rafmacalaba/data-use-mentions-extended huggingface dataset:
nurses/midwives, the combined region‐ and income group‐specific aggregate median density rate was applied to the most recent data year. In a number of countries where no empirical data for nurses/midwives was available but information on physicians was available, a global ratio of 2.517 nurse/midwives to physicians was applied to obtain the estimate for nurse/midwife density. The ratio multiplier for all other health workers was then similarly applied to obtain an estimate for all other health workers, and the estimates for all three types of health workers summed to obtain the total supply of health workers per country (see Annex A for more details).

We explored alternatively specifying Equations 2 and 3 as log‐linear models, which assumes an exponential functional form. The resulting projections from the exponential specification yielded estimates that were magnified compared to those from the linear specification, exaggerating both positive and negative trends. Coupled with the sparse number of data points for many lower income countries, resulting predicted values appeared to be less stable. Within‐ sample specification tests were also not possible given the data constraints. We therefore adopted the more conservative linear specification.

# _Data_

Country‐level data were collated from multiple sources. Historical data on physician and nurse/midwife densities (1990‐2013) were obtained from the WHO Global Observatory Health database (WHO no date). Historical and projected total population and population aged 65 and over were obtained from the United Nations Population Division.

We used the World Bank Development Indicator database to retrieve historical (1995 – 2013) GDP per capita (in constant 2011 dollars, purchasing power parity (PPP) values), total health expenditures per capita (in constant 2011 international dollars, PPP values), and the share of health spending OOP (World Bank 2015). Projected real GDP per capita through 2030 were obtained from the analysis carried out by Patrick Huang‐Vu Eozenou (see Annex B for details). Historical data on OOP expenditures per capita (1995‐2013) were calculated
> 43 Koks, E.E. et al. A global multi-hazard risk analysis of road and railway infrastructure assets. _Nat. Commun._ **10** , 2677 (2019).

> 44 Tolman, H. L. _User Manual and System Documentation of WAVEWATCH III_ TM version 3.14. Technical note, MMAB Contribution 276, 220 (2009).

45 Vousdoukas, M.I. et al. Global probabilistic projections of extreme sea levels show intensification of coastal flood hazard. _Nat. Commun._ **9** , 2360 (2018).

46 Vousdoukas, M. I. et al. Developments in large-scale coastal flood hazard mapping, _Nat. Hazards Earth Syst. Sci_ . **16** , 1841–1853 (2016).

> 47 Scussolini et al. FLOPROS: an evolving global database of flood protection standards. _Nat. Hazards Earth Syst. Sci._ , 16, 1049–1061 (2016).

48 Marconcini, M., Metz-Marconcini, A., Esch, T. & Gorelick, N. Understanding current trends in global urbanisation – the World Settlement Footprint suite _. GI_Forum,_ **9** (1), 33–38 (2021).

49 The WSF-Evo dataset and its technical documentation are available from DLR at <u>https://www.dlr.de/content/de/artikel/news/2021/04/20211111_blick_aus_dem_weltraum_so_entwickeln_sich_ staedte.html</u> 50 Marconcini, M., Metz-Marconcini, A., Esch, T. & Gorelick, N. Understanding current trends in global urbanisation – the World Settlement Footprint suite _. GI_Forum,_ **9** (1), 33–38 (2021).

> 51 Liu, X., Huang, Y., Xu, X. _et al._ High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015. _Nat Sustain_ **3,** 564–570 (2020
Sectoral Productivity Shock, Regional Differences in Intersectoral Linkages, and Structural Transformation in Ghana* Saumik Paul<sup>1</sup> Dhushyanth Raju<sup>2</sup> _Keywords_ : Structural transformation, intersectoral linkages, propagation of productivity shock, subnational areas, mining, Ghana _JEL codes_ : L14, L22, O11, O14, R11, R15 1 Newcastle University and IZA, saumik.paul@newcastle.ac.uk. 2

> World Bank, draju2@worldbank.org.

> * This paper is a substantially revised version of a previous paper titled “Regional Differences in Intersectoral Linkages and Diverse Patterns of Structural Transformation,” published as IZA Discussion Paper 15461. We thank Emanuele Ferrari for guidance on the construction of input-output tables from supply and use tables for Ghana. We also thank Al-Mouksit Akim, Andrew Dabalen, Giorgio Fazio, K. N. Harilal, Marco Lorusso, Sunil Mani, Rayaprolu Nagraj, M. Parameswaran, and seminar participants at the Centre for Development Studies (Trivandrum, India), Newcastle University, and the Centre for the Study of African Economies (CSAE) Conference 2023 on Economic Development in Africa (Oxford University, UK) for helpful comments.
10 FERNANDES-KLENOW-MELESHCHUK-PIEROLA-RODR ́ıGUEZ-CLARE

# **Correlation between intensive and extensive margin, and relation with distance**

We now move beyond the intensive margin elasticities and report additional stylized facts on the correlations between the intensive margin, the extensive margin, and distance. There is a positive and significant correlation between average exports per firm and the number of exporting firms (0.25, standard error 0.01) after taking out origin-year and destination-year effects. Table 6 shows how these margins vary with log distance with alternative sets of fixed effects. The elasticities are all negative and significant when controlling for origin-year and destination-year fixed effects: average exports per firm, the number of firms, average number of products exported per firm, and average exports per product per firm all decline with distance between trade partners.

# **Relation to previous empirical results**

We finish this section by relating our stylized facts to those of EKK, EKS, Bernard et al. (2007) and Bernard et al. (2009). EKK use firm-level export data for a single origin (France) and show that average exports per firm increase with market size of the destination (measured as manufacturing absorption) with an elasticity of 1/3. In Figure 4 we plot market size (horizontal axis) against our estimated destination fixed effects (vertical axis) from a regression of average exports per firm on origin, destination and year fixed effects based on the core sample and country pairs for which _Nij >_ 100. A regression line (not shown) through the points in the plot implies that average exports per firm increase with destination market size with an elasticity of 0.19, a bit lower than the result in EKK.<sup>15</sup> EKK also show that firms exporting to more destinations exhibit higher sales in the domestic (French) market. Our data does not include domestic sales, but we can instead look at sales in the most popular destination market for each origin. Let _xil|j_ denote
whereas under the alternative labor market models, a temporary subsidy can have long-term impacts on firm size.

We find that 24 percent of firms take the subsidy to hire a worker, resulting in an increase in employment in the treated firms during the subsidy period. However, using 12 rounds of survey data to track the dynamics of adjustment for four years post-subsidy, we show there is no lasting effect on employment, firm profitability, or sales. A combination of shedding of workers by treated firms and additional hiring by control firms completely eliminates the employment gap within two years. The only long-term effect is that the subsidy appears to have increased survival rates for firms that initially had low-capital and low profitability.

We use the data generated by the experiment to differentiate between competing views of urban labor markets for small firms. A combination of detailed survey data and an analysis of heterogeneous treatment effects yields no evidence that owners are learning more about their ability to manage workers, and suggests that search is not excessively costly for such firms. Complementary treatments providing either capital or training show that the lack of long-term effect does not appear to be due to lack of complementary capital or skills. Instead, the estimated return to additional labor during the subsidy period appears similar in magnitude to the subsidy offered, suggesting additional workers bring no more value to the firm than their unsubsidized labor cost. As such, our results imply that labor markets appear to be functioning reasonably well for microenterprises, and do not appear to be the constraint to their growth that some theories might suggest.

This paper contributes to a recent experimental literature on frictions in urban labor markets in developing countries. Much of this literature focuses on interventions to help particular job-seekers find jobs, by directly offering the job seekers wage subsidies (Galasso et al., 2004, Groh et al. 2016, Levinsohn et al., 2014); and/or by trying to improve the search and matching process through transport subsidies and skill certification (Groh et al, 2015; Abel et al,
---|
|Directly affected|550,000|1,040,000|
|Affected (incl. indirectly<br>affected)|900,000|1,730,000|
|_*assuming households that we_<br>_flood risk strata)_|_re not reached in follow up survey_|_were not affected (weights based on_|

_**assuming households that were not reached in follow up survey were affected in the same way as the ones with information (weights based on flood risk strata)_ According to these estimates, a large share of residents suffers from the impact of floods in Dar es Salaam, but impacts are not homogenous. Some households suffer significant monetary impacts, associated with housing repairs, asset losses and long periods of time each rainy season when everyday life is disrupted by floods. However, a large share of affected households only misses a few days of work or no work at all, leading to low or even no monetary losses, even though water may have reached their houses. Similarly, while rain causes delays in commuting, roads may still be accessible, and households are able to shift to different transportation methods. It is important to keep this in mind when assessing these numbers. The size of losses that were incurred to households will be addressed in greater details in Section 3.2.

# 3.1.3 Who are the exposed?

Poorer households are over-represented among those exposed to floods in Dar es Salaam. The proxy of per capita expenditure<sup>21</sup> is lower among affected households, indicating that poorer households tend to settle in areas with high risk. Affected households are also significantly more food insecure than non-affected households, confirming the correlation between poverty and exposure to floods. The pattern is consistent for households directly affected by the 2018 flood. However, in the latter case, results are only statistically significant for food insecurity. Figure 7 shows the distribution of the estimate used for expenditure in

> 20
Policy Research Working Paper 11010

# **Abstract**

In 2019, 21.9 percent of the population in Pakistan lived below the national poverty line. Since then, the COVID-19 pandemic, devastating floods in 2022, and a macro-fiscal crisis with record inflation have profoundly impacted economic activity and income-earning opportunities. The absence of a new household survey limits the ability to ascertain the implications of these different shocks on household welfare and poverty. Up-to-date welfare information is crucial for formulating appropriate policy responses to crises that directly affect households’ socioeconomic well-being. To overcome the lack of current data, this paper proposes a microsimulation tool that combines microdata from the latest national household survey with high-frequency macro indicators to produce nowcasts and forecasts of poverty. The tool models household consumption by using individual and household characteristics and accounts for changes in labor markets, inflation, social transfers, and remittances. The results account for household-specific inflation rates, which reflect systematic variation in household consumption patterns and sector-specific growth rates. Using the preferred specification, the findings suggest that in 2024, the poverty rate stood at 25.3 percent, an increase of seven percentage points compared to 2023, with about 13 million additional people falling into poverty. Moreover, in addition to the projected increase in poverty, poor households face disproportionately higher welfare losses and get pushed deeper into poverty.

This paper is a product of the Poverty and Equity Global Department. 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://www.worldbank.org/prwp. The authors may be contacted at obarriga@worldbank.org.

_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, even if the presentations are less than fully polished. The papers carry the names of the authors
China’s WTO accession commitments.<sup>54</sup> This is important since all post-WTO reform options are evaluated on the background of the implemented WTO commitments. For comparison purposes we use the same set of elasticities and tax distortions as in Ianchovichina and Martin (2004) (Table 4A). These originally come from GTAP version 5 database (Dimaranan and McDougall 2002).

The macro closure for the empirical model is one of full employment, perfect mobility of skilled and unskilled workers between nonagricultural sectors, and perfect mobility of unskilled workers within agriculture. However, there are barriers to labor mobility between rural and urban employment (see Ianchovichina and Martin, 2004). To capture the effects of these barriers to mobility, the model allows for both imperfect transformation between unskilled workers in agricultural and unskilled nonagricultural employment, and an implicit tax on nonagricultural employment. The imperfect transformation reflects the substantial differences in farm and nonfarm unskilled workers’ characteristics, and the ability to transform at a cost farm into nonfarm workers through training, experience, and the creation of nonfarm jobs in rural areas.<sup>55</sup> The tax is designed to reflect the pure policy-induced barriers between rural and urban workers, such as the cost of a residence permit and the inability to sell farm land.<sup>56</sup>

> (2004), available also as a World Bank Policy Research Working Paper at http://econ.worldbank.org/files/26864_wps3053.pdf.

> 54 China’s WTO accession commitments include reduction in protection to post-accession (2007) tariff levels, liberalization of the services sectors, restructuring of the automobile sector, removal of quotas on China’s clothing and textile exports, and China’s agricultural export subsidies.

> 55 We set the elasticity of transformation at 1.32. This is the estimate by Sicular and Zhao (2002) used in Ianchovichina and Martin (2004).

> 56 The tax reflecting pure policy-induced barriers between rural and
7.4|n.d|29.0|n.d|30.5|<br>30.5|n.d.|
|**The mixed-skilled group**|**46.0**|**43.9**|**43.4**|**41.3**|**1.2**|**5.6**|**47.2**|**49.5**|**8.7**|<br>**6.1**|**24.4**|
|The agricultural group|38.2|36.7|36.0|34.6|5.2|6.8|43.4|43.5|25.6|<br>18.5|27.6|
|The low-skilledgroup|42.2|41.4|39.8|39.0|1.2|3.4|43.5|44.8|7.7|<br>6.5|15.4|

Source: Own calculations based on ENIGH96 survey 1/ Following OECD, it is assumed that teachers worked on average 41 weeks per year and non-teachers worked on average 49 weeks per year. .

# _Income, Labor Earnings and Salaries_

Teachers salaries have been highlighted as a very important issue in school improvement discussions. The level and structure of teacher remuneration are said to affect teacher morale and ability to focus on and devote adequate time to teaching well. Teacher remuneration could also determine the capacity of the education system to attract and retain good teachers. Table 5 presents several definitions of salaries and personal income sources, since much of the argument over teacher compensation refers to what is meant by the term “underpaid.” An issue that may hamper the comparison of net earnings across occupations and locations is
heating, respectively. Much of the energy budget of the households, particularly of the poor, is spent on heating during the cold winter months. Given heating energy expenditures are a substantial component of the total budget, an increase in price of gas will have important impact on their welfare.

The poorer households spend a relatively larger share of their budget on electricity. Electricity expenditures are more burdensome and account for a larger share of expenditure than gas. As such, spending on electricity is somewhat regressive: the poorest quintile spends 4.0 percent of their budget on electricity while the richest quintile spends only 2.9 percent – which indicates relatively inelastic demand for basic electricity uses such as lighting. This result is similar to the finding of a survey of household electricity expenditure in other ECA countries (Lampietti, et al., 2004; Junge, et al., 004.), which indicates that the poor spend a much larger share of their budget on electricity than the rich. In contrast, household spending on gas is neither regressive nor progressive on which the middle quintiles spend a larger share of their budget (4.8 for third quintile), compared to 3.9 for the richest quintile and 3.0 percent for the lowest quintile.

On the other hand, one of the important observations from Table 6 is that expenditures on heating energy take up a larger portion of the budget of the poor than the upper income groups. Households in the poorest quintile spend nearly twice as much (6.5 percent) of their budget on heating overall than households in the highest quintile of the income distribution (3.4 percent). This may suggest that there is a negative income elasticity of heating energy demand. Since heating needs during the winter months is a necessity, it is not surprising that poor households spend a higher share of total expenditures on heating. While higher energy prices could encourage energy efficiency investments, the immediate impact on the poor and their choice of heating fuel should be of vital
# **2. Overview of Education and School Fees**

Confucian values that strongly encourage education have historically played a key role in Chinese parents’ decision to enroll children in schools. The advent in 1986 of the ‘Law of Compulsory Education’ made school enrollment mandatory for all children age 6 and above and required all children to attend school for a minimum of 9 years. Grassroots enforcement and monitoring of this law by urban resident committees or rural village councils (the smallest administrative units in China) has helped rank the country among those with the highest school enrollment rates. The gross enrollment rate at the primary and secondary school levels reached 108 percent and 96 percent respectively in 2013 (UNESCO, 2016).

Universal compulsory education, however, does not fully alleviate the burden of school fees for families. The education system’s finance is highly decentralized in China, leading to subnational governments bearing most of the costs of public education spending (approximately 95 percent) and the central government funding the rest (World Bank and DRC, 2014). These challenges gave rise to school fees as an important source of revenue for local governments’ public education budget. These school fees are often collected by the local government (through schools) and then transferred back to each school, with the specific amounts being determined in negotiations between the former and the latter. Notably, migrant households are often asked to pay extra school fees, the exact amounts of which vary from city to city (see, e.g., Yuan, (2010)).<sup>6</sup> No official data exist on school fees, but these can be estimated from household education expenditures. The Rural-Urban Migration in China (RUMiC) data set (discussed in more detail

> 6 Despite the repeated calls to give migrant children equal treatment, many public schools in China continue to impose higher tuition fees or other fees on migrant children, often with local government approval (World Bank and DRC, 2014). There are
The decade spanning the years 1973 to 1983 in Ghana saw one of the most spectacular declines in economic fortune that any country has known. All sectors of the economy shrank in real terms. The output in the cocoa sector declined as producer prices declined-fueled by both the decline in world prices, and the domestic policies of the State marketing Board. The producer price had reached a peak in 1956, but declined from a level of around 140 to less than 20 in 1983. Because of the long gestation period in the planting of cocoa trees, cocoa output did not start its downward course until after 1965, but then followed the price index down. The combined effect was that the index of cocoa income in 1983 stood at a level which was one-seventh of the high point of 1960-61.

Cocoa, however, is only a part of the income of the rural peasants, and is moreover concentrate in the South of the country. How did the food sector behave during this period? Tabatabai (1988) has put together the record of eight major crops in terms of the annual growth rates of production, area and yield. His major conclusion is that there was hardly any statistically significant trend in yields, but production shrank with remarkable speed-cereals at an annual rate of 3.4 per cent for cereals, and of 4.7 per cent for starchy staples. Evidently this remarkable shrinkage of output was entirely due to a decline in acreage.

Tabatabai ascribes this decline in acreage-more pronounced in the South than in the Northern ecological region-to the large scale international migration of labor to neighboring countries which were experiencing a mini-boom triggered by the oil price increase. "It has been estimated that between 1974-5 when the exodus began, and 1981 some 2 million Ghanaians had left for Nigeria and Cote-d'Ivoire alone." (Tabatabai, p.718 quoting UNICEF, 1984, pp.165-66). Since employment in the "modern" or formal sector was maintained in the period under consideration (see above), much of the emigrant labor
<!-- Start of picture text --> .2 .3 .4 .5 .6 .2 .3 .4 .5 .6<br>Gini Gini<br>1 1<br>.8 .8<br>.6 .6<br>resilience<br>.4 .4<br>social inclusion<br>.2 .2<br>0 0<br><!-- End of picture text --> Note: Fitted values and 95% confidence interval shown Source: Social Sustainability Global Database 2022 Social sustainability should therefore not be interpreted as just another strong correlate with high income and low poverty, whereby rich countries are inevitably more socially sustainable. Nor should we expect that poor economies inevitably fail in areas of inclusion, cohesion, resilience and legitimacy. Figure 6 illustrates this complexity. Rwanda, a low-income country, Lao PDR, a lower middle-income country, and Namibia, an upper middle-income country, all display middling levels of social inclusion. The level of social cohesion in Ethiopia is similar to that of Ecuador, despite the GDP per capita of Ecuador (US$ 5,600) exceeding that of Ethiopia (US$ 936) by more than five times in 2020.

Post-conflict countries can also display high scores in some surprising aspects of social sustainability. Differences are also wide within regions and despite similar socio-economic profiles. Process legitimacy in three countries that have recently undergone civil conflict—Sierra Leone, Philippines, and Lebanon—is well above that in Cambodia, which underwent a genocide several decades ago. Ghana and Senegal meanwhile have far higher levels of process legitimacy than Sierra Leone, despite also being West African countries.

In fact, top performers in our sample in terms of inclusion, cohesion, resilience and legitimacy vary in terms of income level, population size, country size, region and (recent) history of conflict. See table 3. By contrast, countries recently or currently afflicted by war, civil conflict