rafmacalaba/datause-encoder-data huggingface dataset:
(BRT) system and the Metro system. These findings provide useful insights to understand how households make transport mode decisions for commuting purposes, as well as how they choose the location of their residences.

Our paper builds on a growing literature on the valuation of dwelling unit characteristics but makes several additional contributions. First, for the identification of the employment subcenters, we use the results of Atuesta et al. (2017) that estimate nonparametrically the subcenters following McMillen (2001). These results are more robust than those using specific thresholds of employment and allow comparability through time and among cities.

Second, we consider not only private accessibility to employment subcenters using road distance, but also proximity to public transport infrastructures to determine how much households value location. Since employment subcenters are estimated based only on formal employment, other types of accessibility could be more valuable for households that are not employed in formal jobs. Then, heterogeneity in terms of valuing accessibility is observed when analyzing the socioeconomic level of households or the proportion of residents who are informal workers in each neighborhood. Our results account for these differences analyzing how much accessibility is worth for different types of households.

Third, thanks to a new data set of housing developments compiled by a private market intelligence firm, we are able to base our analysis on repeated sales of very similar housing units, rather than rely on one or repeated cross-sections of market prices (Shiller, 1991; McMillen, 2003). This also helps minimize the omitted variables bias, common in standard hedonic estimates (Wallace and Meese, 1997).

Our results suggest that accessibility to employment subcenters is considered an amenity for households in Mexico City. For every year of analysis, being 1 percent closer to employment subcenters (and controlling for their employment level) increases between one and almost three percentage points housing values. As employment subcenters earn importance in the spatial structure of the city, the Central Business District loses significance. In two of the three specifications analyzed, being closer to the Central Business District is perceived as a dis-amenity that depreciates housing prices, suggesting that employment decentralization is playing an important role on households when making their location choices.

3
Skaperdas, Stergios. 1992. "Cooperation, Conflict, and Power in the Absence of Property Rights." _The American Economic Review_ 82 (4): 720-39.

Squire, Lyn. 1993. "Fighting Poverty." _American Economic Review_ 83 (2): 377-82.

- . 1993. "Fighting Poverty." _American Economic Review_ 83 (2): 377-82.

- Srinivasan, T.N. 1985. "Neoclassical Political Economy, The State and Economic Development." _Asian Development Review (1):_ 38-58.

- Swinnen, Johan F.M. 1974. "Positive Theory of Agricultural Protection." _American Joumal of Agricultural Economics_ 76: 1-14.

- Swinnen, Johan, and Frans A. van der Zee. 1994. "Political Economy of Agricultural Policies." _European Review of Agricultural Economics_ 20 (3): 261-90.

- Thorner, Daniel, Basile Kerblay, and Robert E. F. Smith (eds.). 1966. _A. V. Chayanov on The Theory of Peasant Economy._ Homewood, Ill.: R.D. Irwin.

- Tilly, Charles. 1990. _Coercion, Capital, and European States, A.D. 990-1990._ Cambridge, U.K.: B. Blackwell.

- Tsiddon, Daniel. 1992. "A Moral Hazard Trap to Growth." _International Economic Review_ 33 (2): 299-321.

- van de Walle, Dominique, and Kimberley Nead (eds.). 1995. _Public Spending and the Poor. Theory and Evidence._ Baltimore: Johns Hopkins University Press.

- van Schalkwyk, Herman, and Johan van Zyl. 1996. "The Land Market." In Johan van Zyl, Johann Kirsten, and Hans P. Binswanger (eds.), _Agricultural Land Reform in South Africa._ Cape Town: Oxford University Press.

- Wei, Shang-Jin. 1991. "Gradualism versus Big Bang: Speed and Sustainability of Reforms." Kennedy School of Government, Harvard University (mimeo).

- Weingast, Barry. 1992. "Economic Role of Political Institutions." Institute for Policy Reform Working Paper 46. Washington, D.C.

- Wintrobe, R. 1990. "The Tinpot and The Totalitarian: An Economic Theory of Dictatorship." _American Political Science Review_ 84 (3): 849-72.

World Bank. 1990. _World Development Report: Poverty._ New York: Oxford University Press.

- ---------. 1993. _World Development Report: Investing in Health._ New York: Oxford University Press.

- ---------. 1996. _Poverty Reduction and the World Bank: Progress and Challenges in the 1990s._ Washington, D.C.

- Zietz, Joachim, and Alberto Valdes. 1990. _International Interactions in Food and Agricultural Trade Liberalization: Implications for Developing Countries._ Organisation for Economic Co-operation and Development, Paris, France; and World Bank, Washington, D.C.

75
Table B11: Mechanism at play, controlling for domestic sales concentration in source countries

|VARIABLES|(1)<br>CR5|(2)<br>CR5|(3)<br>CR5|(4)<br>CR5|
|---|---|---|---|---|
|Uncertainty (standardized)|-0.444<br>(0.650)|1.670***<br>(0.554)|1.561***<br>(0.510)|1.946***<br>(0.672)|
|Uncertainty _×_ Developing|1.932**<br>(0.832)||||
|Sales Concentration Source countries (standardized)|-0.412<br>(0.274)|-0.423<br>(0.300)|-0.326<br>(0.257)|-0.309<br>(0.347)|
|Observations|1,964|1,191|1,335|989|
|R-squared|0.833|0.827|0.843|0.806|
|Data Coverage|All countries||Developing||
|Minimum # of|||||
|Foreign Investors|3||1|10|

_Note:_ Each observation corresponds to a destination-year, represented as _it_ . “CR5” denotes the concentration ratio of the top 5 investing firms. Countries averaging fewer than 5 foreign investors are excluded from these calculations. “HHI” refers to the Herfindahl-Hirschman concentration index, calculated using the “CR5” sample. “log(# MNEs)” represents the natural logarithm of the number of foreign investors, and “log(K)” denotes the natural logarithm of the average capital expenditure. “Uncertainty” is based on the “T3” variable from the World Uncertainty database (Ahir et al. 2022), which counts the occurrence of the word “uncertainty” from Economist Intelligence Unit (EIU) reports for 143 countries since the 1950s. A higher value indicates greater uncertainty. Uncertainty in year _t_ is approximated as the logarithm of the average number of uncertainty-related words in EIU reports over the three years preceding the investment (standardized to have a mean of zero and a standard deviation of 1). The analysis includes country fixed effects and (world) region by year fixed effects. Countries are classified into 7 regions based on World Bank classifications. Destination-specific control variables include logarithms of population, GDP, the average exchange rate, and a measure of concentration in domestic sales of 4 FDI sources (i.e., the United States, Germany, the United Kingdom, and France, weighted by their respective destination shares in 2003). Investor country concentration is measured as the share of the top 3 companies in each country’s domestic sales, firm-level sales from Worldscope, aggregate sales from EU-KLEMS. Standard errors are clustered at the destination country. Standard errors are clustered at the destination country level. Significance levels: *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

36
# Abbreviations and Definitions

DTM Displacement Tracking Matrix IOM International Organization for Migration IDP Internally Displaced Person NTL <mark>Nighttime Lights</mark> Author Contributions. _Anais Dahmani-Scuitti_ : Conceptualization, methodology, formal analysis, data curation, writing – original draft, writing – review & editing. _Erwin Knippenberg:_ Conceptualization, formal analysis, writing – original draft, writing – review & editing, supervision. _Walker Kosmidou-Bradley:_ Conceptualization, methodology, software, data curation. _Johanna Lee Belanger:_ Conceptualization, methodology, software, data curation, writing – original draft.

Acknowledgments. Authors are thankful to the International Organization for Migration (IOM) for sharing the Displacement Tracking Matrix (DTM). The team would also like to thank IOM staff members of the Afghanistan office, namely Michael Speir, Henry Kwesi Kwenin, Sebastian Boonstra, Caleb Ikyernum and Saboor Sultani for their technical support in handling the DTM data. This paper benefited from input from Razilya Shakirova, and from guidance of Cesar Cancho, Andrew Dabalen, Andrea Mario Dall’Olio and Ghazala Mansuri. Finally, the team is grateful for comments received at the Data for Policy 2021 conference.

Conflicts of Interest. We declare that we have no relevant or material financial interests that relate to the research described in this paper. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the World Bank or any affiliated organizations, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work.

Funding. This research was supported through the World Bank Bank Budget (BB) for the Afghanistan Poverty and Equity Program FY 21-22 as part of its core analytical activities.

<mark>Data availability statement. The data that support the findings of this study are based on two main sources, for satellite imagery and displacement data respectively. The Nighttime Lights (NTL) data obtained from the NASA/NOAA partnerships through the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite, openly available at</mark> <u><mark>https://earthdata.nasa.gov/learn/backgrounders/nighttime-lights. All displacement data used for this study,</mark></u> <mark>alongside with the agricultural indicator in 2020, were collected by IOM, through their Displacement Tracking Matrix (DTM). The data originates from the Baseline Mobility Assessment (BMA) and the Community Based Need Assessment surveys, openly accessible at</mark> <u><mark>https://displacement.iom.int/afghanistan. Any extraction, translation, reproduction, and</mark></u> <mark>distribution, in any form, or by any means, electronic, mechanical, photocopying, or otherwise, requires the explicit prior written permission of IOM. Source</mark> _<mark>: “International Organization for Migration (IOM), December 2021, Displacement Tracking Matrix (DTM)”.</mark>_ <mark>The population data comes from WorldPop</mark> 13
drawn the figure accordingly. Then AB’C’ > ABC and [∆ e - ∆ I] > 0 . That is, we have greater welfare gains in the partner country market with larger supply elasticities in the partner country.

**Market for excluded country services.** We illustrate the impacts with the aid of figure 3, right hand side. It builds on figure 2, right hand side, where we have drawn two sets of supply curves—a more elastic pair and less elastic pair. To be consistent with the initial equilibrium data point, both the less elastic (S0TI) and more elastic tax ridden supply curves (S0Te) must pass through to point A; and to be consistent with the estimated ad valorem barrier, the undistorted less elastic (S0I) and undistorted more elastic supply curve (S0e) must both pass through the point C. Preferential liberalization with another country results in a downward shift in demand for the excluded countries services. Equilibrium in the market for excluded country services shifts to to B’ with higher elasticities, and shifts to B with lower supply elasticities. What is the impact of elasticities on the new equilibrium price and quantity?  With a higher supply elasticity, in response to the initial shift down in demand, there is a smaller reduction in price and larger reduction in quantity. On the other hand, there is an offsetting second order effect. Since the price of excluded market services initially falls less with high elasticities, there is a smaller second order cross-elasticity effect on the reduction in demand, so the demand curve shifts in less in the high elasticity case. We assume the second order effect is smaller so that we have drawn G’ > G and Q1e  <  Q1I.

With initial rent capture, the welfare change is equal to HJ’B’G’ – J’I’CA in the high elasticity case and equal to HJBG – JICA in the lesser elasticity case; the second terms vanish with no initial rent capture. The difference in the welfare between the high elasticity and low elasticity cases is [∆We - ∆WI] = [HJ’B’G’ – J’I’CA] – [HJBG – JICA] = [HJ’B’G’- HJBG] + 14
The CNRS data make use of annual retrospective employment histories dating to 2000, when the first round of the survey was implemented and of detailed monthly employment histories for the 24 months preceding 2009.<sup>9</sup> Information on employment history was used to calculate the counterfactual level of employment for 2009. The research team used estimates of off-farm employment trends (based on data from 2005 through the second quarter of 2008) and monthly employment data from the 12 months before the crisis and annual 2005–07 growth rates. The results of the extrapolation exercise and actual off-farm employment rates are shown in Figure 6.<sup>10</sup> Absent the financial crisis, Huang et al. (2011) argue that 57.8 percent of the rural labor force would have been working off-farm but that instead only 51 percent had off-farm employment. By April 2009, a gap of 6.8 percent had opened between the counterfactual and the actual share of the rural labor force working off-farm. At the national level, that percentage would imply 279 million rural residents were actually working in nonagricultural activities in September 2008, whereas, under the existing trends and seasonal adjustments, 301 million might have been expected to be working off-farm in April 2009. In fact, the analysis estimates that only 265 million rural individuals were working off-farm in April 2009, implying that the net effect was a loss of 36 million jobs. This number is consistent with a drop in nonagricultural employment of 12 percent or, in terms of the entire rural registered workforce, a decline of 6.8 percent in the ratio of business-as-usual to actual share of those working outside of agriculture **.** The net impact, however, differs from the number of rural workers who were actually laid off. That number cannot be deduced from this net gap. A number of factors affect the gap between the business-as usual scenario in April 2009 and the actual level of employment at that time. First, the gap includes those who were laid off between October 2008 and April 2009 and did not find a job ( _the long-term laid-off workers_ ). Second, the gap is also affected by the difference between the number of workers that were actually laid off between October 2008 and April 2009 and those that found a new job between October 2008 and April 2009 but who had not been 11
robust and cluster options and when cluster options are used these can be at different levels (country, region or smaller units). Most studies use country and/or year fixed effects but the collinearity that these fixed effects can generate with the Gini can be very different depending on the structure of the data and on the estimation procedure for the standard error.

In order to compare results across studies, readers should have full information on the number of countries and years, the number of observations within each country/year data point, the exact procedure used for the estimation of the standard error, the use of country, year or other fixed effects, the number of country or year dummies dropped by the software during estimations and more generally the full estimation model. When some of this information is missing, it becomes very hard to replicate and compare results.

# **References**

- Alesina, A., R. Di Tella, and R. MacCulloch (2004): “Inequality and Happiness: Are Europeans and Americans Different?,” _Journal of Public Economics_ , 88, 2009–2042.

- Amiel, Y., and F. A. Cowell (1992): “Measurement of Income Inequality: Experimental Test by Questionnaire,” _Journal of Public Economics_ , 47(3-26).

- Ball, R., and K. Chernova (2008): “Absolute Income, Relative Income, and Happiness,” _Social Indicators Research_ , 88, 497–529.

- Blanchflower, D. G., and A. J. Oswald (1997): “A study of labour market and youth unemployment in Eastern Europe,” _Warwick Economic Research Papers_ , (499).

- (2004): “Well-being over time in Britain and the USA,” _Journal of Public Eco-_

- _nomics_ , 88, 1359–1386.

- Clark, A. E. (2003): “Inequality aversion and income mobility: a direct test,” _Delta Working Papers_ , (11).

- Clark, A. E., and A. J. Oswald (1994): “Unhappiness and unemployment,” _Economic Journal_ , 104(424), 648–659.

- Diener, E., M. Diener, and C. Diener (1995): “Factors predicting the subjective well-being of nations,” _Journal of Personality and Social Psychology_ , 69(5), 851–864.

- Diener, E., E. Suh, and S. Oishi (1997): “Recent Findings on Subjective Well-Being,” _Indian Journal of Clinical Psychology_ , 24(1), 25–41.

- Diener, E., E. M. Suh, R. E. Lucas, and H. L. Smith (1999): “Subjective well-being: Three decades of progress,” _Psychological Bulletin_ , 125(2), 276–303.

18
in the ESR group are 5.0 pp less likely to report working relative to other older men assigned to group C.

We provide suggestive evidence on potential mechanisms driving these results. We test two potential channels: the prevalence of peers’ informal employment and social norms related to domestic obligations. First, our findings suggest that the incidence of peers’ informality in a community influences the effectiveness of using the ESR survey method in capturing female and youth work and youth employment. Second, we also show that peers’ informality in a community has no differential effect on the impacts of using the LOA module. Third, we document that the LOA module works better to address women’s underreporting of labor market indicators when they live in communities where other women spend more time on domestic work. These findings on mechanisms may indicate that the LOA survey method is needed in settings that are more likely to affect women’s own assessment of what constitutes work or employment, which is the case of incidence of norms around domestic obligations. Furthermore, the ESR survey method seems to be more relevant in contexts where the proxy respondent is exposed to more informal employment, which may affect his assessment of other household members’ labor market participation, particularly those of women and youths. Although these findings provide only suggestive insights, we believe they warrant further exploration, as they can serve as a starting point for new avenues of methodological and policy-relevant research.

Our paper contributes to the literature in several important ways. First, we provide evidence on how different survey methods can help address women’s and youths’ underreporting of labor market indicators, which results in a mis-measured employment and work gaps by gender and age. The majority of published studies focus primarily on understanding how women underreport their labor outcomes when responding to standard survey methods and how alternative survey methods improve women’s reporting of labor indicators relative to men (Ambler et al., 2021; Arthi et al., 2018; Bardasi et al., 2011; Dillon et al., 2012; Discenza et al., 2021; Franck & Olsson, 2014; Kilic et al., 2021; Kilic et al., 2023; Muller & Sousa, 2020). No studies, however, have considered how alternative survey methods affect the reporting of labor outcomes by other population groups such as youth, who are more likely 5
27 economy expenditures varies greatly when the component parts of national economy expenditures are exarnined. Following in each section are the regressions that had the highest explanatory force as measured by the adjusted R2 values.

In all years on average in the Russian Federation, subsidies to housing constituted by far the single largest portion of national economy expenditures, accounting for between 56% and 65% of expenditures in the years 1993-95 (data for the national economy components are not available for 1992 with the exception of food subsidies.) For this reason, it is to be expected that the regression results for housing largely reflect those for overall national economy expenditures.

Table 15 reflects the regressive character of housing subsidies in Russia: residents in wealthier regions received more in housing subsidies<sup>26</sup> . Also, as indicated by the variable 'Doctors per 10,000" (generally, a proxy for urbanization) in all years, the most urbanized regions (not necessarily the wealthiest) also received more in housing subsidies. The same result is reflected in the negative coefficient for the variable 'Rural population" in 1993. The latter reflects another dimension of inequality in housing subsidy allocation: most housing subsidies are provided by local goverrnents through subsidized rates on centralized delivery of heating and hot water, which is a predominantly urban phenomenon. Rural households do not have access to these utilities and, hence, do not receive the subsidies associated with them.

_Table 15. Determinants of Housing Subsidies, 1993-95_

||**1993**|**1994**|**1995**|
|---|---|---|---|
||(N=87)|(N=78)|(N=86)|
|**Pre-transfer regional**|.264|.116|.161|
|**revenue**|(14.4)|(4.5)|(10.0)|
|**Federal transfers**|.231|.245|.217|
||(12.0)|(11.9)|(8.6)|
|**Household income**||.065|.024|
|||(8.5)|(5.3)|
|**Wage arrears (1993 data)**||-.572<br>(-4.0)||
|**Doctors per 10,000**|7.69|3.28|3.54|
||(4.5)|(2.5)|(2.8)|
|**Share of rural population (1**|**994 data)**|-3.11<br>(-3.3)||
|**Constant**|-418.6|-178.1|-209.8|
||(-5.5)|(-3.3)|(-3.9)|
|**AdjustedR **<sup>**2**</sup>|.80|.93|.87|

26 Freund and Wallich (1995) show that in a similar situation in Poland the top income quintile of households spent more than five times as much on fuel as the bottom quintile. Hence, price controls in the energy sector are regressive and involve a lot of "leakage" of the price subsidy to the non-poor.
wholesale and retail trade, accounted for two thirds of the employment gain, and agriculture a quarter. In 2006, employment growth slowed to 2 percent, below the growth of the working age population.

**Figure 28. Recent growth is jobless ... Figure 29.  ...and poverty reduction is slower**

<!-- Start of picture text --> 150 101 70<br>65<br>100 100 EAP<br>60 Philippines<br>50 99<br>55<br>0 98 50<br>45<br>-50 97<br>40<br>-100 96<br>35<br>-150 95 30<br>GDP Growth (1980=100)<br>Employment rate (1980=100 - secondary axis) 25<br>-200 94 1990 1996 1999 2000 2001 2002 2003 2004 2005 2006 2007<br>% 1980198219841986198819901992199419961998200020022004<br>$2-a-day Headcount Index (%)<br><!-- End of picture text --> _Source:_ Author’s elaboration on National _Source:_ Author’s elaboration on World Development Statistics Coordination Board, 2007 and Canlas, Indicators, 2007. Aldaba, and Esguerra, 2006.

**For the past few years, poverty reduction has been slower than in the rest of East Asia (Figure 29), and rural poverty remains high.** In the Philippines, 70 percent of the poor live in rural areas. However, the agricultural sector - traditionally more protected than the rest of the economy (David, Intal, Balisacan, 2007) - is not reducing poverty. In the past years, agricultural GDP grew at modest rates, productivity growth was low by regional standards<sup>41</sup> , and labor productivity and yield stagnated. The recent agrarian reform - while successful in transferring income and wealth from landlords to tenants - did not have a significant pro-poor impact (Balisacan and Fuwa, 2007), as the benefits bypassed landless agricultural laborers (Hayami, Quisimbing, and Adriano, 1990).<sup>42</sup>

**The sustainability of the growth model is exposed to longer term risks** . In the long run, of the two engines of growth (export-led services and remittances-fuelled consumption), the

> 41 Between 1980 and 1998, total agricultural factor productivity has grown at 9 percent, compared to 27 percent in Thailand and 49 percent in Indonesia (World Bank, 2007b).

> 42 Indeed, to succeed, land reform needs to combine access to land, credit, and technology.

34