rafmacalaba/data-use-mentions-extended huggingface dataset:
provided by MPDF/IFC, a list of medium and large businesses provided by MPDF/IFC, as well as a census of firms with at most 50 employees done for the Provincial Business Environment Survey (PBES) in 2006.<sup>2</sup> The total number of firms interviewed in the ICS 2007/2008 survey was 502. For the CRBS 2009 survey, all 502 firms of the 2007/2008 survey were re-contacted and information was collected on their activity status. A total of 89% of these firms were still found to be active while 10% of the firms were no longer active, for reasons of bankruptcy, seasonal inactivity, personal reasons (such as illness) or other.<sup>3</sup> Next, a random sample of 410 active firms was contacted to be interviewed again, creating a panel data set of 242 firms (positive response rate of 59%) with the 2007/2008 survey. In addition, 28 new (non-panel) firms were included in the sample to increase the number of large and exporting firms.<sup>4</sup> While the first survey used a standard investment climate survey instrument collecting information on firm characteristics, firm productivity and investment climate constraints, the second survey was especially designed to capture the impact of the global crisis on Cambodia-based firms as well as their risk coping strategies.

Table 1 summarizes the main results with respect to the impact of the financial crisis on firm performance, input and output prices, employment and worker remuneration. The impact is wide-spread, with the vast majority of firms (80 to 90 percent) reporting a decrease in domestic sales and profits in the first half of 2009 compared to their situation in the first half of 2008. The mean decrease in domestic sales and profits was around 30 percent. The impact of the shock on exports was also significant, although slightly less. This shows that the financial crisis affected firms widely, and not only exporting firms were affected. The wide
benefits in Panel C of $96 billion when all projects built in complement, only slightly higher than the baseline.

One hypothesis is that our single sector model ignores the fact that transportation investments may be especially beneficial to specific sectors. Using data on trade flows in North America, Caliendo and Parro (2015) find an aggregate elasticity of trade to trade costs θ= 4.55, close our baseline value, though they also find that θ can vary by sector. The value θ= 2 is a theoretical minimum (if the variance of the Type-II extreme value distribution underlying city productivity in the model is to be finite; see Donaldson and Hornbeck, 2016). Caliendo and Parro (2015) estimate elasticities in agriculture (θ= 8.11) and mining (θ= 15.72) implying these sectors offer a higher sensitivity of trade to trade cost than in aggregate. Alternative columns of Table 6 consider project benefits as if all trade was in these sectors. While project values are lower with θ= 2, they are almost double the result of the baseline specification when we set θ= 15.72. Nonetheless, the value of all projects in Panel C even under this specification is still just $170 billion, approximately half of costs. Allowing a very high sensitivity of trade to trade costs therefore may increase gains from individual projects but does not lead the present value of benefits from all projects to exceed their costs.

We turn to alternative specifications of income. First, we use alternative estimates of city GDP from the research service Oxford economics, as described in Section 3. These alternative values of city GDP lead to an estimate in Panel C of the present value of project benefits equal to $140.6 billion, 60 percent more than the baseline specification, but still less than half of project costs. Second, we use alternative estimates of city GDP that assume all of countries’ income is allocated to the nodes on the network. Here the present value of project benefits in
# **4. Data**

In this study, we match data on monthly retail prices of six major cereals (teff, wheat, maize, sorghum, barley, and millet) and wages of unskilled workers in 82 markets from 1997 to 2013 with annual weather shock data for the same period. In addition, the price and weather information is combined with data on the type of PSNP transfers households in the beneficiary districts since 2005 (if any) and data on access to towns with population greater than 50,000 in 1997 and 2004. The data is introduced in section 4.1. In section 4.2, the key features of weather shocks are described.

## **4.1. Data sources**

_Grain prices and wages_ : We use monthly price data collected in 119 markets from 1996 to 2013 for the analysis.<sup>12</sup> The data come from the Retail Price Survey, which is conducted every month by the Ethiopian Central Statistics Agency (CSA). CSA has selected 119 representative markets to be visited monthly in this survey. The markets surveyed have stayed remarkably consistent across time. More markets used to be covered prior to 2001, but this data is not included in the analysis. Although there are about 119 markets that were followed in this survey, this analysis focuses only on those located in rural districts for which crop loss data is available. In addition, most of the analysis is focused on 82 markets that are in districts which experience only one major rainy season, _Meher_ season. This is done by excluding markets that also experience another rainy season, _Belg_ . We use nominal prices in the analysis and include year and monthly dummies to control for inflation and seasonal price patterns.<sup>13</sup> _Weather shocks_ : Data on weather shocks is taken from the Livelihoods, Early Assessment and Protection project (LEAP). LEAP combines ground and satellite rainfall data collected throughout the year to provide rainfall data for each district
- Gallup, John Luke, Jeffrey D. Sachs & Andrew D. Mellinger (1999). “Geography and Economic Development”. _International Regional Science Review_ 22(2) (August): 179–232

- Glaeser, Edward L., Rafael La Porta, Florencio L ́opez-de Silanes & Andrei Shleifer (2004). “Do Institutions Cause Growth?” _Journal of Economic Growth_ 9(3) (September): 271–303

- Griffith, Rachel, Stephen J. Redding & Helen Simpson (2004). “Foreign Ownership and Productivity: New Evidence from the Service Sector and the R&D Lab”. _Oxford Review of Economic Policy_ 20(3): 440–456

- Grossman, Gene M. & Elhanan Helpman (1991a). _Innovation and Growth in the Global Economy_ . Cambridge, MA: MIT Press

- Grossman, Gene M. & Elhanan Helpman (1991b). “Trade, Knowledge Spillovers, and Growth”. _European Economic Review_ 35(2–3) (April): 517–526

- Hall, Bronwyn H., Adam B. Jaffe & Manuel Trajtenberg (2001). “The NBER Patent Citation Data File: Lessons, Insights and Methodological Tools”. _NBER Working Papers_ 8498, National Bureau of Economic Research, Inc. URL `http://ideas.repec.org/p/nbr/nberwo/8498.html`

- Hallward-Driemeier, Mary, Giuseppe Iarossi & Kenneth L. Sokoloff (2002). “Exports and Manufacturing Productivity in East Asia: A Comparative Analysis with Firm-Level Data”. _NBER Working Paper_ 8894, Cambridge, MA: National Bureau of Economic Research

- Haskel, Jonathan E., Sonia C. Pereira & Matthew J. Slaughter (2007). “Does Inward Foreign Direct Investment Boost the Productivity of Domestic Firms?” _Review of Economics and Statistics_ 89(3) (August): 482–496

- Hirschman, Albert O. (1964). “The Paternity of an Index”
|Table B5. Characteristics of Informals<br>from Informal Sector Surveys conducted by the author|_<25_hours per<br>Taxicabs|w<br>45 hours<br>Taxicabs|Entire<br>pSampl<br>Taxicabs|Entire<br>e<br>Kiosks|Entire<br>Sample<br>Markets|<br>Entire<br> <br>Sample<br>Street Services &<br> <br>Vendors|0<br>_G)_<br>CA|
|---|---|---|---|---|---|---|---|
|Number of Observations (actual number may vary depending on<br>the question)|99|101|308|245|137|<br>80|0<br>0|
|Gender||||||||
|Male|90.9|95.1|91.9|36.6|52.6|<br>48.8|m<br>**0**|
|Female|9.1|_5.0_|8.1|63.4|47.5|<br>51.3|0|
|Age of Person Interviewed|||||||**I**|
|Mean|37.0|36.8|37.1|38.7|37.3|<br>36.5||
|Married|||||||>|
|Yes|84.9|83.0|82.1|82.7|70.8|<br>60.0|R-|
|Education|||||||RI|
|Primary|2.3|1.1|2.5|1.6|13
This version: 15 Feb 2024 5 sum game, generalized growth creates “congestion” and “social scarcity” and your well-being “depends on an increased extent on your relative position in the economic hierarchy,” which creates a “paradox of affluence” where most people feel much worse off even though they may be materially better off. “Consumers individually find that their access to socially scarce goods and facilities...is determined in accord not with absolute but with relative real income. The determining factor is the individual’s position in the distribution of purchasing power.” (Hirsch, page 6) This results in a loss in well-being; “...the frustration in affluence results from its very success in satisfying previously dominant material needs” (Hirsch, page 7).

Hirsch gives several examples of this in the book - we will highlight two important ones: (a) Education becomes increasingly subject to social congestion because its value now depends on the status of your credentials rather than on your years of schooling, because society has reached a point where almost everyone is reasonably well educated. (b) Common property resources, which are largely free in the early stages of economic development with easy access to land, water and clean air, become privatized and congested with growth to the extent that one has to pay high prices for things like access to greenery and space that used to be free.

A long empirical tradition in the economics of growth which analyzes crosscountry macro-data has argued that growth contributes to greater well-being across a number of dimensions (e.g. Barro, 1997). In a more sophisticated analysis, Dollar and Kraay (2001) analyze 40 years of panel data from 92 countries to argue that “growth is good for the poor;” that incomes of the poor rise proportionately to increases in 5
convergence). The analysis of convergence in the mean per capita incomes of municipalities follows the framework proposed by Barro and Sala-i Martin (1991). It aims at an understanding of whether poorer municipalities have been capturing the income gains resulting from modest growth and social spending and whether there has been a reduction in regional disparities. The analysis of convergence in poverty headcount ratios applies the poverty convergence decomposition by Ravallion (2012) to assess the effects of initial poverty on both the income growth process and the sensitivity of poverty reduction to income growth.

While several studies have looked at short-term (growth) convergence in income among states in Mexico, this paper contributes by leveraging a unique data set on

> 1All figures on GDP growth rates come from the World Bank’s World Development Indicators database. Official income-based poverty rates are from Mexico’s National Council for the Evaluation of Social Development Policy (CONEVAL).

2
check the robustness of the coding and harmonization of the seven adequacy dimensions, we revert to Demographic and Health Surveys (DHS) which are nationally representative household surveys that provide data for a wide range of indicators in the areas of population, health, and nutrition and are primarily funded by the United States Agency for International Development (USAID). DHS collects multi-level information on characteristics of housing units and of residents, particularly focusing on demographic and health topics. Household questionnaires collect information on the housing unit such as the source of drinking water, toilet

> 8  While there is significant effort at the World Bank Group to standardize surveys’ variables into a global database that allows comparison across countries (GMD), this exercise is hitherto largely limited to variables pertaining to the reporting on measuring inequality, poverty, and welfare on a global scale. Across some regions, certain variables e.g., on WASH are also already unified in their answer categories. However, classification into adequate / inadequate housing conditions on these seven adequacy dimensions is hitherto absent.
# **7 Conclusion**

In this paper, we have considered the effects of minimum wages on employment and hours worked in Mauritius. Using data from labor force surveys conducted between 2004 and 2014, we assigned minimum wages to workers covered by 30 categories of remuneration orders and estimated how changes in these minimum wages affected employment and hours of work over the period 2004 – 2014.

Our 2SLS estimates suggest a slightly positive effect of minimum wages on employment in the covered sector where remuneration order specified minimum wages are applicable. The employment elasticity of 0.113 is within the range of elasticities found in previous studies of employment effects of minimum wages in LMI countries and confirms the findings of those studies that increases in minimum wages have benign effects on employment.

We also find that a 10 percent increase in minimum wages was associated with a 2.3 percent increase in average work hours for males in the covered sector but a 1.8 percent decline in average work hours for females in the covered sector. This suggests that industries in which females are more likely to be represented, such as domestic work and services, may be responding to increased minimum wages by decreasing the number of hours worked by female employees rather than firing workers, despite the overall positive employment effect.

In the uncovered sector, a significant hours of work effect, driven by an increase in average male work hours, is found for the sector overall with a 10 percent increase in the minimum wage being associated with an increase in average number of hours worked of 4.2 percent. It seems then that increases in the minimum wage have the effect of increasing average work hours for males in both the covered and uncovered sectors.

We caution, however, that while the overall estimated employment effects seem to be benign, as noted by Bhorat, Kanbur and Stanwix (2015), this may be the result of context-specific factors which interact with the minimum wage to ensure that the effects are on average very small. Ultimately, the impact of any enforced change
place), the net cost of the program is expected to amount to PLN 22.2 million per year (1.3 percent of GDP).

To compare the <u>Figure 9. Spending Efficiency</u> efficiency of the new 0.020 Redistributive efficiency 0.025 program relative to 0.018 Poverty-reducing efficiency 0.016 0.020 other existing 0.014 programs, we 0.012 0.015 calculate the change 0.010 in poverty and 0.008 0.010 0.006 extreme poverty per 0.004 0.005 zloty spent for each 0.002 program. We find that 0.000 0.000 the change in poverty and inequality per zloty spent is lower for the Family 500+ program compared to <u>Source: World Bank estimates based on HBS 2014.</u> social assistance, family benefits, social pension and housing benefits, as these are more targeted, but it is more cost efficient than nursing allowance and nursing benefit (Figure 9). However, it must be kept in mind that last two benefits are not means-tested or targeted to the poor as their aim is to support persons suffering from disability.

# **Increasing the tax-free allowance for Personal Income Taxes**

In addition to the Family 500+, the new government campaigned on a promise to increase the taxfree allowance for PIT from PLN 3,000 to PLN 8,000. However, the plans have not yet been detailed, and the size of the increase might end up being lower, potentially to be implemented in several steps. The simulations below present two scenarios, one in which the tax-free allowance increases to PLN 5,000, and the other where it increases to PLN 8,000. We present the impact on poverty and inequality, along with the estimated fiscal cost of these measures. Given the introduction of the Family 500+, we also show the impacts of these alternative reforms in combination with the Family 500+
/( _n_ + δ ) . For a depreciation rate of 6% and population growth rate of 4%, this implies that the saving rate in the low steady state must be 10%, which is actually just a bit higher than the saving rate of 8% for Sub-Saharan Africa as a whole.<sup>3</sup> We then choose the level of technology, A, to ensure that the 1/(1 −α ) steady state level of the capital stock at the low equilibrium, i.e. _kL_ =  _sL_ ⋅ _A_  is equal to  _n_ + δ  $1500. For a benchmark value of α =0.5, this implies that A=1.22. We have drawn the high steady state under the assumption that saving rates are twice that in the low steady state, but that there are no other changes in any of the parameters of the model. Since the ratio of the capital 3 Note that all calculations are done using PPP-adjusted data. Since the relative price of investment goods tends to be higher in poor countries than in rich countries, saving and investment rates are substantially lower when evaluated at PPP than in local currency terms.

9
# **4.  Data sets under review**

To the best of our knowledge, there is no comprehensive database of household surveys and their disability questions. We searched for household surveys and censuses conducted in low- and middle-income countries between 2009 and 2018. Surveys prior to 2009 would be unlikely to have internationally comparable disability questions as much of the work to develop internationally-comparable disability questions started in the 2000s. Some of the data sets post 2018 were not yet available at the time this research study was conducted. The period under consideration is 2009 to 2018.

The surveys and censuses were retrieved from the online International Household Survey Network Microdata catalog, the World Bank Microdata Library catalog, the International Labor Organization survey catalog, the repository of census questionnaires maintained by the United Nations Statistics Division, and the websites of individual National Statistical Offices. Additional surveys were sourced from the World Bank.

Our data consist of the questionnaires from household surveys and censuses in LMICs between 2009 and 2018. The list of surveys under review includes some of the major international surveys: Living Standards Measurement Study (LSMS), the Survey of Income and Living Conditions (SILC), Global FINDEX, and the Demographic and Health Survey (DHS). The Global FINDEX survey is standardized across countries, hence only the global questionnaire was reviewed for each year. The analysis is restricted to data available from surveys that may be used in monitoring general outcomes among adults with disabilities and their household. It also includes national surveys, general ones, as well as topical surveys such as health, disability, labor force surveys, or surveys focused on water/sanitation, or transition to adulthood. DHS is a major global health data set and in 2014, the DHS program developed a disability data collection optional module based on the WGSS. Some countries have changed questions in the module (Casebolt 2020).

The resulting pool included 734 data sets and 1,297 data set-years and censuses from 133 countries in East Asia/Pacific (22 countries), Europe &
12</sup> has mined coal for the production and supply of electricity to the towns of Arlit and Akouta, and the SOMAIR and COMINAK uranium mines. At the end of 2006, the estimated remaining reserves of SONICHAR (4,933,043 tonnes) were not enough for power generation to support the mining industry, which was growing steadily in the region (World Bank 2010b). Other coal reserves that could serve as sources of electricity have recently been found. The growth of the mining industry depends on expanded generation of power to support extractive industries activity.

As power infrastructure is scarce, access rates to electricity are low in Niger, particularly in rural areas. In 2005 national access to electricity was only 8 percent of the population, one-fourth the level in comparable peer countries. Around 41 percent of the urban population is electrified in Niger, and 86 percent in LICs. Practically no one in Niger’s rural areas is electrified, while 12 percent of rural dwellers are in peer countries (table 12). Since no significant new investments were made in the sector during the period 2000–05, NIGELEC’s investment needs remain substantial (World Bank 2007b).

Low access to electricity negatively impacts Niger’s environmental sustainability. Increasing population growth, at 3.4 percent a year, compounded with low levels of access to power has furthered reliance on traditional sources of energy for domestic consumption. Reliance on traditional fuels has placed undue stress on fuel wood and, consequently, forestry resources, leading to overexploitation of the natural forest cover. Only 5,000 hectares are replanted versus 100,000 hectares of forest cleared every year (African Development Bank 2005). Households’ combustion of biomass for domestic uses also increases carbon emissions.

> 11 Getting Electricity Database 2010. The number of days to obtain a permanent electricity connection captures the median duration that the electricity utility and experts indicate is necessary, in practice (rather than required by law), to complete a procedure.

> 12 Société Nigérienne
in drought years, South Africa produces a modest maize surplus for export.  Yield increases partly reflect deregulation of the industry and the reduction of maize area where it is no longer competitive because of lower yields and higher risks. Commercial farmers have also invested substantially to improve maize production. About one quarter of the area is irrigated. In Northern Cape Province, where all maize is irrigated, yields are around 10 t/ha. Farmers also use advanced maize hybrids, including genetically modified seed, and apply about 75 kg/ha of fertilizer nutrients, much higher than elsewhere in Africa. Given its uniqueness, we have chosen  not to include South Africa in analysis of regional data.

3
.6|1.2|2.1|
|6000+|2.0|1.2|0.6|0.6|0.7|1.0|1.0|0.5|
|Not stated|17.7|24.8|31.3|30.9|27.9|41.8|36.7|33.2|

In this case, we face two types of problems. On the one hand, we only have access to intervalcensored data, which is insufficient to analyze changes in the distribution of earnings in the country, and, on the other hand, we have an increasing proportion of individuals who do not declare income. We apply the imputation procedure previously described to address both problems, estimating the interval-censored regression for each year, with a set of household-level characteristics and job type characteristics. The sample of interest includes all adults who declared to be employed, even if they did not state their income.

We make the simplifying assumption that people who did not state income are randomly distributed conditional on observed characteristics. To account for the fact that characteristics may differ across those who did or did not state their incomes, an inverse probability weighting strategy is used to estimate the interval regression model. Finally, the imputation procedure is implemented as discussed in section 3 but assuming no lower and upper bounds for the imputed wages. Nevertheless, the maximum imputed wage for those who do not state their income is capped at the maximum predicted among those who declare their income. In all cases, imputed earnings are adjusted by inflation.