rafmacalaba/datause-encoder-data huggingface dataset:
17 aggregate offsetting policies and the WRI captures the higher welfare costs of more disparate policies across industries. These scalar measures have the advantage of making policies more transparent, which can facilitate further reforms.

Notwithstanding their contribution, there are a number of limitations to the indexes. Some are empirical. First, the estimates can only take account of agricultural products which have commodity level data in the World Bank’s database. The database has product level data for approximately 70 percent of the 19 African focus countries’ farm production value, and somewhat less of their consumption value. We, therefore, necessarily miss some information about distortions to production and consumption and therefore trade and welfare in Africa. Furthermore, the data in the World Bank’s database are not highly disaggregated, which is not ideal for capturing the full extent of welfare and trade distortions from African policies affecting differentiable processed as well as primary products. Finally, in the absence of reliable, consistently estimated time-series of elasticities of demand and supply, we make simplifying assumptions about those elasticities to compute the scalar index number series. The estimates would be more precise if we had access to reliable elasticity estimates, although probably not a lot different, according to sensitivity analysis conducted by Croser, Lloyd and Anderson (2010).

There are also some methodological caveats worth noting. The methodology in the paper adopts the standard approach still presented in most textbooks on trade policy or welfare economics, based on the benchmark of competitive markets. The methodology ignores the existence of divergences and governance problems, including administrative costs. Thus the trade and welfare reduction indexes reported above may be over or under-stated to the extent that such problems exist. For example, in some cases where there is market failure, we know from second-best theory that policies that increase assistance to a lightly protected industry may increase rather than decrease national economic welfare. In particular, the RRA measure reported in Table 1 suggest that distortions to non-farm tradables sectors in Africa exist. Even so, the series reported in this paper are useful aggregations of data and almost certainly give a better indication of trade and welfare effects of policy than average NRA-type measures.
**Figure 7: Years of education, by gender and location**

<!-- Start of picture text --> 1940 1950 1960 1970 1980 1990<br>Women/Rural Women/Urban Men/Rural Men/Urban<br>12<br>10<br>Years of education 8<br>6<br><!-- End of picture text --> Source: Staff estimates based on HIES 2016.

Note: 1990s cohort refer to individuals born between 1990 and 1995. Average mean years of education by decade-cohort of individual’s year of birth; individuals in 1990 cohort may not have fully completed education at the time of survey (2015), and hence these individuals could attain higher mean years of education.

# 4.2.Defining Trade Policy Scenarios<sup>21</sup>

# 4.2.1. Baseline

**As mentioned above, an ex-ante evaluation requires the creation of a counterfactual simulation that will serve as a business-as-usual scenario with no policy change.** The model relies on the GTAP version 9 database benchmarked to 2011. The CGE model runs up to 2028, replicating the key historical macroeconomic aggregates from the World Bank World Development Indicators (World Bank, n.d.). Productivity growth in the baseline scenario is calibrated to achieve the historical and forecasted GDP growth rates up to 2021. Productivity growth is adjusted thereafter to be consistent with historical trends.<sup>22</sup> The baseline scenario also incorporates tariff reductions under existing FTAs. These are based on the data set provided by the International Trade Center, including all TPP members’ FTA commitments up to 2027 (International Trade Centre 2016). We also assume that Sri Lanka enjoys the Generalized System of Preferences (GSP) preferences on its exports to EU markets.

**The baseline contemplates continuation of recent trends using standard closure rules for macroeconomic accounting.** The scenarios incorporate three closure rules. First, government expenditures are held constant as a share of GDP, fiscal balance is exogenous while direct taxes adjust to cover any

> 21 This section was largely was developed by Guillermo Arenas (Economist, World Bank Group) using the Tariff Reform Impact Simulations Tool.

> 22 These productivity assumptions are not altered in in the counterfactual scenarios.

14
Despite the presence of encouraging findings, the literature also features microeconometric studies that recover either negative or positive but statistically insignificant short-term effects of household agricultural commercialization on welfare outcomes (von Braun, de Haen, and Blanken, 1991; Kennedy, 1989; Immink and Alarcon, 1993; Immink et al., 1995). Complementing these results, Carletto et al. (2011) present evidence on the long-term effects of agricultural commercialization on household welfare by using a panel data set spanning the period of 1985-2005, and focusing on non-traditional agricultural export crop adoption in the Central Highlands of Guatemala. The authors document that while consumption status improved for all household groups in the surveyed communities between 1985 and 2005, the extent of improvement among long-term adopters was lower in comparison to the changes experienced by non-adopters.

Other factors influencing the relationship between household agricultural commercialization and welfare outcomes include unfavorable rainfall outcomes and staple food price shocks. Carter and Maluccio (2002) use South African panel data to show that the inability of communities to insure against covariate shocks results in increased levels of stunting after economic disasters. Similarly, Akresh, Verwimp, and Bundervoet (2007) demonstrate that crop failures in Rwanda lead to disproportionate reductions in the height of low-income girls, and Hoddinott (2006) finds that a Zimbabwean drought has long-term stunting effects on children less than two years old.

# **2.1 The Malawian Context**

The links between household agricultural commercialization and welfare are particularly important to understand in the case of Malawi where the headcount poverty rate stands at 51 percent and 85 percent of the population reside in rural areas, primarily engaged in agriculture (NSO, 2012). The sector is not only an essential part of the social fabric, but also the backbone of the economy. 84 percent of Malawian households own and/or cultivate land, and the contribution of agriculture towards the Gross Domestic Product (GDP) stands at 30 percent.<sup>6</sup> Maize is the main staple crop, accounting for close to 60 percent of household caloric intake and being grown by nearly 100 percent of all farm households.<sup>7</sup> Maize availability typically defines the food security status of the

> 6The estimate of the contribution of agriculture towards the GDP is for 2011, and is obtained from http://data.worldbank.org/indicator/NV.AGR.TOTL.ZS. The estimate of the percentage of Malawian households owning and/or cultivating land is based on the Third Integrated Household Survey (IHS3) 2010/11. 7The estimates are based on the IHS3 2010/11 data.

5
The above argument implicitly assumes that the location decision of an individual bank is made independent of the location decision made by other, competing, multinational banks that are also expanding their business abroad and want to determine their choice of location. When a firm decides to expand its business abroad, however, the choice of location is not just dependent on the firm’s own internal advantage, but also on the competitive advantage that the investing firm has both over domestic firms but also over other foreign firms. To the extent that sources of internal competitive advantages are derived from the ability to work within a certain institutional environment, this implies that it may not just be the difference between the institutional quality in host and source country that matters for location decisions, but rather the difference between host and source country taking into account the institutional quality of the competitors. The impact of this type of competitiveness on location attractiveness of any type of foreign investment has, to our knowledge, not been studied before. The location decision of foreign banks, an institutionally-intensive sector, provides a good test how institutional differences can lead to competitive advantages and how they maybe dealt with.

In our study, we test therefore whether competitive advantage arising from working in a certain investment climate is an important determinant of foreign bank entry. In order to do so, we first construct a database covering most banks in all developing countries, including their ownership and the source country of all foreign owners. This database allows us to test whether better knowledge about a certain business environment compared to a bank’s competitors is a determining factor in its decision to enter a certain market. We next construct a measure of a bank’s competitive advantage as it relates to institutional environments. We find that controlling for a large number of variables already established to determine bank entry, the level of institutional development in the host country itself does not seem to impact cross-border entry activity. The same holds for differences in institutional development between host and source countries. However, we find strong evidence that similarities in institutions between host and source country _compared to_ competitors affect a bank’s entry decision. In other words, it is not the absolute level of the institutional environment faced by a firm that prevents it from entering a certain market, but rather its ability to work within a certain institutional environment better than its competitors. This shows that competitive 3
- K. Diaconu, J. Falconer, A. V. Verbel Facuseh, A. Fretheim, and S. Witter. Paying for performance to improve the delivery of health interventions in low- and middle-income countries. _Cochrane Database of Systematic Reviews_ , (12):CD007899, 2020.

- T. Doran, E. Kontopantelis, J. M. Valderas, S. Campbell, M. Roland, C. Salisbury, and D. Reeves. Effect of financial incentives on incentivised and non-incentivised clinical activities: Longitudinal analysis of data from the UK Quality and Outcomes Framework. _British Medical Journal_ , 342: d3590, 2011.

- E. Duflo, R. Hanna, and S. P. Ryan. Incentives work: Getting teachers to come to school. _American Economic Review_ , 102(4):1241–78, 2012.

- E. Fehr and L. Goette. Do workers work more if wages are high? evidence from a randomized field experiment. _American Economic Review_ , 97(1):298–317, 2007.

- D. Filmer and N. Schady. Does more cash in conditional cash transfer programs always lead to larger impacts on school attendance? _Journal of Development Economics_ , 96(1):150–157, 2011.

- F. Finan, B. A. Olken, and R. Pande. The personnel economics of the state. NBER Working Paper w21825, National Bureau of Economic Research, 2015.

- National Population Commission. Nigeria demographic and health survey 2018 - final report. Technical report, 2019.

- G. B. Fritsche, R. Soeters, and B. Meessen. _Performance-based financing toolkit_ . The World Bank, 2014.

- R. G. Fryer, S. D. Levitt, J. List, and S. Sadoff. Enhancing the efficacy of teacher incentives through loss aversion: A field experiment. Technical report, National Bureau of Economic Research, 2012.

- V. Gauri, J. C. Jamison, N. Mazar, O. Ozier, S. Raha, and K. Saleh. Motivating bureaucrats through social recognition: evidence from simultaneous field experiments. Technical report, The World Bank, 2018.

- J. Goldberg. Kwacha gonna do? experimental evidence about labor supply in rural malawi. _American Economic Journal: Applied Economics_ , 8(1):129–49, 2016.

34
that are far less congested) and would encourage use of vehicles within Mexico City that are not registered, or registered elsewhere.<sup>13</sup>

**Figure 3: Welfare Gains and Mileage Reductions from Auto Mileage Tolls**

<!-- Start of picture text --> 120 30<br>100 25<br>80 20<br>60 15<br>Welfare gain<br>40 % reduction in miles 10<br>20 5<br>0 0<br>0 3 6 9 11 14 17 20<br>Auto toll (Cents per vehicle mile)<br>emissions from autos<br>Welfare gain (US$ per capita)<br>% reduction in mileage, gasoline and<br><!-- End of picture text --> Another option might be to charge by the mile for any driving in Mexico City through global positioning systems (GPS), or other electronic tolling, such as transponders embedded in roads that record vehicles as they pass overhead. Mandating installation of the required metering technologies in vehicles is infeasible at present, given the large incentives for people to evade the mandate to avoid paying tolls as well as important privacy concerns. However, it might be possible to design schemes to circumvent these constraints. For example, owners of vehicles with the required metering technology might be eligible for a lump-sum annual rebate (financed in part by the new toll revenues). This subsidy could be set initially so that a large portion of

> 13 Another option for congestion pricing is a cordon toll, as recently implemented in London and Stockholm. In the London scheme (Leape 2006), tag plates are photographed at all entry points into the priced area, and charges are automatically debited from the vehicle owner’s pre-paid accounts (large fines are imposed for those who have not paid ahead of time). These schemes differ from the uniform mileage toll studied here. In particular, a worry is that they may worsen congestion outside of the priced area as people alter driving habits to avoid the toll, though this does not appear to have been a serious problem in London and Stockholm.

28
17 household-based measure is that it captures only outpatient costs incurred during the last four weeks. Another is that because utilization data were collected only from one randomly-selected household member, we cannot distinguish between households where someone received care but nothing was spent out-of-pocket, and households where nobody received any care. A merit of the individual-based measure is that we _can_ distinguish between these two groups. The other advantage is that we have outpatient expenses incurred over 12 months. A disadvantage is that we have out-of-pocket spending data only for the last contact. The other is that we have spending on inpatient care (if there was an inpatient admission), or on outpatient care (if there was not), but not both.

We need a measure of nonmedical consumption. In the WHS household questionnaire, the household is asked four questions covering its expenditure in the previous four weeks on (i) food, (ii) housing-related costs, (iii) education, and (iv) other goods and services. This is far from ideal, and is a far cry from, say, a typical survey along the lines of the World Bank’s Living Standards Measurement Study; such surveys capture consumption rather than just expenditure (they capture, for example, home-grown food and the use value of family-owned housing), and also collect detailed information through a detailed questionnaire the various components of consumption. Even in comparison with household surveys with slimmer consumption modules, the WHS consumption module is very brief. The consensus seems to be (cf. Lu et al. 2009; Heijink et al. 2011; Raban et al. 2013) that nonmedical consumption may be underestimated in the WHS. Because of the
62 percent of the items were performed. There is a positive association between wealth and each individual procedure of antenatal care. All the coefficients are significant at least at the 95 percent level. A one standard deviation increase in wealth is associated with a seven percentage‐point higher likelihood of having blood and urine samples taken for testing, a five‐percentage‐point increase in the likelihood of being offered an HIV test, and a five‐percentage‐point higher likelihood of receiving nutritional counseling.

Table 2 presents the results of regressions of the quality‐of‐care indexes on the wealth index. Columns 1 to 3 present the results from analysis of the household survey data, and columns 4 to 9 present the results from analysis of the direct observation data. Given that women in the household surveys report on antenatal care coverage over the entire pregnancy, positive wealth gradients could appear due to the difference in individual visits or because wealthier women attended more consultations. For the household survey data, we find that each standard deviation increase in wealth is associated with a 4‐ percentage‐point increase in the completeness of antenatal care. When we focus on the first antenatal care consultation in directly observed visits (column 4), the gradient drops to 1.6 percentage points. In both cases, the coefficient representing the wealth‐quality relationship remains statistically significant at the 99 percent level when controlling for women’s education level, age, and marital status (columns 3 and 6). It is important to keep in mind that the items in the quality indexes are different. That is, although the estimated coefficients both represent percentage changes in protocol compliance, the clinical implications of observed changes might differ.

We use the rich data collected through direct observation of antenatal consultations to create an index of non‐clinical quality of care, which primarily focuses on the interpersonal behavior of the providers. The index gives equal weight to the following six indicators: filling an antenatal care card, taking sufficient time for the consultation (at least 10 minutes), explaining procedures, encouraging questions, suggesting a third person attend the consultations, and using visual aids. On average, the providers score 0.57 on this index. We do not find a relationship between socioeconomic status and this measure of non‐clinical quality, regardless of the regression specification (columns 7 to 9 in table 2).

Table C.3, in appendix C, presents the regression results when each component included in the direct observation quality indexes is regressed individually on wealth. The regressions include the same controls as those used in column 6 in table 2. For eight of the 21 items representing clinical quality, there is a positive relationship with the wealth index, with p‐values below 0.10. The relationship is particularly strong for diagnostic testing, which often requires additional fees and out‐of‐pocket 10
Policy Research Working Paper 10439

# **Abstract**

The Jharkhand State Livelihoods Promotion Society is rolling-out solar powered community lift irrigation systems to provide access to irrigation to 23,580 farming families in 13 districts of the state. This paper assesses these irrigation systems using data collected from 297 farmers in the command area of 50 randomly selected irrigation systems. The study also interviewed 457 nonbeneficiaries. Farmers in the command area of the irrigation systems irrigate more land, have higher cropping intensity, are more likely to grow high-value crops, and had higher gross value of output in the Rabi (winter) season. The beneficiaries also spend less money on irrigation, especially compared with nonbeneficiaries who use their own or who rent diesel pumps to irrigate their fields. Although the solar powered community lift irrigation systems offer multiple benefits, they remain severely underutilized. The median hours of operation of the 50 sampled irrigation systems was less than 100 hours per year, and the average operating time was 192 hours per year. Solar irrigation systems cannot be economically viable with low levels of capacity utilization, indicating that incentives are needed for system managers to increase utilization, gross irrigated area, and irrigation surplus.

This paper is a product of the Agriculture Global Practice. 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 a.kishore@cgiar.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
Figure 2.1 With manufacturing flagging, the share of services in employment has been soaring (percentage, 1996–2015) <!-- Start of picture text --> Primary Industry Secondary Industry Tertiary Industry<br>100<br>80<br>Percent 60<br>40<br>20<br>0<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> _Source_ : data.stats.gov.cn.

Trends in technological advancement and automation Digital technologies are fundamentally changing the modern workplace. Computers have become phenomenally better than humans at performing activities that involve high-frequency codifiable tasks, which Autor, Levy, and Murnane (2003) call “routine,” scripted in algorithms and thus automated. These tasks are prevalent in mid-skill occupations such as clerks, craftsmen, and operators. With the wave of the latest digital technologies, such routine tasks increasingly include cognitive tasks such as mathematical calculations, organizing of information, and some of the manual repetitive work on assembly lines.<sup>2</sup> As the price of computing continues to fall, more and more of these tasks are being automated.

Technological progress has polarized employment, particularly in developed countries. This polarization is reflected by the displacement of labor from the mid-skill routine tasks, the increased labor productivity in high-skill ‘abstract’ tasks, and the fewer technological disruptions of lowskill occupations intensive in nonroutine manual tasks. These changes are largely due to the adoption of new technologies that displace routine tasks. Industries and countries that have seen a faster information and communication technology (ICT) growth have increased the demand for college-educated workers and lowered the demand for workers with mid-level skills without necessarily displacing the low-skill or the least-educated workers (Michaels, Natraj, and Van Reenen 2014).

Although China has been among the developing countries where the employment share of the midskill occupations is not falling significantly, there are already indications that the job polarization may start sooner than anticipated (see Figure 2.2 and Figure 2.3). There are four main factors that may have slowed down the process. First, the structure of labor markets in developing countries tends to be different, with a smaller share of mid-skill occupations (given, for example, the predominance of low-skill agricultural and informal employment). Second, the pace of technological adoption is much slower than in advanced economies. Third, the positive productivity impact of technological progress might spur local demand for goods and services, 11