rafmacalaba/datause-encoder-data huggingface dataset:
Policy ReseaRch WoRking PaPeR 4744

# **Abstract**

Concerns have been raised about the impact of rising food prices worldwide on the poor. To assess the (short term) impact of rising food prices in any particular country it is necessary to look at both the impact on food producers (who benefit from an increase in prices) and food consumers (who loose out when the price increases), with a focus on poor producers and consumers. In Mali the impact of a change in the price of rice is not ambiguous because about half of the rice consumed in the country is imported, so that the negative impact for consumers is much larger than the positive impact for producers. By contrast, for millet and sorghum, as well as corn, the impact is more ambiguous since much of the consumption is locally produced. Using a recent and comprehensive household survey, this paper provides an assessment of the potential impact of higher food prices on the poor in Mali using both simple statistical analysis and non-parametric methods. The paper finds that rising food prices for rice, millet and sorghum, corn, as well as wheat and bread could together lead to a substantial increase in poverty, with the increase in the price of rice having by far the largest negative impact.

This paper—a product of the Development Dialogue on Values and Ethics, Human Development Network—is part of a larger study by the Africa Chief Economist Office and the Development Dialogue on Values and Ethics on the impact of the food price crisis in Africa and the policy responses available to governments. This research was started in the Africa PREM department and benefits from funding from the Africa Region Regional Studies Program as well as the Belgium and Luxemburg Poverty Reduction Partnerships. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The author may be contacted at qwodon@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
**Table 4: The Determinants of Greenfield FDI in the MENA region, Q1 2003 – Q4 2012 Estimations by Broad Industry**

|**The Determin**|**ants of Greenfield FDI in**<br>_Dependent Variable: Log G_<br>_LSDVC E_<br>|**the MENA region Q**<br>_FI (in millions of USD)_<br>_stimation_<br>|**1 2003 – Q4, 2**<br> <br>|**012**<br>|
|---|---|---|---|---|
|_Sector:_|(1)<br>Resources and<br>Energy|(2)<br>Non-resource<br>Manufacturing|(3)<br>Tradable<br>Services|(4)<br>Non-tradables|
|Political Instabilityt-1|-0.02|-1.46***|-1.18***|-0.53|
||(0.52)|(0.40)|(0.35)|(0.43)|
|GFIt-1|0.07|0.02|0.05|0.11***|
||(0.04)|(0.04)|(0.04)|(0.04)|
|ln Industrial Productiont-1|0.96|0.30|-0.17|0.48|
||(0.67)|(0.51)|(0.45)|(0.56)|
|Δ ln Industrial Productiont-|0.79|-0.41|0.18|-0.04|
||(1.08)|(0.82)|(0.72)|(0.89)|
|Inflation t-1|-7.65|-10.26**|-12.46***|-2.18|
||(5.54)|(4.19)|(3.68)|(4.56)|
|Δ Exchange Ratet-1|4.29|3.91|1.45|-10.95*|
||(7.57)|(5.73)|(5.04)|(6.24)|
|Country FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|
|Observations|604|604|604|604|
|Number of Countries|17|17|17|17|

Bootstrapped standard errors in parentheses ***p<0.01, ** p<0.05,* p<0.10 29
**Table 3: Trading Partners of Major Exporters of Tropical Timber Products, 1991**

|Exporter/Importer|Japan<br>Importing cou|Asia a!<br>ntry share of total<br>a share of nati|E.U.<br>U.S.A.<br>exporter exports and (exports as<br>onal production)|
|---|---|---|---|
|Brazil||-|46.54<br>20.17|
||-|-|(1.68)<br>(0.73)|
|Cameroon|3.16|-|67.72|
||(1.26)|-|(27.10)|
|Congo|-|-|88.34|
||-|-|(57.90)<br>-|
|C6ted'Ivoire|-|-|62.45|
||-|-|(34.14)<br>-|
|Gabon|-|-|68.76|
|||-<br>-|(41.52)<br>-|
|Indonesia|33.80|37.73|9.10<br>8.54|
||(24.04)|(26.83)|(6.47)<br>(6.07)|
|Malaysia|32.19|49.62|5.42|
||(25.44)|(39.22)|(4.29)|
|Myanmar|1.62|92.85|-|
||(0.30)|(16.58)|-<br>-|
|Papua New Guinea|56.31|39.35|-|
||(30.49)|(21.31|-<br>-|

Source: World Bank, IECIT, calculations based on FAO, Forest Products direction of Trade. Notes: ./ Asia excludes Japan. Major Asian importers are: Korea, China, Thailand, India, Hong Kong, Singapore, and the Philippines.

- denotes that data were not available. However, it is expected that the share, even if available, would be small. Timber products include: sawlogs and veneer-logs, sawnwood, and plywood.

The table also indicates countries that export large quantities of their production of nonmanufactured tropical timber products that are most likely to be affected by demand in Europe and, to a lesser degree, U.S. for certified timber products. African producers will be most heavily affected, since they export a large percentage of their production to E.U. This is not the case for Indonesia and Malaysia however; they both export little of their overall production and only 12% and 4% of their total exports respectively go to the US and E.U. Other Asian timber exporting countries, such as Myanmar and PNG, may not be significantly affected by certification as these countries export to other markets in Asia in which there is little, if any, demand for certified timber. Although the E.U. and US account together for 66% of Brazil's timber exports, only a 18
Policy Research Working Paper 10255

# **Abstract**

The assignment of social programmes to their target population, known as targeting, is key to effective policy implementation. Proxy means testing is a widely used targeting approach where means testing is infeasible due to economic informality. This paper proposes a novel, practically feasible assessment approach that aims to reduce average proxy means test data collection costs, or allow more extensive data collection within a given resource envelope. Combining variable selection and prediction intervals, it develops a household-level truncated early stopping algorithm, which can reduce average interview length while maintaining predictive accuracy close to a standard proxy means test baseline. Applying the approach to Indonesian data, simulation of a 40 percent population coverage programme shows that targeting questionnaires could be shortened by 61 percent while maintaining PMT-level accuracy. A case study of a large health insurance programme in an urban area suggests that the share of intended beneficiaries who are among the targeted population can potentially be increased from 65.6 percent to 78.3 percent if enumerators conducted more of the shorter surveys that the truncated early stopping algorithm generates.

This paper is a product of the Poverty and Equity 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 email@ timohlenburg.com, jpinxten@worldbank.org, d.fricke@ucl.ac.uk, and f.caccioli@ucl.ac.uk.

_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
a look at the residual plot of the earlier estimates. They show moderate variations during these time periods, in particular since 1999.

The inclusion of the trend variables does not have a substantial impact on the significance level of the long-run elasticities, albeit the magnitude of the coefficients is moderately affected. While the schooling coefficient decreases minimally, the physical capital coefficient is augmented. The time trend for 1985 is significantly positive but there is a negative trend since 1999. Interestingly, both time periods are related to political events. 1985 is the transition year to civilian rule. 1999 is the election year of the Alfonso Portillo government, where compromised representatives of the former military nomenclature are suspected of wielding political power.

To the extent that this association is correct, a loose interpretation would suggest that in Guatemala the strengthening (weakening) of civilian rule has a significant positive (negative) impact on long-run growth. While at first sight this interpretation appears plausible, however, it is obvious that other factors are important as well. Moreover, the growth-enhancing channel of democratic rights might be operating indirectly on some independent variables, such as educational attainment. This complicates the analysis. Hence, further research is needed to strengthen this hypothesis.

# **_Alternative Capital Stock Data_**

Column 2 of Table 8 includes capital stock data with a 4 percent depreciation rate rather than the 5 percent thumb value assumed throughout this study. The data with 4 percent depreciation is essentially identical to the Nehru and Dareshwar (1993) capital stock series, despite some minor discrepancies? when compared with data from Banco de Guatemala? on investment. Assuming 4 percent depreciation of the capital stock has little impact on the results, although in the IV specification the significance of the capital coefficient is weakened. This suggests that a 4 percent depreciation is rather on the low side.

Column 3 includes the capital stock estimate built with disaggregated investment data originally constructed to compute the quality index for capital. This series is robustly correlated with growth. The long-run elasticities for physical and human capital are slightly higher than with the standard estimate of the capital stock. Due to the limited number of observations the regression could only be run by OLS. Thus, the coefficients are likely to be upwardly biased. Altogether, varying the assumptions about the depreciation rate moderately changes capital elasticities but does not change very much the role of human capital on growth.

# **_Alternative Schooling Data_**

The most interesting sensitive test concerns the validity of the conclusions on the importance of human capital to growth. Column 4 uses interpolated education data from Barro and Lee (2001). Column 5 includes the interpolated time series from Cohen and Soto (2001) into the regressions. In both estimates human capital, as measured by average years of schooling, is robustly correlated with growth.

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difficult to draw on it for Public Expenditure Reviews (PERs) and in other contexts for fiscal analysis. Nevertheless, it can provide useful macro insights for strategy analysis and may be developed further to address some of these limitations.

The links between growth, service delivery, and MDG achievements outlined above have demonstrated that a more sophisticated framework is needed. The analysis must consider macroeconomic factors and tradeoffs between objectives. For example, the prospect of significant increases in foreign aid (for most countries in Sub-Saharan Africa, external assistance required to meet the MDGs in 2015 may require more than a doubling of aid flows) leads to concerns over the possibility of “Dutch disease,” characterized by an appreciating real exchange rate, a shift of resources towards non-tradables, and lower export growth. A related critical issue is the pace at which large, aid-financed programs should be scaled up. Rapid initial expansion may drive up costs more quickly and could be more expensive in real present value terms. On the other hand, given time lags, especially in education, expanding investment too slowly may make it impossible to achieve the MDGs by 2015. A coherent analytical framework is needed to capture macro-micro linkages, Dutch disease effects, and timing issues.

The need to evaluate and provide policy advice on such tradeoffs – across sectors and over time – has led to innovative research efforts. This paper presents MAMS (Maquette for MDG Simulations), which was produced within a research program on the MDGs conducted at the World Bank. MAMS is an economywide framework designed to analyze the interactions between delivery of human development (HD) services (health, education, water, and sanitation), the Millennium Development Goals (MDGs), growth, and foreign aid. The framework is equally applicable to the analysis of the same set of policy issues in the context of PRSs (Poverty Reduction Strategies). MAMS belongs to the class of dynamic general equilibrium models, but has been substantially augmented to capture the key processes that generate MDG outcomes, along with the feedbacks to the rest of the economy.

MAMS does not replace detailed sectoral studies, but instead complements and draws on the research that underpins sector strategies for achieving the MDGs. Without sector studies to provide a strong empirical basis, the analysis of MDG strategies in an economy-wide framework (either MAMS or any other) loses much of its power. By fully embedding such strategies in a comprehensive economy-wide framework, MAMS fills a gap in the toolkit that is available to policy analysts. Especially for low-income countries, the policy challenges related to the MDGs cannot be well understood unless sector issues are viewed in the context of constraints in the macro environment and in labor markets.

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mortality for this age group. It is likely that the rates of respiratory disease are higher among irnfmts in the more densely populated cities.

For Indonesia as a whole (all ages), inflanmmation of the respiratory tract was the sixth leading cause of death (after accidents, diarrhea. cardiovascular disease, tuberculoses, and measles), accounting for 6.2 percent of all mortality." 9n the U.S., diseases related to respiratory tract information account for about 4.4 percent of all mortality. However, in Jakarta, this outcome accounts for 12.6 percent of all mortality,96 more than double the rate for all of Indonesia. It is reasonable to hypothesize that exposure to high indoor and outdoor air pollution, along with high population density and limited health care access, may be responsible for these higher rates in the city. For the entire population. upper respiratory tract conditions are the leading cause of morbidity, responsible for 44.8 percent of all reported cases. In Indonesia in 1986, acute respiratory tract infections had the highest reported prevalence rate at 21.3 per 1000. Following this outcome in prevalence were skin infections (7.6 per 1000), gastrointestinal tract conditions (6.9), other infections (6.5) and bronchitis and asthma (6.4) (Ministry of Health, 1989). Data from the 1990 Indonesian census indicates that among children under age five living in Jakarta, 10.7 percent experienced cough or shortness of breath during the two weeks prior to the census survey period. Of these, 65 percent were taken to a health care fadclity. Finally, as the indicator of respiratory disease status, a study of asduna in children reported a prevalence rate of 16.5 percent96 versus approximately 10 percent among children in the United States, 10 percent in Bogor, 6 percent in Jogjakarta, and 3.7 percent in Bali. The health data are revealing particularly when relative rates of respiratory disease in Jakarta are compared not only with these United States, but even with other cities in Indonesia, or with the country as a hole, which may be a more appropriate baseline for comparison. Using this information, we assumed a baseline rate of astha for the population of Jakarta of 8.25 percent or (16.5/10) times the U.S. asthma prevalence rate of 5 percent.
the location of an industry in a region (for example, the tanneries in Calcutta), which are difficult to measure but are important for analytic purposes. In addition, economic diversity of large metropolitan regions encourages inter industry knowledge transfers, which tend to enhance average levels of efficiency in the region. In principle, introduction of the fixed effects terms captures some of these city specific characteristics. There are some pitfalls of taking administratively defined entities (Indian districts in this case) as the unit of analysis for capturing the efficiency gains from location in urban areas. Often times, either the extent of metropolitan areas/agglomerations extends beyond administratively defined boundaries, or there are several specialized sub-centers within a large agglomeration, which cannot be identified using information at the district level.

Figure 3 provides a schematic of these ideas, where L is the metropolitan area or agglomeration and S is the specialized sub center. We consider these cases (A, B, and C) using the Bombay metropolitan areas for reference. In case A, the specialized sub center and the agglomeration are part of the same jurisdiction. Take for example, the Indian motion picture industry which is highly concentrated, and localized in the Bandra suburb of Bombay. In principle, while urbanization benefits can be adequately represented for the motion picture industry as the same benefits accrue to all firms from locating in an urban area (such as Bombay), the estimation of localization benefits is likely to be biased. This is because the industry is not spread throughout the metropolitan area, but clustered in one sub center. If spatially disaggregated data were available (for both firm location and regional attributes), it would be possible to estimate more realistic measures of industry concentration, which take into account localization in tightly clustered geographic space.

In case B, the sub center and the agglomeration are in adjacent jurisdictions. An example is the Bombay metropolitan area and its adjacent district, Thane. Even though these two entities belong to different jurisdictions, they are part of the same agglomeration. Economic activities in Thane are closely linked to activities in the Bombay district. We are likely to get biased estimates of urbanization benefits if the linkages between sub centers of this type and the large metropolitan area, are. not considered. This is because the urbanization benefits accruing to firms in the sub center (Thane) are not only a function of its own level of urbanization but also influenced by the characteristics of the agglomeration to which it belongs (Bombay, in this case). To test if 15
<!-- Start of picture text --> Figure 2.2 Spread of the Industrial Revolution: Late Comers Catching Up<br>(1800-1914)<br><!-- End of picture text --> <!-- Start of picture text --> Length of Railroad Lines open in kilometers (1km=5/8 mile)<br><!-- End of picture text --> <!-- Start of picture text --> 60000<br>Great Britain<br>50000 Late comers<br>France<br>Germany<br>Germany<br>40000 Russia<br>Belgium<br>30000 Austria-Hungary<br>Russia<br>20000<br>G.Britain Italy<br>10000 the leader Netherlands<br>Spain<br>0<br>Sweden<br>1830 1840 1850 1860 1870 1880 1890 1900<br><!-- End of picture text --> Source: Compilation based on the statistical appendices in the Fontana Economic History of Europe Vol 4, accessible on the web, _Modern History Sourcebook_ .

Relative to the Britain, industrialization was delayed in the United States because the country lacked the basic factor endowments, labor and capital to invest in business. When it finally began in the 1820s after the country had enough capital and labor, relative to continental Europe, its growth was ―explosive.‖ Both laborers and capital came from Europe where political revolutions sent immigrants to the U.S. The first locomotive emerged in 1826, the first railroad started in 1827, and the length of railroad surpassed Britain in 1850, reaching 9,021 miles; and then expanding rapidly to the west in 1890, reaching 129,774 miles,<sup>9</sup> longer than the length of railroads in the entire continental Europe. Rapid industrialization and structural transformation then followed. In 1800, 85 percent of the US population was comprised of farmers, but in 1860 this had declined to 50 percent.

Gerschenkron observed that rapid industrialization could start from vastly different levels of ―economic backwardness‖. In fact, ―the more backward a country‘s economy, the greater was the part played by special institutional factors ( _government agencies, banks_ ) designed to increase the supply of capital to the nascent industries,‖ (Gerschenkron 1962, p. 354). Like Akamatsu, a weakness of Gerschenkron‘s theory is that he did _not_ stress that for the latecomer to be competitive, the latecomer must identify industries that are consistent with its comparative

> 9 Source: Chauncey Depew (ed.), _One Hundred Years of American Commerce 1795-1895_ p 111.

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# **2. A Framework to Study the Relation between Corruption, Tax Evasion, Financing and Innovation**

Consider a simple set-up where some of the firms in our survey during the course of doing business, pay bribes to government officials and/or evade taxes. Some of these firms are also innovators who undertake an innovation opportunity that needs to be provided a government license or approval and financing. Below we elaborate on the corruption technology and present a framework to understand the link between corruption, tax evasion, innovation and financing.

First, consistent with Ades and Di-Tella (1999), we view bribe payments by firms as an illegal tax or fee levied by government officials who have the power to hold up a firm by denying services. This interpretation fits the type of corruption that we investigate empirically below. The firms that we analyze in our sample are relatively small and are unlikely to have market power in the market for corruption. Moreover, as discussed below, much of the bribery is to providers of routine services.<sup>8</sup> Safavian (2001) and Svensson (2003) find that bureaucrats tailor bribes to firms’ ability to pay. Thus, the characteristics of firms that will be extorted by officials depend on the opportunities for extortion and the likelihood of punishment. We conjecture that firms in some industries, like construction, which are usually regulated and subject to inspection are particularly subject to extortion by government officials. Below, we use cross-country data to examine the relation between firm size, ownership structure, and industry, and bribe paying. We also investigate whether these firm characteristics predict tax underreporting.

Second, to understand the effect on the innovators among our sample of firms we follow Murphy, Shleifer, and Vishny (1993) who argue that innovators are particularly vulnerable to extortion from government officials because they are not part of the entrenched lobbies; they are often credit-constrained and hence can be more easily deterred by public rent-seeking; and the nature of their projects (long-term, slow accumulation of capital, risky) offer more opportunities for rent seekers.<sup>9</sup> Thus, innovations that involve activities such as changing the physical layout of a factory or office space, installing telephones, acquiring motor vehicles, opening new premises,

> 8 We find no evidence that firms that pay bribes outperform firms that do not.

> 9 We test for this link between innovation and bribes below.

7