rafmacalaba/datause-encoder-data huggingface dataset:
(Measure DHS, 2008). Moreover, the province breakdown shows that the southern (where Maputo city is situated) and central provinces have the highest rates of prevalence of HIV/AIDS in Mozambique (Republic of Mozambique National AIDS Council, 2008). People living with HIV/AIDS are already a vulnerable group because of the negative impact of the illness on labor force participation or the cost and difficulty of accessing treatment. A negative impact of the crisis on their welfare and nutritional status could thus be expected.

Medical research has established that a minimum level of adherence to ARV treatment of 95% is necessary to achieve significantly better health outcomes as assessed by the viral load, immune system and occurrence of opportunistic infections (Paterson et al., 2000). Non adherence predicts disease progressions and survival rates, and increases the risk of transmission of drug resistant viruses (Bangsberg et al., 2001, De Olalla et al., 2002, Falagas et al., 2008). Failure to achieve proper adherence to treatment is thus both an individual and collective risk.

Determinants of adherence depends on several factors such as the treatment regimen (which can be quite complex and include food restrictions, specific schedules, etc.), disease characteristics, the quality of the patient-provider relationship, or the clinical setting. Socio-demographic factors do not consistently predict adherence behavior (Ickovics & Meade, 2002). The meta-analysis by Falagas et al. (2008) on socioeconomic status as a determinant of adherence finds that while the relationship is unclear, there is a positive trend of association between income, education or employment status and adherence. It is worth noting that adherence is not found to be consistently lower in developing countries, and largely depends on access to treatment and financial barriers (Orrel et al., 2003b). When therapy is fully subsidized in developing countries, it can be at least as good as in developed countries (Orrel et al., 2003a).

Even when treatment _per se_ is free, transportation costs to the health facility to get a prescription refilled are found to be a powerful barrier to adherence. Moreover, patients have to make “impossible choices” between competing claims: transport costs and good nutrition of the patients compete with schooling fees or medical costs for children, food for the rest of the family, etc. (Ware et 4
respondent is considered informed about the unsubsidized price if his/her answer is less than 3 US$ away (in both directions) from the ’true’ price. This choice is more restrictive than the baseline case. While in the baseline 216 people are considered as ’informed’, in this robustness check only 121 are considered so. The results of the regressions with the more restrictive definition of ’Information’ are reported in table A.12. The focus in this case is on the coefficient of the variable ’Information’. The magnitude of the estimated coefficient is slightly lower than in the baseline but the estimates remain significant at one percent level in all models. Furthermore, the estimated coefficients of the other two regressors remain highly significant. We also consider a bigger error band (5US$) and report the results in table A.13. The baseline scenario is confirmed also in this case.

Finally, we consider a slightly different definition to the variable ‘Delivery’. In the baseline analysis for the January 2011 survey, the variable ‘Delivery’ is a dummy variable taking the value of ’1’ if the respondent declares to be confident about receiving the subsidy under the announced new scheme. However, it could be the case that the respondent answers ’no’ because she or he does not quality for the transfer. This robustness exercise considers under the variable ’Delivery’ only individuals with a monthly usage of electricity below the qualifying threshold, i.e. those that qualify for the benefit. This choice reduces the number people confident in the delivery (meaning the number of observations taking the value of ’1’) from 373 (baseline) to 305. The results are reported in table A.14. The coefficient of ’Delivery’ is only marginally lower than in the baseline but the variable remains significant at one percent level. Overall these results suggest that the baseline findings are fully robust to the alternative definitions of delivery, information, and partisanship and that these variables play an important role in individuals’ satisfaction.

# **6. Conclusion**

In this paper we analyze the determinants of the citizens’ satisfaction about a reform of the gas subsidy in El Salvador. The reform was expected to improve the welfare of around three-quarters of the population but turned out to be highly unpopular. Our contribution is double. First, using newly collected data on six consecutive surveys we document the evolution of the popularity of the reform across individuals. Second, using Probit/Logit models we test the marginal effects of three key observables: the individual’s level of information (which is especially relevant ex-ante), his/her trust in the government’s ability to implement the 28
PoIIcy RESEARCH WORKING PAPER 1549

# Summary findings

Using economic rates of return from more than 1,200 public and private sector projects implemented in 61 developing countries, Isham and Kaufmann analyze determinants of investment productivity.

Results from Tobit estinmation demonstrate that the degree of countrywide policy distortionsmacroecononmic, exchange rate, trade, and pricingcritically affects the productivity of investments. Countries with undistorted policies are likely to end up with highlv productive investments. In countries with distorred policies, investilments are likely _to_ be unproductive. And within a country, investments become more productive when economic policymaking improves.

The productivity of projects in the tradable sectors are also affected (in a nonlinear fashion) by the size of a country's public investment program.

Isham and Kaufmann discuss possible selection biases in this data set, present tests of robustness, and highlight policy implications. In particular, donor financing for improvements in the policy climate is likely to pay off. A powerful rationale for supporting structural reform is that it raises the productivity of both public and private investments.

- - This paper a product of the Regional Mission in Kiev, Europe and Central Asia, Country Department IV was originally prepared as a backgrotu n d paper for _World Development Report 1991._ Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, D(C 20433. Please contact Sylvia Torres, room H5-169, telephone 202-473-9012, fax 202-522-0010, Internet address dkaufman((@worldbank.org. November 1995. (34 pages)

_The Policy Research WX'orking Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development Issuies._ **_Anr_** _objet tive ol thi series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of tie authors and should be used and cited accordingly. The findings, interpretations, and conclusions are the_ **_authors_** _o n and skouli_ **_not_** _be attributed to the World Bank, its Executive Board of Directors, or any of its member countries._ Produced by the Policy Research Dissemination Center
**Table 1** <u>:</u> _LSMS_ _<u>Surveys with Financial Sector Information and Dates of Crises</u>_

||_Date of LSMS_|_Date of Crisis_|
|---|---|---|
|Albania|2002|1992–96|
|Armenia|1996|1994–96|
|Bosnia & Herzegovina|2001|1992–|
|Brazil|1996/97|1994–99|
|Bulgaria|2001|1996–97|
|China|1995 & 1997|1990s–|
|Côte d'Ivoire|1988|1988–91|
|Ecuador|1998|1998–2001|
|Ghana|1998/99|1982–89|
|Guatemala|2000|_1990s_|
|Guyana|1992/93||
|India|1997/98|_1993–_|
|Kosovo|2000||
|Jamaica|1997|1994|
|Kazakhstan|1996||
|Kyrgyz Republic|1998|1990s|
|Morocco|1991|Early 1980s|
|Nepal|1996|1988|
|Nicaragua|1998-99|Late 1980s–96|
|Pakistan|1991||
|Panama|1997|1988–89|
|Peru|1994|1983–90|
|Romania|1994/95|1990–96|
|Russia|1992|1995|
|South Africa|1993|_1989–_|
|Tajikistan|1999|_1996–_|
|Vietnam|1997-98|1997–|

Note: Italicized dates denote non-systemic crisis. Source for crisis dates: Caprio et al. (2005); for LSMS Honohan (2004c).

**Table 2** <u>:</u> _Mexico: Alternative_ _<u>estimates of Household Inequality Gini 1992-1998</u>_ <u>.</u>

|_Author _|_Concept_|1992|1994|1996|1998|
|---|---|---|---|---|---|
|Luxembourg Income Study|Net Income|54.5|55.3|53.4|54.8|
|Szekely and Hilgert (1999)|Net Income|53.4|53.6|52.8||
|Lopez-Acevedo and Salinas (2000)|Total Income|53.1|53.4|51.9||
|Baldacci et al (2002)|Income|54.1|54.2|51.6||
|Baldacci et al (2002)|Consumption|52.7|51.6|50.2||
|Deininger and Squire (2004)|Gross income|60.7|56.4|54.9|55.4|
|WorldDevelopment Indicators/Povc<br>|al Expenditure|50.3|53.7<sup>a</sup>|51.9|53.1|
|a1995||||||

Source: WIDER World Income Inequality Database (WIID), Baldacci et al. (2002), Lopez-Acevedo and Salinas (2000), Povcal (accessed August 2004).

18
Lower aspirations induced by poverty is another channel through which poverty may affect the decision-making process of the poor, resulting in lower economic growth. La Ferrara (2019) reviews the theoretical literature on aspirations and provides empirical evidence on how they are correlated with poverty rates and income inequality. Data on aspirations are obtained from the tests on academic performance administered through the OECD’s Programme for International Student Assessment (PISA), and are measured as the expectations of students as to what academic degree and job they will achieve in the future. The intuition of this channel is as follows. The poor have lower aspirations than the rich because they anticipate that the lack of resources (including financial buffers to withstand adverse shocks) will impede their success in the future. As result, the poor may lack the incentives to invest in their future income opportunities for their families, such as the education of their children or the adoption of new technologies. This in turn perpetuates their poverty, leading to a vicious cycle in which low growth breeds poverty and poverty promotes stagnation.

All these mechanisms share the common feature that poverty influences the behavior of poor individuals, with negative consequences on the accumulation of capital and long-term growth, hence self-perpetuating poverty. For instance, this behavioral channel is consistent with the empirical evidence that the poor borrow repeatedly at very high rates instead of self-financing through savings (Banerjee and Duflo 2005) or do not invest in profitable small-scale investment such as purchasing fertilizer (Duflo, Kremer, and Robinson 2011), preventing them from escaping poverty.

# **_Political Economy_**

There are two key channels through which inequality has political economy effects that depress long-term growth. The first, the “redistribution” channel, is when inequality generates political pressures from voters for redistribution, which results in an increase in distortionary taxation, and consequently lower investment and growth. The second, “the institutional” channel, is when inequality leads the rich and powerful to influence institutions in such way that laws benefit them but are not conducive to sustained growth for the population at large.

The redistribution channel is illustrated by Alesina and Rodrik (1994) based on the endogenous growth model of Barro (1990), where government spending is productive but is financed through distortionary capital taxation. Taxation and the growth rate of the economy exhibit an inverted “U” relationship. For low levels of tax rates, increasing the tax rate raises growth by funding the expansion of productive public infrastructure. After some point, however, further increasing the tax rate reduces growth because it reduces the incentives to accumulate private capital and may also provide declining marginal return to public expenditure. In the electoral process, the median voter prefers to impose a tax higher than the growth-maximizing tax rate, as they benefit from 33
Box 2. Synthetic panel methodology The issue of economic mobility has gained relevance in academic and wider policy discussion over the last years. Data requirements to analyze this issue are not trivial, though. A proper study requires householdlevel information for at least two periods, not only for income or consumption, but also for other variables that can affect changes in income or consumption. Unfortunately, the availability of panel surveys that contain this type of information is quite limited, and even when existent, many times they suffer from high attrition rates and relatively short survey periods.

The synthetic panel methodology overcomes these shortcomings and builds on an imputation methodology to predict consumption in the second period using two different rounds of data. It relies on time-invariant individual and household characteristics (Figure A5). Consumption in each period is modeled as the sum of two components: a first one associated with time-invariant characteristics (F(X)), and a second one capturing non-observable factors (e). To create the predicted consumption in the first round for the households from the second round, we generate a new component based on how their time-invariant characteristics are associated with consumption, but in the first round. Adding up this new component to the non-observable factors we obtain the predicted consumption in round one. With these two welfare aggregates we construct transition matrices to analyze economic mobility between rounds.

<!-- Start of picture text --> Figure A5: Synthetic panel approach<br>Round one Round two<br>Observed consumption Predicted consumption Observed consumption<br>C1 C1 Ĉ2 Ĉ2 C2 C2<br>C1 Ĉ2 C2<br>C1 Ĉ2 C2<br>C1 X1 Ĉ2 C2<br>C1=F1(X1)+e1<br>C – consumption<br>Ĉ – predicted consumption<br>X – time invariant household and individual characteristics<br><!-- End of picture text --> Depending on the assumptions made about the non-observable characteristics, the method generates a high and a low mobility scenario. For the low mobility scenario, non-observable characteristics do not change in time, whereas for the high mobility scenario they change between rounds. In intuitive terms, low mobility implies that if a shock affects consumption in the first period, it continues to do so in the second one, and in the same direction. High mobility implies that there is no relationship between shocks in time. True mobility should be found within these two boundaries.

The quality of the imputation improves as time-invariant characteristics capture more variation in consumption. Notwithstanding this limitation, synthetic panels allow the use of existing data in a novel way to better understand economic mobility and its determinants.

Source: Adopted by Atamanov, A., Cancho, C. and Meyer, M. from Lanjouw, Louto and McKenzie (2011) 32
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25
sample is a stratified multistage random sample, nationally and regionally (at the governorate level) representative (all 12 monthly samples of the survey spanning part of 2004 and 2005 are independent). In practice, monthly samples are not treated independently, but data collection goes over a quarter, with often filed work for a given month fully completed during a subsequent month in the same quarter. The sample of each quarter is large enough to allow for inferences at the regional and governorate levels, with the exception of Frontier governorates. Due to the large sample size of the main survey (48,000 households), even a one-month sub-sample of 4,000 households in principle is large enough to provide representative data at least for main socio-economic groups. This is of course conditional upon: (i) whether February 2005 sample is not systematically different from other months of the first quarter of 2005 (or third quarter of the survey- see Figure 1)<sup>12</sup> , and (ii) whether attrition between 2005 and 2008 was not excessively large to undermine the sample properties<sup>13</sup> .

> 12 Indeed, some differences were found between the February sample and the full first-quarter sample in terms of regional distribution, household size, housing patterns, sector of economic activities and other aspects. Weights were used for February 2008 and 2005 to reproduce the distribution of the entire quarter of January-March 2005. Since panel structure where no replacement is allowed means that two samples of 2005 and 2008 over which the mean consumption is compared are not fully independent, it is important to make corrections to standard errors calculated based on standard assumptions.

> 13 One of the characteristics of the sample selection method of CAPMAS’s stratified, multistage sample design for 2004/05 is that all PSUs are not represented in each quarter, though the sample in each quarter is nationally representative. The quarterly samples are in turn subdivided into monthly samples, which may happen to be biased. Annex 1 presents details on the correction for for panel attrition

7
percent in 2017. Hence, a potential U.S. disengagement from the world trading system will not have the same level of disruptive effects. Nevertheless, the United States, Europe, and China still account for about half of current world trade. So, the current disputes that involve these countries are expected to have significant spillovers on the rest of the world. As globalization boosted the integration of developing countries in the world economy, would the trade diversion resulting from the trade war benefit these countries? Or would GVC linkages in which developing countries provide more and more inputs to world trade hurt them instead? Are retaliatory measures justified, or should developing countries pursue trade liberalization measures? These are the questions the simulations seek to answer.

# **3 Methodology**

The analysis uses the GLOBE computable general equilibrium model calibrated to version 9 of the data of the Global Trade Analysis Project (GTAP) with 2011 as the base year. The model is comparative static and is a member of a family of CGE models that model trade relationships using principles described in the 1-2-3 model (de Melo and Robinson 1989; Devarajan et al. 1990) and standard multi-sectoral versions for developing countries (Dervis, de Melo, and Robinson 1982).

A range of possible developing country strategies is compared to the baseline scenario, capturing the marginal effects of different counterfactual scenarios. This ex-ante approach follows the rich tradition of using CGE analysis to examine policy issues in developing countries, such as the long-term effects of international trade agreements like the Doha Development Round (Hertel et al. 2009), effects of global protectionism (Kutlina-Dimitrova and Lakatos 2017), disintegration of NAFTA (Robinson and Thierfelder 2018), stress-testing Africa’s growth and poverty performance (Devarajan et al. 2011), trade reform in India (Go and Mitra 1999) or carbon tax and climate change in South Africa (Devarajan et al. 2011).

For the analysis, the world is divided into 18 regions. We define 16 sectors (see Table 2 in the Appendix). Column 2 U.S. tariffs by commodity and region are reported in Table 3 in the Appendix.

A balanced macro closure is specified so that macro expenditures adjust to income changes by fixing the value share of final demand for government purchases, investment purchases, and household purchases. This closure gives an even impact on changes in consumption by government, investors, and households as income changes due to changes in tariff revenues. Such a formulation matters for the sectoral results because different agents have different spending patterns; for example, investors have high expenditure shares on construction.

7