rafmacalaba/data-use-mentions-extended huggingface dataset:
[0.0497]|[0.0559]|[0.0490]|
|Share of male employment in construction (municipality)|-0.0993|-0.0908|-0.105|-0.0964|-0.0952|
||[0.0786]|[0.0942]|[0.0982]|[0.0798]|[0.0947]|
|Share of male employment in white services (municipality)|0.0127|0.0667|0.0561|0.0168|0.0711|
||[0.0833]|[0.0890]|[0.0911]|[0.0828]|[0.0914]|
|Share of male employment in other services (municipality)|0.00105|0.0286|0.0262|0.0108|0.0193|
||[0.0635]|[0.0651]|[0.0684]|[0.0630]|[0.0640]|
|Constant|-0.815|-0.494|-0.781|-0.806|-0.608|
||[0.234]***|[0.264]*|[0.174]***|[0.234]***|[0.256]**|
|Observations|1,696,260|1,696,260|1,696,260|1,696,260|1,696,260|
|R-squared|0.256|0.255|0.255|0.256|0.255|
|Department fixed effects|Yes|No|No|Yes|No|

Sources: 2018 National Census of Guatemala, 2017 and 2018 municipality statistics (FUNDESA and INE), 2017 social spending data (ICEFI).
Data on remittances received from migrants (whether men or women, and if they had migrated for work) is also useful in understanding changes in rural men’s and women’s opportunities and income from ruralurban migration. Among the LSMS-ISA, the 2016-17 Malawi survey is the only one that asks about money sent back to the household from children aged 15 and older who have migrated (including the amount, frequency, mode of payment, and recipient within the household). Extending these questions to include parents who have migrated, across different country surveys going forward, would also be helpful.

# _Skills development_

Despite the narrowing of rural gender gaps in school enrollment over time, rural women face substantial inequalities in skills development and the ability to seek improved economic opportunities. Broad questions remain on (a) understanding how to target interventions to narrow these gaps and (b) how skills and economic mobility translate into better employment outcomes. Several randomized controlled trial (RCT) program-level studies have introduced formal business training for women entrepreneurs but often with little effect, due perhaps to skills not improving enough or the right skills not being taught (see McKenzie and Woodruff, 2014).<sup>6</sup> Underreporting of rural women’s employment also makes targeting training programs and extension services difficult. At present, most nationally-representative household surveys typically do not ask skills-related questions beyond literacy and having formal schooling, which as seen in Van den Broeck and Kilic (2018) may not be relevant for designing policy in contexts where only low-skilled opportunities are available. The World Bank’s STEP skills measurement program has implemented household and employer-based surveys between 2012 and 2017 in 17 countries, spanning a range of cognitive and non-cognitive skills through objective and self-reported measures, but only covers urban adults aged 15-64.<sup>7</sup> To inform areas that surveys should cover on collecting data on different types of skills, and in turn how to evaluate outcomes from different types of employment-related programs, guidelines were developed during the 20
above that of Bangladesh, but below India, Kenya and Vietnam._

Overall, low labor costs cannot fully compensate for low productivity. As a result, Ethiopian firms are among the least profitable (in terms of value added per worker) in our sample (Figure 2 and A.6). However, value added per worker in Ethiopia is actually relatively close to those of the next most profitable country – Bangladesh. If we look at the whole manufacturing sector, value added per worker is higher than in Bangladesh. If we focus on the garment sector, then we find that the profitability of Ethiopian firms is just below that of firms in Bangladesh.

There are three important caveats related the value added data I have just presented. First, the large number of missing values in the material costs variable means that I am working with a selected sample. Second, the surveys for Ethiopia and Vietnam do not record data on rental payments for land or capital. I impute these payments by using 6
**||||
||**1992**||**1993**|**1994**|**1995**||**Panel**|
|Tobin's Q|96.7<br>(52.4)|<br>(|80.3<br>50.1)|78.2<br>(44.1)|76.9<br>(46.3)||81.2<br>(48.2)|
|Profitability|12.1<br>(11.4)|(|13.7<br>11.6)|15.1<br>(11.3)|17.1<br>(12.6)||14.8<br>(11.9)|
|Correlation (Tobin's Q: Profitability)|15.1||23.1|26.9|27.2||21.8|
|*****Standard Deviation in Parentheses.||||||||

****** Residual ownership is by individuals and smaller investment funds.

To calculate Tobin's Q, we use the secondary market prices for firms traded on the PSE at the end of January following the year for which we use accounting and ownership data. This way we can reasonably be assured that the market has incorporated all available information. Using these prices, we calculate Qs as the sum of market valuation and total debt outstanding, divided
For a better understanding of the changes in inequality and poverty presented so far, the final result we present in this section looks at the incidence of fiscal interventions across deciles of the distribution, as a share of market income plus pensions. Results are shown in Figure 6. The strong progressive profile of the education and health transfers, with benefits relatively more concentrated in the bottom deciles, explain the sharp reduction obtained in the Gini of Final income. In addition, these in-kind transfers lift the impact of fiscal policy in the bottom of the distribution. If we consider only in-cash transfers, we observe that they are not sufficient to offset the burden of taxes across all deciles of the distribution. But once in-kind transfers kick in, they have a boosting effect on the final position of low-income households, making them net receivers of fiscal resources. From decile 4 and above, households are net payers to the fiscal authority.

**Figure 5. Poverty rate before and after fiscal policy in Turkey and peer countries**

<!-- Start of picture text --> Market Income (M.I.) Disposable Income Consumable Income<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Argentina Brazil Chile (2013) Mexico Croatia Poland Russia USA (2016) Turkey<br>(2012) (2009) (2014) (2014) (2014) (2014) (2016)<br><!-- End of picture text --> Source: Argentina (Rossignolo 2017); Brazil (Higgins and Pereira 2017); Chile (Martinez-Aguilar et al. 2016); Croatia (Inchauste and Rubil 2015); Mexico (Scott et al. 2018); Poland (Goraus and Inchauste 2016); Russia (Popova et al. 2018). Turkey: Own estimates based on Turkey 2016 HBS. Note: Argentina, Brazil, Chile, Mexico, and
the described minimum-distance restriction could be sufficient to reduce the possibility of spill-over effects in this context based on the following reasons. To start with, it has been recognized that parents usually desire to have their young children attending educational centers located close from home (e.g. see Paes de Barros et al., 2011 for evidence on Brazil). Consistent with this view, 97% of children ages 4 to 6 years old attending a pre-primary in rural Guatemala walk 30 minutes or less to attend an educational center (Living Standards Measurement Survey, 2006). Moreover, the reluctance of parents to have their children attending pre-primaries outside their communities may be heightened in this context due to cultural factors related to a strong

> 14One additional concern is the possibility that a community that does not have a public pre-primary might have a private one (this could attenuate our estimates). As mentioned above, we have data on private pre-primaries and their exact location for 2005 but not for prior years. Consequently, we cannot assess whether the expansion in public pre-primaries affected the supply of private pre-primaries in our sample. Still, we believe that it is unlikely that private supply will play a relevant role in this context because of the overall little demand for private pre-primaries in rural Guatemala. This is well exemplified in the fact that only 1.2% of communities in the comparison group and 0.8% of treatment communities had a private pre-primary in 2005.

14
are collected for a purpose other than that intended for the analysis. In this case, CDRs are collected by MNOs for billing purposes, among other things. Because of this, it is imperative that the outputs generated from these data be subjected to validations and/or adjustments before being put into use. This is to ensure that the results from the analysis of CDRs do in fact match up with the reality on the ground.

In order to ensure that there is no confusion, we explicitly define _validation or evaluation_ here as follows: the process of assessing correctness of outputs (for example, models, indicators, and so on) generated from CDR data as with reference to specified ground truth data. For instance, in order to validate OD flows generated from CDR data, we may compare them with OD flows generated from surveys or manually collected by a human, and then measure the difference. For some use cases, this validation may be enough. However, for other use cases, we may also need to adjust the outputs in order to make them usable for policy. In our OD flows example, we know that the trip counts generated from CDRs represent the subscribers of an MNO or a set of MNOs and not necessarily the actual population. Therefore, to get realistic trip counts, we need to _adjust_ the trip counts from CDRs using actual population data. In figure 6, we provide an overview of the key components in the validation and adjustment framework. In most cases, outputs generated from CDR data sets need to be validated even with very simple strategies. For instance, subscriber population can be compared with actual population by running correlations. While this validation may be enough for some use cases, adjustments may be required for other cases.

11
|Respondent says they are very likely or extremely likely to benefit<br>personally from REDD+ (1 = yes, 0 otherwise)|0.42|0.49|
|---|---|---|
|Reported firewood used per month (kilograms)|298.87|394.84|
|Number of formal CFUGs (number of groups)|1.3|0.73|

We now present illustrative information on the possibility for leakage as fuelwood collection restrictions are put in place. This information comes from our household survey. Respondents were directed to indicate all options that would apply, given restrictions of 25%, 50%, 75% and 100%. Figure 3 shows that faced with a 25% fuelwood collection restriction, over half of respondents would shift to fuels produced on their private lands. About 30% would include reductions in their energy consumption in their response portfolio, about 25% would gather from other forests and substantial portions would shift to biogas and LP gas or gather in CFs. Very limited portions of households would use crop residues, kerosene or dung (not shown).

Figure 3 Alternative Energy Sources Identified for 25% Fuelwood Restrictions in Respondents’ Main Forests 23
of footprint reduction versus housing affordability.

In Section 2 below, we briefly present relevant Cape Town stylized facts regarding job locations (polycentrism), the high level of income inequality that prevails in the city, and the different housing submarkets that coexist. Section 3 then details the theoretical model. Section 4 presents the data and calibration. Section 5 describes a benchmark simulation and the effects of the two sets of policies. Section 6 briefly concludes.

# **2. The Cape Town context**

Cape Town is a sprawling middle-income city of 4.2 million residents, with an ethnically diverse population (46% Black Africans, 40% “Coloured” (Mixed Descent), 13% Whites and 1% Indians/Asians). The city faces a population growth of 2.4% annually, fueled by in-migration from South African rural areas and other African countries. It has inherited high levels of poverty and acute income inequality (which is highly correlated with race) from past Apartheid policies: As a result, the Gini index for Cape Town is among the highest in the world, reaching .62 in 2017.

This high level of inequality is associated with a fragmented housing market, consisting of four main segments: (i) privately developed formal housing (which houses 52% of residents in 2016), (ii) State-subsidized formal housing (29%), (iii) informal structures in informal settlements (9%), and (iv) structures erected illegally in the backyards of formal housing, mainly State-subsidized housing (8%) (source: Statistics South Africa). Informal settlements first appeared in Cape Town with the rapid urbanization of Black Africans that was stimulated by the labor demands of the wartime economy in the late 1940s (Wilkinson, 2000). They re-appeared at scale during the 1970s as a result of inadequate affordable housing provision coupled with the relaxation of Apartheidera "influx control laws" (which sought to limit internal in-migration of rural Black Africans to cities). As in the
# **Figure 3. The poverty rate and per capita income of the districts with the 2006 poverty rate above 40%**

A. Per capita income of households in the treatment B. The poverty rate of households in the treatment and and control districts control districts (in percent) <!-- Start of picture text --> 2004 2006 2008 2010 2012 2014 2016 2018 2020<br>2004 2006 2008 2010 2012 2014 2016 2018 2020<br>Year<br>Year<br>Treatment Control<br>Treatment Control<br>30 90<br>80<br>25<br>70<br>60<br>20<br>50<br>15 40<br>30<br>The poverty rate (%)<br>Per capita income (million VND) 10 20<br>10<br>5<br>0<br><!-- End of picture text --> Note: This figure graphs the per capita income and poverty rate (%) of households in districts covered by the 30A Program compared to other districts during the 2004-2020 period. The per capita income values are adjusted to 2020 prices using the annual Consumer Price Index (CPI). The analysis focuses on households in districts where the poverty rate in 2006 was greater than 40%. The vertical lines indicate the year 2009, which marks the implementation of the 30A Program.

35
expenditures; lower rates of poverty and extreme poverty (the latter for producing districts only); fewer households with basic necessities uncovered; and lower illiteracy rates (producing districts only). At the same time, districts in producing provinces exhibit higher consumption inequality, as measured by a higher Gini coefficient.

When we refine the control group and introduce department fixed effects (regression (2)), the differences between producing districts and districts in non-producing provinces (β0) remain large and significant: on average, households in producing districts display 14 percent higher per capita expenditures, poverty rates are between 3 and 4 percentage points lower, there are 4 percentage points fewer households with basic necessities uncovered, and illiteracy rates are 1.6 percentage points lower.<sup>8</sup> These beneficial impacts contrast with those regarding consumption inequality: the Gini coefficient in

> outcomes in 2007, where a clear causal relation can be asserted. Moreover, to correct for such biases we would need to use data on district characteristics prior to 1993, which are unavailable.

> 8 Observe that the last two estimates show the net impact after controlling for their 1993 levels.

13
schemes, resulting in 2020 employment rates that were largely unchanged from 2019. This was observed, for example, in Hungary, Poland, and Romania, as well as in some Western Balkan economies, including North Macedonia and Serbia. In contrast, employment rates fell and unemployment rose sharply in 2020 in many countries in Eastern Europe, the South Caucasus, and Central Asia, where employment retention schemes were smaller or absent. In many of these countries, where informality tends to be high, increases in unemployment were somewhat stemmed by shifts from wage and salaried work to selfemployment (ILO 2022a).

The labor market recovery since 2020 has been similarly uneven across and within countries, as well as across sectors. In Türkiye, Poland, and Kazakhstan—ECA’s second, third, and fourth largest economies, respectively—employment has returned to prepandemic rates, and in the Central European economies labor market slack has returned to or fallen below pre-pandemic levels.<sup>13</sup> In contrast, the recovery has been more sluggish in some economies in South Caucasus and Central Asia. In some cases, labor market recoveries have been shallower than unemployment data suggest because employment losses have been offset by increases in people outside the labor force—reflecting, for example, job seekers that have become discouraged from long spells of unemployment. High-frequency World Bank Household Survey data indicate persistent financial concerns among the poor and vulnerable, as pandemic-related job and income losses have disproportionately affected them, particularly in lagging regions within countries (World Bank 2022h).<sup>14</sup> As a result, the erosion of human capital from pandemic-induced unemployment has varied in ECA, which could lead to divergences in potential growth over the coming years.

The pandemic has highlighted not only the critical role of digital connectivity for the continuity of public service provision and economic activity, but also the digital divide across income groups and geographic regions. Although access to broadband internet has expanded over the past decade in ECA, with almost all households
to the item as a barrier to access, some by use of independent information. For example, in the case of affordability, the authors obtain data on the all-in cost (cash plus travel time<sup>18</sup> ) of each service and define as excluded those with monthly income less than 50 times that service (in Swaziland 88% of households are seen as excluded on cost grounds from transactions accounts). The idea of this single index seems to be to benchmark a country’s overall performance; the components of the index could point to the direction of policy solutions. While the implementation of the idea might be criticized, it does represent a creditable attempt to distill the information from household surveys in a policy-relevant manner.

A different type of household survey, the financial diary approach, attempts to document in detail the financial transactions of the poor. Pioneered in Bangladesh by Rutherford (2000), (2002), financial diaries can be thought of as an anthropological study exploring the way in which households manage their financial affairs. As such, it is more exploratory of patterns than simple quantification of pre-defined concepts. This approach has helped dispel misconceptions about how the poor employ substitutes for services that only the better-off can afford to acquire from mainstream finance. Awareness of these substitutes is a useful pre-requisite for formal institutions designing new products at reasonable cost to meet the demand of low-income clients with a viable cost structure.

# **3. Business surveys**

Three main methods for obtaining information about business use of financial services are used in advanced economies. The first is from the required public reporting by widely held and listed companies. This data is readily available and has been studied by many researchers. Information about the financial service use of smaller firms is not as readily available, though there are a number of important sources. Amadeus, a commercial service from Bureau van Dijk, draws on sources – mainly credit registries – in 34 European countries to provide a standardized balance sheet and income statement,
the location, time, and percentage juvenile caught by non-SNP sets. After obtaining length distributions for all sets in the electronic logbook data, I match sets to landings events. I then use the percentage juvenile measured by third-party inspectors at landing to update length distributions in the electronic logbook data and calculate juvenile catch for each set.

Specifically, I first identify sets in the full electronic logbook data (reported to the regulator) that are also in the SNP data based on unique vessel identifiers and the time each set occurred. I calculate the number of individual anchoveta (both juveniles and adults) caught by these sets based on their length distribution and tons caught. When percentage juvenile for a set in the SNP data does not match its counterpart in the full electronic logbook data (e.g., the vessel reported a different percentage juvenile to its owner than to the regulator), I shift the length distribution up or down in half-cm increments until I minimize the absolute difference between the implied percentage juvenile (percentage of individuals that are less than 12 cm, as implied by the updated length distribution) and the percentage juvenile reported to the regulator (i.e., a one unit shift of the length distribution in either direction would result in a larger absolute difference between implied and reported percentage juvenile).

I then impute length distributions for non-SNP sets as follows. For each two-weekof-sample by two-degree grid cell, I calculate the individuals-weighted average proportion of individuals in each half-cm length interval caught by SNP sets. Given the percentage juvenile value for each non-SNP set, I adjust the length distribution for that set’s two-week-of-sample by two-degree grid cell to match the set’s percentage juvenile value. For sets with percentage juvenile above (below) the individuals-weighted average percentage juvenile for their twoweek-of-sample by two-degree grid cell, I inflate (deflate) the proportion of individuals below 12 cm and deflate (inflate) the proportion of individuals above 12 cm so that the imputed length distribution for each set implies a percentage juvenile equal to the percentage
represents measures of precipitation shocks. The main precipitation shock is a binary variable that takes the value of 1 if precipitation is 1 standard deviation below the long run average for a specific ADM2 unit. Average temperature is also accounted for in the estimations.

We deploy a small set of firm-level controls (X) in the estimation - whether the firm is a multi-firm, age of firm (in logs), size of firm (in logs), exporter status, foreign ownership, and whether the firm has a checking or savings account. Sector (ISIC 2 digit level) and survey year fixed effects are also included in the specification. The identification strategy does face the challenges of omitted variable bias and selection. Simultaneity bias is less of a concern given that individual firms are unlikely to influence precipitation shocks. To address omitted variable bias concerns, we control for a variety of controls at the firm-level. We also account for time-invariant omitted variables at the ADM1 geographical level through ADM1 fixed effects. The estimations do face the issue of endogeneity due to firm location selection may be affected by precipitation shocks. The best way we account for is through controls that are determinants of firm productivity. The sample of firms in MENA are survivors, therefore our estimates may understate the true effects.

Potential channels through which precipitation shocks may affect sales are explored in the study replacing the outcome variable in equation 1 with various variables identified in the literature as plausible channels. Finally, the more recent rounds of the MENA surveys (around 2019) do have specific questions on green investments and adaptation. This allows us to explore the relationship between precipitation shocks and various green measures.

8