rafmacalaba/data-use-mentions-extended huggingface dataset:
export performance which is a common finding of gravity models. On the other hand, large and remote countries export less. Ten percent increase in the remoteness of the country decreases export sales by 4-5 percentage points. These findings are in according with the results of Djankov et al (2010) and Iwanow and Kirkpatrick (2009). Past export growth also contributes significantly to the current export performance. Lastly, trade restrictiveness index which measures the stringency of domestic trade policies on imports negatively affect exports. This shows that there is complementarity between importing and exporting activities which could be caused by exporters’ extensive usage of imported intermediate goods.

Redding and Venables (2003) emphasize the importance of foreign market access as a factor of export growth. In their theoretical model, they define foreign market access of an exporting country as the sum of market capacities of all partner countries. They find that a substantial part of the differential export growth of numerous countries since 1970 can be attributed to variations in the rate at which their foreign market access has grown. In Table 6, I use the market access restrictiveness index (MATTRI) as a proxy for foreign market access. The table shows that difficulties in market access significantly decrease export performance. In four of the six specifications, its coefficient is negative and significant. Ten percent increase in market access lead to 2 to 8 percentage points increase in exports. Among the

> 9 In 2005 there are 136 countries. Data for Namibia is available for 2006 and 2007.

7
income on average. As a consequence, on the income side too, tariff incidence will vary across households.

Having disaggregated and nationally representative household-level data with information on both consumption and income portfolios is a major advantage since it enables policymakers and researchers to quantify the significant heterogeneity in the impacts of trade reforms across households. At the same time, it is worth bearing in mind some of the limitations of household surveys. For instance, they typically fail to capture households at the very top of the household income distribution. In addition, they do not adequately capture capital incomes. Moreover, they suffer from measurement error, especially in incomes, which are often underreported. To minimize the role of measurement error, we dropped households in the top and bottom 0.5% of the expenditure distribution prior to aggregating.

# **2.2 Harmonizing tariff data**

The next step in the analysis is to convert tariffs at the HS6 level to the standardized product classifications from the household surveys. Each group _i_ from the household surveys contains many finer product groups from the HS classification. To arrive at a product level average, we computed weighted average tariff rates _τi_ for each of the groups in the survey classification.

where _n_ is an HS-category that belongs to survey-category _i_ and _m_ ( _c,n_ ) are imports of good _n_ from country _c_ . To calculate (1), we use tariffs from the latest year for which data are available.

Tariffs vary both across countries and across products. The average tariff across countries is 14.2%. Tariffs are highest on average in Bhutan, notably 48.4% on average, and lowest in Iraq (5% on average), and tend to be lower in countries with higher levels of GDP per capita. There is also significant variation in tariffs across the different products in our data. For agriculture, the focus of our applications, Figure 6 depicts both the mean and the maximum tariff for each of the 53 food items in our data. On
gradient found in 1993 had largely vanished by 2004. Simultaneously with rising landlessness, the distribution of land holdings became more equal across levels of living.<sup>37</sup> Figure 7 is not consistent with our model’s prediction that post-reform landholding should have a positive wealth gradient. However, our model ignored heterogeneity in land quality. On a closer inspection we find that the poor have not seen gains in their land quality. This is evident from Figure 8, which gives the share of annual crop land that is irrigated. We see marked gains over time at all levels except among the poorest. The same pattern was also found when we looked at the distribution of the share of annual crop land that the commune authorities rated as high quality (details available from the authors). A plausible explanation for these differences is the initial inequality in non-land wealth, given credit market failures. Those with wealth were naturally in a better position to invest in their land.<sup>38</sup> The 2004 survey allows us to study how land was acquired.<sup>39</sup> Figure 9 shows how the incidence of land transactions varies with consumption. The totals include all means of acquiring or disposing of land that are allowed under the land laws (purchase, sale, inheritance, bequest, exchange). We also identify purchase and sale. We see that the upper consumption groups were more likely to take up the new opportunities for land-market transactions. These data are clearly not consistent with the view that the poor were selling their land to the rich and remaining poor. (It is likely that some of the transactions observed at middle consumption levels are for those who were previously poor.) This is further supported by Figure 10, which shows how each household’s land-holdings in 2004 were initially acquired. We see that the poor in 2004 had relied heavily on reclaiming land, and hardly at all on market transactions for obtaining land; the purchase of land was more
in Table 10). However, further analysis of heterogeneity suggest that rural road developments profoundly reduced the chance of facing food insecurity in remote areas.<sup>21</sup> The estimated welfare impacts of rural roads are robust against the measure of drought shock. How to identify households/areas affected by droughts is not straightforward. As explained, this study uses an NDVI-based drought exposure indicator. As a robustness check, six dummy variables are created based on different thresholds (z-scores lower than 0, −0.5, or −1.0) and interacted with rural road indicators in estimating the DID matching models to assess how the impacts of rural roads on household consumption change, depending on the degree of abnormalities in vegetation. Except for the extreme threshold (−1.0), the estimated impacts of rural roads on household consumption are not substantially different from the baseline with the threshold being zero (Table A2 in the Appendix).

Moreover, the estimated impacts above do not substantially change when other matching methods are applied. Table A3 in the Appendix summarizes the results based on nearest neighbor matching (one-to-one matching). Overall, the estimated impacts on household consumption are larger than those based on kernel matching. Regardless of setting caliper (row A) or not (row B), the results demonstrate that rural roads increased household consumption by about 20 percent between 2012 and 2016.

# **6. Conclusion**

This study attempts to assess the impacts of recently developed rural roads on household welfare and economic activities. The panel data sets of both household surveys, which include detailed household consumption information, and transport networks make it possible to relate the development of rural roads to welfare changes across space and over time. Moreover, the study period between 2010 and 2016 incorporates both the launching and development of a large-scale rural road development program (URRAP) and historically severe droughts, which allows it to analyze the contribution of rural roads to resilience against disaster shocks.

The study finds that recently
) proposes as a _rule of thumb_ that the minimum wage should be set at about half of the median wage and that there are at least three reasons to pay attention to measures of the minimum wage “bite”. First, a comparison of the minimum wage to the average or especially the median wage offers a guide for how binding a particular minimum wage level is likely to be. Second, the minimum wage “bite” also provides a benchmark for determining how high or low a minimum wage is across time periods or countries. Third, it gives a natural reference point for judging whether the level of the

> 1 According to Vietnam’s census, mean household size in 2009 was 3.84 in rural and 3.66 in urban areas. In 2013, the age dependency ratio was 0.41, the labor force participation rate of individuals aged 15to 64 was 0.82 and the unemployment rate two percent (source: World Bank World Development Indicators). A back-of-the-envelope calculation based on these figures reveals that the average household in rural areas contains 2.2 employed members and 1.7 individuals that are unemployed, out of the labor force or not aged 16 to 64. In urban areas the figures are 2.1 and 1.6, respectively.

- 6 -
this assumption by looking at the association between coffee mills and two key health outcomes that have a significant bearing on the cognitive development of children: high-for-age (HAZ) scores and stunting (Grantham-McGregor et al., 1996; Mendez and Adair, 1999).

Our results in Table 8 provide evidence that coffee mills are associated with an improvement in child health. Children living within the catchment area of coffee mills are 5 pp less likely to be stunted (column 3) and experience 0.1 std increase in their HAZ scores (column 1).

# **6.3 School Attendance**

We conclude our section on mechanisms by evaluating the effects on school attendance. Again, our working hypothesis is that the positive income shocks associated with coffee mills enable parents to increase educational investments in their children thereby allowing (incentivizing) the children to attend school and learn.

Ideally, testing this assumption will require granular data on student attendance at the school level before and after the opening of coffee mills. However, this data is unavailable in the study context. The available data on attendance is aggregated at the district level, thereby limiting its use, at least for this study. To this end, we rely on proximate measures of attendance from the DHS dataset, which asks if a "person attended school during the current school year", and restrict the sample to children within the primary school-going age (7 and 16) in Rwanda. Admittedly, this measure is imperfect as it may reflect enrolment rather than (regular) attendance, but we use it as a proxy for attendance.

We, therefore, combine the DHS data and information on the location and opening of the coffee mills to estimate the effects of the coffee mills on our proxy measure of "school attendance". The results as shown in Table 9 suggest a positive effect of the mills opening on school attendance rates. Starting in column 2, we find that children living within the catchment area of an operational coffee mill are about 2 pp more
of land size collected using GPS devices. Lamb (2003) concludes that by controlling for measurement error in land size, the statistical case for the IR disappears. This paper has shown that introducing rigorous controls for land _quantity_ does not affect the evidence concerning the IR, which is in fact strengthened when the better land data are used.

We also explored how farmers‟ self-reporting of plot size varies systematically with the size of their holdings and other household and land characteristics. We conclude that in Uganda small farmers tend to over-report plot size relative to medium-sized land-holders, while the largest farm groups under-report plot size on average. This is consistent with earlier evidence from Mali reported by De Groote and Traorè (2005).

Self-reported measures of land size are notoriously imprecise. In large household surveys in developing countries, however, they have for a long time been the only option available to practically collect data on the physical dimension of the plots owned or cultivated by the household. More 12
people killed).

# 4.3 Tobit Estimation

An empirical approximation to equation 6 consists of estimating a Tobit model augmented by the exposure to violence in each district. Due to the widely missing data on wages for each worker, we use "education" as a proxy. However, we include for control the average district wage, calculated using available data in each country (relative to the mean) to proxy for the market value of working (or outside option of remaining in domestic work). Finally, we control for the number of children under age seven in the household, as well as country, and time xed eects.

Under this setting, the interaction term _β_ 5 identies the average dierence in hours supplied between a woman whose spouse is employed compared to a woman whose spouse is unemployed when an attack occurs, measured as when one extra person dies or is wounded. We estimate equation (8) using only females who are not head of households, and we disregard presence or absence of other working household members. We remove India from this estimation, as mentioned, because we lack information on number of working hours, and we use the individual-level data (that is, we 10
**Table 5: Relative Quality of Publications in 2001**

|Rank|Re<br>Cit<br>Ind|lative<br>ation<br>ex|
|---|---|---|
|All Fields|20|0.57|
|clinical medicine|22|0.54|
|biomedical research|17|0.58|
|biology|33|0.47|
|chemistry|22|0.63|
|physics|25|0.56|
|earth/space sciences|21|0.56|
|engineering/technology|27|0.54|
|mathematics|32|0.45|
|psychology|28|0.29|
|socialsciences|31|0.34|

Note: Relative Citation Index is the country’s share of cited literature adjusted for its share of published literature, excluding within country citations. An index greater (less) than 1.00 indicates that the country is cited relatively more (less) than is indicated by the share of publications.

Source: Science and Engineering Indicators 2004 (National Science Board 2004) 48
**2008-11**

|Albania|Greece|Pakistan|
|---|---|---|
|Algeria|Guatemala|Panama|
|Argentina|Honduras|Paraguay|
|Armenia|Hungary|Peru|
|Australia|India|Philippines|
|Austria|Indonesia|Poland|
|Bahrain, Kingdom of|Iran, Islamic Rep.|Portugal|
|Bangladesh|Ireland|Qatar|
|Belarus|Italy|Romania|
|Belgium|Japan|Russian Federation|
|Bolivia, Plurinational State of|Jordan|Rwanda (2009)|
|Botswana (2009)|Kazakhstan|Saudi Arabia, Kingdom of|
|Brazil (2011)|Kenya|Senegal|
|Bulgaria|Korea, Rep.|South Africa|
|Burundi (2011)|Kuwait, the State of|Spain|
|Cambodia|Kyrgyz Republic|Sri Lanka|
|Cameroon|Lebanese Republic|Sweden|
|Canada|Lesotho|Tanzania|
|Chile|Lithuania|Thailand|
|China (2011)|Madagascar|Trinidad and Tobago|
|Colombia|Malawi|Tunisia|
|Costa Rica|Malaysia|Turkey|
|Côte d'Ivoire|Mali|Uganda|
|Czech Republic|Mauritius|Ukraine|
|Democratic Rep. of the Congo|Mexico (2011)|United Kingdom|
|Denmark|Mongolia|United States of America|
|Dominican Republic|Morocco|Uruguay|
|Ecuador|Mozambique|Uzbekistan|
|Egypt, Arab Rep.|Namibia|Venezuela, RB|
|Ethiopia|Nepal|Vietnam|
|Finland|Netherlands|Yemen, Rep.|
|France|New Zealand|Zambia|
|Georgia|Nicaragua|Zimbabwe|
|Germany|Nigeria||
|Ghana|Oman||

Data sourced from the previous World Bank exercise (see Borchert et al., 2012), with some adjustments, see Section 2. All data correspond to 2008, except as indicated otherwise.

33
practice, the resulting list was not different from the original list produced by the VVC, suggesting that few names were added or subtracted. The list, however, always contained more individuals than the actual number of voucher packs each village ended up receiving. This means that only a subset of farmers in the list of eligible farmers would become new beneficiaries.

**Beneficiary Selection** : To increase transparency and fairness in the selection of beneficiaries, in half of the study villages, the intervention team facilitated a public lottery to select Xi<sup>2010</sup> new voucher beneficiaries from the list of eligible farmers. In the presence of the Village Assembly, the names of all eligible farmers were placed in a basket and beneficiaries were randomly selected. This list of beneficiaries was then ratified by the Village Assembly and displayed publicly in a prominent place in the village. In practice, it took close to two hours to conduct the lottery.

Figure 1 describes the 2x2 factorial design that divides study villages into four bins according to the interventions implemented in each village. While the public meeting with the Village Assembly allows us to assess whether the identification of eligible households is done following the program eligibility criteria, the lottery assesses whether the farmers with highest marginal returns to inputs are selected as beneficiaries among the eligible households and the scope of elite capture. These interventions answer the following questions: Do VVC members identify all potential eligible households, regardless of their productivity? And among the eligible households, do they select only productive households, or their friends and kin?

# _3.2 Data_

This paper uses survey data from 4 different sources. First, in every study village we conducted a _villager_ household survey of 16 households drawn at random. This survey asked basic socioeconomic characteristics, beneficiary status and whether the household met the NAIVS eligibility criteria. Second, we conducted a survey with one male and one female member of the VVC to assess their understanding of the eligibility criteria, the process of beneficiary selection and their socioeconomic characteristics
No|Yes|No|Yes|
|Observations|13066|10391|13303|10501|13066|10391|13303|10501|

**N** otes: All continuous variables are winsorized at the 1% and 99%.

in sales across rounds of data collection.

Robust standard errors in parentheses

* p<0.10, ** p<0.05, ** p<0.01

-Expectations are higher for necessity firms with respect to the other two types of informal at the 10% level of significance in Panel B, cols. 3 and 4.

-Uncertainty is lower for parasite firms at the 5% level of significance in Panel B, col. 7, but no significant differences are observed in col. 8.

-The coefficients of PCA 1 and PCA 2 are statistically different at the 5% level in Panel C, cols. 1 and 2; and at the 10% level in panel C, cols. 5 and 6. Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).
The slowdown in population growth rate has been preceded by a sharp decline in fertility rates (Figure 11). The number of births of an average Nepali woman declined from over 6 in the 1970s and over 5 in the 1990s to around 2 in 2014. Demographers have variously attributed this fertility transition in Nepal to the spread of mass education, increasing female labor force participation, and mechanization of agriculture (Bhandari and Ghimire 2013; Axinn and Barber 2001). Another potential driver is the increase in urbanization. The share of urban population has been increasing steadily, from 3 percent in the 1960s, to 9 percent by 1990, and to 18 percent by 2014 (World Bank 2016b). Nepal’s urban population growth has been the fastest in the region, although the country remains the least urbanized in South Asia today (Muzzini and Aparicio 2013). More recently, the social and economic changes, brought by the increase in the migration of overwhelmingly male population together with an increase in educational attainment of girls, have led to delayed marriage and reduction in total fertility (Shakya and Gubhaju 2016).

**Figure 11: Population and Population Growth in Nepal**

<!-- Start of picture text --> 30 7<br>6<br>25<br>5<br>20<br>4<br>15<br>3<br>10<br>2<br>5<br>1<br>0 0<br>Rural Population Urban Population<br>Population Growth (annual %) Fertility rate (Birth per women)<br>Total Population (millions)<br>Population Growth (%)/Fertility Rate (birth per woman)<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014<br><!-- End of picture text --> Source: World Development Indicators (World
In essence, the normative debate and its advances are still fairly far from the actual criteria used by donors to allocate aid. Also, while poverty reduction has increased in importance among the overarching development priorities, the actual allocation of aid is not driven mainly by poverty. These outcomes are partly explained by the fact that the measurement of poverty requires instruments that have only recently become widely available. When the World Development Report on poverty was published in 1990 only a handful of countries carried out and made available household consumption surveys. Today, the World Bank has a database of over 1,700 household surveys many of which are for public use. We are now in a position to make much better use of micro data for allocating aid and targeting the poor whether we believe in efficiency, equity or structural criteria.

The IL methodology proposed makes an optimal use of the micro data available and complies to efficiency, equity and transparency criteria. It improves on the simple measurement of poverty by extending the analysis to the non-poor. The concept can help to improve the policy capacities of countries by highlighting the potential for redistributive policies, it complies to the equal opportunity principle by allocating aid according to the monetary capacity of a country to address its own poverty and it is transparent in that responds to principles well established in the tax and distribution theory literature.

# **3 Framework**

The idea that individuals should contribute _equally_ towards the expenses of the government dates back to Mill (1848) theory of _equality of sacrifice_ .

_For what reason ought equality to be the rule in matters of taxation? For the reason that it ought to be so in all affairs of government. As a government ought to make no distinction of persons or classes in the strength of their claims on it, whatever sacrifices it requires from them should be made to bear as nearly as possible with the same pressure upon all, which, it must be observed, is the mode by which least sacrifice is occasioned on the whole. If any one bears less