rafmacalaba/data-use-mentions-extended huggingface dataset:
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- Frese, M., Fay, D., Hilburger, T., Leng, K., and Tag, A. (1997). The concept of personal initiative: Operationlization, reliability and validity in two German samples. _Journal of Occupational and Organizational Psychology_ , 70, 139–161.

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social returns taking into account externalities. Since externalities from education are in general positive, the inclusion of externalities would increase the returns.

# **IV- FINDINGS**

This section presents the estimated returns to education. First, it describes the rates of return in the base case scenario that best answers the title question on profitability of formal lifelong learning. Second, we discuss how various individual and educational factors affect the gains from re-entering the education system.

# **IV-1 HOW AGE AFFECTS RETURNS TO FORMAL LIFELONG LEARNING**

Based on the base-case scenario, we find high rates of return to re-entering the education system. For a 20 year-old, the returns to primary, secondary and tertiary education are 8 percent, 14 percent, and 17 percent. On an international scale, these returns are quite high, and point to the need for continued strong emphasis on investment in education and further reforms to facilitate access to education.

The returns differ across education levels. The return to tertiary education exceeds that of secondary education by three percentage points and primary education by 9 percentage points. This pattern of rising return to schooling is similar to that of several of the other major Latin American countries, Sanchez-Paramo and Schady (2003). The driving force is primarily a rising demand for highly skilled labor with which the education system has not been able to keep pace.

**Figure 4 - Rates of return to education by age (base scenario)**

<!-- Start of picture text --> 20%<br>15%<br>10%<br>5%<br>0%<br>20 25 30 35 40 45 50 55<br>-5%<br>-10%<br>-15%<br>Age at re-entry to Education<br>Primary Secondary Tertiary<br>Internal Rate of Return<br><!-- End of picture text --> Source: Authors’ calculation based on Encuesta de Hogares
sectoral relationship between price differential and productivity differential. We estimate 11 using South African sectoral data. In particular, we assume that agriculture, mining and manufacturing are typically traded sectors, broadly guided by the observations in Figure 3. The rest are treated as non-traded sectors as listed in Table (2).

|**Tradable sector**|**Non-tradable sector**|
|---|---|
|Agriculture|Electricity, gas and water|
|Mining|Construction|
|Manufacturing|Trade, catering and accommodation ser-<br>vices|
||Transport, storage and communication|
||Finance, insurance, real estate and busi-|
||ness services|
||Community, social and personal services|
||sectors|

Table 2: Traded and non-traded sectors Prices are defined as indices of output prices. labor productivity on the other hand is calculated as the ratio of total output (which also includes intermediary output) to total employment per sector. Our data covers the period 1993 to 2017 and were collected from Quantec Research.

As a baseline scenario we begin by conducting a fixed effects estimation of equation (11). The results are reported in Table (3) The results are broadly in line with expectations. We find a positive relationship between sectoral price differential and the difference in productivity between traded and non-traded sectors. In particular, it confirms the intuition that an increase in productivity in non-traded sectors reduces prices of the non-traded relative to traded sectors, thus devaluing the country real exchange rate.

The baseline specification in Table (3) can be improved statistically by taking into consideration the data generating process in the panel, which shows indication of non stationarity in the series. We therefore use the Pool Mean Estimator of Pesaran et al. (1999) to re-estimate the relationship in equation (11) while considering both the long run relationship and the adjustment dynamics. The results are summarized in Table (4).

16
in costs as recorded by companies and establishments.

Although we have arrived at a reasonable reconciliation of the two sources, it seems clear that the Brazilian national accounts understate industrial output by relying almost exclusively on activity as recorded in the industrial census. This understatement of output in the national accounts has been stressed by several observers, e.g. Merrick and Graham (1979), Pfeffermann and Webb (1979), and by the World Bank team who recommended changes in the national accounts (Tyler, Goldberg, Blazic-Metzner, 1984).

There is also a very big discrepancy (see table 2.2) between employment in manufacturing as recorded in the 1980 industrial census (4,839,253) and employment as recorded in the population census (6,939,421) and a similar discrepancy in 1950, 1960 and 1970 as well. As there was no population census in 1975, we cannot check for that year, but it seems clear that the national accounts adjustment for activity by "autc.nomos" is too small.

Finally, it should be noted that the Brazilian industrial census and the national accounts _reat certain primitive agricultural transformation processes (e.g. the more rudimentary kinds of flour milling) as agricultural activities, and some dressmaking activ4i-es are included in "commerce" rather than manufacturing. On the other hand, some repair work, e.g. on motor vehicles is treated as a manufacturing rather than a service activity.
- 74 -

**Fimure** 3

<!-- Start of picture text --> I KID DEFICITS/GDP<br>706<br>DEFTOT<br>I8N Is<br>596 190l7 9018 .1I5<br>In~~~~~~~~~~~~~~~~~~~~~~~<br>DEP 1968 Pbi eco 1965 rsyDeil,Zo 1976 1975 GP(ihu 199 ltors 98<br><!-- End of picture text --> INFD :inflation Rate,<sup>December-to-Decembers,</sup> CPI

- DEFTOT :Total PubILc<sup>Sector</sup><sup>**Deficit,**</sup> % of GDP (includes interest payments). ConceptuallY<sup>equivalent to "PubliC Sector</sup> Borrowing Requireiancs".

- DEW P Public Sector PrLmuary **Deficit,** 2 of GOP (without<sup>interest</sup> payments).

- DEFNAT National Government Deficit, % of GDP (1958-1961 from CONADE)

Source ot Public sector<sup>deficits data (UEFTOT and DEFP) is de Pablo and</sup> Martinez (1987). Same source for DEFP in **Figure** 4.<sup>Deficits are</sup> calculated by "above the line" approach.
4

# 1. **Microstates and per capita income levels**

In this paper, we consider a large cross section of 157 countries for which at least 10 years of annual data on per capita GDP adjusted for differences in purchasing power parity is available. Of these, 33 are microstates defined as having an average population over the period 1960-1995 of less than one million. These countries are listed in Table 1, and range in size from tiny St. Kitts and Nevis with population of 42,000 to Mauritius with 912,000. The income range is similarly wide, from very poor African countries such as Guineau-Bissau and Comoros with real PPP-adjusted per capita GDPs around $600 to wealthy oil-exporting countries such as Qatar with per capita GDP of over $18,000.

If microstates suffer from the disadvantages of smallness, they should be poorer on average than larger states. What do we actually find? Not controlling for any other characteristic, microstates have the same range of per capita incomes as the rest of the samnple (Figure 1). However, if we control for the location by continent of all countries, whether they are oil producers, and whether they belong to the OECD, then microstates are actually significantly _richer_ than other states (Regression 1). Microstates are 50 percent (=-exp(.4025)-1) richer than other states, controlling for location. We note that this result does not reflect the obvious outliers in the sample, since the oil exporting countries Qatar and Bahrain are picked up by the OIL dummy, and Luxembourg and Iceland are picked up by the OECD member dummy. Even if we exclude two other particularly wealthy microstates (Bermuda and Bahamas), we still find that microstates are nearly 40 percent richer than other states. Figure 2 shows the income residual by quintile of population, and we see the very strong income effect in the microstate bottom quintile of population. We also note that the favourable performance of small states carries over to other quality of life indicators
explanatory variables on dependent variables vary along the explanatory variable vector X resulting from the nonlinear nature of the multinomial logit model, we estimated the effect coefficients around the sample average of X.

In order to overcome this issue, we ignored the panel weights of the SILCs and tried to maximize the sample variation by combining all the accessible observations on the annual transitions contained within the different SILC panels. In order to construct our final data set, we began by extracting all the transition observations that occurred within the available SILC panels. For example, we got observations on the 2009–10, 2010–11, and 2011–12 transitions from the 2012 panel, and the 2008– 09, 2009–10, and 2010–11 transitions from the 2011 panel. We repeated this process for all the available panels, from the 2008 to the 2012 panels. As can be seen, transition observations of a particular year can overlap for different SILC panels. Therefore, for a particular year, we picked transitions from the SILC panel that included the highest number of observations for that transition period. Combining these, we ended up with a pooled sample of pseudo-cross sections that contain yearly transitions of 2006–07, 2007–08, 2008–09, 2009–10, 2010-2011, and 2011–12, respectively. The resulting data set contained 97,418 observations.

# **5 Estimation Results**

The effect of social assistance on the status of individuals in the Turkish labor market was investigated through various multinomial regressions. First, we estimated the probability that an individual would remain in one of the four labor market statuses, which are defined as formal-employed, informalemployed, unemployed, and out of the labor force, by using the 2013 SILC data set. Also using this data set, we were able to disaggregate social assistance into three categories: social assistance received for children, for housing, and for other social services. Only social assistance provided by the 13
auto generation capacity installed in 2009 (estimated in around 3,427 MW)<sup>2</sup> would already be above the capacity of the national grid (2,992 MW).

Where most of auto generation capacity is installed in industrial facilities and mines, private households are increasingly using electrical inverters and generators to respond to blackouts. Departing from 2001 data and supposing that imported electricity generation devices not exceeding 75 kvA are for household use, auto generation capacity at the household level would be of around 766 MW in 2009. A second alternative way to contrast electricity generation capacity privately installed for complementary use can be found by departing from ENHOGAR 2007. This survey asks whether the subject has an “inversor” or generator at home, and 18.1% of households in the Dominican Republic count with one. Supposing an average generation capacity of 1.5KW per household, total supplementary capacity installed for private household use in the Dominican Republic would be around 678 MW, well above the 53 MW estimated by the National Energy Commission in 2001. According to sector experts, the increase in private auto generation capacity may have been motivated by the impoverishment in the quality of energy supply following 2003 economic crisis.

Which is the cost of maintaining these auto generation devices? According to some estimates<sup>3</sup> , the charge needs of the inverter ́s battery represent 63% of the average electricity consumption by a middle-income household (453 KWh a month). Considering 3 million households registered in the 2010 census and supposing that around 18-20% of the households count with an “inversor”, it results that approximately 154 GWh a month<sup>4</sup> are consumed just to support individual private auto-generation capacity. This is a very significant cost of opportunity of inefficiency in the system, which burdens both the state and final consumers, who pay around 240 USD million of their annual electricity bill just to keep the inverter working. This approximation seems to be in line with
would group, for example, Istanbul with Kocaeli and Mersin with Adana and result in 109 agglomerations. While a 50 km cut-off would include Bursa in the first group and Hatay in the second group and result in 44 agglomerations (larger than 10 grid cells). We report our results for the larger agglomerations (Figure 5).<sup>22</sup> As with the construction of urban agglomerations, there is no standard approach to delineating peripheral areas. For this paper, we define peripheral areas as being in the same district as an agglomeration but not classified as being a high-density urban area by the GHSL in 2000 (see Figure 6). Once again, there are other choices that may be made. Our choice is based on the reasoning that buffers around urban agglomerations that are not contiguous are challenging to both conceive of in theory and construct in practice. In contrast, classifying grid cells that are not high-density, but in the same district as an agglomeration as peripheral, is both simpler to conceive of and to construct. In the next section, we examine changes in nighttime light emissions across periods using this typology.

# **3.3 Booms and Busts within and across Urban Agglomerations**

In what we follow we restrict our analysis to comparing estimates of Night-Time Light Luminosity (NTL) using one sensor (DMSP) or both (DMSP and VIIRS) but being careful to not compare changes using two different sensors. Table 5 shows average changes in growth rates of NTL by period employing the core-periphery delineation discussed above. We do not include the periods after 2012 (when the DMSP is not available) but include the 2000-2002 recession and the 2008-2009 recession. We find a clear pattern. NTL falls during the two recessions, rises slowly during the 2004-2006 expansion, and rises rapidly during the two rebounds. This confirms (part of) our classification of time periods.

Table 5 also shows that the 2010-2011 rebound was much more pronounced (in terms of increases in NTL) than the 2002-2004
the development of the Russian housing market. Pensioners, constituting by far the largest group of privatizers, are generally not inclined to move and, thus, exert a negative effect on household mobility which, in turn, retards the advancement of the housing market in Russia. The other, much smaller group consists of the relatively well-off who can be expected to become active participants in the housing market.

Survey data indicate some movement towards a more efficient allocation of the housing stock than was the case at the beginning of transition. A certain part of the mover households moved from their original apartments to apartments that were in some respect inferior in reaction to the economic forces affecting them. The housing market allows poorer households to find housing more in keeping with their ability and willingness to pay for it.

That many renters in Russia have thus far chosen _not_ to privatize their apartments is the largely the influence of the sense of "occupation rights" inherited from the Soviet Union. would make evictions. For many Russians there have been few incentives to privatize their housing. Nonetheless, data from surveys in Moscow and two smaller cities indicate that market ideas regarding housing search methods are beginning to penetrate the mentality of the Russian public.

Despite the deterioration in the financial standing of most Russian households, a small but growing number of Russians would like to get long-term loans for the purchase or construction of housing if such a possibility existed. This suggests that a properly developed mechanism of mortgage lending could make a positive difference in the Russian housing market.
# **Highlights**

- We estimated outpatient caseloads and provider competence using nationally representative facility-level survey data from 10 Sub-Saharan African countries.

- The median primary health care provider sees 10.9 patients every day, but the median outpatient visits a provider who sees 25.7 patients daily.

- There is almost no correlation between caseloads and competence in most countries: low-competence providers are nearly as busy as the most competent.

- Hypothetical reallocations that match underutilized high-competence providers to larger caseloads could meaningfully improve the quality of care in half of the sampled countries.

# **1 Introduction**

A prominent consensus in the voluminous literature on “human resources for health” is that there is a global workforce crisis in primary health care (PHC) – a general shortage of providers, which is far worse in poorer countries and especially acute in Sub-Saharan Africa (SSA).<sup>1, 2</sup> The _prima facie_ case for this consensus is unassailable. Even in the OECD countries, doctors and nurses are extremely busy and visibly overworked, with frequent reports of provider burnout. There are 4.5 physicians per 1,000 people in Germany and 3.6 in the United States,<sup>3</sup> but only 1.55 physicians, nurses _and_ midwives for every 1,000 people across the WHO African Region.<sup>4</sup> Given that there are far fewer physicians per population in SSA than in OECD countries, it does not require a huge leap of faith to assume that the workforce crisis must be much worse in SSA compared to the United States or the European Union.

On closer consideration, however, these statistics present a puzzle. Assume each person in SSA seeks outpatient primary care 6 times per year, which is the OECD average for in-person physician consultations in 2021.<sup>5</sup> In the African Region, there are 1.55 providers per 1,000 people,<sup>4<
wage labor were curtailed, and respondents reflected on reduced local labor demand and wages (down by about 15 percent) alongside increases in food prices.

Further qualitative semi-structured interviews in study municipalities in 2010—with two dozen respondents (including local leaders, members of households with a migrant and returned migrants themselves) identified through a snowball sampling method—complemented many of the patterns evident the year before and revealed that the local economy had not yet fully rebounded. Several respondents continued to report diminished remittances, often due to migrants having only part-time work. While migration continued, it was lower than in the past and additional migrants had returned to weakened local economic opportunities. Respondents underscored the potential costs of failed migration or early return for which the initial investment had not yet been recouped, leaving return migrants in substantial debt, usually to another family member or local money lender.

The quantitative household panel data between 2008 and 2012 confirm a drop in migration from the region, consistent with the timing reported in the 2009 qualitative study and demonstrating a slow recovery to previous levels. Figure 1 shows the fraction of households by year who had at least one international migrant in any location and at least one international mi <mark>g</mark> rant in the United States, calculated from the retrospective information on migration experiences of all household members described in Section 2.2. The fraction of households with a <mark>mig</mark> rant in the United States rose steadily until 2008 when it dropped sharply, after which it remained fairly stable through 2012. Total international migration, however, began to recover somewhat with increases in migration to Mexico, consistent with the qualitative evidence on the changing relative advantages of that destination for which the upfront costs were substantially lower. Travel to nearby Chiapas was relatively easy and Guatemalans could enter legally, usually after indicating the name of a farm in which they intended to work.

The panel survey data also confirm a reduction in remittances deeper than seen at the national level, possibly reflecting weaker
relief amount per beneficiary in the sample frame, Rs 33,498 (US$ 740), is higher than the state average of Rs 24,275 (US$ 540). There are sevaral reasons for this. First, landholdings in the (non-drought affected) survey district are slightly larger, the bulk of qualifying farmers in the state, and are more likely to be irrigated than landholdings in the rest of the state. Since there is a positive relationship between landholding, crop value and loan size and also between loan size and relief amount, larger landowners will tend to get more relief. Second, some banks not included in the sample frame, such as rural regional banks and smaller credit cooperatives, are likely to issue smaller loans on average than the larger banks included in the sample frame.

# **5.2 The Debt Relief Survey**

In total, 2,897 households were surveyed in five rural districts of the northwestern Indian state of Gujarat between October and December 2009, approximately one and a-half years after the program.<sup>16</sup> The four sample districts, Mehsana, Gandhinagar, Kheda and Anand<sup>17</sup> form a contiguous band in the central and northwestern part of the state. These districts include relatively rich agricultural land and are slightly more rural than Gujarat as a whole, with 64-80% of households residing in rural areas. Like any of India’s 28 states, Gujarat is unique in some ways and ordinary in others. It is richer than average, with a per-capita income about 26% above the all-India average. It is also more urban than the rest of the country, with 37% of its population living in urban areas versus 28% for India overall. Agriculture makes up about the same share of Gujarat’s economy, however, as for India overall. In terms of banking, Gujarat enjoys slightly higher than average commercial bank coverage, with one commercial bank per 14,220 inhabitants, versus
Concept 2 Gini Concept 2 Theil What Concept 2 inequality does not take into account are within-national inequalities. In calculating Concept 2 inequality, we implicitly assume that each individual within a country has the same per capita income (and thus Gi=Ti=L=0). This last assumption needs to be abandoned if we want to calculate “true” global inequality across individuals. But in order to abandon it, one must have access to national income distributions which are available only from household surveys. It is this “jump” that makes such a big difference in data requirements between Concept 2 and Concept 3. From being “modest”, the data requirements now become huge since, ideally, we should have access to national income distributions from all the countries in the world.

4