflation, Consumer Prices (annual %)|-0.000237|-0.000240|-0.000196||-0.000354|
|||(0.000189)|(0.000191)|(0.000158)||(0.000498)|
||School Enrollment, Secondary (% gross)|0.000815|0.000649|-0.000179||-0.000981|
|||(0.00127)|(0.00122)|(0.00141)||(0.00129)|
||Life Expectancy at Birth, Total (years)|-0.0120*|-0.0120*|-0.0101||-0.00518|
|||(0.00691)|(0.00706)|(0.00739)||(0.00690)|
|tes|Administrative and Civil Service Reform||-0.00188|-0.00232*||-0.00238*|
|varia|||(0.00129)|(0.00125)||(0.00125)|
|Co|Decentralization||0.000832|6.61e-05||0.000346|
|tent|||(0.00192)|(0.00173)||(0.00181)|
|Con|Public Expenditure, Financial Management, and Procurement||-0.00187|-0.00205||-0.00190|
|form|||(0.00146)|(0.00147)||(0.00159)|
|Re|Tax Policy and Administration||0.000579|0.000822||0.00593|
||||(0.00270)|(0.00253)||(0.00388)|
|es<br>age|Committed Amount|||1.77e-05|0.000168|6.97e-05|
|ariat<br>ss St||||(0.000170)|(0.000198)|(0.000186)|
|Cov<br>vene|Lending (~preparation) Costs Total ($ thousand)|||7.25e-05|7.66e-06|5.74e-05|
|ment<br>ecti||||(9.61e-05)|(7.63e-05)|(0.000103)|
|age<br>t Eff|Days from PCN Review to Approval|||-4.88e-05|-3.40e-05|-5.26e-05|
|Man<br>ble a||||(3.76e-05)|(3.17e-05)|(3.70e-05)|
|oject<br>serva|Days from Approval to Effectiveness|||-0.000227|-0.000276**|0.000241|
|Pr<br>Ob||||(0.000169)|(0.000126)|(0.000197)|

48