rafmacalaba/data-use-mentions-extended huggingface dataset:
(2020) found that rural traders in Nigeria lived an average of 6.56 kilometers from markets, while Tanou et al. (2021) reported distances of 5.29 to 7.10 kilometers to health care facilities in rural North Benin. Oldenburg et al. (2021) found that households with young children in Northwest Burkina Faso were approximately 4.82 kilometers from health care facilities. Popoola's (2022) research in Nigeria showed that rural villages were, on average, 6.9 kilometers from the nearest primary school.

Adetunji, Musilimu A. (2020). Households Travel Behaviour to Markets in Rural Communities in Ayedaade Local Government Area of Osun State, Nigeria. Journal of Asian Rural Studies, 4, 202. https://doi.org/10.20956/jars.v4i2.2336 Tanou, M., Kishida, T., & Kamiya, Y. (2021). The effects of geographical accessibility to health facilities on antenatal care and delivery services utilization in Benin: A cross-sectional study. Reproductive Health, 18(205). https://doi.org/10.1186/s12978-021-01249-x Oldenburg, C. E., Sié, A., Ouattara, M., Bountogo, M., Boudo, V., Kouanda, I., Lebas, E., Brogdon, J. M., Lin, Y., Nyatigo, F., Arnold, B. F., & Lietman, T. M. (2021). Distance to primary care facilities and healthcare utilization for preschool children in rural northwestern Burkina Faso: Results from a surveillance cohort. BMC Health Services Research, 21(1), 212. https://doi.org/10.1186/s12913-021-06226-5

> 15 In peri-urban areas, households are typically closer to amenities, with distances ranging from 1 to 2 kilometers. Popoola (2002) observed that households in peri-urban settlements near secondary towns or capital cities were around 1.2
need to control for the removal of quotas at the end of the ATC. In a post-quota world, US and EU markets are expected to be flooded by apparel from larger exporters, such as China and India, which were previously bounded by quotas. Export data and tariff data were compiled from IDB-WTO and TRAINS/WITS at the HS6-digit. However, because 95% of the volume of apparel exports is accounted for by the 7 major exporters, we report results of estimates on this reduced sample where data quality is arguably superior not only because there are positive aggregate exports by each country every year, but also because these countries export a larger number of products.

# **4.2. Econometric Strategy**

Two constraints guided our estimation strategy. First, a lack of plausible instruments at this detailed product level, precluded us from implementing a two-stage procedure in which a decision to export a specific apparel product to a given destination is taken in a first step, then a decision is taken on volume in a second step<sup>10</sup> . Second, we were confronted to a large number of zero-exports –or zeroes– in the data disaggregated at the HS-6 level: 95% of zero observations for the whole sample of 22 exporters, and 86% for the reduced sample of the top7 exporters. However, the product-specific RoO under AGOA were defined at the HS-4 level and the VC rule under EU preferences was defined at the HS-4 level. This led us to aggregate data to the HS-4 level. As a result, the number of zero trade flows is brought down to 60 percent of observations for the top 7 exporters.

Since this is still a large number of zero observations, we deal with it by contrasting several estimators in table 2. Two benchmark estimates are reported. Column 1 reports OLS estimates with ln �Xi,tj,k�as the dependent variable that considers observation for positive-only exports11, and column 2 reports Tobit estimates. To overcome the sensitivity of estimates to
Table D.19. Inequality of the Wealth Index at the Census Tract Level

|||_Wealt_|_h Index_||
|---|---|---|---|---|
||Gini Index|Interquartile Range<br>(p75-p25)|Percentile Range<br>(p90-p10)|Percentile Range<br>(p90-p50)|
||(1)|(2)|(3)|(4)|
|Guerrilla control|-0.00345|66.52|82.59|0.0679|
||(0.0104)|(67.52)|(68.28)|(0.116)|
|Observations|2,975|2,975|2,975|2,975|
|Bandwidth (Km)|2.266|2.266|2.266|2.266|
|Dependent mean|0.278|3.520|47.23|1.981|

_Note:_ The table presents the results of equation 1 for the wealth index constructed from Census of 2007. Each Column represents a different measure of inequality using the real per capita income. Column 2 report the interquartile range, calculated as the difference of the wealth index in percentile 75 minus the wealth index in percentile 25 for each census tract. Column 3 shows the percentile range of the difference between percentile 90 and 10 for each census tract. Column 4 reports the percentile range of the difference between percentile 90 and 50 for each census tract. The unit of observation is at the census tract level. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. The estimates use triangular kernel weights. Robust standard errors in parentheses.

Table D.20. Cooperatives
# **The Cases of Rice and Wheat**

The two food commodities that have received the most attention because of price surges are the key staples of wheat and rice. The length of their international price spikes around 1974 were broadly similar to those around 2008, but the height of the spike – particularly for rice – was greater in 1974. The recent price rises were more gradual except in the final months, so we consider an extra year in the lead-up to the 2008 spike.

Estimates of the Ti’s are available for all key rice and wheat countries, in the form of nominal assistance coefficients (NACs), from three sources. Anderson and Valenzuela (2008) provide them to 2004 for developing countries and to 2007 for high-income countries (summarized in Anderson 2009). They are similarly available for high-income countries for 2008 in OECD (2010). For developing countries, Anderson and Nelgen (2010b) provide estimates based on FAO and World Bank data on producer and border prices, respectively, for 2005 to 2008. The most-recent developing country estimates are less reliable than the NAC estimates in Anderson and Valenzuela (2008), for several reasons. One is that the coverage is not as extensive, because domestic prices are not available for some countries. Another is that the FAO’s producer prices and World Bank international prices are not always as reliable as previously-used domestic and border prices from national statistical agencies. The FAO producer prices in US current dollars (FAO 2010) were converted into an index set at 100 for 2004, and the 2004 US dollar prices in Anderson and Valenzuela (2008) updated using the changes in these indexes through 2008. Likewise, the Thailand 5 percent broken rice and Canadian wheat prices from the World Bank were converted to indexes set at 100 for 2004, and the 2004 border prices in Anderson and Valenzuela (2008) updated using changes in those indexes through 2008.

These NAC estimates are reported in Table 1 for the two upward price spike periods. For each of
trade because of their variety and complexity (see, for example, Hoekman (1996) and the overview by Deardoff and Stern (2008)). We rely on a measure of the restrictiveness of a country’s policy regime for any subsector-mode, the “services trade restrictions index” (STRI), which has the weakness of being subjective but the virtue of being simple, transparent and robust (Borchert et al. 2014). This measure is most convenient to depict broad patterns in policy, across countries, modes, and sectors. Essentially, we assess policy regimes in their entirety and assign them into five broad categories: completely open, i.e. no restrictions at all; completely closed, i.e. no entry allowed at all; virtually open but with minor restrictions; virtually closed but with very limited opportunities to enter and operate; and a final residual “intermediate” category of regimes which allow entry and operations but impose restrictions that are neither trivial nor stringent. It is convenient to assign a value to each of these five categories of regimes on an openness scale from 0 to 1 with intervals of 0.25. We call the resulting score a services trade restrictions index (STRI). Once a score has been attached to each regime, STRI values can be aggregated across sectors and modes of supply taking weighted averages which reflect the importance of the different modes in each sector and the individual sectors in a standardized country. Details of the quantification method are provided in Annex Tables A -8 and A- 9 and Borchert et al (2014).

> number of countries at the end of 2015 in order to compare the TPP commitments with the most recent applied policy information.

> 10 The information was updated for Vietnam, Malaysia, Mexico and Chile.

> 11 For GATS commitments and PTAs that use a positive listing approach (e.g. ASEAN Framework Agreement in Services), if a certain sector is not included, we consider it “unbound” and a score
# **3.2. Reporting**

<mark>Although 90 percent of the women who report having been a victim of intimate partner violence said they were aware of the Intrafamily Violence Law,</mark><sup>14</sup> <mark>only 22 percent filed a formal complaint. Disaggregated by type of violence, the data reveal that 34 percent of the victims of physical violence, 21 percent of the victims of psychological violence, and 15 percent of the victims of sexual violence said they formally reported the abuse.</mark><sup>15</sup> <mark>Additionally, for all types of violence, most women reported the abuse to the police force (C</mark> _<mark>arabineros</mark>_ <mark>). Ninety-seven percent who filed complaints of sexual violence, 92 percent who filed complaints of physical violence, and 82 percent who filed complaints of psychological violence did so to the police.</mark>

## Figure 3

_Distribution of victims of intimate partner violence in the past 12 months, by type of violence and complaint_

## Figure 4

_Distribution of victims who formally reported intimate partner violence in the past 12 months, by type of violence and institution where the complaint was filed_ <!-- Start of picture text --> Did not report Reported Police (Carabineros) Investigation Police (PDI)<br>Courts Prosecutor’s Office<br>100<br>15 Other<br>21<br>80 34<br>Phycological 82 7 5 4 3<br>60<br>40 79 85 Physical 92 8 1<br>66<br>20 Sexual 97 21<br>0<br>0 20 40 60 80 100<br>Phycological Physical Sexual<br>Percentage<br>Source: Own elaboration based on 2020 ENVIF-VCM. Source: Own elaboration based on 2020 ENVIF-VCM<br>Percentage<br><
Figure 9: The 2016 amnesty raised the total value of wealth reported by tax filers <!-- Start of picture text --> Assets value<br>(2015=100)<br>2009 2013-15 2016<br>200<br>Amnesty Amnesty Amnesty<br>180<br>160<br>Reported<br>Assets<br>140<br>120<br>100<br>80<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br><!-- End of picture text --> _Notes:_ this figure plots the total value of wealth reported by tax filers in constant 2015 pesos (2015 = 100). Total declared wealth increased by 60% in 2016 compared to 2015 (or from US$116 billion to US$186 billion using the market exchange rate of Arg$12.9 to US$1 in December 2015). Moreover, declared wealth remained more than 50% greater five years later.

_Source:_ authors’ calculations using data from AFIP statistical yearbooks.

32
targeting. After distributing funds on the village level though, the localities must implement their own intra-village distribution; making this a hybrid approach of geographic targeting and possibly the same village-level lists of poor that are extant now. The reason is that with this methodology, estimates on a household level are probably too noisy to yield useful results.

A related note of concern must be raised on the issue of _elite capture_ . The presented gains from targeting are obtained assuming that whichever spatial unit is targeted, resources distributed within the unit are distributed uniformly. In reality, the power of the local elites is known to affect the way decentralized benefits reach the poor. For instance, Galasso and Ravallion (2005) show that the results of the Food for Education program in Bangladesh is better in communities with more land equality and they argue that this reflects greater elite capture when the poor are less powerful. Araujo et al., (2008) find that localities with more inequality (as measured by the expenditure share of the top 1%, 3%, 5%, 10% and 20% of the population) reduces the probability that latrine projects that are especially pro-poor are implemented in Ecuador, holding the level of poverty in the community constant. These findings may be especially worrying as we show in this paper that poverty is correlated with inequality in Vietnam. Also numerous studies show that that even at the lowest administrative levels the scope for political elite capture should not be assumed away<sup>9</sup> .

On the one hand, one may say that elite capture will challenge our findings as presented in Tables 2-3, but on the other hand it enforces the need to establish an objective framework for poverty targeting. Plus, as this phenomenon occurs at all levels, distributing social assistance from the commune- or village level down to poor households would still be an improvement from the current provincial level.

# **8. Conclusions**

This paper combines the 2009 Vietnam Population Census and the 2010 Vietnam Household Living
of Population and Housing calculated the share of urban population in Bangladesh at 23 percent. A month after completion of the Census, the Bangladesh Institute of Development Studies (BIDS), the independent agency in charge of

> equal the reference population of interest at the time of the survey data collection; and/or weights will vary across PSUs or be constant at the level at which PSUs are allocated. 29 The listing exercise consists of the enumeration of households in a PSU before a survey is implemented. This list is used to select households randomly within a PSU, and it is only conducted in PSUs that were selected in the first stage of a multi-state clustered household survey.

34
make extensive (and perhaps excessive) allowance for informal activity not recorded in the manufacturing censuses. It is also clear that census definitions of value added vary between countries, and need adjustment to bring the comparisons for the three countries to a common conceptual basis as is used in national accounting. Unfortunately, we were unable to adjust the detailed US census data to a national accounts concept of value added. This is a shortcoming of our chapter III, which uses a standardised but inferior notion of value added in neglecting to deduct service inputs (i.e. the US census concept) for our 17 industries. However, at the level of manufacturing branches and for manufacturing as a whole, all our comparisons employ the national accounts concept of value added.

## d) Adjustment to a Common Benchmark Year

Chapter III presents a method for dealing with the problem of comparing countries whose census dates fall in different years. The procedures have general applicability, and they were applied here to the USA, whose performance is often a yardstick for comparison in such studies. In fact, using our approach, US data can be adjusted to any intercensal year needed for purposes of international comparison.
important vehicle for the transfer of technology, but that the host country can benefit from it only if it has a stock of human capital which is beyond a minimum threshold level. They test this idea using data for 1970-89 and find that gross FDI inflows are not significantly correlated with economic growth but that the interaction between FDI flows and the stock of human capital is positively and significantly correlated with economic growth. Wang and Wong (2011) corroborate the original result of Borensztein et al. (1998) by focusing on the same period but by using a measure of education quality rather than quantity.

Alfaro et al. (2004) and Alfaro et al. (2010) also explore the role of local conditions but focus on the role of the domestic financial sector. They suggest that FDI creates positive spillovers and promotes growth through backward linkages and that a well-working domestic financial sector facilitates this mechanism because it allows local entrepreneurs to start new firms that produce intermediate goods for foreign multinational companies. Alfaro et al. (2004) use cross-country data for 1975-95 and show that net FDI flows are not significantly correlated with GDP growth but that the interaction between net FDI flows and financial depth (proxied by credit to the private sector over GDP) is positively and significantly correlated with long-run growth. AzmanSaini et al. (2010) build on the work of Alfaro et al. (2004) and, using a threshold regression model and data for 91 countries over 1975-2005, also find that financial depth matters for the link between FDI and growth.

As mentioned above, a recent meta-analysis by Bruno et al. (2018) qualifies these results and suggests that there is a positive correlation between FDI and growth which does not necessarily depend on local conditions. Bruno et al. (2018) also argue that firm-level studies tend to underestimate the positive effect of FDI on economic performance. In their view, the sum of vertical and horizontal spillovers measured in microeconomic studies underestimates the overall benefits of FDI because technologies and managerial competencies may travel across industries
- Gruber, J and Wise, D (1999) _Social security and retirement around the world_ , Chicago: University of Chicago Press.

- Gruber, J and Wise, D (2004) _Social security programs and retirement around the world: Microestimation_ . Chicago: University of Chicago Press.

- Gruber, J and Wise, D (2010) _Social security programs and retirement around the world: The relation to youth employment_ . Chicago: University of Chicago Press.

- Gustman AL, Steinmeier TL (1984) Partial retirement and the analysis of retirement behavior, Industrial and Labor Relations Review 37: 403-415.

- Gustman, AL, Steinmeier TL (2000) Retirement outcomes in the Health and Retirement Study, Social Security Bulletin 63: 57-71.

- Gustman AL, Steinmeier TL (2004) Social security, pensions and retirement behavior within the family. Journal of Applied Econometrics 19: 723-737.

- Gustman AL, Steinmeier TL (2009) How changes in social security affect retirement trends? Research on Aging 31: 261-290.

- Jalan J and Ravallion M (2002) Geographic poverty traps? A micro model of consumption growth in rural China, Journal of Applied Econometrics 17(4): 329-346.

- Kanbur, R Zhang, X (1999) Which regional inequality? The evolution of rural-urban and inlandcoastal inequality in China from 1983 to 1995, Journal of Comparative Economics, 27(4): 686-701.

- Kinsella, Kevin and Wan He (2009) _An Aging World: 2008_ , US Census Bureau, International Population Reports, PS95/09-1, Washington D.C.: US Government Printing Office.

- Kreuger, AB and Pischke, J (1992) The effect of social security on labor supply: A cohort analysis of the notch generation. Journal of Labor Economics 10: 412-437.

- Lazear EP (1979) Why is there mandatory retirement? The Journal of Political Economy 87: 1261-1284.

- Lee C (2009) Labor force participation of older males in Korea: 1955-2005, NBER Working Paper
period 1997-98. We measure per capita income as the average over all available years 13 Hall and Jones (1999) also use distance from the equator as a proxy for the strength of past Westem European influences. We found this not to be a good instrument inour regressions as we could not reject the null hypothesis that it has a direct influence on per capita incomes in addition to its indirect effec **t** hrough governance. Only if we combine our direct measures of govemance with other measures of policy (such as the Sachs-Wamer index used by Hall and Jones (1999)) does an instrument set including distance from the equator pass tests of overidentifying restrictions. Similarly, in the Hall and Jones (1999) specificaton, tests of overidentifying restrictions fail if the policy variable is excluded from their measure of social infrastructure.

14
|(81.303)|[ -164.3, 178.7]|(82.700)|[ -169.6, 188.7]|
|Controls for:||||||||
|Spousal characteristics<br>Y|Y|Y|Y|Y|Y|Y|Y|
|N<br>661<br>Significance levels: * 10%, ** 5%, *** 1%. Robust standard errors in parentheses.<br>Notes: Unit of observation is individual included in revisit sample. The standard erro<br>confidence interval appears below the coefficient of regressions using simulated data<br>column 6 in Table 6, and column 7 replicates column 7 in Table 6.|661<br>r appears below the<br>in square brackets.|661<br>coefficient in r<br>All regressions|661<br>egressions using the<br>include regressors|661<br>real experime<br>in specificatio|661<br>ntal data (odd numb<br>ns of columns 6 and|661<br>ered columns<br>7 of Table 6.|661<br>). The 95%<br>Column 3 replicates|

Notes: Unit of observation is individual included in revisit sample. The standard error appears below the coefficient in regressions using the real experimental data (odd numbered columns). The 95% confidence interval appears below the coefficient of regressions using simulated data in square brackets. All regressions include regressors in specifications of columns 6 and 7 of Table 6. Column 3 replicates column 6 in Table 6, and column 7 replicates column 7 in Table 6.
Bank, 2021). High inequality rates have been attributed to persistent disparities in labor market outcomes, inadequate coverage of the poor by the social protection system, and a fiscal system characterized by limited progressivity (Vaughn & Cabrera, 2022).

**Figure 2. Rising inequality in recent years, reaching the highest inequality in the EU**

<!-- Start of picture text --> a. Gini Index of equivalized disposable income, Bulgaria,<br>2009-2021<br>43.0<br>41.0<br>38.4<br>39.0<br>37.0<br>35.0<br>33.0<br>29.6<br>31.0<br>29.0<br>27.0<br>25.0<br>Bulgaria EU-27<br>c. At-risk-of-poverty (AROP) rate, EU27, 2021<br>25.<br>20.<br>15.<br>10.<br>5.<br>0.<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>Bulgaria Estonia Latvia Romania Lithuania Spain Italy Greece Croatia Luxembourg Malta EU27 Portugal Sweden France Austria Germany Netherlands Ireland Cyprus Poland Slovakia Belgium Finland Denmark Hungary Slovenia Czechia<br><!-- End of picture text --> <!-- Start of picture text --> b. Gini Index of equivalized disposable income, EU27,<br>2021<br>45.<br>38.4<br>40.<br>35.<br>30.<br>25.<br>20.<br>15.<br>10.<br>5.<br>0.<br>Slovakia Slovenia Czechia Belgium Netherlands Poland Finland Hungary Sweden Denmark Austria Ireland Croatia Germany Cyprus Luxembourg EU-27 France Malta Greece Estonia Spain Portugal Romania Italy