rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 1**

# **Synthetic infrastructure indices: correlation with underlying measures**

_Panel correlations over the period 1960-2005 (5-year non-overlapping observations)_

## **1.1 Synthetic quantity index**

||SyntheticQua<br>|ntityIndex<br>|
|---|---|---|
|Indicator|[IK1]|[IK2]|
|Total telephone lines (main lines and mobile phones)|0.935|..|
|_(per 100 workers)_|(0.000)||
|Main telephone lines|..|0.943|
|_(per 100 workers)_||(0.000)|
|Electricity generating capacity|0.950|0.956|
|_(in MW per 1000 workers)_|(0.000)|(0.000)|
|Total road length|0.644|0.644|
|_(in km. per sq. km. of arable land)_|(0.000)|(0.000)|

## **1.2 Synthetic quality index**

||SyntheticQua|lityIndex|
|---|---|---|
|Indicator|[IQ]|[IQ2]|
|Quality of telecommunication services|0.813|..|
|_(based on waiting time for main line installation)_|(0.000)||
|Quality of electricity|0.746|0.839|
|_(based on technical losses of transmission and distrib.)_|(0.000)|(0.000)|
|Quality of roads|0.754|0.810|
|_(Share of paved in total roads)_|(0.000)|(0.000)|

Note: The numbers in parenthesis under the correlation coefficients are the corresponding p- values. The first synthetic index of infrastructure quantity [IK1] is given by the formula: IK1 = 0.603*K1 + 0.613*K2 + 0.51 * K3, where K1, K2, and K3 denote the logs of (standardized) physical measures of infrastructure in telecommunications (main lines and mobile phones), electricity (electricity generating capacity), and roads (total road length). The measures of telecommunications and electricity are normalized by working population of the country, while roads are normalized by the area of arable land. The second synthetic index of infrastructure quantity [IK2] is obtained as follows: IK2 = 0.606*K1A + 0.614*K2 + 0.506 * K3. The synthetic index IK2 uses the number of main phone lines per 100 workers (K1A) instead of the total number of phone lines (K1). The first synthetic index of infrastructure quality [IQ1] is obtained as follows: IQ1 = 0.608*Q1 + 0.559*Q2 + 0.564 * Q3, where Q1, Q2 and Q3 are (standardized) physical measures of quality in telecommunications (waiting time in years for the installation of main telephone lines), power (the percentage of transmission and distribution losses in the production of electricity) and roads (the share of paved roads in total roads). The second synthetic index of infrastructure quality [IQ2] omits Q1 from the set of underlying indicators, and is given by IQ2 = 0.7*Q2 +0.71* Q3.

36
# Do Public Health Interventions Crowd Out Private Health Investments? Malaria Control Policies in Eritrea<sup>_⇤_</sup>

Pedro Carneiro Alex Armand Andrea Locatelli Selam Mihreteab Joseph Keating

## **JEL codes:** D12, D83, H42, I12.

## **Keywords:** Malaria, Bed nets, Indoor Residual Spray, Information, Beliefs, Behavior.

> _⇤_ Carneiro: University College London and Institute for Fiscal Studies, CeMMAP. Armand: University of Navarra. Locatelli: Economic Research Unit, Bank of Italy. Mihreteab: National Malaria Control Program, Ministry of Health, Eritrea. Keating: Tulane University School of Public Health and Tropical Medicine. This research collaboration was initiated under Development Impact Evaluation’s (DIME’s) Malaria Impact Evaluation Program. We would like to thank especially Arianna Legovini and Edit V. Velenyi for their role in client engagement, research design, and securing funding for the study. We would also like to thank officials at the Eritrean Ministry of Health, the National Malaria Control Program and the data collection team at the Orotta School of Medicine in Asmara. Without them this research would not have been possible. This research was funded by a World Bank grant (as part of the HAMSET II project) through the Project Management Unit of the Eritrea Ministry of Health and a World Bank grant from DIME. We thank Achyuta Adhvaryu, Noel Chisaka, Jean-Paul Clark, Pascaline Dupas, Costas Meghir, Petra Todd and seminar participants at UCL, IFS, the AEL Conference 2011, the 10th Arnoldshain Seminar, DIME-World Bank, PSE, Uppsala, NOVAfrica, Centre for Global Development, and NEUDC Conference 2011 for helpful comments. Carneiro gratefully acknowledges the financial support from the World Bank, the Economic and Social Research Council for the ESRC Centre for Microdata Methods and Practice (grant reference RES589-28-0001), the support of the European Research Council through ERC-2009-StG-240910-ROMETA and ERC-2009-AdG-249612, and the hospitality of the World Bank Research Group.
||Savings<br>Instruments|increase resource<br>control for<br>women-led micro<br>businesses;<br>positive effects<br>are also seen<br>when combined<br>with training|et al. 2022 (RCT),<br>Campos et al. 2023<br>(RCT), Batista et al.<br>2022 (RCT), Buvinic<br>et al. 2020 (RCT),<br>Field et al. 2021<br>(RCT), Somville and<br>Vandewille 2018<br>(RCT)|that can increase<br>control over<br>resources. More<br>evidence is needed<br>for products<br>targeting larger<br>women-led firms.|
|---|---|---|---|---|
||Insurance|A lack of causal<br>estimates is noted|Battaglia et al. 2023<br>(RCT)|More evidence is<br>needed on<br>insurance products<br>tailored for women-<br>led businesses, and<br>on the positive<br>effects of loan<br>repayment<br>flexibility, which can<br>function as<br>insurance.|
||Reducing gender<br>biases among<br>financial<br>intermediaries|There is a need<br>for more causal<br>estimates|Alibhai et al. 2019c<br>(loan application<br>experiment), Brock<br>and de Haas 2023<br>(lab-in-the-field<br>experiment)|More evidence is<br>needed on the<br>effect of reducing<br>agents’ bias; the<br>literature provides<br>evidence that bias<br>does exist and<br>decreases with<br>experience.|
||Encouraging<br>women to enter<br>male-dominated<br>sectors|There is a need<br>for more causal<br>estimates|World Bank 2022<br>(non-causal),<br>Goldstein et al.<br>2019 (non-causal),<br>Gassier et al. 2022<br>(RCT)|Emerging evidence<br>suggests that<br>interventions can<br>encourage women<br>to enter male-<br>dominated sectors<br>(with only one RCT),<br>but there is a lack of<br>evidence on the<br>effects onprofits.|
|Access to Markets<br>and Technology|Fostering trade<br>readiness and<br>access to new<br>markets|There is a need<br>for more causal<br>estimates|Hardy and Kagy<br>(RCT), World Bank<br>and World Trade<br>Organization 2020<br>(review),<br>Verhoogen 2023<br>(review)|More evidence is<br>needed on market<br>expansion and<br>trade readiness<br>interventions; there<br>is only one RCT<br>focusing on a<br>specificproduct.|
||Increasing access<br>to procurement|A lack of causal<br>estimates is noted|Hjort et al. 2020<br>(RCT), IFC 2023<br>(review)|More evidence is<br>needed; there is<br>only one non-<br>gendered RCT on<br>trainingon how to|

33
# 6. **Conclusion**

Our review of the recent literature reveals that there are some indications regarding a positive relation between democracy and equality. This conclusion emerges both when democracy is measured by the extent of the voting franchise on the basis of specific case studies and also in cross country regressions with democracy proxied by an index based on measures of civil liberties and political rights. As such it provides empirical support for the existing theories of income redistribution A la Meltzer and Richard, 1981, as well as others.

An important caveat to the positive relationship is exemplified by the transition experience of post communist countries in East Europe. While the increase in inequality that took place despite continuous democratization may serve as a warning signal against broad generalizations and premature conclusions, it could also trigger additional theoretical as well as empirical research to explain this puzzle.

Note, however, that none of the recent studies has used the length of democratic experience as an explanatory variable, which further confounds the comparison with some of earlier findings. This, of course, leaves open the question of whether the increase in inequality in post communist countries is really a puzzle that begs an explanation, or perhaps just a transitory phenomenon, that will vanish as democracies there stabilize.

While recent research has addressed some of the methodological problems related to the measurement of inequality and democracy, more work is needed to improve the available databases in order to allow for better comparability across countries. In the light of the findings regarding the importance of the length of democratic experience, an analysis of longitudinal data may prove important in future research. Compilation of reliable panel data may enhance our understanding of the 29
# **1. Introduction**

There has been a remarkable growth in small and medium towns in China over the past two decades. Traditionally, scholars have long characterized the pre-reform rural communities as essentially self-sufficient, inward-looking, and isolated. Their economic structure was described as “cellular” and “fragmented” (Skinner 1964, 1985) based almost exclusively on farming, with towns playing only a supporting role for agricultural production.<sup>1</sup> In the modern times, these more dispersed centers are still critical in helping develop and modernize agriculture by providing both consumer and producer services from banking to marketing, and basic services such as health services and educational facilities (Johnson 1970).

The market reforms in the late 1970s and early 1980s provided changes in institutional environment conductive to the growth of towns. Some changes such as increasing foreign and domestic investment (Chen et al., 1995; Fei 1986; Wei et al., 1999), regional development strategy in favor of more developed coastal region, and massive rural-urban migration (Ma 1996; Wei 1997) have great impact on the growth of town and village enterprises (TVEs), which propelled the town development.

Town development in China represents a large economic and social agenda. By the year of 2000, there were about 20,000 small towns, with a population of 166 million (NBSC 2002). It plays an important role in absorbing excessive rural labor force, expanding rural markets, raising rural incomes, and facilitating economic growth in urban centers.

There has been a concern that the growth of towns has been stalled recently and with it the creation of non-farm jobs in rural industries, although there is little agreement why and how. Likely explanations include financial and institutional barriers, product cycles, lower stock of human capital, labor market barriers, and the lack of agglomeration economies. These issues have further been compounded with recent problems in environment and land, as well as regional income disparity.

This paper explores the issue of town development in China by examining town in-migration in three provinces, Zhejiang, Sichuan, and Henan. Specifically, we center around three issues: current status of town in-migration, human capital stock of inmigrants, and labor market barriers. Three provinces represent different macroeconomic conditions in China: Zhejiang is located in the east coast, Henan in the central region, Sichuan in the western region, with Sichuan and Henan legging economically far beyond Zhejiang.

We mainly rely on the three volumes of aggregated statistics from tabulation of 2000 population census data released, supplemented by electronic version of detained tabulations for towns released by the National Bureau of Statistics recently (NBSC 2002). Both short form and long form (9.5% of total population) asked the questions about migration. In the published tables, in-migration into towns is cross-tabulated by some

> 1 For a historical treatment of this issue, see Huang 1990.

2
**Table 9: Land Rights, Decision-Making and Permissions Variables by Cluster (Tanzania)**

||Male L<br>|andowners by<br>|Cluster<br>|Female L<br>|andowners b<br>|y Cluster<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(6)|(7)|(8)|
||Mostly Not<br>Hold<br>(base)|Mostly<br>Joint<br>Rights|Mostly<br>Exclusive<br>Rights|Mostly Not<br>Hold<br>(base)|Mostly<br>Joint<br>Rights|Mostly<br>Exclusive<br>Rights|
|Document Owner|0.01|0.36**|0.10***|0.01|0.08*|0.06|
||(0.10)|(0.48)|(0.30)|(0.12)|(0.28)|(0.25)|
|Plot Decision Making|0.77|0.95***|0.99***|0.82|0.96**|0.98**|
||(0.42)|(0.22)|(0.11)|(0.38)|(0.19)|(0.14)|
|Economic Owner|0.27|0.89***|1.00***|0.51|0.95***|0.99***|
||(0.45)|(0.32)|(0.03)|(0.50)|(0.21)|(0.09)|
|Right to Improve|0.68|1.00***|0.96***|0.73|1.00***|1.00***|
||(0.47)|(0.00)|(0.20)|(0.44)|(0.00)|(0.00)|
|Tenure Security|0.46|0.73|0.87***|0.72|0.9**|0.92***|
||(0.50)|(0.45)|(0.34)|(0.45)|(0.31)|(0.27)|
|Need permission for at least 1 right#||0.95***|0.42*||0.98***|0.31|
|||(0.21)|(0.49)||(0.14)|(0.46)|
|Need permission for some rights (at||0.93***|0.35***||0.91***|0.24***|
|least 3 out of 5 inc. right to improve)||(0.25)|(0.48)||(0.28)|(0.43)|
|Need permission for all rights||0.32***|0.19***||0.51***|0.19***|
|||(0.47)|(0.39)||(0.50)|(0.39)|
|Perc. of rights needing permission||0.82***|0.34||0.92***|0.26***|
|||(0.22)|(0.44)||(0.18)|(0.41)|
|Observations<br>Notes:Permission statistics for non-ho|117<br>lders were no|55<br>t provided|223<br>as very few|235<br>individuals i|86<br>n this categ|88<br>ory held righ|

<u>Notes: Permission statistics for non-holders were not provided as very few individuals in this category held rights.</u> 26
<!-- Start of picture text --> Fisheries Species Population Growth Rate<br>— Log Catch - Exclusive Cooperative<br>== Log Catch - Non-Exclusive Cooperative<br>0.6 : a ,<br>ost oF 3 ae i Pa<br>2 Dt re i 25<br>woay >: fyf Re aay a ieee s<br>¢pos:ftAe fi tNOE ay (o£eo = odwe FaEfeifyce 3 fy 1:'i \AWos 8ua<br>& ‘ x FS 4<br><!-- End of picture text --> <!-- Start of picture text --> o2hi os Ney DONE Vidica<br>Bt wey uf + Ving :<br>I : Al t ae :: wu oe ert<br>a}: : fF i j<br>fq cr FET) a0 30°<br>Period<br>Fisheries Species Population Growth Rate<br>— Nonexe. - Exc, Log Catch Diff, Small Range<br>-- WNonexc. - Exc. Log Catch Diff, Large Range<br>Os on. = a: .<br><!-- End of picture text --> <!-- Start of picture text --> ney ‘ i fs 0.5<br>PrWSheseon caeaefn ‘ i :i ' : é : fest 3 4 ; s<br>aBoalwv ::ivEyed /i weipgit}’ hayt ontf TEEN+“ : Aa! fipo he NES “loo O=|c=)<br>£ F : F i : E -05.£<br>B03 ‘ i ; i ; : g<br>i F 10 Gg<br>o.2} : : i 3 4<br>ol : i : | : 4-15<br><!-- End of picture text -->

<!-- Start of picture text --> 9-5 10 2 30 a) 307°<br>Period<br>~~ Fisheries Species Population Growth Rate<br>— Nonexclusive - Exclusive Log Catch Difference<br>0.6 4 os: - : es<br>Os<br>9 :pea / iBOY i : i ; some oo &<br>Bost : oo<br><!-- End of picture text --> <!-- Start of picture text --> s : i i 4 i i ; -05.£<br>3 03 i : i : 4 he}<br>hs] 0.2 : : ;: ii E: ; i | : F F : F f-1.0 =CTc5<br>oon 10 20 30 a0 307°<br>Period<br><!-- End of picture text -->
Policy ReseaRch WoRking PaPeR 4815

# **Abstract**

Present levels of income inequality in Czech Republic, Estonia, Latvia, Lithuania, Hungary, Poland, Slovakia, and Slovenia remain considerably higher than their pretransition levels, although the relative pace of change over time has varied quite a bit across countries. Using data from the 2006 European Union Survey of Income and Living Conditions, this paper finds that prevailing levels of income inequality in these countries continue to be low by international standards, and that this is in large part due to the very high redistributive impact of direct taxes and public transfers. In addition to the instrumental role of tax and transfer policies in redistributing income, the paper highlights the important role played by differences in education levels and labor market participation rates in explaining observed inequalities across people and across different regions (although not in explaining observed differences across countries). The paper includes an analysis of key factors that help explain observed variation across countries in the level of public support for redistribution, including peoples’ economic background and relative success in life, whether they perceive poverty to be associated with factors within or outside the control of those it afflicts (for example, laziness/lack of willpower vs. injustice in society).

This paper is a product of the Poverty Reduction and Economic Management Department, Europe and Central Asia Region. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at szaidi5@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
<!-- Start of picture text --> 7<br>6<br>5<br>a 4<br>&<br>r<br>3<br>2<br>2<br>1<br>0<br>2004 2005 2006 2007 2008 2009<br><!-- End of picture text --> losses Ounderpricing VRA Sunderpricing ECG @ collection Inefficiencies <!-- Start of picture text --> Nigeria<br>Ghana<br>Cameroon<br>Senegal<br>Cote d'Ivoire<br>Cape Verde<br>Burkina Faso<br>Benin<br>South Africa<br>0 50 100 150 200 250<br>% of revenues<br>Munaccountedlosses Ounderpricing Wcollection inefficiencies<br><!-- End of picture text -->
FIGURE 10 Effects of adverse events on growth of employment, TFP, and investment A. Response of potential TFP growth after recessions B. Response of investment growth after recessions <!-- Start of picture text --> Percentage points Percentage points<br>0.0 0<br>-5<br>-0.5 -10<br>-15<br>-1.0 -20<br>Year 1 Year 3 Year 5 Year 1 Year 3 Year 5<br>C. Response of employment growth after C. Response of employment growth after 5<br>recessions years later<br>Percentage points Percentage points<br>0.5 1.0<br>0.0<br>0.5<br>-0.5<br>0.0<br>-1.0<br>-0.5<br>-1.5<br>-1.0<br>-2.0<br>-2.5 -1.5<br>Year 1 Year 3 Year 5 Banking crises Epidemics<br>E. Response of potential TFP growth 5 years F. Response of investment growth 5 years later<br>later<br>Percentage points Percentage points<br>0.0 2.0<br>0.0<br>-2.0<br>-0.5<br>-4.0<br>-6.0<br>-1.0 -8.0<br>Banking crises Epidemics Banking crises Epidemics<br><!-- End of picture text --> Source: World Bank.

Note: Blue bars are coefficient estimates from local projections model. Orange whiskers indicate 90 percent confidence interval. Recessions are defined as the period from the peak preceding a business cycle trough to the trough, with the troughs defined as years in which output growth is both negative and one standard deviation below the long-term average. Banking crises are identified as in Laeven and Valencia (2020). Epidemics include SARS (2003), swine flu (2009), MERS (2012), Ebola (2014), and Zika (2016). Sample includes unbalanced panel of 32 advanced economies 97 EMDEs for 1981-2020.

30
reduction on the import side is 0.9 percentage points and the average export-tax equivalent reduction is 1.2 percentage points.

As Figures 2 and 3 indicate, trade facilitation policy tends to be better in countries with large trade volumes. We calculate that the correlation between implied tariff reductions and log import volume is -0.31, and the correlation between the export tax equivalent of TFA implementation and the log export volume is -0.33. These correlations might reflect causation flowing in either direction, or that the variables are jointly determined. In any case, the negative correlations between implied tariff reductions and trade volumes limit the possibilities for global gains from trade facilitation policy reform.

# **6.1 What might the TFA be worth?**

The negative correlation between the size of countries’ trade volumes and their implied tariff reductions is significant for our next exercise, in which we attempt to calculate a monetary value of full implementation of the TFA. Our goal is not to provide an exhaustive estimate of the gains from reform, but rather to provide a transparent and conservative calculation of the most plausible source of significant gains from policy changes - reductions in the time needed to clear customs.<sup>40</sup> Our backof-the-envelope calculation assumes that policy-induced changes in the estimated numbers of days in customs are equivalent to reductions in iceberg trade costs. The magnitude of these reductions is primarily determined by the scale of the tariff equivalent estimates discussed above and the value of a country’s trade.

We value these effects by calculating a welfare quadrilateral that is made up of the standard Harberger triangle and a rectangle reflecting the monetary value of trade cost reductions on existing trade flows. Implicitly we posit a model of export supply and import demand, with each country

> 40Some estimates of the gains from the TFA have reported comprehensive global welfare gains, and these are typically calculated in a Computable General Equilibrium model. The figure of US $1 trillion in benefits that received substantial public attention comes from Huffbauer et al. (2012) using estimates from Portugal-Perez and Wilson (2012). These authors consider a broader set of trade facilitation policy changes than are considered here, including valuable but costly infrastructure upgrades. Decreux and Fontagn ́e (2011) consider a halving of selected TAB indicators and calculate world GDP improvements of US $68 billion. Zaki (2014) assumes an even broader set of time reductions from the TAB data (including reductions in inland transport times) and calculates a global trade cost reduction of US $325 billion. Relative to these authors our policy scenario implies larger changes in time in customs, but we consider a smaller set of indicators that improve because of the TFA. Our impacts of policy change are estimated rather than assumed, though we do rely on the Hummels and Schaur (2013) estimate to calculate implied tariff reductions. We use partial equilibrium rather than general equilibrium analysis for purposes of greater transparency.

32