rafmacalaba/data-use-mentions-extended huggingface dataset:
assessing the relationship between the psychology measures included in the baseline survey for business owners in the PI/BSED study with their business profits at baseline. We focus on the relationship between psychological variables and a business success indicator of above and below median profits at baseline.

The OLS regression specification for this relationship takes the form: Where Psychology0i is a psychology outcome (e.g. personal initiative, self-efficacy, error competence, entrepreneurial identity, locus of control and attitude to risk) of entrepreneur i measured at baseline. The coefficient β1 measures the correlation of the psychology outcome with the business success indicator variable BusinessProfit0i that takes the value 1 for profits above the median level at baseline and zero below the median at baseline. X′0i is a vector of control variables where we include controls for other measures of success ascribed at the individual and household level by including an indicator for educational attainment above secondary school level and an index of household asset wealth in the regression in addition to other demographic variables e.g. age, marital status and loan status at baseline. εit is the error term.

Table 4 shows that those women who exhibit higher levels of profitability (above the median) do score more highly across the range of psychology variables at baseline. We believe that the directional link in a business context is that the psychology is driving the economic success as opposed to the economic success is positively impacting psychology (Campos et al., 2017). It may well be possible that a profit windfall might generate a temporary boost in psychological outcomes, but we believe the shift in psychology a priori is what is necessary to change behavior that leads to an improvement in profits and economic success.

Since the PI and BSED trainings mainly attracted the business owners who were towards the lower end of the profit distribution in Ethiopia, perhaps those women could potentially benefit more from a business training since they have a deficit in these skills. They perhaps have pent up demand for these socioemotional skills by having more hardship or difficulty in their life
_h_ consequence<sup>_s_</sup> 0<sup>will be closer to their individual</sup> _s_ 0 than in the case of poor households. Formally, the difference between the individual and the overall CPI is, from (1) and (2), _h h h_ The larger θ0 , the closer _s_ 0 is to _s_ 0 , and thus the closer the aggregate CPI to _cpi_ In the limit, as θ0 _h_<sup>→</sup> 1 for some _h_ , _s_ 0<sup>→</sup> _s_ 0 _h_ and the overall CPI would be identical to that of household _h_ .

In simpler terms, with heterogeneous consumption baskets the overall CPI tends to mimic the individual price index of the better off. This is what Prais (1958) termed the “plutocratic bias” of the CPI. Moreover, for a given level of total and average consumption expenditure across households, the more disperse their individual consumption levels (i.e. the more unequal society is), the less representative the overall CPI will be of the consumption price index faced by those at the lower end of the distribution – i.e., the larger the plutocratic bias.

Finally notice that the difference between the inflation rate affecting household _h_ and overall CPI inflation is given by This is zero when either (i) all prices change in proportion (i.e., _pt_ ,0 = _k pt_ −0,1 for some scalar _k_ ), or (ii) _s_ 0<sup>_h_</sup> = _s_ 0 , so individual and aggregate CPI shares are the same. As we show below, these two conditions are clearly violated in practice.

# _II.2 Inflation inequality and observed inequality_

As noted earlier, when inflation rates differ across individuals the distributions of nominal and real consumption may follow different paths. Observed changes in standard inequality indices (which are typically computed from cross-sectional nominal income or expenditure data) may offer
age refugees has been increasing in recent years. While financing data is only available for 2021, the total cost of providing inclusive education to all school-age refugees in LICs and MICs was estimated to be US$ 9.3 billion in 2022 (Silva, 2023). This represents a US$ 1.6. billion increase in cost on the previous year, driven almost entirely by growing refugee displacement.

<!-- Start of picture text --> Total financing for and costs of refugee Total financing for refugee education in<br>education in LICs and MICs, 2021 LICs and MICs, 2021<br>2022 cost increase<br>(1.6 bn)<br>Host Gov.<br>36%<br>0.4<br>7.7<br>1.7 ODA<br>55%<br>2.6<br>Philanthropy<br>9%<br>Total Financing Total cost (2021)<br>ODA Host Gov. Philanthropy Total cost<br>USD billions<br><!-- End of picture text -->

# _Source: REFD (2024)_

refugee education. Over-stretched education systems and/or significant fiscal constraints limit state capacity to finance refugee education in many host countries. As shown in Figure N, philanthropic financing for basic refugee education in LICs and MICs contributed approximately 8 percent of total financing for refugee education in 2021. However, as discussed in Key fact 5, the majority of philanthropic financing was delivered to nongovernment and civil-society organizations, and did not finance inclusive refugee education. While philanthropic organizations should be encouraged to finance inclusive education, it is clear that significantly more resources, beyond those delivered by the philanthropic sector, are required to meet the current financing gap. With ODA for refugee education representing 10 percent of aid to refugee situations in LICs and MICs, and less than 1 percent of all ODA in 2021, increasing ODA for refugee education in LICs and
businesses: 94% compared to 88% for men. The shares of informal businesses started by their current owner vary somewhat across cities, ranging from 56.2% in Varanasi (India) to 99.1% in Pakse (Lao PDR).</mark> Table 4 presents descriptive statistics on the reasons for starting an informal business. While over 83% mention hours or location as a reason, the inability to find another source of income is also listed by 64% of owners. Importantly, <mark>over 89% of these owners listed the opportunity to develop a profitable business, with men and women equally likely to provide this reason. There is some cross-city variation, however: ranging from 70% in Nampula (Mozambique) to 100% in Sehore (India). For formal micro-enterprises, this variable was only collected in</mark>

> 15 Importantly, the question wording in the India survey was more general, referencing digital payments, rather than mobile money specifically.

> 16 This difference is not statistically significant in Zambia.

17
implication of the Dutch disease and resource-curse hypotheses to the outcome of industrialization in resource rich countries.

Manufacturing has been touted as a key driver for economic development, especially for developing countries (Kuznets 1953, Matsuyama 1992). Jones & Olken (2008) examines the allocation of resources underlying economic growth, documenting that movements into and out of the labor force in manufacturing tend to coincide with accelerations and decelerations in the growth rate. In examining the constraints for sustained growth in Africa based on a comparison to benchmark countries (e.g., Indonesia and Thailand), Johnson, Ostry & Subramanian (2007) finds that, in almost all cases, these benchmark countries have escaped poverty through manufacturing exports, and posits that the manufacturing sector may be crucial for sustained growth in Africa. A small but growing literature documents the manufacturing experience of developed and developing countries with particular emphasis on the evolution of manufacturing output and employment shares. Dasgupta & Singh (2007) examines data for a set of 14 developing countries for the period 1986-2000, and finds evidence of deindustrialization, that is, manufacturing tends to monotonically increase with per capita income until it peaks and begins to decline.<sup>1</sup> They also note that such deindustrialization appears to occur at lower levels of income than developed countries. Similar findings have emerged for Indian states, where Amirapu & Subramanian (2015) document premature deindustrialization and reports that by 2010, manufacturing employment shares peaked at about 45 percent of the corresponding 1988 turning point value. To our knowledge, the only study examining deindustrialization in SSA is Rodrik (2016), which uses a larger set of countries (albeit only 11 African countries are included) and comes to a similar premature deindustrialization for Sub-Saharan Africa and Latin America.

Thus, there is a dearth of literature focused on sub-Saharan Africa’s manufacturing experience, and those that do have utilized a limited number of SSA countries due to data inadmissibility. This paper attempts to fill this void by assembling data on 41 sub-Saharan African countries
been better even in the absence of migration (Mckenzie and Sasin 2007; Mckenzie et al. 2010; Carletto et al. 2011). This selectivity might lead to continued relatively higher outcomes for migrant households and their children, or even larger gaps over time. On the other hand, it is possible that broad based development improvements (for example in health and sanitation services in households and communities) might benefit children in non-migrant households relatively more over time so that they would (partially) catch up, particularly if non-migrant households have less access to private resources for services like health care. The catch-up possibility is consistent with the observed improvements in nutritional status documented over time between the 2001 and 2008 school censuses. If trends in nutritional status are relatively better for children in migrant households, the DD approach would attenuate any estimated negative effects of the economic shock (positive bias); if trends are relatively better for children in non-migrant households, it would overstate the absolute magnitude of the effects (negative bias).

Another reason trends might differ is that the type or selectivity of households that send migrants might change over time in ways associated with child nutritional status. The 2009 qualitative study revealed that the perceived cost-benefit calculations of migration were changing, so it is possible that the types of individuals and households newly undertaking or continuing migration were also changing (Section 4.2). We indirectly examined this possibility in two ways. First, we explored whether there were differences in fertility for migrant and nonmigrant households over time using a DD framework. However, in contrast to the main analyses presented below, the interpretation of significant differences here is not that the economic shock necessarily caused these outcomes, but rather that the DD captures possible changing average characteristics of households with migrants in 2008 and 2012 that could reflect changes in selectivity. Using the sample of all households and a household-level version of equation (1),
3.20)|-0.333*|(-1.83)|
|Female*Years of schooling|0.0280*|(1.90)|0.0165|(0.99)|
|Female*TEEP cohort|0.122|(1.24)|0.0646|(0.58)|
|Mother’s years of schooling|0.00956|(1.39)|0.0165**|(2.02)|
|Mother’s age|-0.000262|(-0.12)|-0.00169|(-0.80)|
|Log(consumer durables)|0.0455***|(3.60)|0.0474***|(3.74)|
|Height|0.00569*|(1.92)|0.00613*|(1.78)|
|Number of students per classroom in high school|-0.000602|(-0.68)|0.000581|(0.52)|
|If phone interview|0.233***|(4.62)|0.242***|(3.18)|
|School fixed effects|yes||yes||
|Number of observations|1,716||1,592||

Source: TEEP Tracking Survey. Notes: t values are in parentheses. * significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

41
its average for October, while for Nampula the two months are similar. Consistent with this, NDVI peaks in March in Manica, while it peaks a month later in Nampula. Unlike in temperate countries, tropical Africa has a green harvest and some local food insecurity is alleviated just after the NDVI has reached its peak.<sup>16</sup> More pronounced within-country differences are evident closer to the equator. In Tanzania (Figure A 1-c), Kilimanjaro, in the Northern Highlands, exhibits a bimodal pattern. In contrast, Iringa and Mbeya, in the Southern Highlands, have unimodal harvest cycles.<sup>17</sup> We have chosen not to use deviations from NDVI as a measure of weather averages disturbances. This is because NDVI deviations may reflect deviations in vegetation unrelated to food. However, it is essential to have a broad understanding of what harvest cycles typically look like for each province, to more accurately estimate the impacts of weather disturbances during a child’s birth season. In what follows, we use this understanding, along with rainfall data, to develop measures of drought shocks.

# B. _Identification of Dry Spells_

A consensus is yet to emerge on best-practice approaches to measuring droughts (Trenberth et al. (2014)). Soil moisture, evapotranspiration and water levels are all important. However, there are significant challenges to developing comprehensive measures for tropical Sub-Saharan Africa and other data-scarce contexts. At the same time, there is little that anomalous rainfall the season affects disagreement during growing adversely crop productivity (Funk et al. (2019)). Consistent with our overall objective, here we develop a simple and transparent approach to measuring droughts. Other than the use of the NDVI to estimate harvest cycles, the only data we use are the CHIRPS Rainfall dataset (Funk et al. (2015)).

Our algorithm involves the following steps. First, we calculate the average and standard deviations of total rainfall (in mm) for every 10-day period or decad in
0.03]|
|**Number of employees per organization**|2.54|2.58|-0.03|
||[0.73]|[0.78]|[0.09]|
|**Ratio of employees to heads**|2.56|2.58|-0.02|
||[0.71]|[0.78]|[0.09]|
|**Ratio of employees to managers**|2.52|2.61|-0.09|
||[0.74]|[0.71]|[0.09]|
|**Span of control (employees per manager)**|5.64|6.20|-0.56|
||[6.06]|[8.43]|[0.90]|
|**Number of organizations**|127|196|323|

**Notes:** Standard deviations are in parentheses.  The unit of observation for the first panel is the civil servant and for the second panel the organization.  Only employees and managers responded to the information module and hence only these individuals are used for the statistics in the first panel.  These statistics are based on the survey sample and not on administrative data.  The span of control is taken from the survey question "How many personnel do you manage?"; the total is taken for each organization if there is more than one manager; and the organization-level statistics are reported (each organization has an equal weight in the `By Organization' statistics).  Heads refers to heads of organization or deputy heads of organization. `Manager' refers to managers within the organization.  Grade is the official civil service grade of the professional civil servant, ranging from 1 to 17 in the sample. The 2013-14 National Civil Service Human Resource Statistics Abstract (Ministry of Civil Service
# The 2018 Health Equity and Financial Protection Indicators Database: Overview and Insights

Adam Wagstaff<sup>a*</sup> , Patrick Eozenou<sup>b</sup> , Sven Neelsen<sup>b</sup> , and Marc Smitz<sup>b</sup> a Development Research Group, The World Bank, 1818 H Street, NW, Washington DC 20433, USA b Health, Nutrition and Population Global Practice, The World Bank, 1818 H Street, NW, Washington DC 20433, USA

**Keywords:** Health indicators; health equity; health and inequality; out-of-pocket health expenditures; financial protection; health and poverty; millennium development goals; sustainable development goals; universal health coverage; non-communicable diseases

**JEL codes:** I1, I3, J13
.005|0.006|0.007|
|81|Stationary plant and machine operators|0.004|0.009|0.004|
|82|Assemblers|0.000|0.001|0.000|
|83|Drivers and mobileplant operators|0.006|0.016|0.005|
|91|Cleaners and helpers|0.001|0.001|0.001|
|92|Agricultural, forestry and fishery labourers|0.000|0.000|0.000|
|93|Labourers in mining, construction, manufacturing and transport|0.001|0.001|0.001|
|94|Food preparation assistants|0.002|0.004|0.002|
|95|Street and related sales and service workers|0.000|0.000|0.000|
|96|Refuse workers and other elementaryworkers|0.013|0.007|0.007|

_Source_ : Authors.

_Note_ : The proportions of occupations in each gender preference are presented.

# 4. Job application and selection

## 4.1 Applications

This section analyzes job market transactions, namely application and selection. One of the unique data features of online job portals is transaction data, which cannot be obtained from labor force surveys. Over the period from 2012, the number of applications per job posting is 25
35 Lipton, M. (1977), **Why poor people stay poor: Urban Bias in World Development** , Harvard University Press, Cambridge, MA.

Mellor, J. (1976), **The New Economics of Growth: A strategy for India and the Developing World,** Cornell University Press, Ithaca, NY.

Mwabu, G. and E. Thorbecke (2004), “Rural development, growth and poverty in Africa”, _Journal of African Economies_ , 13, AERC Supplement 1, ppil16-i65 Ravallion, M. (2002), "On the Urbanization of Poverty," Journal of Development Economics, 68(2), 435-442 Reardon, T. (2001), “Rural Non-Fram Income in developing countries, report to the FAO, available at http://econ.worldbank.org/files/26753_treardon.pdf Sahn, D., D. Stifel and S. Younger (2003), “Exploring Alternative Measures of Welfare in the Absence of Expenditure Data”, _Review of Income and Wealth_ , 49(4), pp463-489 Sahn, D. and Stifel Van de Walle, D. And D. Cratty (2004), “Is the emerging non-farm market economy the route out of poverty in Vietnam?”, _Economics of Transition,_ 12 (2), 237-275 Vernon, V. (2002), “Urbanization in Developing Countries”, _World Bank Research Observer_ , 17(1), pp89-112

# **Bibliography for decentralization and participation**

Ackerman, J. (2004), “Co-Governance for Accountability: Beyond "Exit" and "Voice"”, World Development, Vol. 32 ( 3); p. 447 Bardhan, P and D. Mookherjee (2003), “Decentralization and accountability in infrastructure in developing countries”, Boston University, mimeo, available at: <u>http://www.bu.edu/econ/Working%20Papers/papers/Dilip%20Mookherjee
Health and civil war in rural burundi. Journal of human Resources, 44(2):536563.

- Camacho, A. (2008). Stress and birth weight: evidence from terrorist attacks. American Economic Review, 98(2):51115.

- Case, A. and Paxson, C. (2008). Stature and status: Height, ability, and labor market outcomes. Journal of political Economy, 116(3):499532.

- Central Bureau of Statistics (2012). National population and housing census 2011. National report, Kathmandu, Nepal.

CIA World Factbook (2019). The world factbook - nepal. https://www.cia.gov/library/ 27
result is confirmed by the data for our sample showing that most of the firms regarding access to finance as a major or very severe obstacle are micro or small. The size distribution for firms perceiving access to finance as a minor obstacle or no obstacle is more symmetric.

The findings in this and previous sections suggest that a low level of financial development results in a skewed firm size distribution, with a larger relative share of small firms. Policies favoring the development of the financial sector should therefore have an effect on the firm size distribution and, ultimately, favor the adoption of different technologies and an improved allocation of resources if the industry in question is in line with the country’s comparative advantages (Lin, 2010).

# **9. Conclusion**

Using a newly available data set from the World Bank Enterprise Surveys (2006–10) for 39,538 firms across 98 countries, we investigate the binding constraints on firms’ employment growth. With an econometric model and subjective measures, we find that access to finance and informal sector competition are the most binding constraints—both globally and in each region. Using objective

> 9 Our measure of credit-constrained firms comprises those that applied for a loan and were rejected and those that did not apply for one or more of the following reasons: fear of rejection, collateral requirements too high, interest rates not favorable, or a belief that the application would not be approved.

> 10 Formal sources are private or public banks, nonbank financial institutions, issues of new debt, and suppliers’ credit. Informal sources are friends and moneylenders. Internal sources consist of issuances of new shares and own funds.

16
**_<u>________________________________________________________</u>_**

# **Annex H.   Sample of Infrastructure/Facilitation Indicators (*means available through World Development Indicators 2005)**

# **_Energy_**

- Access to electricity Network (% of population)*

- Electricity production*

- Average end-user prices (US cents/kwh)

- Quality of electricity services

- Electricity supply time  (hours/day)

- Waiting time for electricity connection

- Electricity outage time (hours/day)

- Technical efficiency index

- Electric power consumption (kwh per capita) *

- Electric power transmission and distribution losses (% of output) *

- Commercial energy production/use *

- Combustible renewables and waste (% of total energy)*

- Energy imports, net (% of commercial energy use)*

- Energy production (kt of oil equivalent)*

- Energy use (kg of oil equivalent per capita)*

- Energy use (kt of oil equivalent)*

- Energy use per PPP GDP (kg of oil equivalent per constant 2000 PPP $)*

- GDP per unit of energy use*

# **_Roads_**

- Roads, total network (km) *

- Road traffic (vehicle per km) *

- Roads, goods transported (million ton-km) *

- Access to “all season” road (% of population)

- Road density, population (road km/1000)

- Travel time to work in main cities (minutes/one-way work trip)

- Paved road (% of total road) *

- Perception of services rendered by road department/public works

- Perception of quality of air transport /railroad services

- Road in good/fair condition

- Access to formal transportation service (% of population)

- Passenger cars (per 1,000 people)*

- Pump price for diesel fuel (US$ per liter) *

- Pump price for super gasoline (US$ per liter) *

- Two-wheelers (per 1,000 people)*