rafmacalaba/data-use-mentions-extended huggingface dataset:
Figure 1: Proportion of Land under Restrictions and Out-Migration: Population Census 2012 Figure 2: Proportion of Land under Restrictions and split households, 2002 (HIES) <!-- Start of picture text --> Out-Migration<br>0 .1 .2 .3 .4 .5<br>(mean) area_ldo<br>95% CI Fitted values<br>12.5<br>12<br>11.5<br>11<br>10.5<br><!-- End of picture text --> <!-- Start of picture text --> Split Households<br>0 .2 .4 .6<br>(mean) area_ldo<br>95% CI Fitted values<br>.1<br>.08<br>.06<br>.04<br>.02<br><!-- End of picture text --> Figure 3: Proportion of Land under Restrictions and share of female in total population, 2012 Figure 4: Proportion of Land under Restrictions and change in female share in total population for same age cohort (15-49 in 2002), 2002-2012 <!-- Start of picture text --> Female Share of Population Adult Female Share: Same Age Cohort<br>0 .1 .2 .3 .4 .5<br>0 .1 .2 .3 .4 .5 (mean) area_ldo<br>(mean) area_ldo<br>95% CI Fitted values<br>95% CI Fitted values<br>.04 .04<br>.03 .03<br>.02 .02<br>.01<br>.01<br>0<br>0<br><!-- End of picture text --> Figure 5: Proportion of Land under Restrictions and female share in labor force, 2002 (HIES) Figure 6:
One of the lasting effects of gains in agricultural productivity in Asia and elsewhere has been a relatively steady decline in the price of food staples from the 1970s until recent times. Because of the inelastic demand for rice and wheat as well as the competitive nature of agricultural markets, there were concerns that the benefits of technology gains in agriculture would flow exclusively to consumers, adding further to the gap between agricultural and non-agricultural incomes (Quizón and Binswanger, 1986). Nevertheless, early studies suggested that this was not the case and that significant income gains accrued to rural villages following the adoption of the new varieties (Blyn, 1983; Ahluwalia, 1978; Pinstrup-Andersen and Hazell, 1985; David and Otsuka, 1994). Much of the early evidence was selective and not broadly representative of national economies, but with time, and especially after the launch of representative living standards surveys by the World Bank in the 1980s, better measures of the broad and cumulative effects on poverty of improving agricultural productivity emerged. And, on balance, there is now strong evidence that gains in agricultural productivity, especially in combination with investments in education and infrastructure, are transformational elements of poverty reduction and economic growth and that advances in agricultural techniques have contributed significantly to rising farm incomes and reductions in rural and urban poverty.<sup>8</sup> Eventually, Asia’s Green Revolution came to encompasses technological innovations in a number of crops, but breakthroughs in wheat and rice served as catalysts and conduits for most of the benefits associated with Asia’s Green Revolution.<sup>9</sup> And with the Asian experience in mind, a number of authors have called for a similarly styled African Green Revolution based smallholder agriculture, staple crops and high-input technologies designed to substantively improve agricultural productivity.<sup>10</sup> Even when successful, the approach of expanding agricultural productivity by relying on input-intensive methods is not without problems. Many relate to the poor management of natural resources, especially water and soils, and
which included the place of purchase variables. The countries which ultimately satisfied the criteria for inclusion span four regions of the world, concentrated in Sub-Saharan Africa and Latin America and the Caribbean, as detailed in Table B1. Unfortunately we were not able to include countries in Asia, since the question on the place of purchase was almost always missing from their budget surveys.

Table B1: Regional Survey Representation

|**Region**|**# Countries **|**Pop. of Surveyed Countries **<br>(Millions)|**Total Pop. **<br>(Millions)|**Proportion of pop.**|
|---|---|---|---|---|
|Sub-Saharan Africa|16|379|1078|35%|
|Middle East & North Africa|2|48|449|11%|
|Europe & Central Asia|2|9|918|1%|
|Latin America & Carribean|10|489|641|76%|
|East Asia & Pacifc|1|9|2328|0.4%|

While some surveys appeared from their questionnaire to satisfy our criteria, we ultimately could not include them, either because of issues with data access, or because when we looked more closely at the data one of our criteria was violated. For completeness, Table B2 further details countries that were considered, but could not be included/ Table B2: Discarded Household Expenditure Surveys

|**Country**|**Survey**|**Year **|**Reason not Included**|
|---|---|---|---|
|**Armenia**|Integrated Living Conditions Survey|2016|PoP often missing|
|**Bosnia & Herzegovina**|Household Budget Survey|2007|PoP asked as a purchasing habit|
|**El Salvador*
.85|5.26|
||(20.14)|(21.8)|(18.1)||
|Distance (in km) to regional health authority|14.95<br>(10.09)|15.15<br>(9.34)|14.76<br>(10.86)|0.39|
|Population|15775|15387|16163|-776|
||(8564)|(7465)|(9577)||
|Childbearing-aged women|3739|3647|3831|-184|
||(2030)|(1769)|(2270)||

_Panel B: Monthly number of contraceptives administered by health centers between Jan 2020 and Mar 2022_

|||_Mean/(SD)_||_Mean diference_|
|---|---|---|---|---|
|Sayana Press|8.17|8.47|7.87|0.6|
||(13.63)|(15.2)|(11.96)||
|Any contraceptive dispensed|447.4|445.5|449.3|-3.7|
||(328.5)|(305.1)|(352.6)||

Notes: Mean (SD) represents the mean and standard deviation at the health center level, while the last column shows the mean difference between the treatment and control groups. Significance: ***=.01, **=.05, *=.1 Panel A reports characteristics of health centers, based on data provided by the Burundian Ministry of Health in the first trimester of 2024. The count of women of childbearing age (15-49 years old) at each health center is estimated by multiplying the local population within the health center’s catchment area by the national proportion of this
somewhat slower pace than in periods 1-2. However, we also find a higher rejection rate in Test **I** and a somewhat reduced rejection rate in Test 2. On the whole, these results tend to suggest that the widening band makes verification more difficult.

In contrast, for periods 4-7, characterized **by** a currency basket and much wider bands, none of the estimates in Table **lb -** whether restricted or unrestricted **-** appears close to the announced values even after a reasonably large number of observations. Precision is much poorer than in the earlier periods, although the restricted estimates are in general substantially more precise than the unrestricted ones (see the last two columns in the table). In any case, both sets of estimates appear clearly biased; indeed, some point estimates are even negative. As a result, while Test 2 generally rejects the null of zero weights, Test **1** also rejects the announced weights in most samples, and this applies both to the restricted and unrestricted estimates.

These results can be more easily understood with the help of Figure **3,** which presents scatter plots of the observed exchange rate of the Chilean peso against the central parity. In the first three sub-periods, the points cluster along the 45-degree line, reflecting a relatively close match between the peso and the central parity. As the band widens in the last four periods, the peso can fluctuate further away from the central parity. This is particularly apparent in periods **5-7,** whose scatter plots display little or no clustering along the 45-degree line. Thus, it is not surprising that in the first three periods the basket weights defining the central parity can be estimated fairly precisely from the observed exchange rate data, while this is not the case in later periods.<sup>2 5</sup>

> 2' The scatter diagram, along with Figure **1,** also provides some
to an increase in its price of 2.94%. This estimate is closer to the more inelastic estimates produced in prior studies.

Three main takeaways emerge from our analysis, all of which are consistent with theories of limits to arbitrage such as those developed by Koijen and Yogo (2019) and Gabaix and Koijen (2020). First, there is a broad range of investors (beyond passive funds and ETFs) that have ‘implicit’ mandates to closely track the composition of the indexes they follow so as to deviate very little from benchmark returns.<sup>5</sup> Second, hedge funds and other investors, that are supposed to act as arbitrageurs in financial markets, are too small to soften the price impact of demand shocks generated by investors with such ‘implicit’ mandates. Third, consistent with the other two results, the demand elasticity for stocks is highly inelastic, contrary to what is typically predicted in canonical asset pricing models.

# **2 Data and Institutional Framework**

To study rebalancing events, we exploit episodes of additions to and deletions of Colombian stocks from MSCI international equity indexes, together with transactionlevel data from the Colombian stock exchange, _i.e._ , the _Bolsa de Valores de Colombia_ (BVC, henceforth). In this section, we describe these episodes and discuss the main features of the BVC and the proprietary transaction data.

## **2.1 MSCI Additions and Deletions**

MSCI indexes are the most widely tracked international benchmarks for institutional investors in equity markets. Importantly, many of their flagship indexes contain stocks from different countries, and are thus followed mostly by large international investors. In our analysis, we exploit additions to and deletions of stocks from MSCI Standard Indexes, which are the largest in terms of the AUM benchmarked against these indexes.<sup>6</sup> Starting from the MSCI quarterly reports for the Standard Indexes, we identify 18 episodes of additions and deletions of Colombian stocks in the 2006-2017 period.
to limited data access, outdated survey, or the size of the country unsuitable for further subnational disaggregation.

We use the national and subnational boundaries in GSAP to overlay shapefile boundaries for each region with the gridded exposure data described above. We then take the sum of the population exposed within the defined boundaries to generate an estimate of the number exposed within the administrative unit. For partially covered grid cells, the population is weighted by the fraction of the 1km grid cell covered by the region. The total number exposed is multiplied by the share of people whose per capita income or consumption falls below a given poverty line in that administrative unit to determine the number of people who are both exposed and poor at a given poverty line in that unit. This is then summed by country, region and globally to provide country, regional and global numbers on exposed and poor. Implicitly, we assume a uniform rate of poverty within each GSAP subnational administrative level. The impact of this assumption is ambiguous since poorer households may be disproportionately located in exposed locations within subnational areas in some cases, (rural drought) but not in others (urban flooding). The direction and size of this bias is something that needs to be tested in future work as discussed further in the final section of the paper.

This data is used for the following measures: • **Income** : this is calculated as the share of households that have income or consumption less than 1.5 times the poverty line of $2.15 (2017 PPP) for each administrative unit in the GSAP. The data is available for the common reference year of 2019 and is available for 168 countries. At this level of income and consumption there is very little difference between income and consumption (when assessed using countries with data on both, see Wollburg et al 2023) and for the rest of this paper we refer to this measure as income regardless of whether the data used to estimate this comes from an income or consumption aggregate.

• **
Indeed, only when ‘opportunity entrepreneurs’ (those motivated by innovative and progressive drivers) are distinguished from ‘necessity entrepreneurs’ (those who are selfemployed and pushed by defensive and regressive drivers, such as the fear of unemployment), a positive linear relationship between economic development and entrepreneurship seems to be restored (see Carree _et al_ ., 2007; Acs, Desai and Hessels, 2008; Acs, 2008)<sup>4</sup> . By the same token, when the focus is on DCs, a positive relationship

> 4 However, this positive relationship turns out to be barely significant and so the presence of a positive link between entrepreneurship and economic development in advanced economies is highly contested. Only when entrepreneurship is combined with other development indicators, the relation becomes significant; for instance, Acs and Szerb (2010 and 2012) have put forward a composite “Global Entrepreneurship & Development Index” where actual and potential individual entrepreneurial characteristics are combined with

5
passes the 0.4 load factor mark, which applies to 41 percent of rides, the proportion of men in the reserved space starts to increase, and demand for the reserved space drops accordingly. As the load factor passes 0.8, which is the case in 1.3 percent of rides, the mean takeup of the reserved space drops further to approximately 18 percent; even at this extreme of the distribution, observed takeup is still greater than the 13-17 percent expected if women participants board completely at random, suggesting that this kind of measurement error is unlikely to have a significant effect on our analysis. In addition, above-median crowding does not predict takeup of the reserved space in Table 2, again suggesting that this kind of measurement error is only an issue in a minority of observations with extreme congestion.

# **9 Conclusion**

In this paper, we study the impacts of a women-reserved space policy on the incidence of harassment in Rio de Janeiro’s public transit and the drivers of demand for a women-reserved space. We innovate by providing women a secure platform to report experiences of harassment, experimentally varying tasks to measure differences in the incidence of harassment across public and reserved spaces, varying payouts to measure women’s willingness to pay for the reserved space, and leveraging high-frequency data of riding condition to explore various demand mechanisms such as congestion and presence of men. We formally document two drivers of demand for the reserved space: avoiding men (and, thus harassment) and complying with norms.

These data highlight that harassment is shockingly common on the public transit system and that the reserved space offers women commuters a relative respite. We find that a woman commuter traveling in the public space experiences harassment on average once a week. Riding in the reserved space reduces the incidence of physical harassment by 50 percent. We observe, however, that this protective effect goes to zero as the presence of men increases, which applies to about 40 percent of the rides we record. We show stark 32
not farm but received rent for also were counted as being held. This variable then was normalized. The agricultural and land index displayed in Table 1.4 was the sum of the normalized agricultural asset index and the normalized household land holdings.

Overall households in Southern faced the greatest rainfall shocks in 2013. They also face relatively high coefficients of variation in rainfall shocks, indicating greater exposure to future rain shocks. Households in Central faced the greatest maize price and malaria shocks in 2013. But, Central has relatively low coefficients of variation for rainfall, maize price, and malaria shocks, though they also face the highest variability in off‐farm employment. Households in Northern faced the lowest weather and price shocks in 2013, but also had the highest employment loss shocks and relatively high incidence of malaria. They do face relatively high risks of price and malaria shocks in the future compared to the other regions. To summarize, different regions face different risks, with Southern being particularly susceptible to rainfall shocks, Central being relatively susceptible to employment loss shocks, and Northern being historically most susceptible to price shocks.

# **_Household and community characteristics_**

In the face of all the perils described above, even if most of our sample households could be regarded as poor, there are varying degrees of deprivation between them and over time. As captured in the literature 11
workers’ lifetime welfare rises despite a drop in their wages, because each manufacturing worker understands that there is a probability each year that she will choose to enter the expanding export sector and bene...t from the increased real wages there. A dynamic approach with a full accounting of option value therefore complicates the welfare analysis of income inequality. For example, recall that Ebenstein et al (2009) have argued that o¤shoring to lowwage countries has pushed large numbers of US workers from high-wage manufacturing jobs into lower-wage service-sector jobs. In a dynamic model, these workers may nonetheless bene...t from such o¤shoring, because each manufacturing worker knows that with some probability each year she will move into the service sector anyway; the value of this option is enhanced by any measure that raises the real wage in that sector.

# 4 Directions for Future Research.

Research in the 1990’s undermined the simple Hecksher-Ohlin theory linking trade and inequality, and economists pointed at non-trade factors, such as technology and labor institutions or laws, to explain rising inequality. However, now a number of other channels have been discovered that have led to a vigorous resurgence of the idea that trade can lead to a rise in inequality –with the new features that it can do so through North-North trade; in countries of the South; and within each industry and within each class of workers. These theoretical developments have been in important respects fed by empirical work, and are now in turn giving rise to a rich new empirical literature, partly due to the increasing access to ...rm-level data across an increasing range of countries.

A number of natural directions for future work suggest themselves. The new ideas on consumer-side e¤ects (Fajgelbaum, Grossman and Helpman (2009)) and on higher-dimensional income-inequality e¤ects (Section 2.3.2) await empirical exploration (with the exception of Broda and Romalis (2009) for the former), while the interactions of trade with imperfect contracting have
Table 8 extends the analysis for poverty rates by incorporating the gender dimension in the racial differentials. The evidence is consistent with negligible differences across groups. Table 9 additionally present the combined perspective of age and race. We observe that the impact of the fiscal system is poverty-increasing for children (0-15), youth (16-17) and non-elderly adults (18-59) and very povertydecreasing for the elderly, both for AfroBrazilians and whites. Interestingly, the impact for AfroBrazilian elderly is higher (-19.7 p.p.) than for white elderly (-11.1 p.p.).

**Table 8. Moderate poverty headcount rates and Gini index considering both pre‐ and post‐fiscal income aggregates, separately by household head’s gender and race**

|**Race/gender of the head**|**Prop. of**<br>**individuals**<br>**(%)**|**Pre-fiscal**<br>**income**|**Post-**<br>**fiscal**<br>**income**|**Change in**<br>**poverty**<br>**(p.p.)**|
|---|---|---|---|---|
|White female head|18.2|22.0%|21.6%|-0.4|
|White male head|21.7|17.6%|17.0%|-0.6|
|AfroBrazilian female head|28.5|45.9%|45.3%|-0.6|
|AfroBrazilian male head|30.4|37.9%|37.0%|-0.9|

Source: authors’ estimations based on PNAD-C 2019. Pre-fiscal income considers the household-level market
Rocha, and M. Ruta, “The Belt and Road Initiative: A Structural Analysis”, forthcoming Defourny, P. (2017) ‘ESA Land Cover Climate Change Initiative (Land_Cover_cci): Land Cover Maps, v2.0.7.’ Centre for Environmental Data Analysis.

Djankov, Simeon, Caroline Freund, and Cong S Pham, (2010) “Trading on Time,” Review of Economics and Statistics, 92 (1), 166–173.

Donaldson, Dave, (2018) “Railroads of the Raj: Estimating the Impact of Transportation Infrastructure,” American Economic Review, 108 (4‐5), 899–934.

Duranton, G., P. M. Morrow, and M. A. Turner, (2014) “Roads and Trade: Evidence from the US,” The Review of Economic Studies, 81 (2), 681–724.

Fajgelbaum, Pablo and Stephen Redding, (2014) “External Integration, Structural Transformation and Economic Development: Evidence from Argentina 1870‐1914,” NBER Working Paper 20217.

Fujita, M., P. Krugman and A.J. Venables (1999) ‘The spatial economy; cities, regions and international trade’, MIT press, Cambridge MA.

Gaulier, G. and Zignago, S. (2010), ‘BACI: International Trade Database at the Product‐Level. The 1994‐ 2007 Version’, CEPII Working Paper, N°2010‐23 Glaeser, E. and W. Xiong. (2018), ‘Urban productivity in the developing world’, Oxford Review of Economic Policy, 33.3, 373‐404.

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Lall, Somik V. and Lebrand, Mathilde, (2019) “Who wins, who loses? Understanding the Spatially Differentiated Effects of Belt and Road within Central Asia
15

# **Table 1**

||DATA USED IN INDONESI|AN FERTILITY|**-** MORT|ALITY MODEL||
|---|---|---|---|---|---|
|Svyuol|Variable|Units|Yr|Nean|STD DEV|
|IMR|Infant Deaths per Birth|Proportion|1980|.108|<br>.023|
|TFR|Children born per Woman 15-45|Children|1980|5.228|<br>.815|
|TOIL|Percent of HH with Private<br>Waste Disposal (Toilet<br>or Latrine)||1980|47.838|<br>12.046|
|REXNP|Government Non Hospital<br>Recurrent Health<br>Expenditures<br>per Capita|Rp 000|1984|1.272|<br>.485|
|REXHP|Government Recurrent Hospital<br>Expenditures<br>per Capita|Rp 000|1984|.802|<br>.687|
|REXFPP|Gov. Expenditures<br>on Family<br>Planning per Capita|Rp 000|1980|.427|<br>.191|
|ILLIT|Adult Illiteracy Rate|t|1980|26.870|<br>10.190|
|PURB|Percent Urban Population||1980|21.978|<br>18.248|
|YP|Monthly Household Expenditures<br>Per Capita|<br>Rp 000|1983|17.609|<br>5.318|
|CAL|Daily Calories per Capit%|Calories|1980|1906.2|207.2|

Sources: Indonesia; Department of Health, Bureau of Planning; Central Bureau of Statistics, Census; National Family Planning, Coordinating Board; SUSENAS (National Household Survey, February 1984)

## IV. EmRirical Results

17.