rafmacalaba/datause-encoder-data huggingface dataset:
14 The primary sampling unit,s were the ten southern states in Nigeria. Although the target population was originally intended to include students<sup>from all</sup> states, logistical and financial constraints caused this<sup>to be reduced to the</sup> 10 southern states, which include 90% of the country's<sup>school enrollments. Of</sup> these, acceptable data were received from eight<sup>states. Within each state, a</sup> random sample of schools was selected, with probability proportional to the number of schools in the state. At the second stage,<sup>a random sample of one</sup> class per school was selected, and at the final stage,<sup>about 30 students were</sup> randomly selected in each class, or the entire class<sup>was sampled if it</sup> contained fewer than 30 students.2/ Students were administered a mathematics test and a background questionnaire. Teachers completed several questionnaires about their backgrounds, their general classroom processes, their<sup>teaching practices, and</sup> characteristics of their class. Data about the school were provided by a school administrator. Although a very large<sup>number of measures was collected</sup> in the IEA stuxdy, only those used in this paper are described below.

The analytic sample of students and schools used in this study is described in Table 1. Of the 1,012 students comprising the analytic<sup>sample,</sup> only 227 (22.4%) are female. About half (51.9%)<sup>of the students are in</sup> single-sex schools, with more of the males (56.3%)<sup>than the females (34.7%) in</sup> such schools. This is reflected in the school<sup>distribution,</sup> which is exactly half single-sex (20 of 40 schools).
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 8. Benin's irrigation potential**

|**Cutoff**|**L**|**arge-scale**||**S**|**mall-scale**|||**Total**||
|---|---|---|---|---|---|---|---|---|---|
|**(%)**|**Investment**|**IRR**|**Area**<br>**increase**|**Investment**|**IRR**|**Area**<br>**increase**|**Investment**|**IRR**|**Area**<br>**increase**|
||US$ million|%|hectares|US$ million|%|hectares|US$ million|%|hectares|
|0|3091|6|1,583,665|586|8|113,418|3677|7|1,697,083|
|6|2333|7|1,195,432|188|16|36,414|2521|8|1,231,846|
|12|0|0|-|76|25|14,620|76|25|14,620|
|24|0|0|-|23|40|4,437|23|40|4,437|

_Source_ : Derived from You and others (2009).

If small-scale projects are implemented, water withdrawals are expected to rise. This, in addition to the contamination of groundwater resources by high amounts of fertilizers and pesticide use in irrigation, will augment the water stress.

**Figure 12. Irrigation potential**

<!-- Start of picture text --> 6 percent IRR cutoff<br>a. Large scale b. Small scale<br>18 1400 70 2500<br>16<br>1200 60<br>2000<br>14<br>1000 50<br>12<br>1500<br>40<br>10 800<br>8 600 30 1000<br>6 20<br>400<br>4 500<br>10<br>200<br>2<br>0 0<br>0 0<br>Area increase (thousand ha) Internal Rate of Return (%)<br>Area increase (thousand ha) Internal Rate of Return (%)<br>Area Increase Area Increase<br>Internal Rate of return<br>Internal Rate of return<br>Togo Niger Ghana Mali Guinea Senegal Nigeria Benin Liberia Mauritania Benin Ghana Togo Guinea Senegal Niger Mali Nigeria<br>Burkina Faso Sierra Leone Cote d'Ivoire Mauritania Sierra Leone Guinea-Bissau The Gambia Burkina Faso Cote d'Ivoire<br><!-- End of picture text --> _Source_ : Derived from You and others (2009).

_Note_ : Charts are based on 6 percent cutoff estimates, at which the estimated area increase for southern African countries not included in the figures is zero.

22
# **2. Stock-taking: Where Does Africa Stand?**

Earlier stock-taking exercises of banking and finance in Africa suffered from a lack of data for a broad cross-section of countries in the region (Honohan and Beck, 2007). Most crosscountry studies on financial development included only a few larger African financial markets and their focus was on other developing and emerging regions of the world. This situation has changed over the past five years, with data available for a large part of the region and several segments of the financial system. Global data collection efforts on the depth, outreach, stability and efficiency of financial systems have been much more successful in collecting data on African financial systems.<sup>ii</sup> Aggregate data have been complemented with a number of enterprise surveys, and surveys for some countries now have a panel dimension (i.e. firms being surveyed at several points in time). Similarly, household surveys specialized on financial services, such as the Finscope and Finaccess surveys in several African countries, have provided important insights into individual and household access to and use of formal and informal financial services. In the following, we will therefore use an array of databases and other sources to document the development and structure of banking systems across the region.

# **2.1.Aggregate financial development in Africa in international comparison**

Africa’s banking systems are small, costly, and focused on the short-term end of the yield curve as we will illustrate in the following. However, we will also document the progress Africa’s banking systems have made over the past decade.

To compare banking systems in Africa to a proper benchmark, we limit our sample to low- and lower-middle income countries in Sub-Saharan Africa and compare the median for this group to the median country across a sample of low- and lower-middle income countries outside Africa. We thus explicitly drop several upper-middle income African countries in our 5
|**Table 10: Median Least Squa**|**re Regressi**|**on Results**|**- Wide Int**|**erest Rate**|**Spread**|||
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Overhead<br>Liquidity|0.681<br>(0.000)*** <br>-0.022<br>(0.000)***|0.655<br> (0.000)*** <br>-0.016<br> (0.000)***|0.646<br> (0.000)*** <br>-0.021<br> (0.000)***|0.613<br> (0.000)*** <br>-0.029<br> (0.000)***|0.646<br> (0.000)*** <br>-0.022<br> (0.000)***|0.637<br> (0.000)*** <br>-0.023<br> (0.000)***|0.738<br> (0.000)***<br>-0.021<br> (0.000)***|
|Loan Loss Provisions|-0.043|-0.044|-0.042|-0.032|-0.043|-0.04|-0.043|
||(0.000)***|(0.000)***|(0.000)***|(0.000)***|(0.000)***|(0.000)***|(0.000)***|
|Market Share Deposits|0.042|0.056|0.093|0.001|0.071|0.078|0.067|
||(0.434)|(0.332)|(0.147)|(0.990)|(0.241)|(0.187)|(0.294)|
|Equity|0.057<br>(0.000)***|0.045<br> (0.002)***|0.091<br> (0.000)***|0.075<br> (0.000)***|0.071<br> (0.000)***|0.074<br> (0.000)***|0.055<br> (0.001)***|
|Liquidity* 2004||-0.011<br>(0.019)**||||||
|Liquidity* 2005||-0.024<br>(0.000)***||||||
|Equity* 2004|||-0.035<br>(0.237)|||||
|Equity*2005|||-0.128<br>(0.000)***|||||
|Interest Income Share||||-0.007<br>(0.000)***||||
|Intermediation||||-0.019<br>(0.086)*||||
|Herfindahl Deposits|||||-0.682<br>(0.004)***|||
|Herfindahl Loans||||||-0.937<br>(0.003)***||
|Inflation|||||||0.324|
||||||||(0.000)***|
|Real Treasury Bill Rate|||||||0.315<br>(0.000)***|
|Industrial Production Growth|||||||-0.016<br>(0.715)|
|Exchange Rate Depreciation|||||||0.193<br>(0.001)***|
|Constant|0.091|0.079|0.076|0.112|0.134|0.135|0.026|
||(0.000)***|(0.000)***|(0.000)***|(0.000)***|(0.000)***|(0.000)***|(0.001)***|
|Observations|1,831|1,831|1,831|1,810|1,831|1,831|1,772|

p values in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1% 31
- Lemieux, Thomas, and David Card (1998).  “Education, Eranings, and the `Canadian G.I. Bill’.”  NBER Working Paper No. 6718.

- Li, Hongbin, Pak Wai Liu, Ning Ma, and Junsen Zhang (2005). _Does Education Pay in Urban China?: Estimating Returns to Education Using Twins_ , mimeo.

- Meisner, Maurice J. (1986). _Mao’s China and After: A History of the People’s Republic_ , CollierMacmillan: New York, 1986.

- Meng, Xin, and R. G. Gregory (2002a). Exploring the Impact of Interrupted Education on Earnings: the Educational Cost of the Chinese Cultural Revolution.  Unpublished manuscript, Australia National University.

- Meng, Xin, and R. G. Gregory (2002b). “The Impact of Interrupted Education on Subsequent Educational Attainment:  A Cost of the Chinese Cultural Revolution.” _Economic Development and Cultural Change_ 50(4): 935-959.

- Pepper, Suzanne (1996). _Radicalism and Education Reform in Twentieth-Century China_ . Cambridge University Press.

- Staiger, Douglas, and James Stock (1997).  “Instrumental Variables Regression with Weak Instruments.” _Econometrica_ 65: 557-586.

- Wooldridge, Jeffrey (2002). _Econometric Analysis of Cross Section and Panel Data,_ MIT Press, Cambridge.

- Zhang, Junsen, Yaohui Zhao, Albert Park, and Xiaoqing Song (2005).  Economic Returns to Schooling in Urban China, 1988 to 2001. _Journal of Comparative Economics_ 33: 730752.

26
**Table 1: Number of households covered in census in each of the survey villages**

|**District**|**Upazila**|**Union**|**Village**|**Number of**<br>**Households**|
|---|---|---|---|---|
|Dhaka|Dohar|Moksudpur|Baniabari|200|
|Dhaka|Dohar|Nurisha|Choita Butar|263|
|Madaripur|Kalkini|Sahebrampur|Sahebrampur|286|
|Madaripur|Kalkini|Koyaria|Rampol|349|
|Mymensingh|Gaffargaon|Moshakhali|Kandi|518|
|Mymensingh|Gaffargaon|Panchbag|South Harina|390|
|Chittagong|Putia|Khorna|Khorna|294|
|Chittagong|Putia|Kachuai|Sujanagar|238|
|Comilla|Barura|North Shilmuri|Boro Vatua|285|
|Comilla|Barura|South Shilmuri|Akusar|226|
|Noakhali|Begumgonj|Mir Warishpur|Kendurbag|420|
|Noakhali|Begumgonj|Eklashpur|Eklashpur|381|
|Narail|Narail Sadar|Bhadrabila|Polaidanga|331|
|Narail|Narail Sadar|Chandibarpur|Ratadanga|310|
|Rangpur|Mithapukur|Imadpur|Sarkar Para|174|
|Rangpur|Mithapukur|Mirjapur|Arifpur|328|
|Sylhet|Bishwanathpur|Alongkari|Tengra|371|
|Sylhet|Bishwanathpur|Rampasha|Puran Gaon|204|
|Barisal|Muladi|Muladi|Dorichar Laxmipur|358|
|Barisal|Muladi|Kazirchar|Uttar Kazirchar|356|
||||**Total number of**<br>**households**|**6,282**|

24
Policy Research Working Paper 10505

# **Abstract**

This paper uses data from a survey of 116,061 households in India to study people’s beliefs about inequality and demand for redistribution. The findings show that a household’s beliefs about inequality, implied by the perception of their position on the income distribution, is negatively correlated with support for reducing inequality. This is relevant since there are significant differences between where individuals believe their household stands and their actual position, with the gap between perceived and actual position exceeding two deciles on average. Despite these large differences, informing individuals of their household’s position on the income distribution has no discernible effect on support for reducing inequality. The paper posits that demand for redistribution may be unresponsive to this information because it is based on exclusively on household’s income and does not account for the sharing of resources within communities. In communities where group-specific public goods, such as religious and social goods, are present, class antagonism and redistribution are mitigated by community solidarity. Households benefit from these goods, and such benefits alter the individuals’ beliefs of inequality. Consistent with this prediction, the average individual perceives their household as richer in districts with a greater supply of religious or social goods. The sharing of resources within religious or ethnic groups can shape perceptions of the income distribution and reduce support for redistribution within these groups, and thus requires serious consideration in studies of inequality.

This paper is a product of the Office of the Chief Economist, South Asia Region. 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://www.worldbank.org/prwp. The authors may be contacted at mbussolo@worldbank.org and at akshay_dixit@g.harvard.edu.

_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
The choice of PPPs could be viewed at least in part as a consequence of the behavioral biases of policy makers. Policy makers are influenced not only by the financial, ideological, and political preferences of the government as a collective, but also the time horizons of their political tenure, and thus present bias and myopic inability to make credible commitments. On the one hand, the choice among different potential infrastructure investments may create tensions between interest groups in the population that the government must consider. This choice also affects the costs, potential revenues from user fees and ultimately the financial viability of the project. This in turn affects the feasibility of a private interested partner (as shown in Fay et al. (2021)). On the other hand, infrastructure investments are long term commitments while those holding political office may be working on much shorter timelines. This situation creates tensions both in the maintenance of the investments, and the choice to make the investments in the first place. The main behavioral inhibitor is that their return appears well after the current responsible politicians stay in office ends (Daniel et al. (2019)).

# **3.1.2 Establishing Contractual Arrangements for Service Provision**

<mark>Once the government has decided on the specific investment, the policy maker must elaborate the contract.</mark> We observe a great variety of funding and service provision arrangements across sectors and countries. These range from direct public provision to private regulated contracts, with a variety of types of Private Public Partnerships (PPP) in between. Though there is a growing role for the private sector, it is beyond argument that infrastructure construction is mainly carried out by the public sector. Specifically, 83% of the investment in infrastructure projects in developing countries was executed by public entities and state-owned enterprises in 2017 (World Bank, 2017).

<mark>Conceptually, the principal (policy maker) gives authority and property rights over certain operations to the agent (service). The agent acts and makes decisions (some of these actions and decisions are explored in the next section). The government must design a contract that induces the agent to serve his/her interest of improving access to a quality service. The process for selecting a service provider must include fairness, neutrality, and t</mark> ransparency (World Bank, 2018). Transparent practices are key within the procuring organization, in the absence of which there could be reactions from the contracting parties as well the consumers. In this sense, procurement officers may be tempted to engage in corrupt practices due to present bias, with direct implications on the way that bidders act in the procurement process. For instance, the Procuring Infrastructure PPP Report (World Bank, 2018) <mark>evaluates laws and regulations of 135 economies and determines</mark> 14
wage growth may influence the change in exports, as a rise in wages may hamper export expansion. In the latter case, the change in Export Index and the error term would be negatively correlated, causing a downward bias in the coefficient estimate for the change in Export Index.

Whatever the source of endogeneity may be, I address the potential endogeneity problem using the province-specific measure of the U.S. tariff reduction as an instrumental variable. The U.S. tariff cuts that each province faces are calculated using a methodology similar to the one developed by Topalova (2010) and applied to Vietnam by McCaig (2011) and Coello (2009). Topalova (2010) examines the 1991 Indian trade liberalization episode and finds that rural districts, in which production sectors more exposed to import liberalization were concentrated, experienced slower reduction in poverty in India. Following Topalova (2010), McCaig (2011) constructs a provincial measure of U.S. tariff reductions and finds that provinces that were more exposed to the U.S. tariff cuts in the aftermath of the BTA experienced faster decreases in poverty. My approach differs from the previous studies in terms of explicitly modeling the rise in labor demand induced by exports as a channel through which exports influence wage growth.<sup>39</sup> In order to overcome potential endogeneity problems, I use the U.S. tariff cut measure as an instrument. Furthermore, I extend the tariff cut measure developed by Topalova by including a

> 39 McCaig (2011) also examines the impacts of the BTA on provincial wages, but for different education groups. McCaig (2011) shows that the increase in the U.S. tariff cut measure is associated with an increase in the mean provincial hourly wage growth for workers with “at most a primary education” whereas the relationships are not statistically significant for the other education groups. His findings are consistent with a rise in labor demand for unskilled workers, but the study does not show or model a mechanism through which the tariff cut translated into the rise in wages for unskilled workers. Also, since the education groups of “no formal education” and “primary” are aggregated in McCaig (2011), one cannot know whether the U.S. tariff cut is associated with the rise in wages for the _least_ educated workers (who are the most likely to be the poor). In terms of the data, whereas McCaig (2011) used cross section data aggregated at the province level, I used panel individual data. Finally, McCaig (2011) and most of previous studies used hourly wages in evaluating wage change, I used monthly wages which reflect both the compensation per unit of time and hours worked.

25
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27 lead to lower costs and prices of bednets due to an increasing demand and competition in the ITN and LLIN market.

As shown in Table A1, estimated costs per ITN vary significantly not only by country and by factors mentioned above. They vary as well depending on the methodology that is used for calculation of average unit costs, particularly on the type of costs considered. As a result, there are different estimates of cost per ITN delivered within the same campaign.

Guyatt and Snow (2002) report costs per bednet and insecticide to be around US$ 4.50 – US$ 6 when bought in a bulk. Retail prices of ITNs were US$ 3 – US$ 4 in Tanzania in 2004 (Magesa et al 2005). In 2008, a price for a LLIN was US$ 4.09 – US$ 5.78 in Kenya (Ministry of Health 2009b). WHO estimates an annual cost of protection with LLIN to amount between US$ 1.48 and US$ 2.64 and to average at US$ 2.08 (WHO 2007). Assuming a three year product life, the cost per delivered LLIN can be estimated between US$ 4.44 and US$ 7.92, averaging at 6.24 US$.

Given these estimates and taking into account the fact that there was a gradual shift from distribution of ITNs (lower provision costs) to LLINs (higher provision costs), we consider US$ 5 a reasonable estimate for the average cost per ITN delivered in Kenya over a five year period 2003-2008.

# **7 References**

Abuja Declaration. 2000. http://www.rollbackmalaria.org/docs/abuja_declaration_final.htm. Accessed on 9<sup>th</sup> June 2011.

Ashraf, N., G. Fink, and D. N Weil. 2010. _Evaluating the Effects of Large Scale Health_ _Interventions in Developing Countries: The Zambian Malaria Initiative_ . National Bureau of Economic Research.

De Allegri, M., P. Marschall, S. Flessa, J. Tiendrebeogo, B. Kouyate, A. Jahn, and O. Muller. 2009. ―Comparative cost analysis of insecticide-treated net delivery strategies: sales
# 5.3. Prospects for potential growth in EAP

Potential GDP growth in EAP is projected to slow further to an average rate of 4.6 percent a year in 2022-30, down from 6.2 percent a year in 2011-21. China accounts for much of the projected slowdown, but slowing potential growth is expected to spread to the rest of the region as well. Part of the projected slowdown is due to the pandemic and the war in Ukraine, which are expected to be most severe and longest lasting in the countries that have suffered most from the collapse of global tourism and trade. Growth prospects have also deteriorated for countries that have recently suffered natural disasters, domestic policy uncertainty, and terms of trade shocks.

In terms of the production function framework, each of the three main drivers of potential output growth is expected to contribute to the worsening outlook, with weaker capital accumulation accounting for most of the slowdown followed by falling TFP growth and labor supply growth. Capital accumulation is projected to slow most steeply in China, where policy efforts to rein in credit growth have recently resumed. In contrast, in the Philippines, investment is expected to pick up from depressed levels and boost potential output growth. Heightened geopolitical tensions may weaken investment in the region through higher interest rates, reduced business confidence, and heightened uncertainty.

TFP growth in EAP is expected to be dampened further by maturing electronics technologies and the slowing expansion of global value chains. Geopolitical tensions may also weaken gains from increasing international division of labor and diffusion of technology.

Demographic trends that are already slowing labor force growth are expected to continue, putting the region at risk of growing old before becoming rich (figure 6). The largest decline in the share of working-age population is expected in China. In contrast, for some countries, including Cambodia, Lao PDR, and Papua New Guinea increases in workingage populations are expected, and these countries could continue to reap demographic dividends if they generate sufficient jobs.

Risks to the baseline projection for potential output growth are predominantly on the downside. Downside risks include a worsening of the conflict between Russia and Ukraine, persistent geopolitical tensions, and associated trade disruptions. Worsening geopolitical tensions could further destabilize global economic activity and, in the longer term, cause global trade, investment, technology transfer, and financial networks to fragment (World Bank 2022c). The drag on activity from persistent trade and supply disruptions and high commodity prices could also cause the global economy to become mired in stagflation, with inflationary pressures requiring substantially more monetary tightening than currently assumed.

# 5.4. Policy options to lift potential growth in EAP

The baseline projection for 2022-30 shows a further slowdown in EAP's potential output growth, which will also result in a slower convergence with per capita incomes of advanced