rafmacalaba/data-use-mentions-extended huggingface dataset:
# 5. **Discussion**

Sustained use of improved technologies, such as biomass cookstoves, is critical to the success of promotion programs. Our results suggest that 3.5 years after receiving the stove, which is close to the expected 5-year lifespan of the stove, about 62% of households covered by the survey still have their Mirt stoves in place, which appears to be quite high. Among those who still have their stoves, the average frequency of stove use in period 5 (i.e. 2016) is no different than the latter part of the first year (periods 3 and 4), but in several cases greater than shortly after initial distribution (periods 1 and 2). This suggests that, even after 3.5 years, the Mirt stove still provides positive net benefits to users. At a contemporary cost of $12.00 and considering the benefits of Mirt stove use, which include fuelwood savings, this seems like a good value to users and to institutions interested in promoting the technology.

We find little systematic relationship between treatments and retention of the stove, which suggests that abandonment is due to reasons other than our randomized treatments. Based on our endline survey, most who dis-adopted their stoves reported that it was because their Mirt stoves broke and therefore either were inconvenient to use or could not be used. People therefore do not seem to have abandoned the Mirt stove simply because they did not like it. Breakage after 3.5 years is not unexpected given that the estimated lifespan is about five years.

Analysis of regular stove use, defined as use of the stove at least twice per week, suggests no differences across treatments 3.5 years after initial distribution. After such a long time, we find that initial treatments do not matter. We also examine effects across time by treatment and find that households who were randomly required to pay a subsidized price for the stove show more consistent increases in regular usage regardless of model or cooking event definition (40, 45
- Elbers, C., and R. van der Weide (2014) ‘Estimation of normal mixtures in a nested error model with an application to small area estimation of poverty and inequality.’ World Bank Policy Research Working Paper 6962, The World Bank

- Elbers, C., J. Lanjouw, and P. Lanjouw (2003) ‘Micro-level estimation of poverty and inequality.’ _Econometrica_ 71, 355–364

- (2005) ‘Imputed welfare estimates in regression analysis.’ _Journal of Economic Geography_ 5, 101–118

- Fujii, T (2006) ‘Community-level estimation of poverty measures and its application in cambodia.’ In ‘Spatial Disparities in Human Development’ (United Nations University Press) pp. 289–314

- Fujii, T. (2010) ‘Micro-level estimation of child undernutrition indicators in Cambodia.’ _World Bank Economic Review_ 24(3), 520–553

- Fujii, T., and R. van der Weide (2013) ‘Cost-effective estimation of the population mean using prediction estimators.’ World Bank Policy Research Working Paper 6509, The World Bank

- Grosh, M.E., and Mu ̃noz (1996) ‘A manual for planning and implementing the living standards measurement study survey.’ Living Standards Measurement Study Working Paper 126, The World Bank

- Hansen, M.H., and B.J. Tepping (1990) ‘Regression estimates in federal welfare quality control programs.’ _Journal of the American Statistical Association_ 85(411), 856–864

- Hidiroglou, M. (2001) ‘Double sampling.’ _Survey Methodology_ 27(2), 143–154

- Humphreys, C.P. (1979) ‘The cost of sample survey designs.’ In ‘Proceedings of the Survey Research Methods Section’ American Statistical Association pp. 395–400

- Kijima, Y., and P. Lanjouw (2005) ‘Economic diversification and poverty in rural India.’ _Indian Journal of Labour Economics_ 48(
according to Goñi et al (2011) using the 2003 ECV household survey. Table 1 presents alternative computations provided by Jorratt (2010) using the 2007-08 ENIG survey. According to these estimates, direct taxation reduces the Gini coefficient by no more than 0.3 percentage points. The richest quintile of the population account for about 70 percent of total income tax payments, according to Table 1. The share of income paid by different households does not differ much by income, suggesting very low progressivity. The three poorest deciles, for instance, each pay a higher proportion of income tax relative to household incomes compared to any of the deciles between the fourth and the ninth.<sup>12</sup> 19. **The tax burden on personal income in Colombia is very low.** Colombia ranks 3<sup>rd</sup> lowest in the region raising only 1.1 percent of GDP in personal income taxes compared to the regional average of 1.8 percent of GDP. In comparison, OECD countries raise 9.0 percent of GDP on personal income. As shown in Figure 3, personal income taxes correspond to just 8 percent of total central government tax revenue in Colombia. The redistributive potential is naturally limited by the low amounts raised.

**Figure 3. Sources of Central Government Gross Tax Revenue, 2010.**

<!-- Start of picture text --> Fuel taxes<br>Other<br>Wealth tax<br>Financial<br>Transaction<br>Import tariffs<br>Income tax,<br>VAT<br>persons<br>Income tax,<br>firms<br><!-- End of picture text --> Note: Gross Tax Revenue totals 13.1 percent of GDP in 2010. Source: IMF (2011a).

20. **Less than 2 percent of Colombia’s adult population pay income taxes on declared income, in part, because of a
fire in Godhra on February 27, 2002. While several theories circulate on who set the train on fire, the dominant one is that a Muslim mob set the train on fire due to an altercation between a Muslim tea seller and the Hindu activists and possibly the attempted molestation of the tea seller’s daughter (Khanna, 2008; Simpson, 2017). Several of the 58 people killed on the train were women and children.

Following this incident, a three-day-long violence was perpetrated by Hindu mobs against the minority Muslim population. The riots were considered one of the most severe forms of Hindu-Muslim violence in post-independent India in terms of both geographical spread and brutalities. While previous Hindu-Muslim violence in India had been an urban phenomenon, in 2002, it spread to rural locations as well (Jaffrelot, 2003). According to official figures, the riots killed 790 Muslims and 254 Hindus. However, unofficial statistics suggest that close to 2000 Muslims were killed over the next three months, and many others were injured (Amnesty International, 2005). More than 200,000 Muslims were displaced because of the looting and burning of their homes and businesses (Srivastava, 2011). There were further outbreaks of statewide violence over the next year.

Women were largely victimized the Gujarat riots of 2002. Several women were raped and killed. Survivors reported sexual violence such as rapes, gang rapes, forced nudity, torching, mutilation of the stomach and sexual organs, and insertion of objects into bodies (Hameed et al., 2002). According to Jha and Farasat (2016), Muslim women were not the only victims of sexual violence in the Gujarat carnage of 2002; Hindus were also targeted in some cases. Many of these cases were not reported to the police because of the fear of further attacks, stigma, and ostracism.

During episodes of communal violence, women are often targeted to create fear and also because they are viewed as transmitters of the culture of a particular community. Exposure to such physical and sexual brutalities
|||2004), an extension<br>of the GTAP model<br>(Hertel, 1997)|and of unskilled workers<br>within agriculture;<br>GTAP elasticities of<br>substitution between<br>imports and domestic<br>products|textiles and apparel in US<br>and EU and agricultural<br>subsidies in China,<br>efficiency gains in<br>automobile sector and<br>cross-border trade in<br>services|
|---|---|---|---|---|
|Walmsley<br>and<br>Hertel<br>(2001)|35%<br>cumulative<br>percentage<br>point increase<br>relative<br>to<br>baseline<br>(2000-2005)|Recursive dynamics<br>with adaptive<br>expectations and<br>endogenous capital<br>stock in GDyn<br>documented in<br>Ianchovichina and<br>Walmsley (2012)|Perfect competition,<br>fixed propensity to save,<br>CET function between<br>value added and<br>aggregate inputs, fixed<br>full employment, perfect<br>mobility of skilled and<br>unskilled labor across<br>sectors but not across<br>regions, international<br>capital mobility and<br>ownershi|<br>Version 4 GTAP Database<br>aggregated into 19 regions<br>and 22 sectors; 11.2%<br>reduction in average<br>tariffs between 2000 and<br>2005, removal of quotas<br>on textiles and apparel in<br>US and EU and<br>agricultural subsidies in<br>China|
|Yang (1996)|30%<br>cumulative<br>percentage<br>point increase<br>relative<br
2019.

- Uganda Bureau of Statistics (UBOS) (2016). Uganda National Panel Survey 2013-2014. Public Use Dataset. Ref: UGA ~~2~~ 013 ~~U~~ NPS ~~v~~ 02 ~~M~~ . Downloaded from `https://microdata.worldbank. org/index.php/catalog/2663` on 9 February 2024.

- Uganda Bureau of Statistics (UBOS) (2019). Uganda National Panel Survey 2015-2016. Public Use Dataset. Ref: UGA ~~2~~ 015 ~~U~~ NPS ~~v~~ 02 ~~M~~ . Downloaded from `https://microdata.worldbank. org/index.php/catalog/3460` on 9 February 2024.

- Uganda Bureau of Statistics (UBOS) (2021). Uganda National Panel Survey 2019-2020. Public Use Dataset. Ref: UGA ~~2~~ 019 ~~U~~ NPS ~~v~~ 03 ~~M~~ . Downloaded from `https://microdata.worldbank. org/index.php/catalog/3902` on 9 February 2024.

29
varying across contexts and countries. Despite the immense importance of understanding these challenges and identifying how to mitigate housing market failures, the empirical literature in this area is highly underdeveloped for EMDEs. This can be primarily attributed to a dearth of robust property price data and associated analysis in these markets.

This paper pursues a two-fold approach to address the gap: First, addressing the information vacuum in EMDEs regarding property price data, this paper presents a novel approach to collecting house price data in data-scarce environments common in EMDEs. Instead of relying on survey data or obtaining property price data from various official and unofficial sources, we scrape available price information from EMDEs’ listing websites where private properties are listed for sale. In doing so, we largely constrain our analysis to the formal housing market and disregard informal housing not transacted in online markets, such as the incremental self-building of houses typical in EMDEs. Our approach offers a first foray in providing the distribution of property price data otherwise nonexistent in EMDEs.

In the second part of this paper, we demonstrate the useability of distributional private property price data and present an approach to estimate the price of a “standard house”. We refer to this price as the Basic House Price (BHP). This BHP allows for comparability of prices across EMDEs, standardizing the significant variation in the type of property across markets. Further, the BHP presents a standard measure that can be used in future research to monitor aspects of inclusiveness. When linked to other data such as, for instance, household income or housing finance data, progress on the Sustainable Development Goals (SDGs) may be monitored. The BHP also offers a standard measure to assess and compare housing affordability across population segments if paired with income data.

We present the web scraping approach, descriptive statistics, and the methodology for estimating the BHP exemplarily for five countries: Albania, Costa Rica, Morocco, Pakistan, and South Africa.<sup>2</sup> Overall, we collect residential property price data from over 200
_Marshallian economies_ ) or interactions with diverse sectors ( _urbanization or Jacobian economies)._ Workers may gain human capital by interacting with a larger number and quality of workers, thus contributing to the dynamic learning effects (see dotted diagonal line in figure 2). US cities that experience higher growth in the number of college graduates also see a rapid increase in average wages, beyond the obvious compositional effects, than those with stagnating stock of human capital, suggesting important knowledge spillovers (Moretti 2004). More recently, researchers have recognized that it is not only about bringing more and better “clones” to the city but also a greater variety of them, which is arguably part of agglomeration externalities (see Duranton, 2016; Diamond, 2016; Atkins, Faber, Navarro, 2018; Handbury and Weinstein 2015).

_Agglomeration/Congestion Costs:_ However, cities also present agglomeration costs due to higher crime, disease and congestion in transport, land and housing. Such costs—and the necessary compensation for them-- are often not considered in interpreting estimates of the wage elasticity but they should be as they may create a positive elasticity in the absence of any productivity gains.<sup>5</sup> The same issue affects other measures of city-level productivity, namely, revenue based firm level productivity measure, TFPR, which is the residual of the _value_ of output controlling for capital, labor, and sometimes human capital. Firms charging higher output prices may appear as more productive, even if these gains are actually representing higher costs or market power (Foster et al. 2008). The next section attempts to isolate these distinct effects empirically, focusing particularly on wages, total factor productivity and marginal costs, each of which captures one or more elements discussed above, and has limitations relating to interpretation, measurement and endogeneity.

# **III. Measurement Challenges and Estimation Methodology**

# Data

Table 1 details the sources of data for the four countries used. Each is an annual manufacturing survey containing the
|2014|428,618|1,031|0.24|86.8|13.2|25.2|71.2|
|---|---|---|---|---|---|---|---|
|2015|440,445|1,092|0.25|87.5|12.5|23.7|72.7|
|2016|457,273|1,136|0.25|88.1|11.9|22.9|73.7|
|2017|475,757|1,209|0.25|88.9|11.1|21.4|75.4|
|2018|491,289|1,277|0.26|89.4|10.6|20.5|76.4|
|2019|511,863|1,299|0.25|89.5|10.5|20.1|76.9|
|Total|4,027,783||0.24|87.6|12.4|23.7|72.8|

Source: World Bank staff analysis using Romania MoF firm-level data, 2011-2019. For purposes of providing the summary statistics, the sample is restricted to firms with revenue and employment data. SOEs are defined as firms where national or subnational governments have ownership stake of at least 10%, while POEs are firms where national or subnational governments own 0-9.9%. The percentages for centrally owned or locally owned may not add up to 100 because there are “other” SOEs that are neither owned by the central or local government.

Table 4: Distribution of Firm-Year Observations by Sector, 2011-2019

||Observat<br>|ions|||SOEs|||
|---|---|
30 effects of each programmatic party variable are as large or larger in magnitude as those reported in Table 4. In countries lacking competitive elections, the investment effects are stronger than in Table 4, and significant among those countries that lack competitive elections but have both a programmatic ruling and opposition party. However, the education effects of the average programmatic rating of all parties is weaker in nondemocracies than in Table 4; the effects of programmatic government _and_ opposition parties on corruption and bureaucratic quality are weaker than in Table 4.

# **Poor countries and collective action**

The results in Tables 4 and 5 indicate that organizations that facilitate collective action by citizens are significantly associated with policy outcomes, above and beyond the effect of competitive elections, and often when competitive elections themselves exhibit no influence. These findings are relevant for understanding obstacles to economic development only if these organizations are less common in poorer than in richer countries. In fact, this is the case.

Using data from 2008, for which the latest income data are available, Table 6 compares the three collective organization variables across rich and poor countries in three categories: all countries, all countries with competitive elections, and all countries without competitive elections. The differences are starkest with respect to the last variable, whether both the government and opposition parties are programmatic. More than four times as many rich countries as poor countries, and rich democracies as poor democracies, exhibit programmatic government and opposition parties. These are highly significant differences.

Across rich and poor non-democracies, comparisons are less reliable, since there are only 14 rich non-democracies in 2008, compared to more than 30 countries in each of the other cells. Nevertheless, the fraction of rich non-democracies with programmatic parties
Policy Research Working Paper 7910

# **Abstract**

In low-income, capital-scarce economies that face financial and fiscal constraints, managing revenues from newly found natural resources can be a daunting challenge. The policy debate is how to scale up public investment to meet huge needs in infrastructure without generating a higher public deficit, and avoid the Dutch disease. This paper uses an open economy dynamic stochastic general equilibrium model that is compatible with low-income economies and calibrated on Ugandan’s data to tackle this problem. The paper explores macroeconomic dynamics under three stylized fiscal policy approaches for managing resource windfalls: investing all in public capital, saving all in a sovereign wealth fund, and a sustainable-investing approach that proposes a constant share of resource revenues to finance public investment and the rest to be saved. The analysis finds that a gradual scaling-up of public investment yields the best outcome, as it minimizes macroeconomic volatility. The analysis then investigates the optimal oil share to use for public investment; the criterion minimizes a loss function that accounts for households’ welfare and macroeconomic stability in an environment featuring oil price volatility. The findings show that, depending on the policy maker’s preference for stability, 55 to 85 percent of oil windfalls should be invested.

This paper is a product of the the Office of the Chief Economist, Africa Region and the Macroeconomics and Fiscal Management Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at azeufack@worldbank.org, alexandre. kopoin@oecd.org, jnganou@worldbank.org, ftchanatchana@worldbank.org, and laurent.kemoe@umontreal.ca _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly
expansion of trade according to the lines of Vietnam's comparative advantage should be beneficial to labor demand.

**Table 14: The labor intensity of exports and import substitutes**

|Sector|Output/worker<br>(dollars)|
|---|---|
|Export industries (SOEs)|3,855|
|Export industries (FDI)|4,300|
|<br>Export industries (average)|4,078|
|Import-substituted<br>industries|9,200|

_Source:_ Rows 1 and 4 are calculated from a survey of 191 State-owned enterprises by the Ministry of Finance. The second row is calculated from data from the Ministry of Planning and Investment. Row 3 is an average between row 1 and row 2.

Factor content calculations based on table 14 (and shown in Appendix I) suggest that a tripling of Vietnam's manufacturing exports would create between 1.5 and 1.7 million direct jobs in export-oriented companies, with a best estimate of 1.6 million jobs. In addition, if the production of domestically purchased intermediates for export production have labor requirements similar to exporting companies, another 700,000 jobs would also be created indirectly. Increased imports worth $6.5 billion, on the other hand, are likely to displace between 200,000 and 350,000 workers in import-substituting companies (with a best estimate of 260,000). In addition to these workers - essentially employees in SOEs- another 400,000 jobs may be lost in firms that produce intermediates for such import-substituting companies. The net positive employment effect of an additional $6.5 billion of manufacturing exports and imports is thus likely to lie between 1.3 and 1.4 million direct jobs, with another 300,000 indirect jobs.

17
on the Measurement of Economic and Social Progress 2009.

> 5 World Bank 2015.

> 6 For a more detailed discussion, see Foster et al. 2013.

> 7 World Bank 2019.

> 8 SDG 4 makes this commitment: by 2030, signatories will “ensure inclusive and equitable quality education and promote lifelong learning opportunities for all.” The goal’s target 4.1 is to “ensure that all girls and boys complete free, equitable, and quality primary and secondary education leading to relevant and effective learning outcomes.”

> 9 Other authors have also applied the welfare measurement concept to education. Denny (2002) used the traditional Foster, Greer, and Thorbecke (FGT) class of poverty measures to compare adult literacy across 12 countries using the International Adult Literacy Survey, and Thomas, Wang, and Fan (2000) estimated education inequality using the Gini index.

5
*2.1 Context**

Our study took place in Latur and Nanded districts in the South-Eastern part of Maharashtra, one of the largest states in India. Though it is also one of the richest states in India, there is evidence of severe inequalities within the State, with Latur and Nanded districts regularly classed to be among the most deprived parts of Maharashtra. Data from the 2012-2013 District Level Household Survey (DLHS-4) indicate that around 21% (18.7%) of households in Latur and Nanded (rural India) owned a Below Poverty Line (BPL) card, 56.6% (46.25%) of households owned land, and that household heads had around 4.16 (3.98) years of education. Agriculture is the main economic activity in the rural areas of these districts, with over 70% of people engaged in the primary sector.

Latur and Nanded districts lag behind rural Maharastra and rural India on average in terms of sanitation coverage. The DLHS-4 data indicate that only around 24% of households owned a toilet on average in those districts, compared to 38% in rural Maharashtra and 56% in rural India. Our study focused specifically on clients of a large MFI, who came from relatively more deprived households in rural Latur and Nanded. Indeed, data from a baseline survey conducted in 2014/15 indicate that around 41.9% of client households had a BPL card, compared to 21% for Latur and Nanded in the DLHS-4 data, and only 44% of households owned land (relative to 56.6% in rural Latur and Nanded). Toilet coverage among these households was similar to that for rural parts of the districts: In 2014-15, our survey data indicate that around 27.5% of households with a client of the MFI had a toilet. Very few households reported using community toilets, meaning that over 70% of these households defecated in the open. This corresponds 4
)**<br>_Full S_|**Quarter 3**<br>**(Spring)**<br>_ample_|<br>**Quarter 4**<br>**(Summer)**|
|---|---|---|---|---|
|Nominal value of monthlytotal consumption|2017.79|1902.86|1876.92|1914.64|
|Real value of monthlytotal consumption|2022.00|1718.27|1519.12|1477.56|
|Nominal value of monthlyfood consumption|1196.98|1123.25|1129.01|1182.97|
|Real value of monthlyfood consumption|1201.19|961.26|789.41|797.60|
|Dailycaloric intake|2884.92|2725.03|2445.83|2387.33|
|Food consumption score|67.88|61.28|57.86|57.69|

Note: Estimated population weighted means. All values in per capita terms, except the food consumption score that is calculated for the household. Real values in Afghani reflect adjustments for spatial and temporal price differences, covering 13 months of field work. Caloric intake in kilocalories. _Source:_ NRVA 2007/08 28