rafmacalaba/datause-encoder-data huggingface dataset:
Table D.2: Key indicators by group

||||Revenue ratio (%)|
|---|---|---|---|
|#|Code|Region|2000-2008<br>2009-2016|
|1|EG|Established Graduates|24.63<br>27.16|
|2|RG|Recent Graduates|27.14<br>28.93|
|3|BS|Back to School|28.30<br>26.08|
|4|SS|Still in School|25.07<br>25.73|
||||Tax (%)|
||||2000-2008<br>2009-2016|
|1|EG|Established Graduates|11.53<br>13.04|
|2|RG|Recent Graduates|18.18<br>20.15|
|3|BS|Back to School|8.25<br>8.85|
|4|SS|Still inSchool|12.79<br>14.50|
||||Credit ratio (%)|
||||2000-2008<br>2009-2016|
|1|EG|Established Graduates|44.15<br>60.93|
|2|RG|Recent Graduates|33.28<br>45.02|
|3|BS|Back to School|39.59<br>56.29|
|4|SS|Still inSchool|22.38<br>31.58|
||||Political corruption (0-1)|
||||2000-2008<br>2009-2016|
|1|EG|Established Graduates|0.44<br>0.47|
|2|RG|Recent Graduates|0.47<br>0.54|
|3|BS|Back to School|0.38<br>0.38|
|4|SS|Still inSchool|0.35<br>0.37|
||||Public service corruption (0-1)|
||||2000-2008<br>2009-2016|
|1|EG|Established Graduates|0.42<br>0.45|
|2|RG|Recent Graduates|0.49<br>0.55|
|3|BS|Back to School|0.36<br>0.36|
|4|SS|Still inSchool|0.37<br>0.38|
||||Law and order (0-6)|
||||2000-2008<br>2009-2016|
|1|EG|Established Graduates|3.72<br>3.54|
|2|RG|Recent Graduates|3.35<br>3.20|
|3|BS|Back to School|3.48<br>3.48|
|4|SS|Still in School|3.19<br>3.06|
||||Resource distribution (0-1)|
||||2000-2008<br>2009-2016|
|1|EG|Established Graduates|0.54<br>0.55|
|2|RG|Recent Graduates|0.68<br>0.71|
|3|BS|Back to School|0.53<br>0.52|
|4|SS|Still in School|0.57<br>0.58|
||||Ethnic and religious stability (0-6)|
||||2000-2008<br>2009-2016|
|1|EG|Established Graduates|4.44<br>4.44|
|2|RG|Recent Graduates|4.31<br>4.32|
|3|BS|Back to School|3.55<br>3.66|
|4|SS|Still inSchool|4.05<br>4.01|

Notes: Average levels are computed for the subperiods 2000-2008; 2009-2016.

50
Table 3 Pearson correlation matrix for key COVID-19 variables

|||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|(13)|(14)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|(1)|Survivali, t|1||||||||||||||
|(2)|Sales growthi, t|0.389*|1|||||||||||||
|(3)|Output demand stablei, t|0.275*|0.630*|1||||||||||||
|(4)|Input supply stablei, t|0.284*|0.555*|0.668*|1|||||||||||
|(5)|Liquidity stablei, t|0.309*|0.481*|0.524*|0.441*|1||||||||||
|(6)|New productsi, t|-0.018|0.071*|0.057*|0.019|0.027*|1|||||||||
|(7)|Non-smalli, t-1|0.127*|0.151*|0.100*|0.074*|0.112*|0.073*|1||||||||
|(8)|Log Agei, t-1|0.079*|0.089*|0.039*|0.039*|0.042*|-0.009|0.182*|1|||||||
|(9)|Foreigni, t-1|0.024*|0.021|0.020|0.014|0.027*|0.041*|0.180*|-0.0001|1||||||
|(10)|SOEsi, t-1|0.052*|0.049*|0.048*|0.032*|0.047*|0.009|0.087*|0.094*|0.024*|1|||||
|(11)|Subsidiaryi, t-1|0.019|0.017|0.013|-0.005|0.018|0.057*|0.132*|0.101*|0.158*|0.045*|1||||
|(12)|Resource scorei, t-1|0.106*|0.118*|0.083*|0.054*|0.096*|0.088*|0.770*|0.174*|0.568*|0.238*|0.593*|1|||
|(13)|Log Experiencei, t-1|0.082*|0.070*|0.033*|0.056*|0.033*|-0.028*|0.089*|0.497*|-0.043*|-0.047*|0.048*|0.054*|1||
|(14)|Log GDP perc, t-1|0.225*|0.299*|0.174*|0.225*|0.202*|-0.079*|0.054*|0.066*|-0.098*|-0.043*|-0.087*|-0.048*|0.129*|1|
|(15)|Policy stringencyc, t|-0.185*|-0.058*|-0.053*|-0.074*|-0.021|-0.038*|-0.039*|-0.069*|-0.053*|-0.167*|-0.012|-0.076*|-0.051*|0.216*|

Note: * represents statistical significance at the 1 percent levels. The observations of each variable vary.

27
# APPENDIX REFERENCES

Barrett, Christopher B. (1998) “Food Aid: Is It Development Assistance, Trade Promotion, Both, or Neither?” _American Journal of Agricultural Economics_ , 80(3): 566-571.

Barrett, Christopher B. and Kevin C. Heisey (2002). “How Effectively Does Multilateral Food Aid Respond to Fluctuating Needs?” _Food Policy._ 27(5-6): 477-491.

Barrett, Christopher B. and Daniel G. Maxwell (2005). _Food Aid After Fifty Years: Recasting Its Role._ New York: Routledge.

Barkley, Andrew, Jesse Tack, Lawton Lanier Nalley, Jasong Bergtold, Robert Bowdens, Allan Fritz (2013). “The Impact of Climate, Disease, and Wheat Breeding on Wheat Variety Yields in Kansas, 1985–2011.” Kansas State University Agricultural Experiment Station and Cooperative Extension Service. Bulletin 665.

- Blattman, Christopher and Edward Miguel (2010). “Civil War.” _Journal of Economic Literature._ 41(1): 3-57.

- Cashin, Paul, Kamiar Mohaddes, Maziar Raissi, and Mehdi Raissi (2014). “The Differential Effects of Oil Demand and Supply Shocks on the Global Economy.” _Energy Economics_ , 44: 113-134.

- Hsiang, Solomon M., Marshall Burke, Edward Miguel (2013). “Quantifying the Influence of Climate on Human Conflict” _Science_ , 341, 1235367 (2013).DOI: 10.1126/science.1235367

- Hsiang, Solomon M. , Kyle C. Meng & Mark A. Cane (2011). “Civil conflicts are associated with the global climate” _Nature: Letter._ 476(25): 438-440.

- Iizumi Toshichika, Jing-Jia Luo, Andrew J. Challinor, Gen Sakurai, Masayuki Yokozawa, Hirofumi Sakuma, Molly E. Brown, and Toshio Yamagata (2014). “Impacts of El Niño Southern Oscillation on the Global Yields of Major Crops.” _Nature Communications_ .5(3712).

- International Federation of the Phonographic Industry. “Recording Industry in Numbers.” Accessed 26 Aug 2015. https://musicbusinessresearch.wordpress.com/2010/03/29/therecession-in-the-music-industry-a-cause-analysis/.

80
Table D.6. Impacts of PEP Program on Mayoral and Presidential Elections Migration Test

||Election|Sha|re of Vote|s for|Electoral|
|---|---|---|---|---|---|
||Turnout<br>(1)|Left<br>(2)|Center<br>(3)|Right<br>(4)|Competition<br>(5)|
|_Panel A. Mayoral Elections_||||||
|EarlySettlements1993m_×_I(Post2018)t|0.003|0.016|-0.010|-0.012|-0.000|
||(0.002)|(0.011)|(0.015)|(0.012)|(0.008)|
|q-values|[0.557]|[0.557]|[0.673]|[0.557]|[1.00]|
|R-squared|0.861|0.461|0.368|0.430|0.392|
|Observations|4,954|4,954|4,954|4,954|4,815|
|_Panel B. Presidential Elections_||||||
|EarlySettlements1993m_×_I(Post2018)t|-0.005|0.003|-0.006|0.003|0.004|
||(0.002)|(0.004)|(0.005)|(0.005)|(0.007)|
|q-values|[0.227]|[0.863]|[0.780]|[0.863]|[0.863]|
|R-squared|0.894|0.902|0.860|0.876|0.691|
|Observations|5,177|5,177|5,177|5,177|5,173|
|Municipality FE|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|
|Department FE_×_Year|Yes|Yes|Yes|Yes|Yes|
|Municipal Controls_×_Year|Yes|Yes|Yes|Yes|Yes|

_Notes:_ Early Settlements is the geographical variation from Rozo and Vargas (2021), defined as the ratio of Venezuelan migrants by municipality over total foreigners in Colombia in 1993 (before the onset of the Venezuelan crisis). Electoral competition was estimated following Chac ́on et al. (2006):1 - (% winning candidate - % second-place candidate). The estimates include municipality, electoral year, and department electoral-year fixed effects, and they control for the interaction between electoral year dummies and a set of predetermined municipal characteristics, measured before the beginning of our period and listed in Table C.2. Clustered standard errors at the municipal level are reported in parentheses and False Discovery Rate (FDR) q-value in brackets *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.

79
WPS3924

**What are Public Services Worth, and to Whom?**<sup>∗</sup> _Non-parametric Estimation of Capitalization in Pune_

Somik V. Lall _World Bank and NIPFP_ Mattias Lundberg _World Bank_ _Abstract_ : The availability and quality of basic public services are important determinants of urban quality of life. In many cities, rapid population growth and fiscal constraints are limiting the extent to which urban governments can keep up with increasing demand for these services. It therefore becomes important to prioritize provision of those services to best reflect local demand. We present a strategy to estimate the demand for public services, which is sensitive to heterogeneity in preferences across types of households, and the nonparametric estimation addresses problems arising from functional form restrictions. Using data from Pune, India, we estimate the demand for public services, as represented by the marginal change in the self-assessed monthly rental price of dwellings from the services. We find that the value of publicly provided services accruing to the poor is greater than that going to wealthier households, and even untargeted across-theboard investment in specific services can be progressive.

# **JEL: H40; R21**

World Bank Policy Research Working Paper 3924, May 2006 _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._

> ∗ This paper is part of a larger effort to understand the contribution of urban public services to household welfare and overall quality of life. The research and data collection has been co-funded by a World Bank research grant on “Strengthening Fiscal Capacity of Indian Cities” and the UK DFID’s Urban Knowledge Generation and Toolkits program. The authors can be contacted at <u>slall1@worldbank.org</u> (Lall) and <u>mlundberg@worldbank.org</u> (Lundberg).

1
# **1. Introduction**

Improving maternal and child health is integral to the Millennium Development Goals (MDGs). In particular, Goals 4 and 5 call for, respectively, reduction in child and maternal mortality for the period between 1990 and 2015. Goal 4 targeted a reduction of under-five mortality by two-thirds, while Goal 5 set a 75 percent reduction in maternal mortality. The goals received national and global attention over the last decade and a half; countries and their international development partners mobilized support to expand childhood immunization and increase availability and utilization of maternal health services.

By the target date of 2015, different countries are at different stages in terms of achieving the MDG targets. Country level tracking of the MDGs shows that Ethiopia is one of those with satisfactory progress towards achieving the targets. A recent UN report shows that all the MDGs were either on track or likely to be on track.<sup>1</sup> A similar tracking by the Center for Global Development (CGDEV) reported satisfactory progress on all goals. The CGDEV tracking in 2011 ranks Ethiopia 33 of 137 countries, with an MDG progress index of 4.5 (on a scale of zero to 8 points).<sup>2</sup> The country’s progress in the health sector is also documented in other recent surveys. For example, the 2014 mini-Demographic and Health Survey (DHS) shows reductions in child undernutrition

> 1 A UN tracking for Ethiopia shows that the country is on track on goals 1 and 4 and likely to be on track on goal 5.http://www.et.one.un.org/index.php?option=com_content&view=article&id=14&Itemid=488

> 2 MDG Progress Index: Gauging Country-Level Achievements, Center for Global Development (CGDEV) http://www.cgdev.org/page/mdg-progress-index-gauging-country-level-achievements accessed on 1/31/15.

2
coefficient, when calculated endogenously in the household survey, is also expected to decrease as the level of development of the country increases.

Figure 4 shows the _normative_ Engel coefficient of the most up-to-date urban poverty lines in LAC (scatter plot). All specifications of the poverty lines in the first four rows of Table 3 present an Engel coefficient of approximately 0.47, which is significantly lower than the _normative_ Engel coefficient of 0.62 determined by the currently-used poverty lines in LAC at the 2005 PPP values (i.e., $2.5 and $4 per person per day). There are two possible explanations for this result. On the one hand, it is conceivable that the currently-used poverty lines in 2005 PPP values might be overestimating extreme poverty and/or underestimating moderate poverty. In other words, the extreme and moderate poverty lines are relatively close to each other – in contrast to the empirical findings of this paper using more up-to-date poverty lines – which seems to be biasing the estimations of poverty. On the other hand, the difference in _normative_ Engel coefficients between the two sets of lines could be a reflection of the increasing development of the LAC region during the last decade (World Bank 2014b and World Bank 2015), which is manifested in the ratios of extreme and moderate poverty lines.

- _4.2 Regional poverty estimates in LAC in 2011 PPPs and comparison with currently-used poverty rates in 2005 PPPs_

The primary objective of this paper is to provide inputs for updating the currently-used extreme and moderate poverty lines in order to estimate the regional poverty rate in LAC. Figure 5 presents the sensitivity of extreme and moderate poverty headcounts to updates in the poverty lines and PPPs values. Using the set of extreme poverty lines shown in the first four rows of Table 3 (between $2.5 and $3.2 per person per day), the extreme poverty rate would have ranged from 7.7 percent to 11.9 percent in 2013. Similarly, using the moderate poverty lines (between $5 and $6.8 per person per day), the moderate poverty rate would have varied from 24.2 percent to 35.9 percent in the same year.

The figure also shows an alternative approach that could be used for updating the regional poverty line: the “continuous headcount approach” (Jolliffe and Prydz 2015). This method consists on finding the value of the regional poverty line in 2011 PPPs that results in the same regional poverty rate as the one estimated using the 2005 PPPs. According to this alternative approach, in order to maintain the same poverty rate with the new extreme poverty line as the one obtained with the previous extreme poverty line of $2.5 per person per day (i.e., 11.5 percent), the new extreme 14
- **Figure 1: Experience Union Wage Gap Profile**

<!-- Start of picture text --> Estimated Union Wage Gap<br>0.26<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>2 46 6 10 1 1262022242628303234363840<br>Years of Experience<br><!-- End of picture text --> Source: Estimated from results in Table 3.

The coefficients for gender and ethnicity reveal a significant difference between the union and nonunion sectors with regards to gender and ethnic wage differentials. In the nonunion sector, men enjoy a 17.5 percent wage advantage over women, whereas in the union sector the coefficient for gender is nonsignificant, highly suggestive of greater gender equality in the union sector than in the nonunion sector. Similarly, wage equality for indigenous people seems to be potentially greater in the union sector. The coefficient of the _municipio_ indigenous percent variable in the nonunion sector equation reveals a -C.8 percent earning disadvantage for each percent of indigenous population within a _mwnicipio,_ whereas the same coefficient in the union sector equation is nonsignificant. Variations in the wage differential due to gender and indigenous probability are examined more closely in latter sections.

15
Exploring Geospatial-Based Approaches to Develop a Pre-Census National Sampling Frame in Armenia Saida Ismailakhunova Avralt-Od Purevjav Tsenguunjav Byambasuren Sarchil Qader<sup>1</sup> _JEL Classification_ : C55, C83, C80 _Keywords_ : Sampling frame, Enumeration areas, Census, Geospatial, Household surveys, Developing countries

> 1 Ismailakhunova: Poverty and Equity Global Practice, The World Bank, Tbilisi, Georgia (email: <u>sismailakhunova@worldbank.org); Purevjav: Poverty and Equity Global Practice, The World Bank, Washington, DC 20433,</u> United States (email: apurevjav@worldbank.org); Byambasuren: Dyson School of Applied Economics and Management, Cornell University, 438 Warren Hall, Ithaca, NY 14853, United States & Poverty and Equity Global Practice, The World Bank, Washington, DC 20433, United States (email: <u>tb497@cornell.edu); Qader: WorldPop, University of Southampton, University</u> Road, Southampton SO17 1BJ, United Kingdom (email: s.qader@soton.ac.uk). Acknowledgments: We thank Obert Pimhidzai, Ambar Narayan, Carolin Geginat, Juan Muñoz, Walker Kosmidou-Bradley, Narine Tadevosyan, Hans Peterson, and the UNICEF Armenia team for helpful feedback and comments. We are also grateful to CRRC-Armenia for sharing the sampling frame based on the 2011 census settlements and the 2023 electoral precincts.
<!-- Start of picture text --> Response to Cholesky One S.D. (d.f. adjusted) Innovations + 2 S.E.<br>Response of z to D(ffr) Response of D(tbeugdp) to D(ffr) Response of D(tbusagdp) to D(ffr)<br>010 sy \<br>\ \ 003 \<br>.008006 \\\ 002 \ \N .002 \\\<br>00d ae 001 — 001 a<br>000 ad -.001 \ /<br>~002 —_lennnneeen"onL ad ~ NYoo -,002 \\ /<br>eee -001 SY Vv<br>12 3 4 5 6 7 8 9 10 11 12 12 3 4 5 6 7 8 9 10 11 12 12 3 4 5 6 7 8 9 10 11 12<br>Response of D(bmgdp) to D(ffr) Response of D(domint) to D(ffr) Response of gygapto D(ffr)<br>1 A 0012 \ “ —<br>SS \ 005“ a<br>ws 0008 \. ae<br>.00 (Wott revna anna nec eennnnnnn \ to<br>01 Ooooi“ ——_0004‘0000 ONnn 005‘0 — au a<br>-02 f -.0004 “ -010 TM a“<br>12 3 4 5 6 7 8 9 10 11 12 12 3 4 5 6 7 8 9 10 11 12 12 3 4 5 6 7 8 9 10 11 12<br>Response of infl to D(ffr)<br>.0020 \\<br>0016<br>O02 \ON<br>.0008 a<br>0004 Tee<br>0000 —_——<br>0004,\ cpeneeeenenneeeeneee 7.<br>-0008 \<br>12 3 4 5 6 7 8 9 10 11 12<br><!-- End of picture text -->
municipalities had electricity revenue of less that R1 million per annum.<sup>21</sup> It is generally accepted that electric power distribution is characterized by significant economies of scale. Thus, most of these municipal distributors suffer from substantial average cost disadvantages due to their small scale of operation. Indeed, the 1998 White Paper reported that average distribution costs ranged from 23.9 cents per kWh for distributors of less than 1 GWh in annual sales to only 13.4 cents per kWh for distributors of more than 1000 GWh—a 46 percent average cost differential largely reflecting basic economies of scale that were simply lost.<sup>22</sup> The bulk of the electricity in South Africa is consumed by the industrial sector which in 2003 accounted for almost 40 percent of total electricity consumption (figure 1). The next two largest consumers, mining and residential, accounted for 17.9 and 17.6 percent of total electricity sales ((MWh) respectively and have been showing the greatest growth in demand in recent years. The industrial and mining sectors are even more important for Eskom, having accounted in 2003 for over 48 and 30 percent of the utility’s electricity sales respectively, while the domestic and commercial customers accounted for just over 7 and 6 percent of its sales, respectively.

> 21 Davidson and Mwakasonda (2003).  “Southern Africa Sub-regional Study: South Africa and Zimbabwe.” Energy & Development Research Centre, University of Cape Town.

> 22 http://www.dme.gov.za/publications/wp_ene/whitepaper1998.htm

14
include year effects interacted with regency-level base-year controls that might be related to industrial structure. Specifically, we control for labor market size differences with logpopulation. As a measure of overall economic development we include log-real output per capita and the share of population with tertiary education. To capture productivity differences, we include log-real manufacturing output per worker. Finally, as an inverse proxy of employment opportunities in non-manufacturing industries, we include the share of manufacturing output.<sup>30</sup> We cluster errors at the regency-level to match the variation of the Bartik instrument.

It is worth noting that the regency-level Bartik design departs from the assumption of product market-specific labor supply underlying our empirical and theoretical approaches used so far. Instead, the assumption here is that labor supply is homogeneous across product markets and hence firms tap into the same labor market—the entire commuting zone _m_ that we identify with a regency—to employ workers. Under this assumption, regulation in product market _i_ would create local spillovers through affecting labor market power—and thus the wage markdown—of _all_ firms in _m_ . Such a spillover effect works through the impact on total labor market demand and the unique equilibrium wage, as shown in Online Appendix E.

# **6 Results**

The results of estimating Equation (6) are presented in column 1 of Table 1. Imposing entry barriers in a product market increases the wage markdown in that market by 25%. The effect is statistically significant at the 95 percent confidence level. The estimated impact is economically relevant. Given the employment-weighted sample average of _nil_ (0.186), the coefficient implies that markdowns are 4.6% higher due to barriers to entry across manufacturing products. This result is consistent with the framework laid out in Section 2 and fits the predictions of Equation (5).

> veloped regencies might be more densely populated by specific industries and have better transportation infrastructures, which would result in low labor supply elasticity and lower markdowns.

> 30 Regency-level data comes from the national statistical institute BPS in a dataset assembled by the World Bank.

19
25

**Table 5. OLS regressions on impact of migrant exchange rate shocks in the Philippines, 1997-1998**

||_No controls_<br>_With controls for region*_<br>_urban and pre-crisis_<br>_household and migrant_<br>_characteristics_<br>(1)<br>(2)|
|---|---|
|**_Panel A. Household expenditure _**|_(household-level regressions)_|
|Total household expenditures||
|Food expenditures|-0.01<br>-0.007|
||(0.036)<br>(0.034)|
|Non-food expenditures|-0.032<br>-0.041|
||(0.057)<br>(0.063)|
|**_Panel B. Household educational _**|**_expenditures_** _(household-level regressions)_|
|Education expenditures||
|(as fraction of initial|0.016<br>0.026|
|householdincome)|(0.010)<br>(0.013)**|
|**_Panel C. Labor supply of children_**|**_aged 10-17_**_(individual-level regressions)_<br>Females<br>Males|

||Fe|males||Males|
|---|---|---|---|---|
||No controls<br>(1)|With controls<br>for region*<br>urban and pre-<br>crisis household<br>and migrant<br>characteristics<br>(2)|No<br>controls<br>(3)|With controls<br>for region*<br>urban and pre-<br>crisis household<br>and migrant<br>characteristics<br>(4)|
|Total hours|-2.753|-2.14|-1.448|-3.234|
|worked|(2.044)|(2.246)|(1.711)|(1.411)**|
|Hours worked:|||||
|For employer|-1.276|-0.547|-0.52|-0.268|
|outside household|(1.392)|(2.023)|(0.978)|(1.411)|
|As worker without<br>pay in family-|-1.693|-1.837|-2.786|-4.942|
|operated farm or<br>business|(0.793)**|(0.936)*|(1.297)**|(1.533)***|

Notes: Each cell of table presents coefficient estimate on exchange rate shock. Standard errors in parentheses. Number of observations for household-level regressions is 1646; number of observations for individual-level regressions is 579 (females) and 609 (males).

*  Significant at the 0.10 level

- **  Significant at the 0.05 level

***  Significant at the 0.01 level

Source: Yang (2005: Table 4).