rafmacalaba/data-use-mentions-extended huggingface dataset:
out of non-employment.

The SMM is based on comparing a vector of empirical moments computed from rm and worker actual data, with moments computed from rm and worker simulated data. The simulated moments depend on the choice of Γ through optimal investment _Iijt_ and equilibrium labor allocation _N_<sup>_jt_</sup> . The estimator for the adjustment costs minimizes the weighted distance between the empirical and simulated moments.<sup>10</sup> Within the SMM algorithm we further estimate the worker utility function parameters _η_ (sector quality shifters) and _ν_ (worker shock variance) using a linear regression derived from the workers' Bellman equation. This step is done within the SMM because the Bellman equation is a function

> 10 We use six moments: the serial correlation of the investment rate, _corr_ ( _Iijt, Iijt−_ 1) ; the correlation between the investment rate and the protability shocks, _corr_ ( _Iijt, Aijt_ ) ; the negative spikes rates, dened as the percentage of rms with investment below negative 10 percent; the correlation between sector deviations in wages and employment with respect to the sector mean, _corr_ (( _wjt −_ _<u>wj</u>_ ) _,_ ( _Njt − N j_ ) ; the serial correlation in non-employment, _corr_ ( _N_ 0 _t, N_ 0 _t−_ 1) ; and the correlation between the change in non-employment and the change in the average wage across sectors, _corr_ (( _N_ 0 _t − N_ 0 _t−_ 1) _,_ <u>(</u> _<u>wt − wt−</u>_ 1)) . We search over values of Γ using a combination of ne grid search and coordinate descent search, which works better than second-derivative Newton-Raphson or quasi-Newton methods in a case like ours with a discretized state space.

16
and the standard deviation of regional nonfarm productivity levels are 398.5 thousand and 69.3 thousand cedis per worker, respectively. Moving from the actual to the efficient allocation of labor across sectors (that is, to where regional output wedges are eliminated) increases national nonfarm productivity to 448.5 thousand cedis per worker (a 111.6 percent increase from the level of national nonfarm productivity under the actual distribution of regional output wedges); in addition, it increases the standard deviation in regional nonfarm productivity levels to 169.3 thousand cedis per worker.

Table 2 presents analogous results regarding the effects on national aggregate labor productivity from the three types of simulations. National aggregate labor productivity is a weighted average of farm and nonfarm labor productivities. To obtain the effects on national aggregate labor productivity, apart from the simulation results on the effects on nonfarm productivity (presented in table 1), we use data from Phase II of the IBES to estimate productivity in farm enterprises, as well as GGDC data to estimate the farm employment share, the change in the nonfarm employment share, and the change in farm labor productivity.<sup>18</sup> As the table shows, under the actual distribution of regional output wedges, we obtain a national aggregate productivity of 164.4 thousand cedis per worker. Moving from the actual to the efficient allocation of labor across sectors increases national aggregate productivity to 316.7 thousand per worker (an increase of 92.6 percent from the level of national aggregate productivity under the actual distribution of regional output wedges).

As both Tables 1 and 2 show, the simulation results from adjusting regional output wedges in all regions or in the five richest regions suggest a strong negative association between the extent of disparity in regional output wedges and the level of national labor productivity. In addition, the results from adjusting output wedges in the five poorest regions suggest little or no association between the extent of variation in regional output wedges and the level of national labor productivity. These results
Statistic Division of the Government of Pakistan - PSLM report (8) Sri Lanka Department of Census and Statistics (2015) Household Income and Expenditures Survey (9) Atolls are considered rural areas.

(10) There is a further 5% in the Estate sector, which consists of tea plantations.

_Food consumption questionnaire and data collection methods_ The questionnaire design methodologies for recording the data vary considerably across countries in the region. A comprehensive study analyzing survey questionnaires from 100 low and middleincome countries noted the large variation in the way surveys are designed in each country (Smith, Dupriez and Troubat, 2014). This variation is also apparent across the South Asian household survey questionnaires. Table 5 lists the contents of the questionnaires from South Asian countries, which shows significant differences in the way food consumption data are collected.

7
are civil servants receive further benefits that are not likely to be monetized in regular earnings (e.g., retirement benefits; job security) and hence not included in the response to the earnings questions – and these are therefore not included in our analysis.

Indeed, in our data we find that teachers are significantly more likely to report receiving benefits such as paid leave, medical benefits, and a pension than other workers (Table A3). Across our sample, teachers are 31 percentage points more likely to receive paid leave, 9 percentage points more likely to receive medical benefits, and 30 percentage points more likely to have a pension. On average, even teachers on fixed term (as opposed to permanent) contracts are more likely to receive benefits: for example, 49 percent of teachers on fixed term contracts report access to paid leave versus only 38 percent of other wage workers (Table A4).<sup>17</sup> As a result of these other benefits, our earnings differential estimates likely represent an underestimate of the teacher premium.

In our analysis, we separately consider monthly and hourly earnings since hours worked differ systematically between wage workers who are teachers and those who are not. For all but one survey we analyze reported earnings along with the corresponding time unit of the earnings, for example, daily, weekly, monthly, quarterly, or yearly. Using these two indicators, we first derive implied monthly earnings. For example, if an employee reports her quarterly earnings, then her monthly earnings are calculated as the quarterly earnings divided by three. If daily earnings are reported, we multiply the daily earnings by how many days the person usually works in a week, then multiply that number by four to get monthly earnings. To estimate hourly earnings, we divide the monthly earnings by four times the reported weekly working hours for each worker. When daily working hours are reported, we calculate the weekly working hours by multiplying the number of days worked in a week. The Gambia survey
Figure 2: Tax revenue (% of GDP) of Cambodia and ASEAN countries, 2019 <!-- Start of picture text --> Cambodia<br>Thailand<br>Philippines<br>Singapore<br>Malaysia<br>Indonesia<br>Myanmar<br>0 5 10 15 20<br>Source: WDI database.<br><!-- End of picture text --> Note: Data for Vietnam, Lao PDR and Brunei Darussalam were not available.

Cambodia’s improved fiscal space allowed the government to boost spending. <mark>Government expenditure increased from 20.2 percent of GDP in 2011 to 24 percent in 2019,</mark> largely driven by spending on social services and on defense and security (World Bank, 2023). However, Cambodia’s expenditure boost partly reflected salary increases for civil servants. <mark>Between 2011 and 2019, the public sector wage bill as a share of GDP almost doubled from 4.4 percent of GDP in 2011 to 7.1 percent of GDP in 2019</mark> (World Bank, 2023) <mark>and p</mark> ublic sector minimum wage rose from around US$ 50 a month in 2012 to US$ 250 in 2018 (World Bank, 2019). Social sector spending increased from 4.6 percent in 2011 to 6.8 percent in 2019 largely due to increased education spending, which almost doubled 1.5 percent of GDP in 2011 to 2.9 percent of GDP in 2019 (World Bank, 2023 and WDI). However, health spending only grew marginally in the years before the pandemic. Electricity subsidies to eligible households and businesses amounted to US$ 33 million (0.12 percent of GDP) in 2019.

# **Tax system in Cambodia**

Revenue collection in Cambodia is highly centralized with a small number of taxes collected at the municipal or provincial level. In total, central government revenue amounted to
It should be noted that the choice of population density map is important for the purpose of this study. Smith et al. (2019) provide a sensitivity analysis for flood exposure assessments using different population density maps, including GHSL-POP 2015 (3-arc second, ~90 m resolution). They show that high-resolution population density maps perform best in capturing local exposure distribution, in particular the HighResolution Settlement Layer (HRSL) with 1 arc second, or ~30 m resolution, produced jointly by Facebook, Columbia University and the World Bank (2018). While HRSL is only available for a limited number of countries, GHSL-POP is shown to perform better than alternatives with global coverage, such as LandScan data (30-arc second, ~900 m resolution; Bright et al. 2016).

## **3.3. Administrative boundaries**

The definition of national administrative boundaries follows the standard World Bank global administrative map. However, national boundaries are further disaggregated into subnational units for all countries where World Bank household surveys are available with subnational representativeness. These subnational units are typically provinces or states (i.e. admin1) but can also include custom groupings of subnational regions determined by the sampling strategy of household surveys. Overall, this study covers 189 countries, which are disaggregated into 2,260 subnational units.

## **3.4. Poverty estimates**

For each of the 2,260 subnational units, the World Bank’s Global Monitoring Database offers several poverty estimates, which are all derived from the latest available Living Standards Measurement Survey (LSMS) for the respective country. For the purpose of this study, the standard World Bank definitions of poverty are used to determine the number of poor people in a given subnational administrative unit. Specifically, poverty is defined by the daily expenditure thresholds of $1.90, $3.20, and $5.50.

# **4. Methodology**

## **4.1. Concept of analysis**

To
/sup> After calculating taxes and transfers for all households, we aggregate them and compare the aggregates to the public administrative records. Table 3 below present the results of the comparison. We find that HBS does a relatively good job in capturing the magnitudes of fiscal interventions. Overall, HBS captures 46 percent of total government revenue (as presented in Table 1), including 74 percent of social contributions and 50 percent of direct and indirect tax revenue; and about 50 percent of government spending (as shown in Table 2), including 87 percent of social spending.

**Table 3. Fiscal Interventions in the HBS: Amounts and Comparison with Administrative Records**

> 10 The approach is described in more detail in Annex A.

15
their offerings along the life cycle of digital tech firms in Africa. Following the literature on firm dynamics and entrepreneurship, which suggests that young firms (startups) are more innovative (Decker et al., 2014), the paper compares the differences in the incorporation of disruptive technologies along the life cycle of tech startups. Third, the paper evaluates to what extent incorporating these disruptive technologies into the firms’ products and services is associated with the amount of funding received. It is expected that these high-potential firms incorporating disruptive technologies should exhibit a higher probability of attracting funding, but there could be different market failures affecting this process.

To address these questions, we use the technologies identified as disruptive by Bloom et al. (2021), based on patents for the U.S., and the frequency with which these technologies are mentioned in earning calls. Using textual analysis, we identify those firms in Crunchbase and Pitchbook<sup>2</sup> that include of the associated with these tech- any bigrams nologies, either in their short or in their long description. We focus on three main digital sectors: Fintech, E-commerce, and Information Technology (IT),<sup>3</sup> which were selected

> 1Latin America represents a good comparison group because it comprises emerging markets and, similar to Africa, it has significantly increased its tech adoption and entrepreneurship in recent years, and these dynamics are relatively inward-related, in sharp contrast with other emerging regions like Asia, where the dynamics tend to be trade and outward-related.

> 2Crunchbase (2023) data obtained from www.crunchbase.com and PitchBook (2023) data from https://pitchbook.com/.

> 3It refers to the provision of digital services, such as software and data.

2
person in the ESS 5 context and over the phone as part of the high frequency phone surveys, but there is a lag of nine months between these interviews, rendering the direct comparison of estimated vaccination rates difficult. Instead, we propose a more indirect test of this hypothesis through a comparison of vaccination rates according to administrative records to vaccination rates estimated in the in-person survey. We argue that if the phone survey mode effects were responsible for the observed misalignment between surveys and administrative records, the misalignment should disappear in in-person surveys. The prediction we test is the following.

**Prediction 3:** In-person surveying will display reported vaccine uptake close to administrative records.

where _Vi_ is the vaccination status of individual _i_ , _α_ is a constant, A is the COVID-19 vaccine coverage rate as reported in administrative data and _t_ the test statistic obtained from a standard t- Test. If our prediction is confirmed, comparing _t_ to the critical value of a Student t-distribution will no longer reveal a statistically significant difference between in-person survey estimates and the administrative data.

<u>Hypothesis 4: Panel conditioning/Survey participation effects</u> The phone surveys we conduct are longitudinal, meaning that the same households (and often the same respondents) are interviewed multiple times regarding their COVID-19 vaccination status, willingness to get vaccinated, and related information. It is possible that this leads to a behavioral response in which respondents become more likely to get vaccinated (or report to be vaccinated) as they are repeatedly interviewed over time. Such ‘panel conditioning’ effects have been hypothesized to affect survey data across a wide range of topics but require experimental data for reliable identification (Struminskaya and Bosnjak 2021). Our hypothesis reads as follows.

**Hypothesis 4:** Repeatedly interviewing respondents on the topic of COVID-19 vaccination makes them more likely to get vaccinated or to report to have been vaccinated.

> 10 As data collection for the in-person survey covered a period of multiple months, we conservatively take the administrative coverage rate from the
productivity because productivity in both sectors change as a result of resources flowing from the low productivity sector to the high productivity sector. An important point is that the growth impact of changes in distortions depends crucially on the amount of resources that get reallocated. If the sector-specific distortions in Industry and Services are positive, any decline in distortions in either sector leads to an outflow of resources from Agriculture. However, the gains are capped by the resources in Agriculture initially. An economy that begins with fewer resources in Agriculture is bound to realize lesser gains compared to an economy that begins with a larger share of its resources allocated to Agriculture.

I use the labor shares data from GTAP together with sector value added and employment share data from the World Development Indicator (WDI) database to estimate distortions for many country-years. There are two major concerns with regards to using the sectoral share of employment data obtained from the WDI database. First, the source survey used to estimate the sectoral share of employment for many country-years is not representative in certain respects. For example, the surveys often were conducted in urban areas only, or only sampled workers in a very restrictive 2
the globe, they show that only one in four firms received any type of public support at the time we conducted the surveys. These results show stark differences by income levels: the probability of receiving some public support is 11% in low-income countries, 15% in lower-middle-income countries, 30% in upper-middle-income countries, and 53% in high-income countries. They show that policy reach has been limited, especially for the more vulnerable firms and countries. Using data from the second wave, we provide three novel findings.

_First_ , through time the probability of accessing public support increased significantly from 19% to 32% for an average firm in 38 countries surveyed in the second wave (Figure 20, panel a). _Second_ , there is significant heterogeneity in access associated with country-level characteristics. Firms in countries with more severe initial mobility drop witnessed a significant increase in the probability of accessing support in wave 2 (Figure 20, panel b). Firms in higher income countries and those with lower indebtedness (within their income-group) have a higher probability of accessing support in both waves (Figure 20, panels c and d). Firms in highly indebted countries are also at a disadvantage. These findings suggest that countries with more public resources significantly responded to the COVID-19 pandemic by providing a wider coverage to businesses.

26
29 Table 2: Decomposing growth and poverty reduction from migration, Kagera, Tanzania

|**2010 location**|**N**|||**Poverty headcount**||
|---|---|---|---|---|---|
|**Migrants only**<br>**Destination**||**1991‐94**|**2010**|**Change in Poverty**<br>**headcount**|**Share in poverty**<br>**headcount change**|
|**Rural**|1086|0.56|0.35|‐0.21|0.40|
|**Town**|720|0.45|0.14|‐0.31|0.38|
|**City**|285|0.45|0.02|‐0.42|0.21|
|**Total**|2073|0.50|0.23|‐0.27|1.00|

Source: Christiaensen, De Weerdt and Kanbur (2015).
(Table 9), and they did not affect parenting behaviors (Appendix Tables B6 and B7).

Neither the program as a whole, nor the D2D and HBP interventions, seems to have affected the way parents perceived education. While the program did have an effect on the reported optimal age for starting preschool, these impacts are not larger in T2 and T3 (Table B8). In addition, there are no impacts on the perceived return to school, maybe because parents already considered the return to primary and secondary school to be high (6.8% per school year in primary and 8.5% for secondary in the control group) and even higher than those reported by Humphreys (2015) based on Cambodia’s Socio-Economic Survey (CSES) in 2010. Unlike other contexts (for example in Madagascar (Nguyen, 2008) or in Dominican Republic (Jensen, 2010)), Cambodian caregivers did not appear to have under-estimated the return to education. We cannot rule out the possibility that a more intensive set of programs (e.g., more frequent home visits, more intensive HBP) might have had bigger impacts. But it is also possible that, in the context of these small rural villages, the construction of the CPS itself had a demand-side “information” component in the sense that it was likely an important village event that households would have known about. In such a context, it could have been the case that the additional demand-side programs did not convey any information over and above the construction itself given what parents already believed about the return to education.

# **6 Conclusion**

Consistent with findings in other studies, our results show that a relatively wellimplemented preschool program—90% of treatment villages received a community preschool (CPS)—can initially increase the cognitive and socio-emotional performance of young children. Even though the midline ITT impacts are low in magnitude (+0.04 SD), the complex substitution patterns make these results consistent with moderate-to-large impacts on
educational opportunities. Since school construction in Indonesia weakened the impact of a father's education on his sons' schooling attainment, the policy should be considered eective, even though it failed to inuence the deep-seated institutional matrix in Indonesia in the 1970s and 1980s.<sup>49</sup> The Inpres schools were eective for girls also in the sense that the girls born to uneducated fathers had a much higher probability of primary completion when exposed to the new schools.

# (9) Conclusions

Exploiting a dramatic expansion of primary schools in Indonesia that doubled the number of primary schools in ve years, we provide evidence on an important policy question relevant for most of the developing countries: does public investment in primary school construction improve intergenerational educational mobility of the disadvantaged groups such as girls and children born to uneducated parents? We take advantage of a large data set from the full count census 2000 and rely on a credible identication scheme developed by Duo (2001). Our empirical specications are based on recent theoretical analysis that suggests that the intergenerational educational mobility curve can be concave or convex, with the workhorse linear model as a special case.

We nd that Inpres schools made the mobility CEF less concave (or more convex), except for girls' years of schooling for which there is no signicant eect on the shape of the mobility curve. This led to substantial improvements in relative mobility of the most disadvantaged children (fathers with no schooling) at the primary completion level irrespective of gender, but also reduced relative mobility among the more educated households. The new schools thus resulted in a stronger persistence in the educational advantages enjoyed by the most educated segment of the society.

The eects on the educational opportunities of children beyond the primary level are, however, dramatically dierent across genders. While the eects on sons' completed years of schooling are qualitatively similar to that on primary completion, surprisingly, there are no signicant e
|**N.E Nigeria**|Host Community|0.107|(0.045, 0.169)|17.0%|62.7%|71.5%|
|---|---|---|---|---|---|---|
||IDP|0.139|(0.103, 0.175)|23.3%|59.8%|28.5%|
|**Somalia**|Non-IDP|0.286|(0.245, 0.327)|44.8%|63.8%|64.3%|
||IDP|0.396|(0.337, 0.454)|63.3%|62.5%|35.7%|
|**South Sudan**|Host Community|0.119|(0.057, 0.180)|19.5%|60.9%|86.7%|
||IDP|0.203|(0.004, 0.402)|34.1%|59.5%|13.3%|
|**Sudan**|Host Community|0.054|(0.033, 0.074)|9.4%|57.0%|67.5%|
||IDP|0.255|(0.232, 0.277)|43.6%|58.5%|32.5%|

Note: Own elaboration based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017).

Another important trend concerns the Ethiopia and Sudan samples, which surveyed refugee and/or IDP households in camps, whereas the Nigeria,