rafmacalaba/data-use-mentions-extended huggingface dataset:
Thus human capital variables, such as age education and experience and household variables such as income and household size effects an individuals participation decision.

# **3. The Absorption of Labor**

## **3.1 Sectoral Employment**

The growing labor force was absorbed in the different segments of Ghana's labor market. In this section we analyze the distribution of formal and informal workers, the public-private composition of wage workers-especially the formal workers. The questions one would like to ask is: Is public sector the main provider of wage jobs? Does female employment distribution differ from that of male employment distributions? What is the main source of wage employment for females? Does public employment depend on educational level? Are most of the wage jobs in the rural or urban areas? Is agriculture still the main source of employment in the rural areas or has there been a decline in the agricultural sector employment. The objective here is to identify the important sectors of employment and changes -if any -in the relative importance of sectors during this period. A related issue will be to find out to what extent are the employment differentials - occupational or industrial- justified? Another broad distinction is the difference between private and public wage employment. The GLSS data set allows us to make this distinction within the formal labor<sup>4</sup> market.

Looking at the broad employment distribution we find that agriculture is still the main form of employment for the labor force as a whole. In terms of formal and informal employment, in 1992, 21% of the workers were wage workers, of which 13% worked in the public sector; in 1987, 24% of the workers were wage workers, of which 14% were formal sector workers. Over 74% of the workers were self employed, of which 48% were engaged in agriculture (a decline from 49% in 1987). Among the wage workers, 62% were formal sector workers and 38% were informal sector workers.
]|
|List experiment argument index|‐0.135|‐0.132|
||(0.147)|(0.149)|
||[0.343]|[0.322]|
|Observations|1,523|1,495|

_Note: P‐values are in parenthesis displayed below the estimated coefficients. * (**, ***) indicates statistical significance at the ten‐percent (five‐percent, one‐percent) level. Adjusted Benjamini‐Hochberg p‐values are reported in square brackets. All regression control for gender‐state fixed effects. Control variables of column (2) include all baseline controls that were significant determinants of attrition and of selection between receiving “training and grant” vs “training, but no grant”. In particular, these are age, marital status, employment status, business ownership, food consumption, non‐food consumption, formal bank account, formal loans, informal loans, education level, literacy level, numeracy level, household size, number of children, number of rooms, number of buildings at baseline, and exposure to conflict between baseline and endline._ 29
such as women, for example), depending on how binding such constraints are.

## **6.1 Economic development**

We analyze how the effects of roads and electricity on employment differ by level of economic development. Theoretically, heterogeneous impacts could be driven by differences in occupational choice between countries that differ in level of economic development. Agricultural occupations, for instance, might be more prevalent in less developed economies, whereas the manufacturing and service sectors might be more present in more developed economies. We classify countries according to their 2019 GDP per capita (Table 1).

The results are reported in columns 7 and 8 of Table 7 in the Appendix. The effects of road expansion on employment differ by level of economic development. In areas with high access to electric grids, increasing proximity to the road network by 10 km marginally increases employment probability by 22.1 percentage points in more-developed countries in the LSMS sample and by 32.5 percentage points in the DHS sample. In less-developed countries, the corresponding effects are 2.5 percentage points (LSMS)

> 11In locations along a main road, greater electricity access increases farming employment for the LSMS sample. The OLS results obtained from the DHS sample are consistent with the IV estimates, but with smaller magnitudes. The effects of road access in locations that have high levels of access to electricity and of electricity in locations that have high access to roads are in the same direction as the IV effects.

25
<!-- Start of picture text --> 0.6<br>0.4<br>0.2<br>0.0<br>-0.2<br>-0.4<br>-5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10<br>Years to management by African Parks<br>probability any iNaturalist visits<br>D<br><!-- End of picture text -->

**Fig. S9** : **Effect of AP management on probability of positive iNaturalist visits, excluding potential protected area staff** . We exclude potential protected area staff from iNaturalist data, then repeat the procedure which produced Figure S8 (SI A.3). Points are regression coefficients, bars are 95% confidence intervals, standard errors are clustered at the protected area level, the unit of observation is the area-year, the number of observations is 3,625, and the control variables are area fixed effects, year fixed effects, a third-order polynomial in precipitation in m, and 12 degree day bins (Section 4). The ATT is 0.175 (standard error = 0.040).

<!-- Start of picture text --> 0.50<br>0.25<br>0.00<br>-0.25<br>-5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10<br>Change in probability of positive eBird v Years to management by African Parks<br><!-- End of picture text -->

**Fig. S10** : **AP management increases the probability of positive eBird visits.** Points are regression coefficients, bars are 95% confidence intervals, and standard errors are clustered at the protected area level. The unit of observation is an areayear and the
br>0.286**|<br>0.185**|<br>0.092|<br>0.131***|<br>0.132***|
|quarterly data lagged 1 quarter|(0.039)|(0.114)|(0.084)|(0.057)|(0.046)|(0.043)|
|<br>_LMP_PASSIVE_: Passive labor|9.253**|28.345***|12.591*||||
|market measures spending, %<br>GDP per percentage point of<br>|(4.368)|(0.755)|(7.748)||||
|unemployment (lagged 1 yr)|||||||
|<br>_UBSA_(defined in Table 4),|-0.301***|-0.481***|-0.342***||||
|lagged 1 year|(0.047)|(0.053)|(0.104)||||
|Gini index||||0.262***|0.119||
|(lagged 1 year)||||(0.068)|(0.116)||
|Country-level random eff. s.d.|||3.066|||3.191|
|Residual s.d.|||0.918|||1.278|
|LR test vs. a model without|||||||
|random effects,_p_-value|||0.0001|||0.0000|
|Root MSE|0.8500|1.2822|0.6209|2.1074|2.7608|0.9271|
|R-squared|0
Figure 11: Formal and informal employment distribution by age cohort in manufacturing: Cameroon (2008) <!-- Start of picture text --> Entrant 1−5 years<br>6−10 years 10+ years<br>Employment in formal establishments Employment in informal establishments<br>1 − 4 5 − 9 10 − 19 20 − 99 100+ 1 − 4 5 − 9 10 − 19 20 − 99 100+<br>1 − 4 5 − 9 10 − 19 20 − 99 100+ 1 − 4 5 − 9 10 − 19 20 − 99 100+<br>Share of employment (%)<br>80<br>60<br>40<br>20<br>0<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> _Source:_ Establishment censuses obtained from the statistical agencies of the selected countries; see section 3. _Note:_ The reference is the total employment for each age cohort and formality status. The figure is constructed based on data for establishments in the manufacturing sector. Establishments with missing employment or age data and state-owned are excluded.

24
supported household surveys should 1) allow for the creation of a comprehensive welfare aggregate to track poverty and shared prosperity, and 2) be multi-topic, building on the World Bank’s intellectual leadership in setting standards in multi-topic household survey design and implementation through the Living Standards Measurement Study (LSMS) program.<sup>13</sup> These surveys are non-uniform across countries in terms of questionnaire design, sampling design, fieldwork organization, and approaches to data entry and processing. Understanding the local context, the institutional capacity for survey design, implementation and analysis, and the incentive structures for headquarters- and field-based survey staff are therefore crucial for formulating survey implementation budgets and understanding cross-country variation.

The first input into our database implementation unit cost estimates is a database of implementation unit costs that was compiled by the LSMS team on the basis of the detailed survey implementation budgets tied to selected surveys that are supported by the Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) initiative in sub-Saharan Africa.<sup>14</sup> The detailed survey implementation budgets are sourced from 6 countries and are associated with the Ethiopia Socioeconomic Survey (ESS) 2011/12, Malawi Third Integrated Household Survey (IHS3) 2010/11, Niger Enquete Nationale sur les Conditions de Vie des Menages et l’Agriculture 2011, Nigeria General Household Survey (GHS) – Panel 2010/11, Tanzania National Panel Survey 2008/09, and Uganda National Panel Survey 2009/10.

Although these surveys are not used to estimate official poverty statistics, with the exception of Malawi IHS3 2010/11, they match the multi-topic household survey design criterion recommended by the World Bank Household Survey Strategy, and are integrated into the respective country NSSs. The specific LSMS-ISA supported survey waves that inform our analysis are the baseline for the respective panel survey programs; thus, their unit costs are applicable in the context of the cross-sectional household surveys that will be supported by the World Bank over

> 13 As recommended by the World Bank Household Survey
the labor market, which is the main transmission channel of<br>macroeconomic shocks and adjustment policies to economic activity, employment, and relative prices.<br>The labor-market specification permits a disaggregation at the urban and rural levels and, within each<br>level, in the formal and informal sectors. Second, IMMPA links real and financial sectors through an<br>explicit treatment of the financial system. Third, the model emphasizes the negative effect of external<br>debt on private investment and therefore incorporates the possibility of debt overhang. Finally,<br>IMMPA accounts explicitly for the channels through which various types of public investment outlay<br>affect the economy.|
|**Data/information:**<br>**ents**|The greatest drawback of any fully specified CGE model is the time and data required to construct it.<br>The model must be constructed from combined national accounts and survey data. These are first<br>compiled into a SAM, which is then used as the foundation for the model. IMMPA, for example,<br>consists of 131 equations, more than 30 exogenous variables, and more than 200 endogenous variables.|
|**Time:**<br>**irem**|The process can take more than a year and rarely less than a few months.|
|**Skills:**<br>**equ**|Experienced modelers with substantial prior exposure to CGE models are required.|
|**Supporting**<br>**software:**<br>**R**|EViews, Excel.|
|**Financial cost:**|US$75,000 to develop the IMMPA general equilibrium model.|
|**Limitations:**|CGE simulations depend to a large extent on the assumptions made in the model, especially those that<br>are required to close the model
# **Table 15: Deposit penetration and financial inclusion agenda**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|GDP per capita (log)|0.643***|0.653***|0.681***|0.660***|0.673***|0.675***|0.692***|0.664***|0.619***|
||(0.061)|(0.057)|(0.056)|(0.057)|(0.056)|(0.055)|(0.056)|(0.055)|(0.061)|
|FI index|-0.077*<br>(0.044)|||||||||
|FI index (effective)||-0.075<br>(0.046)||||||||
|Resource gap|||-0.031<br>(0.058)|||||||
|FI deposits index||||-0.091<br>(0.059)||||||
|FI deposits index (effective)|||||-0.075<br>(0.060)|||||
|Responsible for consumer protection||||||-0.430***<br>(0.154)||||
Our interest in New Zealand, in turn, lies in the starkness of its containment policies. It implemented a very intense, but brief shutdown in the second quarter of 2020 and, except for some time intervals and locations, lifted mobility restrictions thereafter. In this sense, New Zealand fits very well our benchmark experiment of a one quarter shutdown.<sup>6</sup>

> 6Figure 16 in Appendix B shows, using data from Google mobility reports on visits to shopping venues

4
.7|

Source: NCS, 1985/86 and 1992/93 urban are shown in Table 4.3. In Lagos urban, the Gini coefficient increased from 0.340 to 0.399. For other urban excluding Lagos, the value increased from 0.366 to 0.441 and in the rural areas, the Gini increased from 0.391 to 0.456 showing a worsening of income distribution. This worsening is reflected in the national Gini coefficient which increased from 0.387 to 0.450.

**Fig 4.1: Evolution of Household Expenditures by Expenditure deciles**

<!-- Start of picture text --> 35 __.. .w<br>Z 30<br>25<br>-s20<br>20<br>15<br>00<br>0 5-<br>0.<br>1 2 3 4 5 6 7 8 9 10<br>Population deciles<br>*1985 01992<br><!-- End of picture text -->
describes the data. Section 3 explains our hypothesis and empirical strategy. Sections 4 and 5 present the results and robustness checks. Section 6 concludes.

# **2 Description of the data**

Our analysis exploits data for Brazil (State of Cear ́a), Senegal, and Vietnam from the Firm-level Adoption of Technology Survey (FAT) and the COVID-19 Business Pulse Survey (COV-BPS). In 4
case of developed countries. Along these lines, Chetty, Hendren, Kline, and Saez (2014) state that the United States can be better described as a collection of societies, some of which are “lands of opportunity” with high rates of mobility across generations, and others in which only a few children escape poverty. Thus far, this type of analysis has not been conducted in LAC countries as a whole due to the inadequacy of most survey data for this purpose. This paper fills that gap in the literature by generating estimates of IGM in education at smaller

> 1See for example Messina and Silva (2019) for an analysis of wage inequality over the last two decades.

2
lower productivity than the average one. One approach to dealing with this concern is to use migration lottery programs, in which a group of applicants apply, and then a random ballot determines which individuals get to migrate. McKenzie et al. (2010) and Gibson et al. (2018) examined such a program for Tongan migrants coming to New Zealand, finding they gain a 263 percent increase in (cost-of-living adjusted) income in the first year, with this gain in income persisting for at least the next 10 years, for a net present value of migrating of at least US$237,000. Mobarak et al. (2021) also use a migration lottery to estimate the income gains from migrating between Bangladesh and Malaysia, finding that migration doubles household incomes.

These estimates suggest that there are indeed large gains in income to be had from international migration. However, there has been more debate about whether there are really gains in income to be had from more internal migration. There are very large productivity gaps between the agricultural and non-agricultural sectors (e.g. Young, 2013; Gollin et al. 2014). However, a large part of this gap appears to reflect differences in the human capital and selection of who works in these different areas. Papers which use panel data to track rural-urban migrants over time have found the income gains to be smaller, but still positive. For example, Hamory et al. (2021) find wage earnings to be 27 percent higher in urban areas than rural areas for movers in Kenya, and 4 percent higher in Indonesia; while Beegle et al. (2011) find in Tanzania that individuals who move away from their rural villages have consumption gains of over 30 percent. Lagakos et al. (2020) use panel data from six countries - China, Ghana, Indonesia, Malawi, South Africa and Tanzania – and find an average gain in (spatially price adjusted) consumption of 23 percent from migrating, ranging from 17 percent in Indonesia to 34 percent
supply shocks, where production in MENA is more volatile than in the rest of the world.

Interest rates and per unit storage charges are assumed to be the same for each region at 5 percent per annum and $22.40 per ton per annum. The storage costs are based on recent findings in a World Bank study on wheat markets in MENA (World Bank, 2012).

The rational expectations storage model is known to lack a closed-form solution and so has to be approximated numerically. The numerical algorithm that we use is based on a projection method inspired by Miranda and Glauber (1995) and is described in detail in Gouel and Jean (2012, Appendix D).<sup>16</sup>

> 14 Average annual prices are for wheat (US), no. 1, hard red winter, ordinary protein, export price delivered at the US Gulf port for prompt or 30 days shipment as reported by the World Bank (2011b).

> 15 See, for example, FAPRI’s elasticity database, available on the internet at <u>http://www.fapri.iastate.edu/tools/elasticity.aspx).</u>

> 16 A description of the solution algorithm and solver written for MATLAB is is freely available on <u>https://github.com/christophe-gouel/recs.</u>

11
Figure 1: Magnitude of the COVID-19 Shock across Countries

**Note** : This figure shows the performance of stock markets across countries. Panel (a) shows a comparison between the cumulative changes in the S&P 500 since February 24th, 2020 for the COVID-19 pandemic and since September 14th, 2008 for the 2008-2009 Global Financial Crisis (GFC). Panel (b) shows the cumulative changes in stock markets indexes across countries since February 24th, 2020. The dotted vertical line in each panel shows the day in which the Federal Reserve announced a set of measures to support the U.S. economy (March 23rd, 2020). For EuroNext 100 index, France, Netherlands, and Belgium are, respectively, the countries with the largest number of companies as of August 2020.

**Source:** Authors’ calculations based on Refinitiv data.

2
Table 2: Relation between initial share of employment in mining and growth in per capita income (1996-2011)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Dep. Var.: Growth inper capita income(19|96-2011)||||
|Homeland (=1)|1.049**|1.152**|0.660|-0.032|
||[0.491]|[0.469]|[0.454]|[0.970]|
|Share Employed mining1996|-0.210|-0.004|-0.447*|-0.103|
||[0.215]|[0.247]|[0.258]|[0.226]|
|Homeland*Share Employed mining1996|0.641**|0.540*|0.840***|0.510**|
||[0.291]|[0.324]|[0.284]|[0.241]|
|Observations|9,583|9,583|9,583|9,558|
|R-squared|0.592|0.599|0.634|0.711|
|1996 Controls|Yes|Yes|Yes|Yes|
|2011 Controls|-|-|-|Yes|
|Province FE|-|Yes|-|-|
|MunicipalityFE|-|-|Yes|Yes|

Notes: Data from the 1996 and 2011 population census at the community (sub-place) level. Each column corresponds to a separate OLS regression using a sample of communities within 30 kilometers of the former homeland borders (on both sides). The dependent variable is the difference in log per capita income for 2011-1996
this finding is that, in measuring government size and per capita GDP, Summers-Heston data has managed to correct for cost of living differences across countries. With the World Bank data, however, the relative ranking of countries based on government size or per capita GDP has on average remained the same. Relative prices of government consumption of goods and services continues to be negatively signed and significant at the 1 percent level no matter whether the data is measured in constant 1987 US dollar prices or constant 1985 intemational prices.

The point estimates of the relative price variable in the government size regression show perhaps an unusually large responsiveness of government consumption to its price measured relative to the price of private consumption goods (-0.644 OLS and -0.724 IV in _Table 2)._ To the extent that relative prices of public and private goods differ systematically over time and across countries, this discrepancy is expected to affect the real quantity of goods and services purchased by the government. However, to the extent that government purchases of goods and services may not be comparable across countries (e.g., due to subsidies and taxes; monopsony power of government) the point estimates in _Table 2_ could show too high a price elasticity. An alternative estimate of price elasticity - can be derived using World Bank constant dollar prices which shows an elasticity of 0.225 (IV _Table 11)._ This discrepancy can be traced to marked differences in the measurement of relative prices across the two data sets. In the Summers-Heston data, services are more expensive in high income countries whereas in the World Bank data, 51
Figure 7 – Great Britain, by survey <!-- Start of picture text --> GreatBritain<br>Relative inequality of opportunity Return to education<br>1990 1995 2000 2005 2010 2015<br>survey<br>st.deviation logs mean log dev. 1990 1995 2000 survey 2005 2010 2015<br>Parental networking Intergenerational persistence in education<br>1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015<br>survey survey<br>.5 .35 .25<br>.3<br>.45<br>.25 .2<br>.4<br>.2<br>st.deviation logs mean log dev.<br>.35 .15<br>.15<br>.3 .1<br>.1<br>.025 .15<br>.02 .14<br>.015<br>.13<br>.01<br>.12<br>.005<br>0 .11<br><!-- End of picture text --> Regressors include gender, age, age2, born in England and foreign citizenship Data trimmed at 99.5th centile - vertical dashed line indicates change of survey Figure 8 – Great Britain, age-cohort decomposition <!-- Start of picture text --> GreatBritain<br>Inequality of opportunity - Standard deviation of logs personal incomes Return to education<br>30 40 50 60 70 80 30 40 50 60 70 80<br>Age Age<br>. .<br>1920 1940 1960 1980 2000 1920 1940 1960 1980 2000<br>Born in... Born in...<br>. .<br>Parental networking Intergenerational persistence in education<br>30 40 50 Age 60 70 80 30 40 50 Age 60 70 80<br>. .<br>1920 1940 Born in...1960 1980
Madagascar and Mozambique the year-to-year increase was already large before FPE, and appears to continue after it. It is hard to make out a trend in Kenya due to the year-to-year fluctuations.

We next turn to the year that current grade 4 teachers in the SDI surveys report having begun teaching. It is important to note that the sample of teachers in the SDI reflects only those who have remained as teachers and the results need to be interpreted in that light, in particular because this could induce a “survivorship” bias to the results. If, for example, less-qualified teachers were hired following FPE and teachers with this profile are also more likely to have left the teaching profession by the time of the survey, then we would not be capturing them in the sample. Moreover, it is important to note that the students we test are in grade 4 and so would have been exposed to 3 years of teachers prior to this grade. These teachers may have been hired before or after FPE, meaning that total FPE effects may be hard to detect with knowledge of just the grade 4 teacher’s status. These caveats mean that this analysis should be understood as an exploration of whether the impacts of the FPE scale-up are still present in the long term using this particular window into that exploration.

These data do seem to suggest a noticeable change in the density of grade 4 SDI teachers hired after FPE (Figure 2). While it is only in Tanzania and Uganda that it would appear that this happened exactly in the FPE year, all the countries display a change around that date (although in Togo there are other spikes in hiring that do not correspond to the FPE year). In the spirit of RDD, we test for whether there is a change in the density at the time of FPE by implementing the “test for manipulation” as developed by McCrary (2008).<sup>18</sup> The results are presented graphically in Appendix Figure 1: in all cases the density is indeed