rafmacalaba/datause-encoder-data huggingface dataset:
- 12 -

regional redistribution. For this purpose, the equivalent Rain from targeting is defined as the uniform (untargeted) lump-sum gain needed to achieve the level of poverty under optimal regional targeting (Ravallion and Chao, 1989).

# 3. Average Consumption Levels. Inequality and Poverty in India: A Regional Profile

Our main source of data is the National Sample Survey (NSS) 38th round survey or, consumer expenditure for 1983 (NSSO, 1986). The survey gives the size distribution of per capita monthly consumption expenditures in urban and rural areas of different states and union territories. Our analysis relates to 40 regions in India, namely the urban and rural sectors of 20 states (see Table 1). accounting for 98.4 percent of India's total population (RGCCI, 1982).7 Many of the following calculations were also performed on the data for the 20 states, not split into urban and rural sectors. The poverty reduction attainable through regional redistribution will generally increase with finer regional detail.

One important limitation of these data (in common with most other surveys) is that they only provide single cross-sections. Our analysis will thus be static. We do not consider possible effects of combining spatial and temporal redistributions; for example, transfers from the urban sector to the rural sector during lean seasons or poor crop years can be expected to have a greater impact on aggregate poverty than at other times.8
level, gender, and family status, play an important role in employment and wage. As expected, the well-educated have better chances of getting jobs and earning higher salaries. Interestingly, although the average wage difference between male and female is small, female workers in general have higher education than their male counterparts.

As labor market opportunities and returns to education and other individual talents significantly differ across regions, implementation of appropriate regional labor market policies is important. Human capital, however, remains a key determinant in employment and wage determination. Concerted efforts to improve education will also be important. The design and implementation of relevant policies are of interest for future work.

# **References**

- Balisacan, A.M. and N. Fuwa. (2004, May). “Changes in Spatial Income Inequality in the Philippines.” Research Paper No.2004/34, World Institute for Development Economics Research. (United Nations University, Helsinki).

- Balisacan, A.M. (2003), “Poverty and Inequality”, in: The Philippine Economy: Development, Policies and Challenges, New York Oxford University Press; Quezon City, Ateneo de Manila University Press , 2003

- Blinder, A.S. (1973). “Wage Discrimination: Reduced Form and Structural Estimates. Journal of Human Resources, 8: 436-455.

- Manasan R.G. and S. Chatterjee (2003), in: The Philippine Economy: Development, Policies and Challenges, New York Oxford University Press; Quezon City, Ateneo de Manila University Press , 2003

- Oaxaca, R. (1973). Male-Female Wage Differentials in Urban Labor Markets. International Economic Review 14: 693–709.

- Schady, N.R. (2000, September). “What Education Pays? Non-Linear Returns to Schooling among Filipino Men” World Bank (mimeo).

- Yamaguchi, F. (2005). “Why Do Schooling Returns Differ? Screening, Private School, and Labor Markets in the Philippines and Thailand.” _Economic Development and Cultural Change, 53_ : 4, 959-81.

17
|Figure 17. Telecom coverage in Zimbabwe|40|
|---|---|
|Figure 18. Zimbabwe’s Internet market and southern African peers|41|
|Figure 19. Zimbabwe’s infrastructure spending needs are among highest in the region relative to GDP|44|
|Figure 20. Zimbabwe’s existing infrastructure spending is one of the highest in the region|45|
|Figure 21. Zimbabwe’s pattern of capital investment in infrastructure differs from that of comparator countries|46|
|Figure 22. Underpricing of power and water in Zimbabwe is burdensome|47|
|Figure 23. Consumption of infrastructure services in Zimbabwe is highly differentiated by budget|48|
|Figure 24. Zimbabwe’s power and water utilities: The burden of inefficiency|48|
|Figure 25. Zimbabwe needs to attract more private investment|50|

# List of tables

|Table 1. Achievements and challenges in Zimbabwe’s infrastructure sectors|6|
|---|---|
|Table 2. Benchmarking Zimbabwe’s power infrastructure|15|
|Table 3. Massive inefficiencies are related mainly to underpricing and undercollection of bills|18|
|Table 4. Zimbabwe’s possible hydropower expansion|20|
|Table 5. Zimbabwe’s annualized costs of capacity expansion (over 10 years)|20|
|Table 6. Zimbabwe’s irrigation potential|23|
|Table 7. Benchmarking water and sanitation indicators|25|
|Table 8. Water and sanitation needs|29|
|Table 9. Zimbabwe’s road indicators benchmarked against Africa’s low- and middle-income countries, as of 2008|31|
|Table 10. Quality of classified roads in Zimbabwe, 2010|31|
|Table 11. Railway indicators for Zimbabwe and selected other countries, 2000–05|34|
|Table 12. Benchmarking air transport indicators for Zimbabwe and select other countries|36|
|Table 13. Zimbabwe’s transport needs|37|
|Table 14. Benchmarking ICT indicators|38|
|Table 15. Illustrative ―ideal‖ investment targets for infrastructure in Zimbabwe|42|
|Table 16. AICD annual spending needs estimates over a 10-year period|43|
|Table 17. Financial flows to Zimbabwe’s infrastructure, 2009*|45|
|Table 18. Zimbabwe’s potential gains from greater operational efficiency (annualized)|46|
|Table 19. Funding gaps by sector|49|

iv
- Meng, X. and Kidd, M. P. (1997). Labor market reform and the changing structure of wage determination in china’s state sector during the 1980s. _Journal of Comparative Economics_ , 25(3):403–421.

- Ren, W. and Miller, P. W. (2012). Gender differentials in the payoff to schooling in rural china. _Journal of Development Studies_ , 48(1):133–150.

- Wang, L. (2012). Economic transition and college premium in urban china. _China Economic Review_ , 23(2):238–252.

- Xie, Y. and Hannum, E. (1996). Regional variation in earnings inequality in reform-era urban china. _American Journal of Sociology_ , 101(4):950–992.

- Zhang, J., Zhao, Y., Park, A., Song, X., et al. (2005). Economic returns to schooling in urban china, 1988 to 2001. _Journal of Comparative Economics_ , 33(4):730–752.
in the agricultural sector (Kondylis, Mueller, and Zhu, 2014).<sup>1</sup> The sample consists of 4,000 households residing in ve districts of the Zambezi Valley, Mozambique (Chemba, Maringue, Mopeia, Morrumbala and Mutarara) (Figure 1). The survey is composed of two parts: one Computer Assisted Personal Interviewing (CAPI) questionnaire, lled indoors by dierent respondents and at various levels (household, individual, and plot); and one paper-based plot survey conducted in the eld on household's main plots.

Subjective and objective knowledge and adoption outcomes were asked of two individuals per household, the household head and his/her partner or spouse.<sup>2</sup> Eight sustainable land management (SLM) practices were documented based on the curriculum of the education intervention and prior adoption practices in the region. We focus on the measurement of the three most common techniques: intercropping, mulching, and strip tillage. A ctitious practice was introduced as a placebo, planting in squares, to check for the sensitivity of self-reporting to response bias.

# 2.2 Knowledge and Adoption Measures

During the interview, enumerators asked the respondent to name any conservation agricultural technique that he knew. When the respondent answered one of the nine SLM techniques, enumerators marked it on the pop-up window as known. The process was repeated until the respondent listed all of the techniques he knew. Our rst self-reported measure, know by memory, reects the respondent's ability to recall the name of SLM techniques.<sup>3</sup> After the respondent recollected the techniques he knew, enumerators read the remaining techniques (including the placebo) from the survey list and asked whether the respondent knew each technique. Our second self-reported knowledge measure, know by name, is based on these responses. Logically, if a respondent knows a technique by memory, he will also know the technique by name. Therefore, for each person, the techniques known by memory are a subset of the techniques known by name, and knowing by memory is a sucient condition to knowing by name.

> 1This RCT was designed and implemented under the Market-led Smallholders Development in the Zambezi Valley Project, which aims to improve the income of smallholder farmers, soil fertility, and ecosystem resilience to climate change (World Bank, 2007).

> 2In the case of a polygamous household, only the main spouse was interviewed. A single response for knowledge and adoption was given when a household head lacked a partner.

> 3Note, in this case, it is improbable that a farmer would state his familiarity with the placebo technique since it does not exist. We therefore treat any response indicating an individual's knowledge of planting in squares as a routing error (enumerator clicked on the technique by mistake).

5
# **How Relevant Is Infrastructure to Growth in East Asia?***

## Kalpana Seethepalli

Operations and Policy Unit, East Asia Sustainable Development Department The World Bank <u>kseethepalli@worldbank.org</u>

## Maria Caterina Bramati

Belgian National Institute of Statistics <u>Maria-Caterina.Bramati@economie.fgov.be</u>

## David Veredas

ECARES, Université Libre de Bruxelles CORE, Université catholique de Louvain <u>david.veredas@ulb.ac.be</u>

* We have benefited from valuable comments and suggestions from Michel Kerf, Michael Warlters, and Antonio Estache. Any shortcoming is however ours and should not be attributed to anyone else, in particular to the institutions we are affiliated with.
Table B5: Mechanism at play, no restriction on the minimum number of foreign investors

|VARIABLES|(1)<br>CR5|(2)<br>CR5|(3)<br>CR5|(4)<br>CR5|(5)<br>HHI|(6)<br>log(# MNEs)|(7)<br>log(K)|
|---|---|---|---|---|---|---|---|
|Uncertainty||||||||
|(standardized)|0.863**|-0.227|1.329***|1.404***|0.047**|-0.061**|0.010|
||(0.389)|(0.579)|(0.445)|(0.439)|(0.022)|(0.025)|(0.032)|
|Uncertainty||||||||
|_×_ Developing||1.590**<br>(0.648)||||||
|Observations|2,691|2,691|1,831|1,777|1,777|1,777|1,777|
|R-squared|0.856|0.856|0.840|0.844|0.775|0.905|0.332|
|Controls|No|No|No|Yes|Yes|Yes|Yes|
|Data Coverage|All co|untries|||Developin|g||

_Note:_ Each observation corresponds to a destination-year, represented as _it_ . “CR5” stands for the concentration ratio of the top 5 investing firms, with no restriction on the minimum number of foreign investors. “HHI” refers to the Herfindahl-Hirschman concentration index, calculated using the “CR5” sample. “log(# MNEs)” represents the natural logarithm of the number of foreign investors, and “log(K)” denotes the natural logarithm of the average capital expenditure. “Uncertainty” is based on the “T3” variable from the World Uncertainty database (Ahir et al. 2022), which counts the occurrence of the word “uncertainty” from Economist Intelligence Unit (EIU) reports for 143 countries since the 1950s. A higher value indicates greater uncertainty. Uncertainty in year _t_ is approximated as the logarithm of the average number of uncertainty-related words in EIU reports over the three years preceding the investment (standardized to have a mean of zero and a standard deviation of 1). The analysis includes country fixed effects and (world) region by year fixed effects. Countries are classified into 7 regions based on World Bank classifications. Destination-specific control variables include the logarithms of population and GDP (source: Conte et al. (2022)) and the average exchange rate over the three years preceding the investment (national currency/USD, Penn World Tables, mark 10.1, Feenstra et al. (2015)). Standard errors are clustered at the destination country level. Significance levels: *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

30
- Lee, Jean N., Jonathan Morduch, Saravana Ravindran, Abu Shonchoy, and Hassan Zaman. 2021. “Poverty and Migration in the Digital Age: Experimental Evidence on Mobile Banking in Bangladesh.” _American Economic Journal: Applied Economics_ 13(1): 38-71.

- Mahler, Daniel Gerszon, Christoph Lakner, Raul Andres Castaneda Aguilar, and Haoyu Wu. 2020. “Updated Estimates of the Impact of COVID-19 on Global Poverty.” World Bank Blogs: Data Blogs, June 8, 2020. https://blogs.worldbank.org/opendata/updated-estimatesimpact-covid-19-global-poverty.

- Muralidharan, Karthik, Paul Niehaus, and Sandip Sukhtankar. 2016. “Building State Capacity: Evidence from Biometric Smartcards in India.” _American Economic Review_ 106(10): 2895-2929.

- Pazarbasioglu, Ceyla, Alfonso Garcia Mora, Mahesh Uttamchandani, Harish Natarajan, Erik Feyen, and Mathew Saal. 2020. _Digital Financial Services._ World Bank Group, Washington DC.

- Prina, Silvia. 2015. “Banking the Poor via Savings Accounts: Evidence from a Field Experiment.” _Journal of Development Economics_ 115(July): 16-31.

- Rojas-Suárez, Liliana, and Maria Alejandra Amado. 2014. “Understanding Latin America’s Financial Inclusion Gap.” CGD Working Paper No. 367, Center for Global Development, Washington DC.

- Rojas-Suárez, Liliana. 2016. “Financial Inclusion in Latin America: Facts, Obstacles and Central Banks’ Policy Issues.” IDB Discussion Paper No. DP-464, Inter-American Development Bank, Washington DC.

- Sahay, Ratna, Ulric Eriksson von Allmen, Amina Lahreche, Purva Khera, Sumiko Ogawa, Majid Bazarabach, and Kim Beaton. 2020. _The Promise of Fintech: Financial Inclusion in the Post COVID-19 Era._ IMF Monetary and Capital Markets Department paper series No. 20/09. Washington, DC: International Monetary Fund.

- Saroy, Rajas, Sakshi Awasthy, Naveen K. Singh, Sonali M. Adki, and Sarat Dhal. 2022. “The Impact of COVID-19 on Digital Payments Habits of Indian Households.” _Bank Indonesia Bulletin of Monetary Economics and Banking_ , 25(Special issue): 19-42.

- Staschen, Stefan, and Patrick Meagher. 2018. “Basic Regulatory Enablers for Digital Financial Services.” CGAP Focus Note 109, Consultative Group to Assist the Poor, Washington, DC.

- Suri, Tavneet, William Jack and Thomas Stoker. 2012. “Documenting the Birth of a Financial Economy,” Proceedings on the National Academy of Sciences, 109 (26), 1025710262.

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J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and 37 Productivity

- Cheng, Y; Zhao, L; Wan, W; Li, L; Yu, T; Gu, X (2016). Extracting urban areas in China using DMSP/OLS nighttime light data integrated with biophysical composition information. _Journal of Geographical Sciences_ , 26(3), 325–338. https://doi.org/10.1007/s11442-016-1271-6

- Chuvieco, E (2016). Fundamentals of Remote Sensing. An Environmental Approach (2nd ed.). Boca Raton: CRC Press.

- Ciccone A; Hall RE (1996). Productivity and the density of economic activity. _American Economic Review_ , 86: 54–70.

- Couch, C; Ldontidou, L; Petschel-Held, G (2007). Landscapes, land-use change & policy. _Urban sprawl in Europe_ . Blackwell Publishing, Oxford.

- Duranton, G; Puga D. (2004). “Micro-foundations of urban agglomeration economies”, in J.V. Henderson and J.-F. Thisse, eds., _Handbook of Urban and Regional Economics_ , Volume 4, New York: North Holland.

- Ellis, Peter, and Mark Roberts. 2015. _Leveraging Urbanization in South Asia: Managing Spatial Transformation for Prosperity and Livability_ . World Bank Publications.

- Fallah, B; Partridge, M; Olfert, M (2011). Urban sprawl and productivity: Evidence from US metropolitan areas, _Paper in Regional Science_ , 90(3): 451-473.

- Forbes, DJ (2013). Multi-scale analysis of the relationship between economic statistics and DMSPOLS night light images. _GIScience & Remote Sensing_ , 50(5): 83–499. https://doi.org/10.1080/15481603.2013.823732

Freire, S; Pesaresi, M (2015): GHS population grid, derived from GPW4, multitemporal (1975, 1990, 2000, 2015). European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/jrc-ghsl-ghs_pop_gpw4_globe_r2015a.
Figure B.9: Placebo. Direct and spillover effects for the pre-intervention Sep’20 bill <!-- Start of picture text --> Treated groups Untreated groups<br>vs. pure controls vs. pure controls<br>Blocks with 80% treated Blocks with 80% treated<br>7 Due date of Interventionbegins 7 Due date of Interventionbegins<br>6 Sept 2020 bill 6 Sept 2020 bill<br>5 5<br>4<br>4<br>3<br>3<br>2<br>2<br>1<br>1 0<br>0<br>01sep2020 08sep2020 15sep2020 22sep2020 29sep202 01sep2020 08sep2020 15sep2020 22sep2020 29sep202<br>Calendar date Calendar date<br>Blocks with 50% treated Blocks with 50% treated<br>7 Due date of Interventionbegins 7 Due date of Interventionbegins<br>6 Sept 2020 bill 6 Sept 2020 bill<br>5 5<br>4 4<br>3 3<br>2 2<br>1<br>1<br>0<br>0<br>01sep2020 08sep2020 15sep2020 22sep2020 29sep202 01sep2020 08sep2020 15sep2020 22sep2020 29sep202<br>Calendar date Calendar date<br>Blocks with 20% treated Blocks with 20% treated<br>7 Due date of Interventionbegins 7 Due date of Interventionbegins<br>6 Sept 2020 bill 6 Sept 2020 bill<br>5 5<br>4 4<br>3 3<br>2 2<br>1 1<br>0 0<br>01sep2020 08sep2020 15sep2020 22sep2020 29sep202 01sep2020 08sep2020 15sep2020 22sep2020 29sep202<br>Calendar date Calendar date<br>Treatment effect (p.p.) Treatment effect (p.p.)<br>Treatment effect (p.p.) Treatment effect (p.p.)<br>Treatment effect (p.p.) Treatment effect (p.p.)<br><!-- End of picture text --> _Notes_ : These figures show the coefficients and 95% confidence intervals from a saturated regression that computes, at each calendar day, the payment rate difference between each treated and untreated group relative to the pure control group (i.e., blocks where no accounts were treated). The top panel shows the effect on treated (left) and untreated (right) units in blocks with 80% treated ( _Tg_ = 3). The middle panel shows the effect on treated (left) and untreated (right) units in blocks with 50% treated ( _Tg_ = 2). The bottom panel shows the effect on treated (left) and untreated (right) units in blocks with 20% treated ( _Tg_ = 3). Standard errors are clustered by block. The first vertical bar shows the due date for the September 2020 bill. This corresponds to a bill issued and due for payment before our intervention began, thus serving as a placebo. The second vertical bar indicates the start of the intervention. The letters were delivered between September 28th and October 7th.

B-15
reduction rate of 6 percentage points or larger. At the regional level, the (unweighted) regional poverty rate declined by almost five percentage points, or 9 percent (= 4.7/50.4).

The estimates in Table 2 are cross sectional estimates, meaning that the poor in each period are anonymous. They only show the net change in poverty over time, but not the composition of the change as measured by quantities (1a) and (1b). Analysis of the latter provides insights into the dynamic process of poverty mobility, but would require panel data that track households (or individuals) over time as discussed earlier. However, panel data are more often than not affected by various issues such as attrition, measurement errors, and sample selection bias which can severely reduce the accuracy of estimates. In addition, even though more household panel surveys have been implemented in recent years with the introduction of the LSMS-ISA program, most of the panel surveys in Africa (which are not part of the LSMS-ISA) are not nationally representative.<sup>11</sup> We turn next to the analysis that is based on the synthetic panels.

# **III. Welfare Analysis Using the Synthetic Panels**

We discuss in this section the results on poverty mobility (Section III.1) before discussing the results on welfare dynamics. While the former focuses on two-by-two transition matrixes (i.e., by cross cutting a household’s poor/non-poor status in the 1<sup>st</sup> period against its poor/non-poor status in the 2<sup>nd</sup> period), the latter concerns the more general two-by-two transition matrixes (i.e., by cross cutting the household’s poor/vulnerable/middle-class status in the 1<sup>st</sup> period against its poor/non-poor status in the 2<sup>nd</sup> period).

# **III.1. Poverty Mobility**

> 11 Reviewing studies that use the existing panel data for African countries, Beegle et al. (2016) find much variation in the estimates for chronic poverty and transient poverty. Furthermore, chronic poverty estimates for the same country, and in some cases using the same data sets, could also vary widely depending on the method being used. How much of this poverty mobility is due to measurement errors is still a matter of debate. Some researchers argue that up to 50 percent of the transitory poverty may be accounted for by measurement error in income or consumption (Dercon and Krishnan, 2000; Glewwe, 2012; Lee et al., 2016).

13
with the strongest competition for UK manufacturers coming from producers in the EU and US. Freeman and Revenga (1999) report a similar result for Europe, Gaston and Trefler (1997) found significant employment responses to import competition in some sectors in Canada, and Gourinchas (1999a, b) found a significant effect of exchange rate fluctuations on movements of jobs across and within sectors in France, using firm-level job creation and destruction data. In the case of the US, Bernard and Jensen (1999b) find that intra-industry reallocations to higher productivity exporters explains up to 20 percent of productivity growth in US manufacturing. For developing countries, among other studies, Aw, Chung and Roberts (2000) find that exposure to trade forces the exit of the least efficient producers in Korea and Taiwan, while Pavcnik (2002) finds that market share reallocations contributed significantly to productivity growth following trade liberalization in Chile.

# **5.  EXPORTS, INTERMEDIATES, FDI AND GLOBAL PRODUCTION SHARING**

Research that focuses on the differential role of exports as opposed to imports as a source of labor market effects concludes that exports tend to positively and imports negatively affect labor employed in the sectors concerned. Thus Davidson and Matusz (2003) find higher sectoral net exports to be associated with less job destruction and more job creation. Harrison and Hanson (1999), find that trade reforms result in employment expansion in export sectors/firms in Mexico, and Milner and Wright (1998) find the same for Mauritius. None of this is surprising of course, but it is important to bear in mind that greater imports have to be paid for, thus requiring and inducing output and employment in export sectors. More interesting is the relative effects on different types of labor.

Exporters in an industry tend to be more productive than other plants. This finding is by now very well established—e.g., Clerides, Lach and Tybout (1998), Bernard and Jensen (1999a) and Aw, Chung and Roberts (2000). One reason is that there are generally large sunk costs associated with contesting an export market (see Roberts and Tybout, 1996; Bernard and Jensen, 1999b). Hallward-Driemeier, Iarossi and Sokoloff (2002) find that in a sample of East Asian countries, both firms with foreign ownership and firms that export are significantly more productive, and the productivity gap is larger the less developed is the local market. Using a firm-level dataset to explore the sources of exporting firms’ greater productivity, they argue that 19