rafmacalaba/data-use-mentions-extended huggingface dataset:
.5% today. For this reason, cement production should be a very good proxy for cement use.

We on the same model as for tall but use as our rely long-difference buildings dependent variable the log sum of cement production over the period 1970-2020 while controlling for log urban population in both 1970 and 2020, thus capturing _cement consumption per urban capita_ . The results in Cols. (1)-(2) of Table 7 show that cement production is positively correlated with MFGSERV and NRXGDP (significant for MFGSERV), consistent with the tall construction results. However, once we control for urban economic development, no differences are observed.

If we use the COMTRADE data to reconstruct cement consumption, we find that the log sums of cement consumption and production over the period 1970-2020 are highly correlated (corr. = 0.98). Likewise, using the log sum of cement consumption produces the

> 32We obtained the data from the _Minerals Yearbooks_ of the U.S. Geological Survey (USGS).
Without the adjustment for the disutility of manual labor the pre-NREGS distribution is derived from the post-NREGS distribution simply by subtracting the net gains from the scheme, as given by gross wages less the imputed forgone income as reported by the household. The gain from the scheme is then defined as (in obvious notation) Gi = Cipost −Cipre. The calculation is more complicated for the adjusted consumption, for which the gain is Gi∗ = Ci∗post −Ci∗pre where: We analyze then the relationship between the net gains and the position along the consumption distribution, both original and adjusted, following a similar non-parametric approach described above for participation.

In aggregating these gains we shall use the popular headcount index (H), given by the proportion of the population living in households with mean consumption below the poverty line. However, given that our adjustment for the disutility of work is changing distribution below the line, it is of interest to also look at two “higher-order” measures, for which we use the poverty gap (PG) index, to also reflect the depth of poverty, and the squared poverty gap (SPG) index, which penalizes inequality among the poor, and can thus be interpreted as reflecting the severity of poverty. All three measures are members of the Foster-Greer-Thorbecke (1984) class of additive measures of poverty.

# **5. Results**

Using the NSS data (rural only) and using the median (Q50 Wi ) as our estimator, we � Ci � find a disutility parameter α� of 0.52.<sup>13</sup> We find similar values in the other datasets described above, namely 0.59, 0.52 and 0.57 for respectively NSS Bihar, Bihar Survey sample as a whole and the Bihar Survey for un-rationed households only. Figure 2 shows a kernel estimation of the distribution of Wi⁄Ci and Table 2 gives corresponding summary statistics for the four datasets.

> 13 To help interpret this number, imagine a
Policy Research Working Paper 10664

# **Abstract**

In addition to the conventional determinants of labor market participation and the choice between wage employment and self-employment, there is a growing interest of the significance of gender role attitudes and personality traits. This study uses data from the 2022 Ghana Informal Sector Measurement Study to investigate the influence of these factors on employment outcomes in the Northern and Ashanti regions of Ghana. The findings are based on a series of analyses, including descriptive, multinomial logistic, and linear probability model regressions. The empirical results show the critical role played by both gender role attitudes and personality traits in shaping individuals’ decisions on labor market participation and employment choices.

Notably, personality traits emerge as significant drivers of observed employment outcomes. However, the impact of these personality traits is often mitigated or even reversed in the presence of heightened traditionalism. Furthermore, the gender-disaggregated analysis reveals that possessing at least a secondary education level is a pivotal factor in the selection of men into formal employment, whereas this criterion holds less significance for women. Conversely, once the decision to participate in the labor market has been made, having at least a secondary education becomes relevant for securing wage employment, regardless of an individual’s gender.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at aamankwah@worldbank.org. A verified reproducibility package for this paper is available at http:// reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>
Additionally, access to timely information from local authorities regarding evolving guidance and safety recommendations and dissemination of such information to patients and communities can also happen through the health facilities. Therefore, all facilities, regardless of their location or level of care offered, should at the minimum, have access to a functioning phone and preferably in today’s world, access to the internet.

Do health facilities in Kenya, Sierra Leone and Tanzania have access to a minimally adequate level of communication tools to coordinate clinical care and health service plans with local authorities and neighboring health facilities? The SDI surveys provide insight on the availability of a functional telephone, either a fixed or mobile phone and a functioning computer with internet access. These data indicate that only between 33% and 43% of facilities across all three countries have a functioning fixed or mobile phone. Similarly, access to a functioning internet connection is poor across all countries, with less than 30% of facilities with internet access. Across all three countries, availability of a functioning phone or internet is poorer in lower-level facilities compared to hospitals, as shown in Figure 4, and poorer in facilities in rural as opposed to urban areas. This has potential implications for care coordination between lower-level facilities and hospitals, with the risk of overcrowding in the latter or challenges in initiating appropriate linkages to care from the former.

> 14 Lippincott Nursing Education. “The Importance of the Optimal Nurse-to-Patient Ratio”. November 10, 2016. <u>http://nursingeducation.lww.com/blog.entry.html/2016/11/10/the_importance_ofth-GCAE.html</u>

11
**Figure 7. Star graphs of household asset portfolios**

(Pre-earthquake and June conditions) <!-- Start of picture text --> Housing Index Housing Index<br>80 80<br>70 70<br>60 60<br>50 50<br>Durable Goods Index 40 Agricultural Assets Index Durable Goods Index 40 Agricultural Assets Index<br>30 Overall 30 Overall<br>20 Quintile 1 20 Quintile 1<br>100 Quintile 2 100 Quintile 2<br>Quintile 3 Quintile 3<br>Quintile 4 Quintile 4<br>Quintile 5 Quintile 5<br>Productive Assets Index Social Index Productive Assets Index Social Index<br>Education Education<br><!-- End of picture text --> Source: Own computations using June 2010 (EFSA II) surveys. Note: The sample is restricted to the households residing in the six strata that cover areas directly affected by the earthquake.

Figure 7 first shows that physical capital, productive capital and agricultural capital have decreased after the quake in the directly affected areas. Indeed, physical capital has decreased after the earthquake in February, and increased slightly between February and June, though not yet to the pre-earthquake level. Productive capital has decreased during both time intervals, while agricultural capital has also decreased after the earthquake and between February and June. Second, Figure 7 shows that the correlation between the asset index and other indexes is generally positive, except with the social capital index (no correlation appears). The correlation is negative with the agricultural asset index. Finally, we note that the correlation between the asset index and the durable goods index has decreased after the earthquake: the between quintiles gaps are lower in June than they were before the earthquake.

# _(d) Directly affected areas_

Table 4 presents descriptive statistics for households living in directly affected areas. The food consumption score is calculated based on the number of different food groups consumed by the household, to represent diversity, and
While a utility may recover costs through the average tariff level, it is pertinent to examine whether all customer groups are facing cost recovery tariffs. Average revenues from residential customers are above full cost recovery level<sup>17</sup> for MERALCO and just a bit lower for BENECO ( **figure** _20_ ). MERALCO has a greater disparity between residential customers’ share of revenues and consumption than BENECO. MERALCO’s residential customers make up 30 percent of consumption and 37 percent of revenues. BENECO’s residential customers make up 52 percent of consumption and 55 percent of revenues. MERALCO’s industrial consumption (30 percent) outweighs its share of revenues (22 percent), but this relationship may be due to industrial customers imposing lower costs on the system than the average cost of service for all customer classes. Industrial consumption only makes up 0.5 percent of overall consumption for BENECO. **Figure 21** compares the percent of consumption for each customer class to the percent of revenue from that class.

**Figure 19: Average tariff revenue and cost recovery by customer group, 2016**

<!-- Start of picture text --> MERALCO BENECO<br>0.20 0.20<br>Full capital cost<br>0.15 Full capital cost 0.15<br>Limited capital cost<br>Operating cost<br>Limited capital cost<br>Operating cost<br>0.10<br>0.10<br>0.05<br>0.05<br>0.00<br>0.00<br>Source: Rethinking Power Sector Reform Project<br>Average revenues (USD/Kwh)<br>Average revenues (USD/Kwh)<br><!-- End of picture text -->

**Figure 20: Percent of revenue against percentage of consumption by customer group, 2016**

# **MERALCO BENECO*
the 69 BAMs (7% of the treatment group). We present these results in the Appendix (Tables A8 and A9). The magnitudes of our estimates are smaller but the effects on our summary outcome measures remain statistically significant. Appendix Figure A1 indicates that monthly incomes for the BAMs were higher than for other public works participants in treatment sites, peaking in the range of 30,000 CFA, which is close to the 37,000 CFA monthly payment for BAMs working in the childcare centers. Figure A2 similarly indicates a rightward shift in the employment index. Finally, Figures A3 and A4 indicate that the income distribution and employment index in the treatment group substantially converge towards those of the control group once we remove the BAMs from our analysis sample, suggesting that the direct employment channel was important in this context, albeit not the only mechanism at work.

# **5 Discussion**

There are certain limitations of the research design worth noting. First, we cannot measure the effects of access to childcare on the decision to participate in the public works program because the childcare intervention was introduced several months after participants had already been recruited and commenced their work assignments. Second, the World Bank project team provided increased monitoring and safeguards to the public works program in sites with childcare centers, so we cannot perfectly isolate the effects of access to childcare from the effects of the additional accompanying measures provided to treatment sites. Third, public works participants in both the treatment and control sites received parental education training from social workers, which could have improved their ability to stimulate their children and could reduce the differences in early childhood development outcomes observed across the two groups. Finally, the COVID-19 pandemic reduced childcare exposure to eight months, instead of one year as planned. There is evidence from nationally representative data collected at the early stages of the COVID-19 pandemic in Burkina Faso that the pandemic has reduced economic opportunities, which may limit the employment and earning effects of the intervention. Our observed impacts are therefore suggestive of potentially larger impacts
The overall savings pattern, ranging from the age and income pattern to the apparent importance of borrowing constraints, resembles prior findings for developing market economies. Controlling for these standard determinants, the surveys allow us to indirectly test savings theories. Specifically, we examine the empirical support for the prediction that households likely to emerge from the transition process as net winners with upward sloping expected lifetime income profiles and low income uncertainty save systematically less compared to households likely to be net losers of the transition from plan to markets or to face high income uncertainty, as implied by both smoothing and precautionary savings motives.

We find partial support for the hypothesis. The specifics of the transition experience suggest that households headed by individuals with above average education, by individuals who are self-employed, and by individuals employed in the private sector are likely to be net winners compared to households headed by individuals who are older, less educated or working in the contracting public sector. Savings rate indeed decline in education, and are higher for households deriving income from the social safety net compared to households with earned income. More puzzlingly, it appears to make little difference for savings whether the head of household is employed in the shrinking lowwage public or the expanding high-wage private sector.
low-income families, which can be used to identify vulnerable people in the society and develop appropriate benefits for them. _CadUnico_<sup>_ ́_</sup> aims to include families with monthly income of up to half a minimum wage per person, or total monthly income of up to three minimum wages. Families with an income above half a minimum wage can also be registered, as long as their inclusion is linked to receipts of social programs, implemented by municipalities, states or the federal government. _CadUnico_<sup>_ ́_</sup> enrollment records show a significant increase in the total number of Venezuelans enrolled in the register, jumping from 1,969 in January 2018 to 74,185 in July 2020 and 77,291 in September 2020. Due to unavailability of recent census data and PNAD surveys not including nationality, there is not enough information at present on the Venezuelans to explore the determinants of registering, thus, this paper concentrates on the determinants of receiving treatment under _Bolsa Familia_ , once registered in _Cad Unico_<sup>_ ́_</sup> .

_Bolsa Familia_ is one of the social assistance programs that uses the _Cadastro Unico_ to identify the low-income families and assess their socioeconomic conditions. _Bolsa Familia_ is a conditional cash transfer for households living in poverty and extreme poverty. In July 2020, 13.5 million families, reaching 44.5 million people - approximately 21% of Brazil’s total population - benefited from _Bolsa Fam ́ılia_ . The total amount disbursed is approximately R$ 30.6 billion (USD$ 6.1 billion) per year, equivalent to 0.45% of the national GDP.

_Bolsa Familia_ benefits are given depending on the family’s composition and per capita income. The basic benefit is equal to R$ 89.0 (USD$ 18.0) and is paid only to extremely poor families, whose income per capita does not exceed 89.0 reais per month. Variable benefits are R$ 41.
apply for FONCODES funding. If institutional capacity and "tastes" for education are unobserved and positively correlated with better educational outcomes, the naive estimate of 6 may be bias,-d up.

If two cross-sections of data are available, one from time 0 and one fiom time 1, n .o re estimation strategies are possible. If the same districts and households are represented in l: o1:h cross-sections, then a fixed effects estimator is a good alternative. However, this has an important disadvantage for our analysis because fixed effects models can be estimated on only a subset of the data. The panel of households interviewed in both 1994 and 1997 consists of only 25% of all households interviewed, while the corresponding fraction of households living in districts which were included in both the 1994 and 1997 LSMS accounts for 71% of the sa:.)nple. Although the use of district (household) panel data would enable us to handle district (hou.;ehold) level heterogeneity, it comes at the cost of fewer observations and less precision.

An attractive alternative is to use a "difference-in-difference" estimator. Assume i lat the error tern in (3) takes the special form _ehd,= yFd + Shd,._ This is equivalent to a specificatioi with district-level fixed effects, with the added restriction that the fixed effects are proportional _;0_ the level of FONCODES funding received. We then estimate: 13
First follow-up <u>(2004):</u>

- Self-employment: same as baseline.

- Migration: defined according to response to survey information, question 2 part C (“Is household living in the same cluster as 10 years ago; nearby village; elsewhere in Kagera; elsewhere in Tanzania; neighbouring country; other country”).

Second follow-up <u>(2010):</u>

- Self-employment: positive answer to section 2D, question T2dQ16 (“During the past 12 months, has [NAME] worked for him/herself or his/her household in a non-farm business? For as an merchant or example, independent fisherman,

- lawyer, doctor, or other self-employed activity?”) and primary activity is not farm/livestock work (section 2D, question T2dQ18).

- Migration: same as first follow-up.

# **B.8 Panel Survey of Income Dynamics (PSID)**

The PSID collects detailed information on occupation and income for the head and (if present) for the ”spouse/partner” of each surveyed household.

Baseline (2001):

- Income: defined as the sum of farm income (ER2042), business income (ER20422 and ER20444), and labor income (ER20443 and ER20447) in the previous year. Losses are not reported for both farm and business income (i.e. these variables are truncated at zero).

- Occupation type: defined according to status in main job (ER17221 and ER17791; “On your main job, are you (HEAD) self-employed, are you employed by someone else, or what?”). We exclude individuals who say they work both for someone else and for self.

- Education: completed education level (variables ER20457 and ER20458).

Midline (2007) and endline (2017):

- Occupation type: same as for baseline (reference variables are ER36134 and ER36392 for 2007, and ER66198 and ER66473 for 2017).

57
Our strategy is to estimate equation (6) with the benchmark diary data, and then compare with the coefficients estimated using the data from the other designs. Treating those other designs as more error-ridden, a cross-module comparison shows how measurement error affects β<sup>ˆ</sup> and γ .ˆ We then estimate Engel curves on simulated data with three types of measurement error: (i) random, (ii) negatively correlated with true values, and (iii) negatively correlated with household size, so as to see which type of measurement error best matches the empirically observed cross-module pattern. In these simulations, the parameter values for the Engel curve with error-free data are based on the empirical results from the benchmark diary, in keeping with our maintained assumption that this is closest to truth.

The motive for the first simulation is that random (classical) error is the typical view of measurement error in consumption data. For example, authors using instrumental variables to treat attenuation bias (e.g., Alderman _et al._ 2006) implicitly assume random errors in measured consumption. For the second simulation, the small literature on errors in consumption surveys supports the hypothesis that these errors negatively co-vary with true values since survey reporting tasks become harder as the household gets richer and the consumption pattern more varied (Pradhan, 2001; Ahmed _et al._ 2010). For the third simulation, a negative correlation with household size is plausible since one person often reports on behalf of the household, and larger households consume more within a given period, increasing the reporting burden. For example, Gibson (2002) finds the understatement by recall surveys especially apparent for the food consumption of larger households.

> the more restrictive assumptions of classical measurement error are used, γ ˆ is biased upwards but no result is reported for β<sup>ˆ</sup> .

10
the capital stock estimates include investment in vehicles that use transport infrastructure (aircraft, trains and trucks) and the value added of transport services.

Generating a clean series for value-added in government and services is not without problems. The aggregate estimates for the service sector “Other” in the statistical yearbook only correspond exactly to disaggregated data for the period after 2010. Prior to that date there are differences that are particularly large in 2006 and 2007. Moreover, prior to 2004 there are no official published official estimates. For this period, the sectoral GDP breakdown produced for the China Industrial Productivity (CIP) project have been used. The IT/Telecom sector corresponds exactly to the requirement for this paper. However, the other two sectors are parts of larger sectors in the CIP. In these cases, it has been assumed that the two sectors used in this paper remained a constant share of the sectors of which they are a part prior to 2004. Even then, the data in CIP for the sector cultural and household services appear to have a break in 1994. Real value-added for IT/Telecom has been estimated by deflating value-added by the gross output price for telecommunication services. Household services have been deflated by the overall service sector deflator. The two discontinuities in the data series mentioned above mean that the derived growth rates for real value added in 1994, 2006 and 2007 appear to be incorrect. The estimated growth rates for 2006 and 2007 for government and infrastructure have been averaged, while the growth rate of real value-added in the household services sector has been set at 10% per year between 1990 and 1994.

34