rafmacalaba/datause-encoder-data huggingface dataset:
2 de Janvry and Sadoulet argue that the unequal distribution of land and income in Latin America (and other developing countries) mean that only a handful of landowners benefit from the income effects of agricultural growth.<sup>3</sup> Since these large landowners prefer to buy luxury items produced by imports, they do not demand the type of labor-intensive goods and services which are produced by the poor in the rural nonfarm sector. For this reason, de Janvry and Sadoulet argue that in land-constrained areas of the developing world - like Latin America and certain parts of the Middle East and Asia - focusing directly on the rural nonfarm sector might provide a better way of increasing the income and employment opportunities of the poor. In this view, income earned in the rural nonfarm sector represents the agent of positive change for the poor in the rural economy, rather than income earned from the traditional agricultural sector.

Despite this cnanged view, there is still no agreement in the empirical literature on two key issues, namely: (a) what is the impact of rural nonfarm income on income inequality?; and (b) vhat is the link between land, nonfarm income and overall rural inequality? On the one hand, studies by Lanjouw in Ecuador, Adams in Pakistan and Chinn in Taiwan indicate that nonfarm income reduces rural income inequality.<sup>4</sup> According to Adams,<sup>5</sup> nonfarm income benefits the poor because the share of nonfarm income varies inversely with both size of land owned and total rural income. On the other hand, studies in Africa have generally produced very different results. For instance, Reardon and Taylor in Burkina Faso, Collier, Radwan and Wangwe in Tanzania and Matlon in Nigeria find that nonfarm income has a negative impact on rural income distribution because it is mainly large landowners who receive nonfarm income.<sup>6</sup> Part of this inconsistency may be explained by differences in the key factor noted above, namely, the distribution of land. While many factors affect land distribution,' on
imbalances in the starting conditions between treatment and control villages. The results prove to be robust to such controls, although a small number of results lose their significance when individuallevel controls are added due to the reduction in the sample size caused by individual-level attrition.

An important characteristic of the two eastern districts is that they are predominantly Pashtun. To check whether the difference in the results for the eastern districts is driven by their ethnic composition, rather than the level of violence, we also examine whether the effect is different in the other two predominantly Pashtun districts in our sample (Balkh and Farsi). The results indicate that there is only a small difference in the effect of the program in Pashtun regions as compared with other districts, suggesting that the divergence of results in the eastern districts are driven by insecurity and/or regional specificity.

## **VI.V. Heterogeneity of Results**

To examine whether the above effects are affected by progress in implementing projects, we use data from NGOs indicating rates of project completion at the time of the follow-up survey. This data is used to construct a dummy variable which assumes a value of one for the 113 treatment villages in which at least one project was at least 80 percent complete. Estimates of interaction effects between this variable and the treatment effect indicate that for, almost all measures, the effect is stronger in villages that made more progress in project completion.

We also explore potential heterogeneity of effects on economic outcomes and attitudes at the individual level. In particular, we examine whether the effects are driven by respondents’ age, education, land ownership, unemployment status or their involvement in subsistence agriculture. The results indicate that the effect of the program is smaller for older respondents, who are more likely to be skeptical of development interventions as they have lived through over three decades of war but also less likely to be themselves actively involved or recruited in the insurgency. However, there is no evidence that any other individual characteristics that we consider have a consistent effect on the effect of the program.

# **VII. Discussion of Results**

Overall, the results lend support to the “hearts and minds” theory. The presence of a governmentsponsored development project in a village positively impacts subjective measures of economic wellbeing, improves attitudes towards the government and allied entities, and has a positive effect on how 19
that unfaithful males are the channel through which HIV gets transmitted from high risk groups to the general population. The paper goes to great lengths to explore other potential explanations that could be the driving force behind that result. But it concludes that the sizeable fraction of discordant female couples is extremely difficult to explain without infidelity among married women. It might be because infidelity is more common than reported, or because, even if infrequent, women are very vulnerable to infection during extra-marital sex, for example, because they are less likely to use condoms than single women and than married men.

The point of this paper is not to play some type of “blame game” where married women would be shown to be equally “guilty” as married men in transmitting the HIV epidemic. The fact that female marital infidelity can be, in many cases, forced should certainly be kept in mind. But, in any event and whatever its causes, female marital infidelity seems to be an important source of vulnerability to the HIV/AIDS epidemic that should not be ignored and needs to be targeted, as much as male infidelity, in prevention efforts.

The two findings from this paper emerge from a very simple analysis, but an analysis that relies on data that contain results from HIV tests, an objective bio-marker. The contradiction between self-reported female infidelity and the proportion of discordant female couples, as well as the examples of discordance in couples about their reported behaviors, suggest that such self-reported behaviors are likely to be biased and that they should be treated carefully as a source of information for prevention policy. As much as possible, policies should rely on evidence including objectively measured HIV status.

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Policy Research Working Paper 6579

# **Abstract**

Three recent rounds (2003, 2006, and 2009) of the Family Income and Expenditure Survey are matched to rainfall data from 43 rainfall stations in the Philippines to quantify the extent to which unusual weather has any negative effects on the consumption of Filipino households. It is found that negative rainfall shocks decrease consumption, in particular food consumption. Rainfall below one standard deviation of its longrun average causes food consumption to decrease by about 4 percent, when compared with rainfall within one standard deviation. Positive deviations above one standard deviation have a limited impact. Moreover, for households close to a highway or to a fixed-line phone, consumption appears to be fully protected from the impact of negative rainfall shocks.

This paper is a product of the Poverty Reduction and Equity Department, Poverty Reduction and Economic Management Network. 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:// econ.worldbank.org. The author may be contacted at eskoufias@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
but perhaps more money to spend on them. Migrants may also transfer knowledge and attitudes to their remaining family members. For example, Hildebrandt and McKenzie (2005) find contraceptive knowledge to increase with emigration of household members from Mexico to the US. Absence of decision-makers may also lead to changes in the bargaining power of remaining members in the household leading to a reallocation of household spending priorities (Chen, 2006). Separation from family members may impact on mental health. Finally, migration of some family members may make it more likely that others will migrate in the future, changing the incentives to acquire education.

The result of all of these different potential channels is that the overall impact of migration on various measures of the welfare of remaining family members is theoretically uncertain. The effects are also likely to vary with the amount of time the family member is away. For example, Lucas (1987) finds emigration from Botswana, Lesotho and other Southern African countries to South Africa decreases domestic crop productivity in the short run as labor is removed from the farm, but appears to enhance crop productivity and cattle accumulation in the long run through invested remittances. Many other empirical studies are unable to control for the length of time migrants have been away, resulting in an averaging of short-run and long-run effects.

# **2.2. Selection and Identification**

The main challenge facing empirical analysis of the impacts of migration and remittances on sending households is a double-selectivity problem. First, households choose whether to engage in migration.<sup>4</sup> Households which send migrants are likely to differ along a number of observable and unobservable dimensions from households which do not send migrants, with some of these characteristics likely correlated with outcomes of interest. For example, an unobserved asset shock may make the sending household poorer and encourage emigration. Households with aptitude and knowledge of foreign languages may be more inclined to engage in migration, and also have children who do better in school. Second, amongst households which decide to engage in migration, some decide to move with their

> 4 To be precise, they choose whether to engage in migration given the existing policy environment. In most cases this is a policy environment which also involves substantial selection from the receiving country side with employers and government officials screening interested potential migrants to determine which ones can actually move. Since there is less screening at destination in our case than in the case of much legal migration, the degree of self-selection is likely to be less in our example than in cases where employers and governments are also involved in selecting the migrants. As a result, our results will be, if anything, conservative in terms of showing the potential bias from self-selection.

5
consumption estimated at around 38, the trend in inequality in subsequent years goes in opposing directions. HBS 2011/12 indicates a decline of inequality to around 36 while NPS 2010/11 suggests a stagnation of inequality and NPS 2012/13 indicates an increase in the Gini index to around 41.

**Figure 2: Levels and trends of inequality in Tanzania Mainland (Gini index of monthly real consumption per capita)**

<!-- Start of picture text --> 41 40.6<br>40<br>39 38.5 38.4<br>38.1<br>38<br>37<br>35.8<br>36<br>35<br>34<br>33<br>HBS 2007 HBS 2011/12 NPS 2008/09 NPS 2010/11 NPS 2012/13<br><!-- End of picture text --> _Source_ : HBS 2007 and 2011/12 and NPS 2008/09, 2010/11 and 2012/13.

At the regional level, both NPS and HBS reveal that inequality is higher in Dar es Salaam and secondary cities than in rural areas **.** HBS data suggest that the distribution of consumption equalized over time in all the regions, with the most substantial improvement occurring in the rural areas, as can be seen from the changing shape of the Lorenz curves in Figure 3. Much of the reduction in inequality seems to have been driven by an increase in the welfare share accruing to the poorest segment of the population, as the consumption share of the poorest quintile grew by more than 16 percent between 2007 and 2011/12, with an increase exceeding 20 percent in the rural areas.

5
<!-- Start of picture text --> Panel A Panel B<br>Resilience Fa<br>© / (ie: -maae<br>2 LN natoutsoay = Be |<br>5 g¢<br>E | E \<br>® | ® \|<br>re] ‘ 5 | Friendshoring<br>{ ~—— ffCS _ China plus one<br>- SS es<br>r=) J<br>2000 2005 2010 2015 2020 2000 2005 2010 2015 2020<br><!-- End of picture text -->
8 sector" (Gillis 1990, p. 84), while the U.K. VAT uses extfnsive zero-rating for many services (e.g., food, all housing and some clothing and services), which co'nld comprise 40 percent of the potentially broad VAT base. This means that whan making country comparisons of obFeved differences in revenue productivity, when rates are identical across countries, it is not possible to infer that one VAT is more "efficient" in terms of collection. Observed differences probably reflect largely, if not entirely, the differences in the size of the base which, in turn, ref.ects different distr.butional considerations built into the VAT systems.

Table 1: Determinants of VAT RIevenues: Regression Results

|**DEPENDENT**<br>VARIABLE:<br>VAT revenue to GDP<br>(%)|(1)<br>All countries|(2)<br>Single rate<br>Countries|(3)<br>Multiple<br>Rates<br>Countries|(4)<br>Differences<br>(2 - 3)|
|---|---|---|---|---|
|Censtant|-0.880<br>(-0.953)<br>|-1.746<br>_<br>(-1.907)0|-4.805<br>(-1.909)|3.059<br>(1.168)|
|RATE|0.389<br>(6.293)a|0.503<br>(7.519)-|0.339<br>(3.025)|0.164<br>(1.276)|
|BASE|1.426<br>(1.930)-|1.313<br>(1.937)0|4.490<br>(2.641)b|-3.177<br>(-1.774)'|
|RANGE|-0.071<br>(-1.863)_|---|0.021<br>(0.383)|-0.021<br>(-0.394)|
|R<sup>2</sup>|0.61|0.77|0.66||
|R<sup>2</sup>(adjusted)|0.57|0.75|_0.55_||
|F|15.85a|29.83a|6.35a||
|n|34|20|14||

Notes: rhe number in parentheses are t-statistics where stands tor signiticance at the Y% level, denotes significance at the 5 % level, and **0** stands for significance at 10 % level.

The RANGE variable, as postulated, is negatively associated with the revenue, albeit weakly statistically significant, indicating that rate differentiation - 'es the tax costs and hence may generate losses in terms of revenue. The result seems to provide ea.ipirical support to Tait's argument that both official administrative costs and traders' compliance costs rise sharply as the number of rates multiply, leading to potential revenue losses. Interestingly, in terms of welfare, Ballard, Shoven and Whalley (1982) reached the same conclusion.

15/ Practical problems in including banking services in the base of the VAT have been quoted as the main reason that led EC countr;es to exempt this sector from the VAT base.
Figure 1. Rates of Growth of Real Hational Income <!-- Start of picture text --> p<br>e<br>-sr -~~~~~~i<br>e ...............<br>A %<br>Ht ..........<br>i929 19a9 1931 1932 1933 I9 :4 9395 1i963 1931<br>Heavy Li i ti -4<br>- De tau Ie Ps ....... ie1<br><!-- End of picture text --> 14
83

**Figure 28   Distribution of Risks by Category**

<!-- Start of picture text --> Swiss Life Group Winterthur Group<br>6%<br>13% 21%<br>8%<br>71%<br>81%<br>Market Risks Underw riting Risks Credit Risks Market Risks Underw riting Risks Credit Risks<br><!-- End of picture text --> Source: Swiss Life (2004), p.59 and Winterthur (2004), p.63 Winterthur Group for instance, evaluates risks and its capital requirements by the measurement method of _Economic Risk Capital (ERC)_ . This method measures all risks on the basis of their potential economic loss, irrespective of how they are handled. Thereby it distinguishes between (c.f. also Figure 28):

- _Market Risks_ : Market risks cover adverse changes in interest rates, foreign exchange, equity prices, private equity, and real estate values. ERC calculates for each risk type separate figures, which are measured on a 99 percent confidence level and for a 1- year holding period. Assets and liabilities are marked to market, where the cash flows of the liabilities are determined by actuarial projection methods. Equity prices, interest and foreign exchange rates are calculated by applying historical simulations. Numbers related to the real estate ERC are obtained by using a variance–covariance approach. It uses historical country-specific data, i.e., real estate indices.

- _Credit Risks_ : Expected losses in the portfolio are calculated by the use of ratings. A component that captures the possible change in the rating of the underlying assets as well as a spread risk component on traded positions are included.

- _Underwriting Risks_ : Central reference figures from ALM (asset and liability management) are used to calculate the required ERC from unplanned risk accumulations, such as deviations from expected mortality, disability, longevity, and expected surrender rates (in the life and pension business part).

- _Business Risks_ : In general, the risks that the ongoing income from the business is not able to cover the ongoing expenses subsequent to a severe crisis.

- _Operational Risks_ : Operational risks result either from inadequate or failed internal processes, systems or employees or from external events like e.g., fraud or system breakdowns.

The method ensures that Winterthur Group remains able to pay its liabilities up to a specific prescribed level of solvency, even in the case of extraordinary market, business and operational situations. Since ERC is the common denominator for all risks it strengthens the ability to monitor and control them. Furthermore, it contributes to greater risk transparency and it facilitates the analysis of risk management issues within the Group.
Energy subsidies in Libya are regressive (in absolute value), or pro-rich. This can be more clearly seen when looking at the distributional analysis on a per-capita basis. Indeed, the per-household data mask the fact that poorer households are larger in size. This implies that, on a per-capita basis, lower income brackets are less well-off. Figure 6 shows per-capita subsidies ( _y-axis_ ) across population percentiles ( _xaxis_ ) for each subsidized energy product. All curves are positively sloped, which indicates that richer households receive higher amounts of subsidies per capita. The regressive feature of energy subsidies is less pronounced for the cases of kerosene and diesel, consistent with the proposition that these products are consumed more intensively by the poorer population. This feature is most pronounced for gasoline and electricity, the two products whose subsidies generate the biggest cost to the government budget.

**Figure 6: Per Capita Benefits Accruing From Subsidies on Energy Product (Libyan Dinars)**

<!-- Start of picture text --> 400<br>Gasoline<br>Diesel<br>Electricity<br>LPG<br>300<br>Kerosene<br>The total<br>subsidies<br>per capita<br>200<br>100<br>0<br>.01 .198 .386 .574 .762 .95<br>Household Percentiles<br><!-- End of picture text --> Source: Libyan Household Consumption Survey 2007/8; Libyan authorities; and Bank staff calculations.

# **5.2.Simulations of energy subsidy reforms**

Energy subsidy reforms are expected to have a significant direct impact on households. Consistent with the fact that gasoline and electricity are the main energy products consumed by households, we find that reducing subsidies on these two items would have a far larger impact on household real income and poverty, as well as on the government budget, than reducing subsidies on other energy products.

24
after a sufficiently high price increase) so that even an ex-ante net consumer can benefit from a price increase. In what follows, we illustrate various sources of adjustment and discuss attempts to quantify them.

# **4.1 Consumption Responses**

We begin with consumption because research in this area has been prolific. To incorporate consumption responses, research needs to estimate a system of demand elasticities (ownand cross-price elasticities) to identify the pattern of substitution in consumption. The estimation of such a demand system is difficult, however.

There is a long tradition in economics in estimating systems of demand equations. The type of estimation method depends critically on the available data. In the early days, when only national account data were available, estimation relied on time series of various consumption aggregates and price indices. The search for a utility-consistent empirical model of demand converged to the Almost Ideal Demand System (AIDS) model of Deaton and Muellbauer (1980), which is now the leading framework to study demand elasticities.

The mass-production of household surveys in more recent years—a process in which the World Bank has been instrumental— has broadened the options for demand analysis. However, while expenditure surveys provide detailed accounts of what people consume, they collect little information on prices. In some cases, community questionnaires collect price data at the village level, and this variation can be exploited to identify demand elasticities. For those cases in which the surveys collect information on quantities purchased as well as on expenditures, demand elasticities can be recovered from unit values—the ratio of expenditures over quantity. Deaton (1987, 1988, 1990) developed a model of demand for quantity and quality that allows for the extraction of the price signal embedded in the unit values at the level of a market (or cluster of households residing near each other and thus shopping for consumption goods in the same markets) and these price signals identify the demand elasticities. Because the estimating equations are not necessarily derived from a utility framework (that is, the equations do not form a full-blown AIDS model), the estimated parameters cannot be used to recover the parameters of the utility function. However, they 20