rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 5000

# **Abstract**

The World Bank is a leading intellectual institution on development. It is a world leader in analytical studies in areas including poverty measurement, delivery of social services, impact evaluation, measurement of development outcomes, international trade and migration. It is also a leader in development data, including the Living Standard Measurement Surveys; the enterprise surveys, and the International Price Comparison Project. World Bank research is resolutely empirical and policy oriented. By both learning from past policies and operations and thinking critically about future policies, research plays a critical role in the formulation of policy advice to developing countries.

This paper reviews the intellectual and institutional forces that have shaped research at the World Bank since the latter started lending to developing countries in the early 1950s. It provides an overview of the shifts in development economics that have influenced Bank research and briefly surveys the changes in research organization, structure and approach.

The first section, after a short introduction, examines the shifts in positive and normative views about development during the past half century that have influenced Bank thinking. The Bank itself has been an active participant in the rise and fall of long-lived development dogmas about the nature of development; the most appropriate policies and actions for achieving it; and the respective roles of government and markets. The second section examines how the World Bank has adapted its organization to keep abreast of emerging issues and produce relevant policy research of good quality. On the one hand, the Bank has experienced several reorganizations that have affected the research unit(s) as well as its relationship with operational units. On the other hand, the Bank’s research units themselves have been reorganized at several junctures, leading to new priorities and new means of achieving them.

This paper—a product of the Development Economics, Operations and Strategy Department—is part of a larger effort by the World Bank to strengthen the complementarities between research and operations and to disseminate high-quality research that is relevant for policymakers in developing countries. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. For information, contact jdethier@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
Estimating the Economic and Distributional Impacts of the RCEP scenario, while exports and imports within RCEP increase around 14.1 percent, and 14.5 percent, respectively, exports for the rest of the world grow by only 7.7 percent, and imports decrease by 1.5 percent, indicating a small trade diversion. Intra-RCEP trade is higher when a common set of rules of origin are implemented, while trade with the rest of the world is lower. Exports to the rest of the world increase 7.7 percent under the productivity kick scenario. In imports from the rest of the world, the impact is negative in most scenarios, with the productivity kick scenario having a reduction of imports of 1.5 percent, relative to baseline.

Figure 18. Impact on Vietnam trade (volume): Percentage change relative to business-as-usual scenario, 2035 <!-- Start of picture text --> Intra-RCEP Exports<br>Intra-RCEP Imports<br>Exports to ROW<br>Imports from ROW<br>-6.00 -4.00 -2.00 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00<br>Tariffs alone RCEP ROO liberalization Productivity kick<br><!-- End of picture text --> Note: ROW = rest of world.

Source: World Bank staff calculations using ENVISAGE model Most of the tariffs imposed and faced by Vietnam within the RCEP bloc have relatively low initial levels due to the high level of integration that already exists in the region (Figure 19). Vietnam imposes the highest tariffs on motor vehicles and parts (4.5 percent), non-metallic minerals (3.3 percent), and other transport equipment (3.2 percent), and the sectors where its exports face higher levels of tariffs are crops (6.5 percent), food and beverages (5.1 percent), and meat products (2.6 percent).

The tariff reductions applied here follow the official RCEP tariff reduction schedules which consist of specific schedules, that vary depending on the source and destination of trade flows. Vietnam has imposed tariffs against other RCEP member countries, with the highest reductions in motor vehicles and parts (-3.3 percent between 2020 and 2035), other transport equipment (-2.4 percent), and petroleum and food (- 13 percent), sectors that had high initial levels of tariffs in 2020. For the tariffs faced by Vietnam, the sectors with the highest reductions are food and beverages (-4.3 percent), petroleum, and coal products (-1.4 percent), and meat products (-1.2 percent).
the migration alternative. Beegle, Joachim and Stefan (2011) document that precipitation anomalies increased both the probability of people leaving the village and the distance moved in Northern Tanzania. Mueller et al. (2020) combined NASA’s high-resolution climate data with longitudinal microdata on migration, labor participation, and LSMS-ISA data (see also Section 4), to test whether climate variability affects temporary migration to rural and urban East Africa and whether climate-induced migration coincides with a lack of local job opportunities. The data included surveys conducted in Ethiopia, Malawi, Tanzania, and Uganda over six years (2009–2014). They found that climate variability significantly affects temporary migration decisions in eastern Africa, specifically that temperature and rainfall shocks cause a reduction in temporary urban out-migration. Mueller et al.’s (2020) findings are consistent with the results of Hirvonen (2016) for rural Tanzania and challenge the narrative that temporary migration acts as a safety valve in response to climatic push factors.

Grace et al. (2018) found that rainfall did not affect temporary migration rates in two Malian villages. The authors combine unique data from highly detailed stories of migration collected over 25 years in two rural communities in Mali, and document that a poor rainy season is not correlated with extreme or even above-average emigration rates. Even accounting for some known sources of variability (age, gender, etc.), a decrease in rainfall does not directly lead to a higher emigration rate. Instead, the results suggest that during low-rainfall years outmigration is lower. Henderson et al. (2017) estimate the effects of climate variability and change on African urbanization patterns over two different temporal and spatial scales: i) local, within-district urbanization for an unbalanced 50-year panel of census data from 359 districts in 29 countries; ii) urbanization patterns from 1992 to 2008 in 1,158 cities. Their estimates show that climatic conditions do affect urbanization rates, with better conditions delaying urbanization and adverse conditions leading to faster urban population growth, but that these effects are confined to a subset of about 20%-25% of Sub-Saharan African districts.

In a study already mentioned above, <mark>Martinez Flores, Milusheva and Reichert</mark> (2021) leverage highfrequency (daily) migration data on the place of origin of migrants and the time of migration, collected from the International Organization for Migration in 17 West and Central African countries over the period 2018-2019, and estimate that droughts, as measured by soil moisture anomalies during the growing season, strongly reduce the number of international migrants. As they detect these effects only in middle-income areas but neither in rich nor poor ones, they conclude that climate-induced liquidity constraints and income losses are the key mediating channels.

Finally, in a recent working paper, Di Falco, Kis, and Viarengo (2022) exploit LSMS-ISA panel data 15
interventions are scaled up and unit costs need to be extrapolated, demand-side constraints have to be taken into account—at least when the unit cost refers to social service utilization, for instance, vaccinations actually delivered or school enrollment. As an example, if the lack of education and the prevalence of traditional norms prevent mothers from having their children vaccinated against measles, the marginal unit cost of a supply intervention that effectively adds to the number of vaccinated children may be infinite in the absence of a complementary, relatively cheap information campaign aimed at the demand side. In the presence of such a demand-side intervention, on the other hand, the marginal unit cost may be very moderate. In the discussion of the costs of achieving universal primary education, Glewwe and Zhao (2006) argue that the cost estimate based on a demand-side model can be three to four times higher than cost-accounting estimates.<sup>26</sup> These cost-accounting exercises, which are common practice in policy reports, typically assume a linear and separable cost function. Although this may be a roughly valid procedure on the margin, for example, in cases where existing interventions are scaled up somewhat, the costs of different components of policy packages are very unlikely to be additive beyond the margin.

Not only the level but also the composition of costs is likely to change considerably when efforts focused on the demand side are included. Bangladesh’s transfer programs in the education sector, for example (see the box above), imply a major shift in the cost structure of primary education. While teacher costs account for approximately 75 to 80 percent of the total cost of primary schooling in the average developing country (Bruns et al., 2003), this figure decreases to only 55 percent in Bangladesh when the demand-side interventions are taken into account.

Ultimately, cost-effectiveness studies need to constitute the basis of MDG planning and costing efforts. We therefore conclude this empirical section with three “best-practice” examples that examine not only the impact of a specific intervention, but the costeffectiveness of different supply- and demand-side interventions. All of them at least implicitly consider demand-side constraints. Two of the three (Handa, 2002; Glick and Sahn, 2006), are _ex ante_ simulations, while the third study (Coady and Parker, 2004) uses results from an _ex post_ evaluation to construct (cost) counterfactuals.<sup>27</sup> For the case of Mozambique, Handa (2002) compares three policies intended to increase primary enrollment: building schools, an unconditional cash transfer, and adult literacy

> 26 Cost-accounting approaches consider supply-side inputs into social service utilization only. 27 Another very insightful contribution on the costs and benefits of alternative policy designs for PROGRESA is Todd and Wolpin (2006). Their counterfactual analyses, which look at different subsidy schemes, are based on a structural model of schooling and fertility choices.

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CGE MODELS FOR THE ANALYSIS OF TRADE POLICY IN DEVELOPING COUNTRIES .. ...

# Jaime de Melo (*)

# Table of Contents

|1.|**Intro**|**duction••••••••••••••••••••••••••••••••••••••••••••••••••••**|1|
|---|---|---|---|
|2.|**AOne**|**SectorModel••••••••••••••••••••••••••••••••••••••••••••••**|3|
|3.|TheD|omesticPriceSysteminMulti-SectorModels••••••••••••••••|8|
|4.|Appli|cationstoTradePolicY••••••••••••••••••••••••••••••••••••|11|
||4.1|The WelfareCostsofQRaand Rent-SeekingActivities•••••|11|
||4.2|InteractionsBetweenQRForeignTrade Regimes<br>andIndustrialOrganization••••••••••••••••••••••••••••••|16|
||4.3.|Optimal TradePolicyinthePresenceofa<br>Government Revenue .Constraint••••••••••••••••••••••••••••|20|
|_s._|Appli|cationstoInternal-ExternalBalance and Growth••••••••••••|23|
||5.1.|CausesofTurkey'sForeignExchangeCrisisin1977•••••••|27|
||5.2.|AbsorptionUnder aDisequilibriumReal ExchangeRate:<br>**Chile1977-81••••••••••••••••••••••••••••••••••••••••••••**|30|
||5.3.|SimulatingAlternativeDevelopmentStrategies••••••••••••|33|
|6.|Appli|cationstoIntertemporalIssues••••••••••~•••••••••••••••••|35|
||6.1.|OptimalForeignBorrowingStrategies:<br>A Case Study<br>ofThailand(1985-92)••••••••••••••••••••••••••••••••••••|38|
|7.|**Concl**|**usions•••••••••••••••••••••••••••••••••••••••••••••••••••••**|42|
||**Footn**|**otes**••••••••••••••••••••••••••••••••••••••••••••••••••••••|43|
||**Refer**|**ences**•••••••••••••••••••••••••••••••••••••••••••••••••••••|**47**|
|||Tables||
|Table|1:|AOneSectorSmall-CountryModelwith<br>DifferentiatedTrade•••••••••••••••••••••••••~••••••••••|_5_|
|Table|2:|WelfareBenefitsfrom Removing QuotasinTurkey•••••••••|15|
|Table|3:|A ComparisonoftheWelfareCostsofRationing<br>underAlternativeMarketStructures•••••••••••••••••••••|19|
|Table|4:|TariffRates••••••••••••••••••••••••••••••••••••••••••••|22|
|Table|5:|AOneSectorSmall-Country GrowthModel•••••••••••••••••|24|

- (*) An earlier version was present at the International Symposium on the Social Accounting M&trix Methods and Applications, June 17 - 19, 1987. I thank Bela Balassa and Sherman Robinson for comments and Jackson ·Hagargee and Julie Stanton for much appreciated support. The views are those of the author not those of the World Bank •

. -· . ., ' .
Table 7 Analysis of the Structural Change In United States Nontarift Barrier Protection: 1966-1986

||Imports C<br>Type I|overed by 19<br>NTBs 2/_<br>T|66<sup>Nontariff</sup><br>ype I+IIN|Barriers<br> TBs 2/|Imports Affect<br>1966NTB|ed by 1966<sup>and</sup><br>s<br>|<sup>1986 Type</sup><br>1986|<sup>I andIINTBs2/</sup><br>NTBs<br>|
|---|---|---|---|---|---|---|---|---|
|Product groupI/|**S**<br>of lmoarts<br>covered|Value of<br>imports<br>covered|**S**<br>ofimports<br>covered|Value of<br>imports<br>covered|**s**<br>ofimports<br>affected|Value of<br>Imports<br> affected|t<br>ofimport<br>aflected|Valueof<br>imports<br>affected<br>|
|||**(S**million)||(S million)||(S million)||(S million)|
|AllProducts|11.9|27.254|16.8|38,373|36.4|9,3793/|<br>45.0|103,105|
|of which:<br>Foodstuffs|14.7|2,517|36.7|6,284|32.3|1,577|73.9|12,655|
|Agricultural<br>Raw Haterials|1.6|92|32.4|1,865|13.8|230|45.1|2,596|
|<br>Fuels|0.0|0|0.0|0|91.8|2.088|0.1|75|
|Ores and Metals|2.6|266|6.4|656|0.0|0|15.7|1,609|
|Manufactures|20.2|24,379|24.5|29,568|38.7|5,484|71.4|86,170|
|ofwhich:|||||||||
|Iron and Steel|76.1|7,589|85.0|8,477|73.0|1,002|99.4|9,913|
|Chemicals|0.0|0|2.9|254|55.0|532|36.4|3,194|
|<br>Electrical<br>Machinery|1.4|176|10.9|1,370|72.0|821|86.7|10,897|
|<br> <br>Nonelectrical<br>Machinery|0.0|0|2.9|375|1.0|19|37.2|4,805|
|<br> <br>Transport Equipment|41.1|9,702|47.3|11,166|73.0|2,017|87.5|20,656|
|<br>Textiles<br>andClothing|66.8|6,297|68.3|6,439|54.7|800|98.5|9,286|

- I/ See Tables 2 and 3 for<sup>the SITC groups includod in each product category.</sup>

- 2/ See Table I for a listing of Type I and Type II nontariff barriers.

- 3/ This is equivalent to a value ot $52.8 billion measured in 1986 prices.

- TABLE NOTE: The actual<sup>1986 U.S. NTB trade coverage ratios</sup> for total imports have been decomposed<sup>into trade coverage ratios</sup> for some of the more important<sup>typesofnontariff</sup> barriers. These ratios (shown In parentheses) are<sup>as follows:</sup> tariff quotas (1.2); variable import levies (1.4); antidumping and countervailing duties (3.5); reference import prices (0.0); minimum import prices (0.0); "volunatry" price restraints (0.2); special import taxes (1.0); import authorization requirements (1.0); non-automatic import licensing (0.0); quantitative restrictions other than _WA_ or textile quotas (2.0); "voluntary" export restraints on volumes (11.3); WA restrictions (3.2); other quantitative textile restrictions (0.9). Trade coverage ratios for other Type 11 NTBs were<sup>not calculated.</sup>
and programs like REDD+ can potentially credit the carbon portion of these external effects if adoption reduces emissions of greenhouse gases.

We find that biogas adoption reduces greenhouse gas emissions in rural Nepal. Using a nationally representative household sample, we estimate that adopting biogas reduces household firewood collections, which are primarily used for cooking, by about 1.1 metric tons per year. We use central values from the latest literature on the greenhouse gas content of fuelwood burned in traditional biomass cookstoves in developing countries and find that biogas plants reduce CO2e emissions on average by 1.6 tons per household per year compared with what would have been emitted had biogas not been adopted. This is considerably lower than the figures being used by the limited existing aid programs for biogas. However, it is still significant. Using a modest carbon price, we conclude that the carbon savings from adoption of a biogas plant are high enough for current international carbon markets to be able to serve as an important source of finance for Nepali villagers who would like to adopt biogas, but do not have the resources.

# **References**

- Altonji, Joseph G., Todd E. Elder, and Christopher R. Taber. 2005. “Selection on Observed and Unobserved Variables: Assessing the Effectiveness of Catholic Schools.” _Journal of Political Economy_ 113 (1): 151.

- Arnold, M., G. Kohlin, R. Presson, and G. Shepherd. 2003. _Fuelwood Revisited: What has changed in the last decade?_ Jakarta, Indonesia: Center for International Forestry Research (CIFOR).

- Baland, Jean‐Marie, Pranab Bardhan, Sanghamitra Das, Dilip Mookherjee, and Rinki Sarkar. 2010. “The Environmental Impact of Poverty: Evidence from Firewood Collection in Rural Nepal.” _Economic Development and Cultural Change_ 59 (1): 23–61.

- Barnes, D. F, S. R. Khandker, and H. A. Samad. 2011. "Energy poverty in rural Bangladesh." _Energy Policy_ 39 (2):894‐904.

- Bluffstone, R., E. Robinson and P. Guthiga. 2013. “REDD+  and community‐controlled forests in low‐income countries: any hope for a Linkage, _Ecological Economics_ . Vol. 87: 43‐52.

- Bond, Tami C., Sarah J. Doherty, D. W. Fahey, P. M. Forster, T. Berntsen, B. J. DeAngelo, M. G. Flanner, et al. 2013. “Bounding the Role of Black Carbon in

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The behavior of prices in the Argentine gasoline market motivated academic research aimed at determining the intensity of competition in this market. The conclusions of the papers written about this topic are contradictory. Aspiazu (1995) argues that the observed differences in levels and adjustment patterns of gasoline prices between Argentina and the United States (which is used as a benchmark for a competitive market) is a consequence of an oligopoly operating under collusion. On the contrary, FEEL (1999) and Coloma (1997) do not find any evidence of the existence of an implicit agreement. Serebrisky (2000) presents different econometric tests to evaluate<sup>the</sup> hypothesis of tacit collusion, finding no conclusive evidence of this type of behavior.<sup>But</sup> he agrees with a study the Argentine Antitnist Commission did in 1998 and concludes saying that "after almost a decade of deregulation, price competition in the gasoline market is less intense than what the Argentine government expected to be".

In 2000, the newly created Secretariat of Consumer Affairs and Defense of Competition (SDCyC), elaborated a report<sup>3</sup> of the gasoline market which<sup>recommended</sup> the adoption of specific policies to increase the competition in this market. The most important policy recommendations were: (a) limit the duration of contracts between oil companies and dealers that operate gasoline stations and (b) establish a ceiling to vertical integration, measured in a percentage of the network of gasoline stations that an<sup>oil</sup> company can own and operate.

The Argentine president, following this report, issued a decree that implemented new policies in the gasoline market. This decree is one of the few examples of successful advocacy achieved by the Argentine SDCyC. In Latin American countries it is<sup>not</sup> common for competition agencies to act on their own initiative and ask governments to adopt pro-competitive policies. In general, they limit their role to merger requests or<sup>to</sup> solve accusations of anticompetitive conducts. However as explained in a recent survey (International Competition Network, 2002), competition agencies are increasing their advocacy role to promote market structures more supportive of intense competition. Advocacy allows a competition agency to expand its reach and play an important role in areas where its role is usually ignored. In particular, advocacy can give a competition

> 3 httD://www.minproduccion.gov.ar/secdef/basehome/merCadodecombustibles.pdf

2
provided in the Appendix, which also reports the results of standard Augmented Dickey-Fuller stationary tests. These results indicate that all the variables, as defined here, are stationary. A "standard" VAR approach (that is, one that ignores cointegrating relationships between the variables in level form), can therefore be used. The real minimum wage is used as a proxy for the unskilled real wage; evidence for Brazil indicates that these two series are indeed highly correlated. Nevertheless, to verify that the real minimum wage "belongs" to the VAR, I applied the block exogeneity test described earlier. As reported in the Appendix, the results indicate that the null hypothesis (exclusion of the cy:l,ical component of the real minimum wage from the VAR) is soundly rejected by the likelihood ratio test.

Figure 1 shows the evolution of the cyclical components of all the variables included in the VAR. The data illustrate fairly well the pro-cyalical behavior of the real minimum wage and the counter-cyclical behavior of the unemployment and poverty rates." Given my 4 percent threshold, the data indicate that 1983, 1984, and 1992 can be identified as "crisis" periods.

The impulse response functions over 24 years associated with a one standard deviation shock to the innovation in the output gap are shovin in Figures 2, 3 and 4, for both the symmetric and asymmetric cases. The solid lines in the figures represent the impulse responses themselves, whereas the dotted lines are the associated 95 percent upper and lower confidence bands.12 G3iven the relatively small size of the sample, the use of standard criteria for choosing the optimal lag length (such as the Akaike criterion) is not feasible; instead, I relied on a visual inspection of the impulse response functions associated with an output shock in the symmetric case and compared the results with one and

> 11Note the sharp drop in the cyclical component of the poverty and unemployment rates in 1986, which reflects movements in the original series. The data compiled by Wodon et al. ,2001) indicate also, as in the dataset used here, a sharp drop in poverty in that same year.

12 The impulse responses and their associated confidence intervals are computed using Monte Carlo simulations employing 1,000 draws.

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