rafmacalaba/datause-encoder-data huggingface dataset:
of completed surveys was 3,413 because some respondents were not Colombians, did not consent to participate in the experiment, were not living in Bogot ́a, or answered the survey more than once. Figure A.2 illustrates the actual location of respondents.

All respondents answered 31 questions divided into five modules: (i) basic sociodemographic characteristics, (ii) COVID-19, (iii) crises, (iv) attitudes towards migrants, and (v) social desirability.<sup>8</sup> Table A.2 presents the order in which the treatment and control groups answered each module. We chose the order carefully to avoid priming the control group before asking them about prosocial behaviors and attitudes towards migrants. The treatment group answered the modules in the order listed above, whereas the control group answered them in the following order: (i) basic sociodemographic characteristics, (ii) attitudes towards migrants, (iii) crises, (iv) COVID-19, and (v) social desirability. Respondents were not able to identify the different modules as we applied a continuous questionnaire. Specifically, each module collected the following information, respectively:

1. _Basic sociodemographic characteristics:_ included gender, age, education, economic strata (measured in Colombia according to the area of residence), religion, and political orientation (measured in scale of one to 10 from left to right).

2. _COVID-19:_ this module asked individuals to think about their situation in March 2020 (before the pandemic) and compare it with their current situation to assess whether someone in their family had lost their job or experienced a reduction in working hours. They were also asked how many people they knew who had contracted COVID-19, and to report their perceptions on poverty trends in Colombia as a consequence of the pandemic.

3. _Crises:_ this module’s objective was to verify whether the COVID-19 module made the crisis more salient to the treatment group. For this purpose, we asked individuals two questions. The first was an open-ended question about the worst crisis faced by Colombia in the last 50 years. Using the answers to this question, we created an indicator variable equal to one for any answer that included the words “pandemic,” “COVID-19,” or “Coronavirus.” The second question asked individuals to rank the following three crises in Colombia from worst to least bad: illegal drug trafficking, internal armed conflict, and COVID-19. We then created a variable that gave a score of three to anyone who ranked COVID-19 as the worst crisis, two if COVID-19 was the second-worst crisis, and one if it was the third-worst crisis.

> 8We used a reduced scale since Facebook surveys must be short to increase response rates. Since the original index counts each question as one or zero depending on the answer, summing the number of questions still yields changes in the same direction. Dahr et al., 2022 also applied a reduced-form version of the scale.

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differ on what percentage of the top incomes the RLMS can capture, as well as the methods and auxiliary data sources that can be employed to correct for these missing values.

We employ a modelling approach to measure inequality that addresses this under-coverage issue (see Jenkins (2017) for more discussion on this approach). In particular, we obtain an inequality estimate for the poorer _p_ percent in the RLMS data using non-parametric methods, and then derive an inequality estimate for the richest ( _1 - p_ ) percent by fitting a Pareto Type I distribution to the top income observations from the same source. The adjusted Gini coefficient can then be obtained by adding together three inequality measures: one for the top incomes (i.e., the richest ( _1 - p_ ) percent), another for the non-top incomes (i.e., the poorest _p_ percent), and another between these two population groups. Using three different values of p that include 90 percent (Panel A), 95 percent (Panel B), and 97.5 percent (Panel C), we plot the results in Figure 1.8 (Appendix 1), which shows larger values for the adjusted Gini coefficients, but a reassuringly similar downward trend over time.

As a further check on whether (and how much) the RLMS missed out on the top incomes, we examine another high-quality household survey that is commonly used for (cross sectional) income and poverty monitoring purposes in Russia, the Household Budget Survey (HBS). The HBS has been conducted quarterly on an annual basis since 1987, covering 47,800 households across the country, but the micro data are only made publicly available since 2003. However, while the HBS collects detailed data on household expenditures, it does not collect data on household income. Consequently, we construct an income variable for the HBS on the basis of indirect accounting of household consumption items, where household money income is the total of household cash expenditures and financial assets (savings). We plot the income and Gini coefficient using the HBS 25
12

- where y - fraction of total revenue collected through seignorage

- x - vector of variables measuring the available tax bases (such as size of the manufacturing, mining, and agricultural sectors, size of imports and exports, per capita income, etc. -- see Tait, Gratz and Eichengreen (1979)).

p * vector of political variables measuring the political instability and/or polarization of the country. The key empirical issue addressed by CET refers to the explanatory power of the political variables, once we control for the structural economic variables. CET use an estimated cross-country probit equation in order to compute an index of the probabillty of government change for a particular country in any given year. This probit equation, which is explained in great detail in Appendix II, regresses instances of actual government changes against political variables (riots, repressions, and so on), economic variables (consumption growth, inflation, income per capita) and institutional variables. With respect to polarization they use two alternative proxies: (i) freqtency of coup attempts; (ii) an index of income distribution. This constructed indicator of political instability differs from the index of actual frequency of government change used in Table 2, in that it provldes a measure of the expects probability of government change, derived from broad cross country evidence.

In addition to the political instabillity index, in thelr regressions on seignorage, CET included the following structural variables: (a) share of agriculture in GDP. Its sign **is** expected to be positive: since it is relatively costly to tax agriculture, governments with a large agricultural sector will tend to rely more heavily on taxes with low administering cost, such as seignorage and trade taxes; (b) share of mining and manufacturLng
returns is to compare the GDP growth, investment ratios and efficiency of investments of the different types of borrowers in the region.

**Figure 20. Growth, investment and efficiency of investment across types of borrowers in Sub-Saharan Africa, 2012-17**

<!-- Start of picture text --> Real GDP Growth Investment Efficiency of Investment<br>6 30 25<br>5 25<br>20<br>4 20<br>15<br>3<br>15<br>10<br>2<br>10<br>1 5<br>5<br>0<br>Light Moderate Heavy 0 0<br>Light Moderate Heavy Light Moderate Heavy<br>Percent, 2012-17 (group median)<br>Percent of GDP, Avg. 2012-17 (group median)<br>Percent per year, 2012-17 (group median)<br><!-- End of picture text --> _Source: World Bank, World Development Indicators. Elaboration: AFRCE Staff estimates._

**Figure 20** reports the median rate of GDP growth, total investment (as percentage of GDP) and the efficiency of investment (as proxied by the growth multiplier of investment). It shows that heavy borrowers exhibit the highest investment coefficients with a median of 28.4 percent of GDP as opposed to the 26.3 and 20.9 percent of GDP coefficients displayed by moderate and light borrowers’ rates of investment. However, heavy borrowers show the lower returns for that higher investment coefficient —as captured by the growth rate of GDP. Total output of heavy borrowers grew at an average annual rate of 3.1 percent. Note that the GDP growth rate of light and moderate borrowers totaled 4.2 and 4.9 percent per year. These findings imply that heavy borrowers might be financing greater investment; however, the efficiency associated to that spending is significantly lower than that of the other groups of borrowers within the region. Finally, light borrowers have lower investment spending (relative to the size of their economy) that it is conducted more efficiently.

An alternative way to examine whether the sustainability of the funds borrowed by the private sector is to indirectly compute the drivers of growth from the aggregate demand perspective. The idea is that sustainability of the funds borrowed would be associated to higher growth of investment rather than higher growth in (private or public) consumption.

**Figure 21** reports the median growth rates per year of private consumption, public consumption, and gross capital formation (investment) for the different types of borrowers across Sub-Saharan Africa during the period 2012-17. Private consumption growth among heavy borrowers was the largest (5.1 percent per year) as opposed to growth rates per year of 4.7 and 3.4 for moderate and light borrowers. In the case of public consumption, it is the group of moderate borrowers that exhibit the largest growth rate (3.9 percent per year). It can be inferred then that moderate

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nature of work acceptable to women who are not in the labor force. The NSS survey fields a specific set of questions to persons who are primarily engaged in performing domestic duties or free collection of goods and are not counted in the labor force. Almost 100% of these were women in 2011-12. Of these women, 72% of those who were willing to accept work if made available, favored “regular, part-time” jobs over “regular, full-time” jobs.

The results presented in the previous section could also be questioned if the population considered for the empirical analysis was not the most relevant, or if some of the variables included in the regressions were not adequately measured. The latter concern is particularly relevant given the various definitions of jobs in the NSS, and the fact that the female LFPR varies substantially with the definition used. A third robustness check, thus, consists of re-running the analysis with the PS definition of jobs, instead of the US definition used above (table 6). The estimates are very similar to the ones using the US definition (table 4) and the five hypotheses advanced by this paper hold.

As for the former concern, it can be argued that individual preferences differ between married an unmarried women. The next estimation is carried out for a sub-sample of married women only (table 7). Again, the results do not differ substantially from those presented in section 4, and the five hypotheses hold. If anything, it is worth noting that the effect of share of young children in the household is weaker for this sub-group. The coefficients of the employment variables are as large as in the larger group, showing that demand-side considerations are relevant for married women too.

# 7. Interpreting the results

The results presented in this paper can be used to decompose the change in female LFPR between 2004-05 and 2011-12 into changes due to inadequate measurement and changes reflecting deeper economic forces. The former include the underestimation of female labor force participation resulting from the excessively stringent definition of unemployment used by the NSS. They also include the potential misclassification of urban areas as rural, given that female LFPR is substantially lower in urban areas than in rural areas. Among the latter are the income effect, the household composition effect and the reinforcement of the patriarchal culture over time, emphasized by the conventional wisdom. Another relevant economic force is “true” urbanization. Last but not least is the key hypothesis of this paper, namely the decline in local job opportunities – and especially in job opportunities considered suitable by women – along the rural-urban gradation.

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Table 1. Poverty Headcount Rates

# A. Income-Based Poverty Headcount Rate

|||<br>US$4-a|<br>-dayPPP|||<br>US$2.50|<br>-a-dayPPP|||<br>US$1.2|<br>5-a-dayPP|<br>P|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Initial<br>period|Final<br>period|Total<br>reduction|Annual<br>change|Initial<br>period|Final<br>period|Total<br>reduction|Annual<br>change|Initial<br>period|Final<br>period|Total<br>reduction|Annual<br>change|
|Argentina, 2000–2010|27.5|14.6|-13.0|-6.2%|14.2|6.4|-7.8|-7.7%|5.1|1.8|-3.3|-9.8%|
|Brazil, 2001–2009|43.1|27.6|-15.5|-5.4%|27.4|15.1|-12.3|-7.2%|11.8|6.1|-5.7|-7.9%|
|Chile, 2000–2009|23.2|11.8|-11.4|-7.2%|9.0|4.3|-4.7|-7.9%|2.3|1.3|-0.9|-5.6%|
|Colombia, 2002–2010|61.6|39.5|-22.1|-5.4%|42.3|22.0|-20.3|-7.8%|20.7|8.2|-12.6|-11.0%|
|Costa Rica, 2000–2008|29.2|18.9|-10.2|-5.3%|14.7|7.6|-7.1|-7.9%|5.5|2.4|-3.1|-9.8%|
|Ecuador, 2003–2010|51.5|33.4|-18.1|-6.0%|31.5|15.9|-15.6|-9.3%|12.2|4.6|-7.6|-13.0%|
|Honduras, 1999–2009|66.1|52.1|-14.1|-2.4%|47.9|36.2|-11.6|-2.7%|24.9|17.8|-7.1|-3.3%|
|Panama, 2001–2009|43.4|29.9|-13.5|-4.6%|28.7|16.1|-12.6|-6.9%|15.4|4.6|-10.8|-14.0%|
|Paraguay,1999–2010|43.3|32.8|-10.6|-2.5%|26.7|18.4|-8.3|-3.3%|14.0|7.2|-6.8|-5.9%|

# B. Consumption-Based Poverty Headcount Rate

||<br>US|<br>$4/US$5-|<br>a-dayPPP|<br> _a/_||<br>US$2.50|<br>-a-dayPPP|<br>||<br>US$1.2|<br>5-a-dayPP|<br>P|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Initial|Final|Total|Annual|Initial|Final|Total|Annual|Initial|Final|Total|Annual|
||period|period|reduction|change|period|period|reduction|change|period|period|reduction|change|
|Bangladesh, 2000–2010|...|...|...|...|89.2|84.0|-5.2|-0.6%|57.7|40.3|-17.4|-3.5%|
|Ghana, 1998–2005|...|...|...|...|71.8|58.3|-13.5|-2.9%|38.2|23.5|-14.7|-6.7%|
|Moldova, 2001–2010|93.8|58.7|-35.1|-5.1%|71.4|12.9|-58.5|-17.3%|27.5|0.5|-27.0|-35.4%|
|Nepal, 1996–2003|...|...|...|...|94.3|84.9|-9.5|-1.5%|54.0|25.9|-28.2|-10.0%|
|Romania, 2001–2009|75.3|33.2|-42.1|-9.7%|23.7|4.2|-19.5|-19.6%|2.6|0.0|-2.6|-100.0%|
|Peru, 2004–2010|45.8|30|-15.8|-6.8%|22.9|11.7|-11.2|-10.6%|3.5|0.8|-2.6|-21.1%|
|Thailand,2000–2009|31.3|16.6|-14.7|-6.8%|7.9|2.5|-5.3|-11.8%|3.7|1.4|-2.3|-10.3%|

_a/_ Moldova and Romania measure moderate poverty at rates close to US$5-a-day, while Peru and Thailand measure at rates close to US$4-a-day. _Source:_ Author’s calculations with data from RIGA for Ghana and Nepal, and from household surveys for Bangladesh, Moldova, Romania, Peru, and Thailand.

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15 Nor is the available data ideal on the expenditure side. BBS (1984, Table 15.18) gives a figure of about 0.75 for the share of expenditure going to food by the rural poor, and this shows negligible variation according to income amongst the poorest half of the income distribution. BBS (1984) does not, however, give a more detailed breakdown of food expenditures. For an earlier (1973-74) survey, BBS (1980, Table 4.12) does give more detail. An Engel curve was calibrated to i.he earlier data and used to estimate expenditure shares devoted to staple foodgrains turing 1978-79, assuming a stable demand function.<sup>9</sup> The estimated foodgrain share of expenditure for the poorest half of the rural population is approximately 50 per cent and this varies little with income. As a proportion of income the mean is 0.47.

Combining these assumptions and sources, it appears that two of the three figures necessary for calculating V* can be estimated with considerable precision, while the third (the income share from wage labour) appears to be rather more variable amongst the poor. I shall consider the implications of this later. At the mean points, the above sources indicate a value of V* - 0.43 for the rural poor, again defined as the poorest 55 per cent of households according to household income.

On the basis of these results it is plausible that the rural poor will typically be worse off in the short-run after an increase in the price of food staples. Boyce and Ravallion's point estimate for shortrun v of 0.22 is well below the above estimates of 9* and, indeed, the latter are also outside the Boyce and Ravallion 95 per cent confidence interval for short-run v of (0.14, 0.30).
process we observe seems to produce different outcomes from that which a simple model posits (Harns and Zink, 2003; Tridimas and Winer, 2005).

The pervasive use of micro data on income, mostly from the Luxembourg Income Study, and the emphasis on market (or "inherent") income distribution, can be traced to the methodology introduced in my paper published as the World Bank Working Paper No. 2246 in December 1999 (“Do more unequal countries redistribute more? Does the median voter hypothesis hold?”), later published in the _European Journal of Political Economy_ (Milanovic, 2000) and which for the first time used household level data derived from household income surveys to test the hypothesis, and in the process reformulated the hypothesis itself. I proposed that the correct data to test the hypothesis had not been previously used. I tested the median-voter hypothesis by calculating the gain realized by different deciles of income distribution when people are ranked by their preredistribution (market) income. This was in the spirit of the hypothesis as originally formulated by Meltzer and Richard (1981): based on their _pre- redistribution_ income, people vote for the level of taxes and type of government expenditures. Previously, however, the test was done in such a way that people appeared to vote on tax-and-transfer combinations based on their ranks in _post-distribution_ income or the distribution of disposable income, which of course is logically wrong, since disposable income is the outcome of the redistributive process.<sup>1</sup> The reason for this approach, so obviously wrong at the slightest reflection, was probably because the authors were unfamiliar with household surveys and may not have realized that a given (say, bottom) pre- and postredistribution income deciles may be composed of very different people. Moreover, the authors might not have been aware that micro data on pre-redistribution income deciles, or more generally fractiles, even if not widely available, do exist.

While the approach that I used is obvious, the interpretation of the sharegain (the difference in the share of a given decile in post- and pre-redistribution income) to directly measure redistribution has been questioned. Four types of criticisms have been leveled at

> 1 Market income _minus_ direct taxes _plus_ government cash transfers equals disposable income.

3
geographical area around existing agglomerations. These findings have important implications for targeting provision of services in the coming years. Expansion of provision of services in the case of Ghana should be based on its existing concentration of population.

The main finding of the paper that expansion of human settlements happened mostly around the existing built-up areas also raises the question of what lies behind this observed persistence particularly in terms of policies and infrastructure investments. This should be a priority for future research.

# References

Banerjee, Abhijit , Esther Duflo and Nancy Qian (2020). " On the Road: Access to Transportation Infrastructure and Economic Growth in China " , Journal of Development Economics, 145: (forthcoming June).

Arshad, Sidra, Shougeng Hu, and Badar Nadeem Ashraf (2018). “Zipf’s law and city size distribution: A survey of the literature and future research agenda Author links open overlay panel”, _Physica A: Statistical Mechanics and its Applications_ , 492: 75-92 Bellefon, Marie-Pierre de, Pierre-Philippe Combes, Gilles Duranton, and Laurent Gobillon (2019). “Delineating urban areas using building density”, Journal of Urban Economics (in press) Bird, Julia and St ́ephane Straub (2020). " The Bras ́ılia Experiment: Road Access and the Spatial Pattern of Long-term Local Development in Brazil " , World Development, 127: (in press) Baum-Snow, Nathaniel , Loren Brandt, J. Vernon Henderson, Matthew A. Turner and Qinghua Zhang (2017). " Roads, Railroads and Decentralization of Chinese Cities " , Review of Economics and Statistics, 99(3): 435-48.

Bleakley, Hoyt and Jeffrey Lin (2012). " Portage and Path Dependence " , _Quarterly Journal of Economics_ , 127(2):587-644.

Chandra, Amitabh and Eric Thompson (2000). " Does public infrastructure affect economic activity? Evidence from the rural interstate highway system " , _Regional Science and Urban Economics_ , 30(4): 457-490.

Christaller, Walter (1933). _Die zentralen Orte in S ̈uddeutschland (The Central Places in Southern Germany)_ , Gustav Fischer, Jena.

Donaldson, David (2018). " Railroads of the Raj: Estimating the Impact of Transportation 27
in the country. We take our budget _S_ and divide it by the total number of poor persons in the country, _Np_ . Our budget divided by the total number of poor persons yields the transfer _a_ that will be distributed to each person. We select the poorest geographic area and transfer _a_ to all persons in that area. If the budget has not been exhausted in the first region, we move to the next poorest region and transfer _a_ to all persons in this second region. We continue until the budget is exhausted. In the marginal region - that in which the budget is exhausted - we do not transfer _a_ , but transfer an equal share of whatever remains in the budget to the population of that last region. Note that this scheme does not guarantee some amount of transfer to all regions. The scheme also implies that households will be receiving differing amounts according to their overall size.<sup>15</sup>

# _Budget and Poverty Lines_

As has been mentioned in Section I, we assume that the budget available for distribution has been exogenously set. As is intuitively clear, the potential benefits from targeting will vary with the overall size of budget. In the limit, as the budget goes to infinity, there is no need for targeting, as even a uniform transfer will eliminate poverty. In this study we employ a number of different budget levels in order to assess the performance of geographic targeting mechanisms under different financial scenarios. As a benchmark, we identify the per capita consumption value of the 25<sup>th</sup> percentile of the consumption distribution.<sup>16</sup> We scale this consumption value by the total population. Our benchmark

> 15  We experimented also with a “constrained” optimal scheme that combined simplicity with a limited degree of optimization.  In this secheme, a uniform benefit is offered in all areas that the scheme operates in , and no benefits are provided elsewhere.  The optimal list of areas is then solved for, conditional on the benefit being the same everywhere.  This scheme resembles the naïve scheme described above, but the benefit size allocated is also optimal, rather than determined arbitrarily.  Unsurprisingly we find that this scheme performs a bit better than our naïve scheme, but the improvement is only slight, and it comes at a cost of being somewhat more complicated to explain.  We are grateful to a referee for this suggestion.

> 16 The consumption distribution is constructed on the basis of the average, across _r_ replications, of household-level predicted percapita consumption in the population census.

10