rafmacalaba/datause-encoder-data huggingface dataset:
“broad” classification that adds to the “typical” one the columns that pertain to utilities, half of the columns with postal and telecommunication services, and one third of the land transport column. The empirical evidence that we present in this and in the next sections relies on the “typical” definition, and we check the robustness of our main results to the use of the “narrow” definition (the results are shown in the Appendix). Because the TiVA data are not detailed enough (i.e., land transportation is included in a broader “transport and storage” sector), we cannot appropriately implement the “broad” definition.

Once public procurement is defined, we identify a private market that is suitable for comparisons. The sum of the other columns in the ICIO table and the “Household expenditure” column in the final demand section is the most immediate and comparable definition of ‘private procurement’. This choice can nevertheless lead to an overlapping with government procurement to the extent to which public authorities operate outside the “typical” definition of government (i.e., outside the Public Administration, Health, and Education columns). Such an overlap between the public and private markets should work against finding significant differences in trade barriers between the two.

# **4 Descriptive trends**

Before turning to the empirical estimates of the gravity models, we investigate descriptive trends in the data. The objective here is twofold: (i) to identify patterns of expenditures across goods and services in public and private markets as these affect estimated home bias at the country-level; and (ii) to have a first look at trade barriers by looking at import penetration ratios. First, we compute the service expenditure share for each country in the ‘typical’ public and private markets as defined above. Figure 2 reports this share for 1995 and 2015, the first and the last years of our sample. One pattern stands out: government procurement is vastly about services. The average government in our sample devotes to services around 90 percent of total procurement purchases in 1995 and 2015. Importantly, the share of pubic purchases on various services is higher than the same share for the private market.<sup>22</sup>

> 22This pattern is confirmed when we adopt the “narrow” definition of government procurement, as shown in Figure A1 in the Appendix.

17
- Korinek, Anton, Johan A. Mistiaen, and Martin Ravallion. "Survey nonresponse and the distribution of income." The Journal of Economic Inequality 4, no. 1 (2006): 33-55.

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Niehues, Judith and Peichl, Andreas, Bounds of Unfair Inequality of Opportunity: Theory and Evidence for Germany and the US (May 15, 2012). CESifo Working Paper Series No. 3815. Available at SSRN:http://ssrn.com/abstract=2060014

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- Paes de Barros, Ricardo, Francisco H.G. Ferreira, José R. Molinas Vega, and Jaime Saavedra Chanduvi. “Measuring Inequality of Opportunities in Latin America and the Caribbean”. World Bank (2009).

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Piketty, Thomas. "Capital in the 21st Century." Cambridge: Harvard Uni (2014).

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Roemer, John E. Equality of opportunity. Harvard University Press (1998).

Sailesh Tiwari, Gabriel Lara Ibarra, and Ambar Narayan (2015). “How unfair is the inequality of wage earnings in Russia? Estimates from panel data”, Policy Research Working Paper; no. WPS 7291. Washington, D.C. : World Bank Group.

Sen, Amartya. Development as freedom. Oxford University Press (1999).

Skoufias, Emmanuel, and Sergio Olivieri. “Inequality and the distribution of gains from growth in Thailand between 2000 and 2009” (2013), mimeo.

18
Although the disparities in GRDP per capita across districts have declined since 1999, they are still higher after the crisis than before, with a Gini coefficient of real GRDP per capita across districts of 0.39 since 2005, compared with 0.37 in 1993. Table 1 shows that this is driven by a large decline in inequality between districts in Papua and, to a lesser extent, Sumatra, whilst inequality within the other major island groups has remained constant or increased. Although, almost every island group experiences a long-run increase in inequality across districts, the national increase is driven by widening disparities between districts in Java, with little change in the overall level of inequality across districts off Java.

But the recent decline in inter-district inequality is mostly due to the continued decline in interprovincial inequality. This seemingly contradictory evolution of spatial inequality can be explained by decomposing inequality into two components: that due to the change in inequality between provinces; and that due to the change in inequality between districts within provinces. Interestingly, the decline in inter-district inequality since 1998 shown in Figure 4 does not result from improvements in the within-province distribution of per capita GRDP. Table 2 decomposes the change in national inter-district inequality<sup>19</sup> into changes in between-provincial inequality and changes in within-province inequality. Within-province inequality barely changes between 1998 and 2003, with almost all of the change in inter-district inequality being driven by the continued decline in inequality between provinces.

**Table 2: Decomposition of changes in inter-district inequality**

|||GRDP PC||GRDP|nonoil&g|asPC|
|---|---|---|---|---|---|---|
||1998|2000|2003|1998|2000|2003|
|GE(0)|0.297|0.263|0.242|0.26|0.229|0.213|
|within province|0.168|0.165|0.157|0.157|0.154|0.147|
|betweenprovince|0.129|0.098|0.085|0.103|0.075|0.066|

Source: Authors’ calculations based on GRDP data from BPS.

The results in Table 2 imply that poorer provinces have generally grown faster than richer ones. But large investments tend to be localized so that, if there were no compensating movements of people, they would tend to increase within-province inequality. The fact that there has been little change in within-province inequality suggests that changes in district GRDP within a province and changes in population have gone together. Figure 5 confirms this. Provinces that experienced increases in the share of their population in the top 20 percent of districts (by population) also tend to experience increases in the share of provincial GRDP coming from the top 20 percent of districts (by GRDP). Thus, as the economy becomes more concentrated spatially, so does the population and vice versa, with the result that within-province distribution of per capita GRDP has remained relatively stable.

> 19 Using the Generalized Entropy class of decomposable inequality measures.  Similar results were obtained using other measures.

10
housing units would have to be built to reach target 11.1 of the sustainable development agenda by 2030. This estimation assumes that governments do not put in place ambitious programs to upgrade existing inadequate housing, improve service delivery, refurbish existing units, and build new adequate housing units. In order to provide adequate housing by 2030, four million _additional_ adequate housing units would be required every year.

Given the expected migration to urban areas, we differentiate the flow deficit by urban and rural areas. By 2030, within the sample, at least 103 million urban HHs<sup>20</sup> and 202 million rural HHs are projected to live in inadequate housing units if governments and private sector participants continue on business-as-usual policies and practices without further addressing the already existing housing deficits.

Within the sample, India, given its predicted population growth in both rural and urban areas is expected to have the largest adequate housing deficit. In urban areas, India is responsible for the lion’s share (31 percent) of the overall adequate housing deficit in the sample, with 21.6 million urban housing units predicted to be inadequate by 2030. The second largest adequate housing deficit in urban areas is projected for Nigeria (10 million inadequate housing units in 2030). In Bangladesh, the adequate housing deficit in urban areas is projected to be 6.2 million by 2030, followed by the Indonesia (5.9 million by 2030 given the predicted population growth in urban areas). The five countries with the largest predicted urban population by 2030 (India, Brazil, Indonesia, Nigeria, Mexico) are likely to have a cumulative adequate housing deficit of 43.4 million housing units in urban areas.

In rural areas, the three countries with the largest predicted adequate housing deficit include India (73.3 million), Ethiopia (13.4 million), and Bangladesh (9.6 million). Together, these three countries account for about 48 percent of the overall predicted adequate housing deficit in rural areas in the sample. The five countries with the largest forecasted rural population by 2030 (India, Pakistan, Indonesia, Nigeria, and Ethiopia) are likely to have a cumulative adequate housing deficit of 112.9 million housing units.

# **6. Conclusion**

In this paper, we introduce a new _Adequate Housing Index_ , which allows for a micro-founded comparison of housing adequacy across 64 emerging economies. To the best of our knowledge, this is the first paper to present cross-country microdata pooled from household surveys on housing conditions on a series of countries across different income groups and continents.

20 Due to the absence of location marker in Albania’s and Turkey’s HCES, urban estimations are excluded here.
Developing countries, even if we separate them as low or middle income, are much more heterogeneous. There are small and large low income countries and their performance might be different. China and India, with their large populations and GDP overwhelm other country groups. Some middle income countries, in Asia and Latin America are major exporters. There are industrialized East Asian countries that are major importers. Therefore we have separated developing countries into six groups. These are: small low income countries; large low income countries; China and India; middle income large exporters; middle income East Asian importers; and other middle income countries.<sup>11</sup> A contentious issue in the literature has been the trade performance of lowincome countries. Many analysts have argued that the low-income countries have not benefited from the expansion in global trade. This is only partially true in agriculture. From table 5, Low-income countries’ share of world exports increased from 3.7 percent 1990/01 to 4.7 percent in 2000/01 and to 4.8 percent in 2006/07. But if intra-EU and intra-NAFTA trade are excluded, their share increases from 6.2 percent in 1990/91 to 7.2 percent in 2006/07. Thus as measured by agricultural export and import performance, they have not been marginalized during this period. Their overall trade surpluses, however, have risen throughout the period, from $7.8 billion in 1980/81 to $17 billion in 2006/07. Low-income developing countries have a trade surplus with industrial countries and with middle-income developing countries, and both of these surpluses have increased over the last two decades. Their exports have increased as well, primarily to other developing countries.

**Table 7 Agricultural Trade Flows of Developing Countries by Group, 1990/91–2006/07**

|||Tra<br>|de Value<br>|($ billion)<br>|
|---|---|---|---|---|
|CountryGroup|Period Avg.|Exports|Imports|Net Imports|
|Low income, small countries|1990-91|9.8|1.6|-8.2|
||2000-01|18.0|8.8|-9.2|
||2006-07|32.2|14.2|-18.0|
|Low income, large countries /a|1990-91|1.5|1.6|0.1|
||2000-01|2.5|4.1|1.6|
||2006-07|4.1|10.0|5.9|
|Middle income, large exporters /b|1990-91|29.1|4.3|-24.8|
||2000-01|55.6|18.4|-37.2|
||2006-07|117.4|23.0|-94.4|
|Middle income, East Asian Importers /c|1990-91|9.2|12.7|3.5|
||2000-01|10.6|36.3|25.7|
||2006-07|13.4|45.7|32.3|
|China and India|1990-91|12.5|5.8|-6.7|
||2000-01|27.1|22.1|-5.0|
||2006-07|51.0|60.2|9.2|
|Other middle income countries|1990-91|60.2|36.7|-23.5|
||2000-01|100.7|101.4|0.7|
||2006-07|202.7|214.9|12.2|

Notes: /a Bangladesh, Ethiopia, Nigeria, and Pakistan. /b Argentina, Brazil, Indonesia and Thailand. /c Republic of Korea, Hong Kong (China), Singapore, Taiwan, China. _Source_ : Based on mirror data from UN COMTRADE Statistics and World Bank calculations 11 Within the other middle income country group, there are the 12 new EU members. One option was to separate them into another subgroup. However, their inclusion or exclusion does not change any of the conclusions.

10
# **6. Code availability statement**

The Random Forest Classification machine learning model is open-source and was used through the Python package scikit-learn: <u>https://github.com/scikit-learn/scikit-learn. Sensitivity</u> analysis was performed using the Python application programming interface of the Gdal package: https://gdal.org/.

# **7. References**

- Ahmed, B., 2021. The root causes of landslide vulnerability in Bangladesh. Landslides 18, 1707–1720. https://doi.org/10.1007/s10346-020-01606-0

- Ahmed, B., Rahman, M.S., Islam, R., Sammonds, P., Zhou, C., Uddin, K. and Al-Hussaini, T.M., 2018. Developing a dynamic Web-GIS based landslide early warning system for the Chittagong Metropolitan Area, Bangladesh. ISPRS International Journal of GeoInformation, 7(12), p.485.

- Ahmed, B., 2015. Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh. Landslides 12, 1077–1095. https://doi.org/10.1007/s10346-014-0521-x

- Ahmed, B., Rahman, M.S., Rahman, S., Hug, F.F., Ara, S., 2014. Landslide Inventory Report of Chittagong Metropolitan Area, Bangladesh.

- Ayalew, L., Yamagishi, H., 2005. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65, 15–31. https://doi.org/10.1016/j.geomorph.2004.06.010

- Breiman, L., 2001. Random Forests. Machine Learning 45, 5–32. https://doi.org/10.1023/A:1010933404324

- Chisty, K.U., 2014. Landslide in Chittagong City: A perspective on hill cutting. Journal of Bangladesh Institute of Planners 7, 1–17.

- Comprehensive Disaster Management Programme II, 2012. Landslide Inventory & Land-use Mapping, DEM Preparation, Precipitation Threshold Value & Establishment of Early Warning Devices.

- Crozier, M.J., 2010. Deciphering the effect of climate change on landslide activity: A review. Geomorphology 124, 260–267. https://doi.org/10.1016/j.geomorph.2010.04.009

- Feizizadeh, B., Shadman Roodposhti, M., Jankowski, P., Blaschke, T., 2014. A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping. Computers and Geosciences 73, 208–221. https://doi.org/10.1016/j.cageo.2014.08.001

- Fell, R., Corominas, J., Bonnard, C., Cascini, L., Leroi, E., Savage, W.Z., 2008. Guidelines for

29
Costinot, A., and J. Vogel (2010) “Matching and Inequality in the World Economy,” Journal of Political Economy 118(4), pp. 747–786 Cruz, M., M. Bussolo and L. Iacovone (2016) “Organizing Knowledge to Compete: Impacts of Capacity Building Programs on Firm Organization,” Policy Research Working Paper Series 7640, The World Bank Fieler, A., M. Eslava and D. Xu (2017) “Trade, Quality Upgrading, and Input Linkages: Theory and Evidence from Colombia,” forthcoming American Economic Review Friedrich, B. (2016). “Trade Shocks, Firm Hierarchies and Wage Inequality,” Northwestern University, mimeo G ̈org, H., L. Halpern and B. Murak ̈ozy (2010) “Why Do Within Firm-product Export Prices Differ Across Markets?,” Kiel Working Paper No. 1596 Hallak, J. C. (2006) “Product Quality and the Direction of Trade,” Journal of International Economics, 62, pp. 238–265 Hummels, D., and P. Klenow (2005) “The Variety and Quality of a Nation’s Exports,” American Economic Review, 95 (3), pp. 704–723 Johnson, R. (2012) “Trade and Prices with Heterogeneous Firms,” Journal of International Economics, vol. 86(1), pp. 43–56 Kugler, M., and E. Verhoogen (2012) “Prices, Plant Size, and Product Quality,” Review of Economic Studies, vol. 79 no. 1, pp. 307–339 Lileeva, A., and D. Trefler (2010) “Improved Access to Foreign Markets Raises Plant-level Productivity...For Some Plants,” Quarterly Journal of Economics, Vol. 125 (3), pp. 1051–1099 Manova, K., and Z. Zhang (2012) “Export Prices Across Firms and Destinations,” Quaterly Journal of Economics, 127, pp. 379–436 Matsuyama, K. (2007) “Beyond Icebergs: Towards A Theory of Biased Globalization,” The Review of Economic Studies, 74 , pp. 237–253 Munch, J.R., and J.R. Skaksen (2008) “Human Capital and Wages in Exporting Firms,” Journal of International Economics 75 (2), pp. 363–372 Park, A., D. Yang, X. Shi and Y. Jiang (2010). “Exporting and Firm Performance: Chinese Exporters and the Asian Financial Crisis,” The Review of Economics and Statistics, vol. 92, No. 4, pp. 822–842 Pellandra, A. (2017) “Export Destinations, Employment and Wages: Firm-level Evidence from Chile,” mimeo CEPAL 13
estimate a societal poverty headcount of 2.16 billion people in 2013 (2.18 billion when based on the lower-bound variant). This count is almost three times more than the global count of people living on less than $1.90 per day (estimated at 767 million in 2013). Figure 6 displays the change over time in both the count and rate of societal poverty, as measured by our proposed SPL, and the SPL with lower bound; and similarly displays the count and rate of absolute extreme poverty as measured by the international poverty line of $1.90 per day. Our estimates indicate that the societal poverty rate has fallen steadily since 1990, but at a slower rate than the decline of extreme poverty. This divergence in the rate of decline amplifies the distinction between the two measures. In the early 1980s, societal poverty (46.5%) is estimated to be about 10 percent more than the extreme poverty rate (42.1%); by 2013, the rate of societal poverty is almost three times greater than extreme poverty. In a growing global economy, this divergence is an expected outcome, but the magnitude of the change (in first differences of the rates) over the decades highlights the distinction in the informational content in both measures.

Figure 6 also reveals a slight divergence in societal poverty with the $1.90 lower bound in the 1990s, but this essentially disappears by 2010. Figure 6a shows the trajectories for poverty rate as measured by the SPLs and the international poverty line, with aggregate societal poverty falling by about 1/4 from 43 percent in 1990 to 30 percent in 2013, while the absolute extreme poverty rate fell by more than 2/3, from 35 percent in 1990 to 11 percent in 2013. Figure 6b reveals that while the rates have been decreasing, the total headcount of societal poverty is essentially at the same level it was in 1990 due to the increasing global population.

The levels and trends in poverty headcounts and rates based on our proposed SPLs differ substantially from those reported by Ravallion and Chen (2013) based on their weakly relative poverty line. They estimate a weakly relative poverty rate of 49.8 percent in 1990 which drops to 27
|iT able <br>Panel A: 2012<br>|2 --Treatment <br>|Effects on Stu<br>|dent Enrollmen<br>|t: School-Gra<br>|deLevel||
|---|---|---|---|---|---|---|
|Population Group|All St|udents|Bo|ys||Girls|
|Treatment|(1)|(2)|(3)|(4)|(5)|(6)|
||0.162|0.655|-0.043|-0.030|0.206|0.684**|
||(1.302)|(0.539)|(0.716)|(0.303)|(0.737)|(0.336)|
|Controls|NO|YES|NO|YES|NO|YES|
|Observations|690|690|690|690|690|690|
|R-squared|0.000|0.694|0.000|0.653|0.000|0.633|
|Meanofdepvarincontrol|15.643|15.643|7.188|7.188|8.455|8.455|

|Panel B: 2013<br>Population Group|All St|udents|Bo|ys|Gi|rls|
|---|---|---|---|---|---|---|
|Treatment|(1)|(2)|(3)|(4)|(5)|(6)|
||0.801|1.286*|0.352|0.399|0.450|0.887**|
||(1.276)|(0.682)|(0.696)|(0.392)|(0.716)|(0.395)|
|Controls|NO|YES|NO|YES|NO|YES|
|Observations|690|690|690|690|690|690|
|R-squared|0.001|0.583|0.001|0.525|0.001|0.532|
|Meanofdepvarincontrol|14.185|14.185|6.586|6.586|7.598|7.598|

‘Notes: Robust standard errors in parentheses, clustered by village. *** p<0.01, ** p<0.05, * p<0.1. The sample includes students in grades 3,4, and 5 in 230 schools. Observations are at the school-grade-level. Controls include 2011 (pre-program) enrollment by gender and cohort fixed effects where indicated, in Columns 2, 4, and 6.
|Source|Aliaga and Ren (2006)<br>Aliaga and Ren (2006)<br>Aliaga and Ren (2006)<br>Aliaga and Ren (2006)<br>Aliaga and Ren (2006)<br>Aliaga and Ren (2006)<br>Aliaga and Ren (2006)<br>Aliaga and Ren (2006)<br>Pettersson and Sisouphanthong (2005)|Pettersson and Sisouphanthong (2005)<br>Humphreys (1979)|Grosh and Mu ̃noz (1996)|Personal communication|Personal communication<br>Personal communication|Personal communication|
|---|---|---|---|---|---|---|
|Survey|Demographic and Health Survey<br>Demographic and Health Survey<br>Demographic and Health Survey<br>Demographic and Health Survey<br>Demographic and Health Survey<br>Demographic and Health Survey<br>Demographic and Health Survey<br>Demographic and Health Survey<br>Third Lao Expenditure and Consumption Survey|Third Lao Expenditure and Consumption Survey<br>Ada Baseline Survey|Generic LSMS|Malawi Third Integrated Household Survey 2010/11|Nigeria General Household Survey 2012/13<br>2011 National Survey on Household Living Conditions and Agriculture|Household Budget Survey 2012|
|_c_|10<br>10<br>12<br>12<br>15<br>27<br>48<br>52<br>3.9|6.1<br>7|12|39|24<br>31|10|
|ntry/Area|bodia<br>nda<br>an<br>opia<br>i<br>key<br>kina Faso<br>o<br>s-Urban|s-Rural<br>opia|_−_|awi|eria<br>er|nia|
|u|m<br>a<br>d<br>hi<br>it<br>r<br>r<br>g<br>o|o<br>hi|_−_|al|g<br>g|to|
|Co|Ca<br>Ug<br>Jor<br>Et<br>Ha<br>Tu<br>Bu<br>To<br>La|La<br>Et|_−_|M|Ni<br>Ni|Es|

41