rafmacalaba/data-use-mentions-extended huggingface dataset:
firms, country and product variations suddenly explain significantly more of the total variation in the total values exported per product group–13 and 7 percent, respectively—while the variance decomposition in the number of firms remains unchanged. And dropping the top 5 percent of firms more than doubles the explanatory power of product groups. In other words, in the absence of superstars and large firms, country and product characteristics are far more relevant. This implies that the other (non superstar) firms conform more to typical product group and country characteristics.

This exercise also addresses another potential concern about the data, which has to do with the fact that the sample varies by year across countries. As noted above, we use averaged data from 2006-2008, to control for this, but there is still some variation since not all countries have data for the full period. If this variation was important, year effects would show a significant share in the variance decomposition. As shown in Table 4, the variation due to time effects is null in all cases.

In sum, from this first analysis of the data, we identify superstars—a very unique group of firms—and their importance in defining trade patterns at the country level. We begin with the observation that the relative number of firms by product group is similar across countries, but the relative exports by product groups are different across countries. We then show that the explanation is that while most firms in a sector are similar in terms of relative size across countries, superstars are very different and can be found in different sectors across countries, driving sectoral trade patterns. We also show that relative firm size, which is largely determined by superstars, explains about two-thirds of the variation in trade patterns and firm count explains the remaining one-third. The results imply that a small group of firms have important 16
.

The DiD strategy relies on the assumption that any pre-existing difference between the households benefitting from the project and those forming the control group would be constant over time in the absence of the project (“parallel trends assumption”). One of the advantages of DiD is that it can account for systematic differences between the treatment and control group that prevailed before the start of the intervention. The existence of such differences is to be expected in the context we study, because the allocation of project locations was not determined randomly. The estimation of the program effect β3 in equation (1) will be robust as long as these differences are constant over time in the absence of the project (i.e., in the absence of the project the households residing in project areas would have featured the same time trend as the control group). There is no statistical test to verify (or reject) this assumption. However, a notion in the literature is to use data on the time before the start of the intervention to visually verify whether trends appear to be parallel. Since the LSMS data are available for Malawi in multiple years (2010, 2013, 2016 and 2019), we are able to follow this approach (see Section V). In addition, it is possible that the parallel trends assumption holds only after conditioning on relevant covariates. Since our data allow us to control for a rich set of household characteristics, it will be sufficient for our identification strategy if trends are parallel when conditioning on these covariates.

In addition to estimating equation (1) based on household survey data for two years (pre and post program implementation), we use grid-level data from satellites (which are available annually for the period 2012-2020) to estimate a more general spatial DiD specification of the form: where Yct is the nighttime radiance in grid cell c at time t , Yeart is a set of dummy variables for each year (i.e., year fixed effects), Cellc is a set of dummy variables
In complicated cases, two municípios are reorganized into three. To address this problem, we merged municípios into 4267 Minimum Comparable Areas (MCAs) – consisting of sets of one to three municípios whose borders were constant over the study period.

Much of the data stem from the Brazilian Population Censuses of 1991 and 2000.<sup>4</sup> The key variables of interest were employment: total number of people reporting any employment and wage: reported labor income divided by the number of workers. Self-employed workers and farmers are counted as workers, and their earnings are counted as labor income. Table 6 gives the sources and definitions of variables employed in this paper. Table 7 provides summary statistics for these variables. Some of them are mapped in the appendix.

A unique feature of this analysis is the use of market potential and related variables. Market potential for a target MCA i is defined as the weighted sum of personal incomes of all other MCAs, where the weight is a decreasing exponential of distance to the target município:

> 4 To be precise, they stem from the 12,5% Census sample of 1991 and 5% Census sample of 2000. Both samples represent município level data.
9 R2 = 0.9216<br>0.8 0.8<br>0.7 0.7<br>0.6 0.6<br>0.5 0.5<br>0.4 0.4<br>0.3 0.3<br>0.2 0.2<br>0.1 0.1<br>0 0<br>0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1<br>Individual Country Maps Individual Country Maps<br>Pooled Pooled<br><!-- End of picture text --> 68
Household weights are subsequently calibrated using census data from the 2017 census. Let h represent these model-adjusted weights for households. Design-based weights for children are calculated as jj ww (FF) 1 1 = ∗ h e e e e , 3� cc jj ww jj Where e e e e is the number of eligible children in the household. Weights were adjusted via ww (FF) min�nn iterative proportional fitting to reflect population-level margins for Khyber Pakhtunkhwa on the categorical variables taken from the 2017 census described above (e.g., area of residence, roof nn material, etc.). Survey weights were applied to all analyses, with household weights for the descriptives and child weights for the regressions.

# <mark>Key Variables and Tools</mark>

Basic demographic data were collected from each respondent for child gender, age, maternal educational attainment, birth registration (respondents confirming the child was registered were asked to provide proof of the document), and area of residence (i.e., urban, rural, semi-urban, inner-city). All instruments were administered in Urdu. In circumstances where children were unable to speak Urdu, enumerators effectively engaged children using various methods, such as teaching them Qaida in Madrasa.

## <mark>Outcome Variables</mark>

### _<mark>Early Childhood Development Measures</mark>_

_Children 0-35 months._ The Caregiver-Reported Early Development Instrument (CREDI) longform was used for children under 36 months of age. The CREDI is a globally-validated developmental assessment tool (Waldman et al., 2021), and the short-form version of the CREDI has previously been validated and used in Pakistan (Hentschel et al., 2024; McCoy et al., 2018). The tool consists of up to 100 caregiver-reported items developed according to typical abilities by age within the birth to age 3 range in 6-month increments (e.g., for children 0-6 months, 6-12 months, and so forth) and administration ends based
income as a result of the expansion in CCTs among the poorest. In the rest of the subperiods, the coefficients are statistically indistinguishable from zero.

To investigate these results further, the analysis tested the previous augmented model by adding extra controls for concepts of either public spending or revenues and with and without CCT data. Invariably, the story holds under different specifications: the positive and significant effects of initial food poverty headcount ratios on income growth are found over 2000–14, in particular during the expansion of CCT coverage in 2000–05.<sup>17</sup> One such specification is shown in table 7, panel c, in which the point estimates for the annualized growth rate in the number of beneficiary families exhibit positive and significant effects in the first years of the program’s expansion, consistent with the findings in the conditional income _β_ -convergence model above.

The second channel, that is, the growth elasticity of poverty reduction, can be analyzed through a variation of equation (5) by regressing _gi_ ( _Pit_ ) on the growth rate in mean per capita income interacted with the initial poverty headcount ratios. This adjusted rate is given by the growth rate in municipality’s mean per capita income, multiplied by 1 minus the municipality’s initial poverty headcount ratio ( _Pit−τ_ ), which penalizes more the sensitivity of food poverty to subsequent growth rates in municipalities starting out relatively poorer. The poverty-adjusted growth elasticity of poverty reduction is then defined as follows: The estimates for the whole sample of municipalities are shown in table 8 (panel a). Notice that they increased in absolute value in all periods relative to the ordinary elasticities in table 5. To illustrate the implications of the poverty-adjusted elasticity, consider, for instance, the value of –1 _._ 983 in 1992–2014. If a municipality’s initial food poverty rate is 10 percent and the municipality experiences a 4 percent annual growth rate in mean per capita income, then that municipality would expect an
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**11,** number 4.

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59
**Table 2**

**Explanatory Sectoral Policy Variables Influencing Power Sector Performance** **<mark>(</mark> Ex** **<mark>p</mark> ected Relationshi** **<mark>p)</mark>**

|||**Expec**<br>|**ted Sign **<br>|
|---|---|---|---|
|**Variables**|**Definition**|**Performance**|**Environmental**|
|||**indicators**|**indicators**|
||**DEGREEOF VERTICALUNBUNDLING**<br>|||
|**Partial Vertical Unbundling**|= 1 from the year of separation of generation from transmission<br>and distribution|**+**|**-**|
||= 0 otherwise|||
|**Full Vertical Unbundling**|= 1 from the year of separation of transmission from generation<br>and distribution<br>|**+**|**-**|
||= 0 otherwise|||
||**DEGREE OF DISAGGREGATION**<br>|||
|**Degree of disaggregation**|= the reverse of the Hirschman-Herfindal index (HHI),<br>computed separately for the generation, transmission, and<br>distribution segments; in the case of generation is expressed<br>in installed generating capacity (MW); in the case of<br>transmission expressed in terms of km line length without<br>discrimination between transmission voltage levels; in the<br>case of distribution expressed in terms of total retail sales<br>(MWh)<br>**DEGREE OF PRIVATE SECTOR PARTICIPATIO**<br>|**+**
zones based on the long‐term (30 years) annual mean of monthly rainfall (157mm) and mean temperature (25.4°C) across the communes in the data set (Figure A2) in order to define _Dry‐cold_ , _Dry‐hot_ , _Wet‐cold_ and _Wet‐hot_ climate zones. Long‐term rainfall and temperature conditions as well as their inter‐annual variation greatly varies by zone (Figure A.3). Different climate zones mostly coincide with the different regions (Table A.3). In what follows current weather variation is described by five sets of variables measuring annual, seasonal, abnormal and extreme weather conditions and weather events.

# **4.1 Annual weather conditions**

Variables measuring annual rainfall and temperature levels control for the effect of average annual conditions and variation between years. The use of annual values is common in the literature exploring linkages between weather and economic outcomes (Burke et al., 2011; Dell et al., 2009; Feng et al., 2010; Schlenker and Lobell, 2010).

Based on the CRU data, annual values are calculated as the mean of the monthly rainfall and mean temperature values in the 12 months prior to the survey. Accordingly, 2010 values are not limited to the 2010 calendar year, but measure conditions in the 2009/10 season.

Figure 5 _Annual weather conditions in rural communes by climate zone in 2010, 2012 and 2014_

## _a. Annual rain_

## _b. Annual temperature_

<!-- Start of picture text --> dry_cold dry_hot wet_cold wet_hot dry_cold dry_hot wet_cold wet_hot<br>2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014<br>400 30<br>300<br>25<br>200<br>20<br>monthly rainfall in mm<br>100 monthly temperature in C<br>0 15<br><!--
||Satellite Measures||
||Hourly<br>(1)|Daily<br>(2)|Weekly<br>(3)|Hourly<br>(4)|Daily<br>(5)|Weekly<br>(6)|
|Satellite Measures|55.4|34.8|20.6|.|.|.|
|Low-cost (unadjusted)|59.9|52.1|46.9|60.2|53.5|46.6|
|Jafe Calibration|61.8|60|56.4|66.4|61.8|56.6|
|Local Calibration|39.5|26.9|21.3|42.7|29.8|18.8|
|Satellite Calibration|44.9|32.1|26.5|42.2|28.5|18|

**Note** : The date range used is for the full year 2023. The regulatory monitor values come from the average of CGQA regulatory-grade monitors. Satellite measures are generated from the MERRA-2 assimilation. The satellite value is the MERRA-2 PM2.5 estimate closest to the Bel-air regulatory-grade monitor station. The regulatory monitor values come from the average of CGQA regulatory-grade monitors. The low-cost unadjusted and calibrated values use averages across all low-cost monitors active for a given time interval. Jaffe calibration uses PM1/10 ratio to identify dust events. The local calibration is the unadjusted Purple Air values calibrated to the local context using data from two local regulatory monitors. The satellite calibration uses the MERRA-2 measure to calibrate the unadjusted low-cost monitors.
sources of information were replicated for SSBFs.

We illustrate the validation methodology using the example of business administration, a GBF that includes finance, accounting, and human resources processes. Table 1 summarizes the three-step validation procedure for each technology in business administration. The least sophisticated technology, handwritten processes, can only perform basic manual administration tasks such as transaction entry, bookkeeping, or employee records handling, without any integration or automation features. Standard software like Microsoft Excel or Google Sheets helps with basic functionality to perform mathematical and statistical operations, including charts, and handle financial account, and HR records. However, it requires manual inputs and knowledge to build specific applications, with limited integration and automation. Mobile apps, such as QuickBook online, are pre-designed to perform these tasks with some

> 5Estimates based on Enlyft dataset (Cirera, Comin and Cruz, 2022). These companies are recognized as key players by various specialized sources estimating market potential for ERP (e.g., Research and Market, Fortune Business Insight), even if there are variations in their market share estimations.

8
||(0.4081)|(0.6600)|(1.1238)|
|---|---|---|---|
|**Observations**|307|136|94|
|**Peru:**||||
|**Management score**|‐0.0908|‐0.0584|1.3459**|
||(0.3032)|(0.6178)|(0.5818)|
|**Observations**|818|313|205|
|**Uruguay: **||||
|**Management score**|1.1181**|1.5504**|2.1498**|
||(0.4724)|(0.7493)|(0.9753)|
|**Observations**|263|100|67|

Notes: Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1. Survey weights are used in all regressions. All regressions control for establishment characteristics.

In addition to looking at difference across size and country, it is interesting to consider what other factors may impact an establishment’s ability to benefit from improved management practices. **Error! Reference source not found.** Table 12 below shows that the premia associated with improved management differs by the degree of technological or knowledge intensity of the sector in which the establishment operates.<sup>6</sup> The results show no significant benefit from improved management practices in low‐tech sectors, while in medium and high‐tech sectors there is a positive and significant relationship between improved management and labor productivity. Considering total factor productivity, it appears that only establishments operating in high‐tech sectors benefit from improved management practices, although this result is based on a small sample
|---|---|
||2015/16|2016/17|2015/16|2016/17|
|GDP growth|-2.6|1.2|1.4|1.4|
|Govt Revenue|15.4|15.1|9.9|12.0|
|Govt Expenditure|18.3|11.8|0.5|3.5|
|Net Lending|-2.9|3.2|9.4|8.5|
|Gross Public Debt|13.5|17.8|-9.0|-9.0|

_The figures in the table show the difference between the outcome for each variable and the projection made just before the disaster. Net lending is equivalent to the overall fiscal balance._ <u>Source: WEO database</u>

> 12 The government debt data for Samoa were not available in the 2012 WEO.

12
the Foster-Greer-Thorbecke (FGT) family. The acronym TIP stands for the _three I’s of poverty_ because the curve provides a graphical summary of _incidence_ , _intensity_ and _inequality_ dimensions of aggregate poverty based on the distribution of poverty gaps (Jenkins and Lambert 1997)<sup>9</sup> . These dimensions are shown as follows: (1) the length of the non-horizontal section of the curve reveals poverty _incidence_ ; (2) the _intensity_ aspect of poverty is represented by the height of the curve; and (3) the degree of

> 8 No indication is provided as to whether this salary increase reflected gains in productivity.

> 9 This curve is constructed in four steps: (1) rank individuals from poorest to richest on the basis of the welfare indicator **y** ; (2) compute the relative poverty gap of individual **i** as **gi=max{(1-yi/z), 0}** where **z** is the poverty line; (3) form the cumulative sum of the relative poverty gaps divided by population size; and (4) plot the resulting cumulative sum of poverty gaps as a function of the cumulative population share.

7
For CATI Henderson and Rosenbaum (2020) caveat these values with the indication that a “portion of these estimates do not include fixed costs and underestimate total survey costs”. They also report large standard deviations for both IVR and CATI.

> 13 For example, in the Nigeria LSMS-ISA survey, direct transport costs represented approximately 23 percent of the overall survey budget while these costs are nonexistent in the Nigeria HFPS.

23
McKenzie 2011). Another critique of the approach is that by aggregating average trends for a given group (or cohort) it assumes away key intra-group mobility, which may be equally or more important than aggregate mobility. A recent approach developed by Lanjouw et al. (2011) explores movements in and out of poverty by imposing fewer restrictions than earlier literature on pseudo-panels. The method produces lower and upper bound estimates of mobility which are expected to sandwich _true_ mobility estimates obtained from actual panel datasets.

This paper seeks to validate this “synthetic panel” approach by estimating intragenerational mobility by means of repeated cross-sectional surveys. We focus on Chile, Nicaragua, and Peru; three countries for which we have panel datasets that follow households for more than two rounds. This allows us to compare true estimates of mobility using the three panel datasets against mobility estimates from applying the Lanjouw et al. (2011) synthetic panel approach in which individual rounds of the panels are treated as though they were repeated cross-sectional surveys.<sup>1</sup> In doing so, the paper makes four contributions to the existing literature. First, the study performs a range of sensitivity analyses and robustness tests to help validate the methodology proposed by Lanjouw et al. (2011) and probe its underlying assumptions.

Second, we improve on Lanjouw et al.’s (2011) inability to use retrospective information to improve the estimates. In their application to Vietnam and Indonesian data, Lanjouw et al (2011) force cross-sectional information to be time-invariant and retrospective by making use of the two-year panel structure of the dataset – i.e., they insert the value of variables from the first round into the second round of the panel. This provides an “ideal” but not entirely realistic context within which to apply the method. By contrast, our surveys include the information

> 1 The use of “true” mobility in this paper relates to how close the approach comes to replicate panel data results. It remains that the typical panel
and Rule of Law variables). These effects are among the largest (in absolute terms) in most specifications and for all country groups.

In emerging Europe (East and South) and in wealthy Europe (West and North) alike, as well as in Europe at large, the share of non-contracted employees is positively related to _economic growth_ (plausibly, through demand side channels - the scale effect and the time pressure). The same is true for informal self-employment in Eastern and Southern Europe and in Europe at large, whilst in Western and Northern Europe this effect is absent. These findings suggest that _informal [at the main job] employment is a pro-cyclic phenomenon in Europe_ . This is in line with finding by Hazans (2011a), based on 70 observations of 27 European countries, that the rates of dependent informal employment and unemployment never went up simultaneously between 2004 and 2009<sup>32</sup> . The level of _GDP per capita_ has a positive and significant effect on the share of non-contracted employees in Europe at large and on both types of informal employment within Eastern and Southern Europe, again suggesting that the scale effect overcomes both the substitution effect and the supply side effects. Positive association between the income level and employment informality in Europe is in a sharp contrast with the situation found in large samples of either high-income and developing countries across the world, or only developing countries, or in Latin America and the Caribbean (see Loayza and Rigolini, 2006; Perry et al., 2007; their results partly reported in Table 10). However, we find a negative effect of income level on employment informality when the wealthy part of Europe (the West and the North) is considered separately. Fialova and Schneider (2011) find a similar effect on self-employment for the ―old Europe‖ (the West, the North, and the South).

Note that our results are not directly comparable to those of Schneider et al. (2010) and Bajada and Schneider (2009) on