rafmacalaba/datause-encoder-data huggingface dataset:
WPS3721

# **Economic Policy Responses to Preference Erosion: From Trade as Aid to Aid for Trade***

## Bernard Hoekman

Groupe d’Economie Mondiale, Institut d’Etudes Politiques, Paris and World Bank

## Susan Prowse

## Department for International Development, UK

**Abstract:** Trade preferences are a central issue in ongoing efforts to negotiate further multilateral trade liberalization. “Less preferred” countries are increasingly concerned about the discrimination they confront, while “more preferred” developing countries worry that WTO-based liberalization of trade will erode the value of current preferential access regimes. This tension suggests there is a political economy case for preference-granting countries to explicitly address erosion fears. We argue that the appropriate instrument for this is development assistance. The alternative of addressing erosion concerns through the trading system will generate additional discrimination and trade distortions, rather than moving the WTO towards a more liberal, nondiscriminatory regime. We argue that prospective losses generated by MFN liberalization should be quantified on a bilateral basis, using methods that estimate what the associated transfer _should have been_ and ignoring the various factors that reduce their value in practice (such as compliance costs or the fact that part of the rents created by preference programs accrue to importers in OECD countries). Given that many poor countries have not been able to benefit much from preference programs, a case is also made that preference erosion should be considered as part of a broader response by OECD countries to calls to make the trading system more supportive of economic development. The focus should be on identifying actions and policy measures that will improve the ability of developing countries to use trade for development.

## World Bank Policy Research Working Paper 3721, September 2005

**_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._**

_*_ Presented at the international symposium "Preference Erosion: Impacts and Policy Responses," Geneva, June 13-14, 2005. We are very grateful to Sheila Page for detailed suggestions that improved the paper and to Werner Corrales, Joe Francois, Nuno Limão, Patrick Messerlin, Dominique Njinkeu, Marcelo Olarreaga, and participants in the symposium for helpful comments and suggestions. This paper draws on a number of background papers prepared for the DFID project “Global Trade Architecture and Development.”
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Sustainability of tariff liberalization efforts 77. The World Bank has released interim conclusions os a study on lessons to be derived from trade liberalization episodes based on a study of various countries. According to this study, some resource-poor and small countries have undergone successful trade liberalization efforts. Since Central American countries fit well into this category of countries, it can be inferred that being small and resource-poor should not preclude any of the countries in the Region from undertaking a successful trade liberalization program. The main conclusions for the successful liberalization episodes include the following:

- (i) maintaining a depreciated exchange rate in real terms;

- (ii) having a low budget deficit;

- (iii) achieving favorable export performance;

- (iv) being implemented under extreme duress;

- (v) introducing strong reforms ("strong liberalization attempts tend to be sustained. These are mostly episodes which start with significant, rather than minor and marginal steps of liberalization"); and

# (vi) maintaining political stability.

78. This report does not analyze to what extent the conditions in countries in Central America fit into the criteria deemed to be desirable for achieving and sustaining a successful trade liberalization effort. However, it is important to note that further changes in trade liberalization should be strong to lead--presumably--to robust export growth--through eliminating the anti-export bias of the trade regime-needed for the survival of the reform efforts.

# Revenue implications of a tariff reform

79. This report suggests that the next steps in the tariff reform would need to take account of revenue implications of the reforms. Assuming that both the exchange rate and import volumes remain unchanged following trade liberalization, reductions in tariffs on final consumer goods would lead to a loss in fiscal revenues by the amount of the decrease in tariffs. If the reform also includes an increase in the floor rate, revenues would likely increase. Given the share of intermediate and capital goods in total imports and depending on the amount of the increase in the floor rate, overall tariff revenues would likely expand. However, if import volumes respond to changes in tariff rates, fiscal revenues could increase slowly, given that price sensitive category of capital goods and some intermediate goods would likely decline if tariff rates are increased. Expansion in final consumer goods imports, however, could offset the rate decline proposed for final consumer goods. If a real devaluation accompanies the tariff reform, then tariff revenues could increase, despite lower volumes and lower rates on final consumer goods.
for the pilot phase of _Pantawid_ to inform program scale-up (Chaudhury, Friedman, and Onishi 2013). The Household Assessment Form to estimate proxy means scores for beneficiary selection was fielded in these eight municipalities between October 2008 and January 2009; we use these data to calculate potential program saturation at baseline. A total of 130 villages in the eight municipalities were then randomly assigned to treatment or control status with equal probability.<sup>12</sup> A follow-up detailed household survey was conducted in October and November 2011, thus allowing for a program exposure period of 30 to 31 months.

# IV. EMPIRICAL STRATEGY AND DATA

# _Empirical Strategy_

Our analysis uses variation across villages in treatment assignment to identify program impact on local prices and child nutrition.<sup>13</sup> To study the effects of these price changes on household behavior, we analyze the direct program impacts among eligible households and then estimate the spillover effects among nonbeneficiary households, defined as non-poor households that nonetheless have eligible children. Since potential program saturation is a key mediator of the cash transfer’s impact on prices of non-tradable goods, we allow the indirect effect to vary by program saturation. A secondary analysis exploits the staggered rollout of the program in a national sample of villages to estimate the covariation of food prices with village-level program exposure.

# _Data_

> 12 The experimental sample selection occurred in three stages. First, provinces that did not have the program as of October 2008 were selected. Three of the 11 available provinces were excluded due to security concerns. Four of the remaining eight provinces were chosen (Lanao Del Norte, Mountain Province, Negros Oriental, and Occidental Mindoro) to span the three macro areas of the country (North, Visayas, and Mindanao). Second, in each province, two municipalities were selected to represent the average poverty level of areas covered by the program in 2008. Third, villages in each municipality were randomly assigned to treatment or control by a computer based random number generator.

> 13 We thus identify impacts of the program by comparing similarly (non) poor households in treated and control villages. Our SUTVA thus relies on a lack of spillovers across villages. However, even if SUTVA is violated across villages, i.e. prices are higher in control villages due to connected markets or something, we might except higher stunting in those villages, leading our estimates to be a lower bound of the impact of Pantawid on prices and stunting. In addition, we have no evidence even of within village effects on wages.

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new infections, supporting the sick and vulnerable, and mitigating the effects of the epidemic. The financial crisis of 2008, however, turned the upward funding trend into a plateau, and there are concerns that it may now go in reverse. At the same time, after years of increasing funding until 2008, there is now an acknowledged need to understand better which community responses are most effective, efficient, and sustainable and how such responses can best complement the actions of governments and other actors. Robust data are needed.

An evaluation of the World Bank’s HIV/AIDS assistance efforts by the Bank’s Independent Evaluation Group (formerly OED) in 2005 highlighted the need for a better understanding of community responses. Such an understanding would include how they are organized and developed, what their strengths and weaknesses are, what types of activities and services they undertake, and how effective and of what quality their activities and services are.

To address these questions, the World Bank’s Global HIV/AIDS Program team, HNP ‐in collaboration with DFID, the UK Consortium on AIDS and International Development, and other partners ‐launched in 2009 an evaluation exercise that includes collecting primary data to assess the results achieved by community responses to HIV and AIDS. The primary objective of this effort is to build a more robust pool of evidence on the impact and added value of community‐based activities and programs in a cluster of countries, with a strong concentration on the Sub‐Saharan African Region.

As part of that effort, the present desk study was conducted to inform the approach of the overall evaluation. The paper describes ways in which the typology of community responses to HIV and AIDS can be characterized or defined and proposes a framework for classifying the types of community responses and analyzing them in a systematic way. The typology can also be applied to different themes and sectors, not only to HIV and AIDS. Thus, the paper provides a framework for analyzing complex communities and developing community typologies. It helps lay the groundwork for and describes the operational context of the larger evaluation that will address the issues of community response. This paper does not report empirical findings, as the actual field studies are currently ongoing.

# **2. Defining Community and Community Responses**

_Communities_ can be described in at least two different ways: culturally and geographically, or a combination of both. (See box 1.)

**Community as cultural identity** : Members belong to a group that shares common characteristics, circumstances, experiences, interests, concerns, or behaviors. Communities can form when people work together or find that they share common needs and challenges. A

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statistically significant and similar in size and sign as compared with the OLS estimations in Column 1 of Table 4. In Table 6, null hypothesis that σ<sup>2</sup> u<sup>= 0 is rejected at better than</sup> 1 percent level of significance. This implies that the difference between the observed rice productivity and the frontier rice productivity is not due to statistical variability alone but also due to technical inefficiency of the households.

Column 2 adds the heteroskedasticity component to the error term associated with the technical inefficiency error term _u_ in the equation **(4)** . The second part of Column 2 shows the estimates of heteroskedastic estimation of **(5)** . The coefficient for IMT in **(5)** is negative and significant at better than 1 percent level. This implies IMT reduces the variability in the technical inefficiency. That is, households in IMT areas would have lower variability in technical inefficiency. Thus, the IMT coefficient in column 2 of Table 6 is not a measure of ATT – it is not directly comparable to the coefficient of IMT in column 2 of Table 4, which is a measure of the impact of IMT on yield. In this case, ATT is calculated from this coefficient using predicted yield, based on **(6)** .

The ATT impact of IMT on rice yield is presented at the bottom row of Table 6. The average increase in yield associated with the presence of IMT for the households in IMTIAs is 2.2 percent. As expected the estimates of increase in yields using stochastic frontier methods are lower than the increase in yield estimated by the OLS results.

# **5.4. Rich and Poor**

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**Figure 4.  Selected Countries with Peak in Youth Population in 2010-2030 Population Aged 12-24 (1950=100), U.N. Medium Variant Projections**

<!-- Start of picture text --> 600<br>Philippines<br>Malaysia<br>500 Egypt<br>Peru<br>India<br>400<br>300<br>200<br>100<br>0<br>1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050<br>Year<br>Population (1950=100)<br><!-- End of picture text --> 30
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Figure 6. Exports from selected energy-intensive industries, 2007 <!-- Start of picture text --> 18 17.9<br>16<br>14<br>12<br>10<br>8 6.8<br>6 4.8<br>4.1<br>4 3.1<br>2.4 2.1<br>2 0.5 1.1 0.4<br>0<br>products (% of GDP)<br>Export value of non-metal manufacturing, iron and steel Albania Bosnia and Herzegovina Bulgaria Croatia Macedonia, FYR Romania Serbia Czech Republic Greece Slovak Republic<br><!-- End of picture text --> Source: Author’s calculation based on _WDI_ and WITS _COMTRADE Database_ .

On the micro level, our enterprise data, in which “energy” covers not only electricity but also other fuel, show that Bulgarian companies are using energy most intensively. There are certain similarities to the above figures, but not completely. The average share of energy spending in total costs is about 9 percent in Bulgaria, which is followed by 8.6 percent of Romania and 7.6 percent of Albania (Figure 7). Of particular note, there is a large variation in energy intensity across firms even in a country. Some companies in Bulgaria are spending more than 60 percent of total costs on energy. They are considered especially energyintensive enterprises. On the other hand, there are a number of firms that expend less than 10 percent on energy of their total costs. This firm-level heterogeneity is an important fact for designing micro-data analysis like the current paper.

Figure 7. Energy spending in firm costs <!-- Start of picture text --> 14 70<br>Mean (left-scale)<br>12 Max (right-scale) 60<br>10 50<br>8 9.1 8.6 40<br>6 7.6 7.2 6.8 7.1 30<br>5.8<br>4 20<br>2 10<br>0 0<br>Share of energy expenditure in total cost (%)<br>Albania Bosnia and Herzegovina Bulgaria Croatia Macedonia, FYR Romania Serbia<br><!-- End of picture text --> Source: Author’s calculation based on BEEP data.
the researcher developed a set of codes and categorized the relevant interview data across all research sites following the coding system. Finally, the researcher analysed and linked the different codes together into cohesive, overarching themes related to social cohesion and refugee-host relationships. Once this analytical process was completed, the qualitative data was integrated with quantitative results. Our rich qualitative data enabled us to interpret the results of survey, explore causal relationships for the patterns we observe and to substantiate our data through narratives. In order to protect the privacy of interviewees, all respondents are either anonymized or given pseudonyms in this paper.

# **4.3 Identification Strategy**

Focusing on the Uganda sample (because of data availability), we first explore the correlation between host community attitudes towards refugees and refugee-host interactions. We use OLS regressions of the index of the perception index ( _Pi_ ) on the interaction index ( _Ii_ ) using individual level data: Each observation is a respondent from the host community. We control for the perception index among other household members ( _PHH_ ) and among neighbours ( _PEA_ ) to account for the fact that perceptions are partly formed at the group level through discussions, gossips, and common narratives.<sup>10</sup> The geographic area considered as a neighbourhood when creating _PEA_ is provided by the Enumeration Areas of the Uganda national census by the Ugandan Bureau of Statistics (UBOS).<sup>11</sup> We also control for a vector of socio-economic and demographic variables _Xi_ to limit the risk of omitted variable bias. We use two approaches to select the variables included in _Xi_ . First, we selected a list of variables that could explain perceptions or interactions based on our knowledge of the literature (see Tables A.3 and A.4 in the Appendix for descriptive statistics). We include enumeration areas fixed effects ( _μEA_ ) or household fixed effects ( _μHH_ ) to control for unobserved characteristics that are constant within EAs/households. Second, we implement the double LASSO method of Chernozhukov et al. (2017) using the original list of potential controls augmented with fourth-degree polynomials along with all first-order interactions and the inverse hyperbolic sine transformation of continuous variables (Knaus et al. 2020). We also estimate specifications without control variables.

– Three main methodological challenges could afflict this OLS regression and more gen-

> 10 _PHH_ and _PEA_ are standardised to facilitate interpretation. Before standardisation, the variable _PHH_ is set as equal to 0 (= the mean of the perception index _Pi_ ) in households with only one adult.

> 11When describing host perceptions across all sites in the later OLS analysis without a measure for interaction (Table 4), the neighbourhoods are defined by similar Enumeration Areas from the Kenyan census for respondents in Nairobi, by villages around Kakuma, by the five main towns close to each of the five Dollo Ado camps, and by specific sublocations to the official woredas in Addis Ababa.

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Aside from the observed factors that affect migration, individual and household migration decisions are also shaped by forces often unobservable to researchers. For example, it has long been assumed that individuals more willing to take risks are more likely to migrate. Indeed, studies of rural-urban migrants in China show that less risk-averse individuals, as revealed by self-reported risk preferences, are more likely to engage in migration (Akgüç et al., 2016), and the distribution of other household members’ risk attitudes also shapes individual migration decisions (Dustmann et al., 2023). Hao et al. (2016) posit that out-migration involves risk-taking, just like launching a new business, and that migrants are behaviorally like entrepreneurs. The study uses a field experiment to confirm that migrants are less risk-averse and more willing to engage in risky competition. While risk preferences have been considered in studies of migration in China, other unobserved preferences, including time preference, or the extent to which people value current over future consumption, have not been analyzed. From evidence collected in other developing countries, e.g. Ghana and Indonesia, migrants are not only more risk-tolerant, they are also more patient (Goldbach and Schlüter, 2018). Given that both higher levels of risk tolerance and patience are associated with higher cognitive ability (Dohmen et al., 2010), migration is selected on both unobserved cognitive abilities and unobserved characteristics of such personality traits.

Given the presence of unobservable individual characteristics and community-level characteristics, including both features of villages and unobserved shocks affecting both well-being and migration decisions, estimation of the impacts of migration in sending communities present challenges when using observational data. Use of randomization for identification, such as lotteries into a visa program (Gibson et al, 2011) are a gold standard in the international migration literature, but not available in China. Researchers have thus appealed to difference-in-difference and instrumental variable strategies to identify impacts of migration. In the sections below, we will highlight identification strategies employed and point out those studies for which outcomes are simply correlated with migration.

# **4. The Impacts of Migration on Earnings, Consumption, Investment and Risk-Coping**

Migration presents both challenges and opportunities for the economic well-being of migrant-sending households. On the one hand, the departure of a family member can lead to a loss of labor, which may result in a decrease in household income. This is particularly significant when the migrant was a key contributor to the household’s earnings or engaged in essential tasks that are difficult to replace. On the other hand, remittances sent by migrants to their families can provide a vital source of income, potentially offsetting the reduction caused by the loss of labor. Ultimately, the net effect of migration on a migrantsending household's income depends on whether the remittances received can adequately compensate 14
more quickly than in the rest of the country.<sup>11</sup> Table 4: Tests of _β_ -convergence conditional on total public spending, 1992–2014

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|---|---|---|---|---|---|---|
||**1992-2014**|**1992-2000**|**2000-2014**|**2000-2005**|**2005-2010**|**2010-2014**|
||||**a. All mu**|**nicipalities**|||
|_ln yit−τ_|_−_0_._012***<br>(0.001)|_−_0_._016***<br>(0.005)|_−_0_._020***<br>(0.001)|_−_0_._047***<br>(0.003)|_−_0_._020***<br>(0.004)|_−_0_._015***<br>(0.003)|
|Obs.|2,234|2,234|2,193|2,193|2,116|2,045|
|R<sup>2</sup>|0.166|0.056|0.342|0.318|0.089|0.061|
|||**b. Municipa**|**lities in stat**|**es along the **|**U.S. border**||
|_ln yit−τ_|_−_0_._017***<br>(0.004)|_−_0_._028***<br>(0.010)|_−_0_._022***<br>(0.004)|_−_0_._051***<br>(0.012)|_−_0_._060***<br>(0.009)|_−_0_._044**<br>(0.017)|
|Obs.|267|267|262|262|267|266|
|R<sup>2</sup>|0.250|0.113|0.226|0.256|0.198|0.055|
|||**c. Munici**|**palities in n**|**on-U.S. bord**|**er states**||
||_−_0_._011***|_−_0_._020***|_−_0_._019***|_−_0_._044***|_−_0_._014***|_−_0_._017***|
|_ln yit−τ_|(0.001)|(0.006)|(0.001)|(0.003)|(0.004)|(0.003)|
|Obs.|1,967|1,967|1,931|1,931|1,849|1,779|
|R<sup>2</sup>|0.154|0.052|0.307|0.274|0.056|0.089|

_Source_ : World Bank calculations. _Note_ : The table presents estimates of the parameter _β_ in equation (4), weighted by municipal population at the initial year of each period under study. The dependent variable is the annualized growth rate in the mean per capita income of municipalities over the period. _ln yit−τ_ and public spending are for the initial year and are in log-scale and in real per capita terms at August 2014 prices. The coefficients for per capita public spending and the intercept are shown in tables 2–6 and 12–16 in the ancillary file. Robust standard errors are in parentheses. *** _p < ._ 01, ** _p < ._ 05, * _p < ._ 1 The difference in the speed of convergence between non–U.S. border municipalities (1.4 percent) and municipalities in border states (6.0 percent) over 2005–10 may also be explained by the following growth patterns. Income growth averaged almost 8 percent annually among the poorest 10 percent of municipalities in both groups, while it decreased among the top 10 percent. The difference lies in the magnitude of this loss: it averaged –1 _._ 6 percent annually in non–U.S. border municipalities, whereas it was –5 _._ 4 percent annually in border states. In this case, then, it seems that the United States–originated housing bubble, which unleashed the global financial crisis, had a strong regional bias, with disproportionate effects on those municipalities most

> 11As reference, growth in mean per capita income over 1992–2000 was positive in municipalities located in border states, with an annual rate of 0.3 percent, whereas it was negative among non–U.S. border municipalities: –0 _._ 9 percent.

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