rafmacalaba/data-use-mentions-extended huggingface dataset:
fixed premium on top of a (variable) electricity price (premium FiT). They can sometimes be guaranteed for a fixed number of years (as in Denmark, Norway, and Netherlands) or come with no such guarantee (in which case the length of contract will be 0).<sup>21</sup> Because wholesale electricity prices are generally not observed, the final remuneration price under a premium FiT is unobtainable. In the empirical analysis, we restrict samples to standard and variable FiTs, and introduce policy dummy variables to differentiate these two types of policy design.

Finally, four countries in the sample, i.e. Poland, UK, Italy and Sweden opted for a quota obligation scheme facilitated with TGCs to achieve renewable targets<sup>22</sup> . There is a rich literature on the comparison of policy effectiveness of FiT and TGC. Although this is not the focus of this paper, we nonetheless collect data on the annual average certificate prices and estimate the correlation between the value of TGCs and the wind installation results.

It should be noted that in addition to FiTs and TGCs, many countries have enacted additional renewable energy provisions, such as investment tax credits, grants, or VAT exemptions. State and local renewable policies are also prevalent (Fischer and Preonas, 2011). Country fixed-effects will reflect these additional incentives if they are stable across years. However, if additional incentive policies are adjusted according to FiT remuneration levels, then our estimated investment elasticity with respect to the FiT rates will be biased.

Data on annual wind generation are obtained from IEA world energy statistics and balances database. All price variables are deflated by the CPI and are expressed in 2000 prices.

> 21Spain, Slovenia, and the Czech Republic allow generators to pick between the standard and premium FiTs. In the dataset, the standard FiTs are assumed to be the relevant ones, because these represent the more guaranteed incentive. No definitive reference was found as to which policy wind generators are likely to
cartels. In the United States, freight transport prices average around $0.04 per tonne-km, compared with $0.11 per tonne-km along the Douala- Ndjamena corridor (USITC 2009).

The regulatory framework is based on market sharing and the centralized allocation of freight. This limits vehicle mileage (to around 2,000 km per month versus 12,000 in the developed world) and undermines incentives for investing in service quality. Hence, the truck fleet is largely composed of poorly maintained secondhand trucks that are typically overloaded to obtain maximum revenue from their restricted usage. There is typically excess supply, with too many vehicles chasing modest freight volumes. In southern Africa, by contrast, a much larger share of freight traffic is allocated through competitive bilateral contracts between clients and shippers.

The cartelization of the Central African trucking industry is at an extremely high level. For example, the truckers surveyed indicated that they were compelled to join truckers’ associations; without membership, getting a load would be far more difficult. In more deregulated environments such as those found in East Africa, being a part of a cartel or truckers’ association counts for less than the individual professionalism of a trucking company.

These considerations make the liberalization of the trucking industry an important step toward improving surface transport in Central Africa. Indeed, until the regulatory framework for the trucking

> 1 The LPI is an annual survey of international freight forwarders conducted in 155 countries. This survey aggregates the responses of survey participants to questions concerning several factors—such as transport, warehousing, and border clearance—into an index that is used to rank and rate global logistics for trade.

> 2 Most countries aggregated as ―West and Central Africa‖ by the LPI are countries that belong to the Economic Community of West African States (ECOWAS).

10
sample size of the uninterested firms, coupled with lower response rates, however makes the confidence intervals on sales and profits reasonably wide, encompassing a change of 15 percent in either direction.

# **5. What does this experiment tell us about the nature of firm competition?**

Our data provide a rare look into the pricing of multi-product small retailers of perishable products in a developing country, and enable us to establish several key facts that are important for understanding how firms compete with one another in these markets, and how firms charging different prices to nearby firms are able to survive over time.

# **5.1 Facts about price changes in these markets**

_Fact 1: Prices change frequently and closely follow the Corabastos wholesale prices._ As noted above, 76 percent of firms at baseline say that they change prices daily. Our six-month follow-up survey asked several questions about how they set prices. Only 9 percent of firms say they know the sale prices of fruits and vegetables at the nearest shop to theirs in the neighborhood, and only 4 percent say they aim to set a price explicitly below that of a competitor. Instead, 91 percent of firms say they just add a mark-up to their purchase price.<sup>19</sup> The result is that retail sale prices closely track vendor purchase prices, which in turn closely track the prices charged at Corabastos. This is seen in Figure 5.

In a simple model of perfect competition, there should be one-for-one pass-through of market prices into retail prices. In contrast, if retail store owners have market power, then they will not pass-through cost changes automatically to consumers. In Table 7, we regress the daily sale prices of our core five Agruppa products charged by retail store owners on the daily price at Corabastos and on block fixed effects. The coefficients range from 0.9 to 1.2, and are thus close to one in magnitude, showing high pass-through of input price changes, and suggesting a high level of
., Combrinck, L., Munghemezulu, C., Dube, E., 2017. Forecasting winter wheat yields using MODIS NDVI data for the Central Free State region. South African Journal of Science 113 Matasov, V., Prishchepov, A.V., Jepsen, M.R., Müller, D., 2019. Spatial determinants and underlying drivers of land-use transitions in European Russia from 1770 to 2010. Journal of Land Use Science 14, 362-377 27
included as part of core questionnaires, not as optional and changeable modules as currently in the DHS (Casebolt 2020). Two widely available data sources in LMICs, the global FINDEX and the LSMS of the World Bank, do not generally have questions on disability and if they do, they sometimes do not apply the best evidence for disability measurement (e.g. the 2012 LSMS for Haiti (ECVMA)). The systematic adoption of the WGSS in the core questionnaires of international surveys such as DHS, the global FINDEX, and LSMS would go some way in producing the data that are needed to assess whether people with disabilities are left behind or if they have proper opportunities and human rights.

5
.09|0.07|

Calculations assume 1 rand _≈_ 0.167 USD in purchasing power parity terms; 4.33 weeks per month. Household poverty lines assume households of four people with only one earner. Control group respondents work 29 hours per week conditional on being employed; earnings for those in full time work will be higher than mean earnings here. Poverty lines are from Isaacs (2016, p.22) and minimum wages are from the Department of Labor for 2015. Minimum wages are for large urban areas (Area A). They are for hospitality businesses with less than 10 employees and shop assistants in the wholesale and retail sector.

51
relative to any other extraction method.

Despite the failure to reject the null based on either the strong or weak criteria, the pattern of which extraction methods result in the largest adjusted R<sup>2</sup> values is remarkably consistent. Bilinear extraction methods for household coordinates, EA centerpoint, and modified EA centerpoint always make up three of the top four models. Recall that the bilinear method computes the distance weighted average of values at the four nearest cell centers. Thus, unlike the simple extraction method, the bilinear method accounts for the point location within the arbitrary cell boundaries of the gridded data product. This approach seems to produce better results than the simple extraction method or the zonal means for small areas (households or EAs). Administrative area appears to be too large of an area to produce strong results, as using Administrative area, regardless of extraction method (simple, bilinear, or zonal mean), tends to produce the smallest adjusted R<sup>2</sup> values. While the pattern is consistent, it should be remembered that differences between each extraction method is not substantial enough to pass even our weak test, and we fail to reject the null.

# **4.1.2** _p_ **-values**

Next, we consider if different extraction methods produce substantially different numbers of significant coefficients. Recall that our maintained assumption is that the weather metrics we examine are significantly correlated with the outcome variables as these weather metrics are commonly used in the literature for just such a purpose. While when examining adjusted R<sup>2</sup> values we disaggregated each bin of regression results by model specification, when examining _p_ -values we disaggregate by 22
# **1 Introduction**

This paper studies the role of firm dynamics in aggregate total factor productivity (TFP), job flows, and wage inequality in Ecuador. A large strand of literature has documented that policy-related drivers and structural frictions impact firm performance with significant effects for aggregate TFP (Restuccia and Rogerson [2008, 2013], Hsieh and Klenow [2009], Bartelsman et al. [2013], among others). Recent literature has found that distortions that impact firm-level dynamics are also important for worker-level outcomes, including employment flows and wage inequality (Haltiwanger et al. [2013, 2016], Decker et al. [2014], Barth et al. [2016], Manning [2021], Criscuolo [2020, 2021, 2022], among others). To examine how firm performance drivers affect aggregate TFP and, simultaneously, impact job flows and wage inequality, the paper leverages a comprehensive employer-employee panel dataset encompassing formal firms in Ecuador. At the firm level, this paper documents a substantial overlap in the productivity distributions of incumbent firms versus entrants and survivor firms versus exiters. Additionally, firm productivity growth is negligible across firms’ life cycle. These firm dynamics suggest underlying market frictions, patterns that are corroborated by macro-level empirical trends. By decomposing aggregate TFP growth, the paper identifies factor misallocation across firms as the predominant channel contributing to Ecuador's aggregate TFP deceleration. Moreover, the distributions of firms by size and age and job flow trends are consistent with persistent allocative frictions, as larger and more productive firms are underrepresented in the economy, new jobs are predominantly created by small, unproductive entrants, and existing jobs are primarily destroyed by old, unproductive firms that continue operating instead of exiting. To reconcile these firm-level and aggregate trends, the paper investigates structural and policy drivers of allocative inefficiencies. Regression analysis at the firm level shows that firms in the top quintile of the productivity distribution face distortive non-wage labor costs that are 3.7 times higher than the bottom productivity quintile,
by taking advantage of various sources of credit that were available in a village prior to microfinance introduction). Thus, our estimates on two comparison groups can be thought of as straddling the biased upward estimates (relative to those without loans) and biased downward estimates (relative to those with other loans) and hence provide valuable information on the magnitude of entrepreneurial spirit bias for future studies of microfinance. Finally, we use data on self-reported loan purpose to identify entrepreneurial borrowers (those who borrow for new business or to repay old business loans) and nonentrepreneurial borrowers. We perform sample splits and compare expenditures among more entrepreneurial borrowers to nonentrepreneurial borrowers.

We have four main results. First, we present a profile of MFI borrowers, who are more likely to be middle-aged, have low education, be relatively poor (i.e., they have overcrowded living conditions), and have prior MFI experience. The characteristics of MFI borrowers are mostly similar when compared to households without any loans versus those with other types of loans.

Second, we find a significant increase in many of the expenditure categories: increased durable purchases, home repairs, festivals, and temptation goods. However, the categories of expenditure that are significantly increased represent a relatively minor share of total expenditures. Thus, food and nondurable expenditures, which are the largest shares of the total expenditure, show no significant changes. This explains why we do not find a significant increase in the total expenditure either.

Third, our results suggest that the entrepreneurial spirit bias is likely to be relatively small because the differences between our two comparison groups are relatively small. In addition, we find increased ‘nonproductive’ expenditures (such as festivals, temptation goods, and home repairs), which are unlikely to be subject to the entrepreneurial spirit bias. These expenditures, while improving utility in the short term, are unlikely to lead to any significant longterm transformation.

Fourth, we find that durable expenditures increase significantly more for entrepreneurial borrowers versus nonentrepreneurial borrowers. However, the temptation goods
foreign trade and foreign exchange rate regime that Poland had already in 1990 only in 1994 and stayed at this level until 1996. Bulgaria reached in 1998 the level of progress in first stage liberalization reforms achieved by former Czechoslovakia in 1992, i.e., during the second year of the transformation program, Poland in 1993 and Estonia in 1994. Bulgaria’s performance was in stark contrast to Estonia—both countries began their transition from the same level in terms of state micromanagement (Table 1). While Estonia moved rapidly in dismantling vestiges of central planning, Bulgaria was falling behind fast reformers.

**Table 1:** _Progress in first generation reforms as revealed in values of EBRD-based aggregate index in 19902004 in selected transition economies (in percent)_

||1990<br>|1991<br>|1992|1993|1994|1995|1996|1997|1998|1999|2000|2001|2002|2003|2004|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Bulgaria|9.5|38.1|38.1|44.5|**57.1**|**57.1**|**57.1**|66.7|**76.2**|82.5|85.7|88.9|<br>88.9|<br>88.9|88.9|
|Poland|**57.1**|57.1|66.7|**76.2**|76.2|76.2|82.5|82.5|95.1|95.1|
firm financial statements to augment the set of firm-level outcomes considered. This is a longitudinal database covering all private formal firms and including information on firm financial characteristics that we use to construct measures of productivity, profits, and nonlabor inputs. We link this firm panel database with the IESS firm panel database based on a common unique firm identifier. We merge the location information in the firm registry to the worker panel database. A couple of definitions of variables used for firm outcome measures differ slightly from Brazil are: capital is defined as purchases of capital goods minus sales and disposals for any purpose, plus values registered by the assets constructed by the employees of the firm and value added defined as gross revenues minus intermediate input expenses.

Our analysis for Ecuador relies on more than 89 thousand observations (about 795 thousand worker-year observations) as seen in Appendix Table E1. On average workers are employed 9.2 months per year and the sample includes 13% of observations with zero months worked. Average monthly real earnings are 1,059 in 2010 USD. Appendix Table E2 shows the workers in the Ecuadorian sample average 33 years of age, with a third being female and 11% having a higher education degree. Workers were employed in the formal sector about 80% of the time before the GFC. Firms have on average 129 workers and 73% of firms are importers. Firms experience on average substantial annual growth in employment prior to the GFC. We ensure the representativeness of our IESS worker panel database by showing that the demographics and job characteristics of workers in the worker sample for Ecuador are similar to those of workers in the complete IESS database. In unreported estimates we find that estimates from standard Mincer wage regressions on the Ecuador worker panel show expected patterns: e.g., a male wage premium of 15% and a higher education premium of 75%.

We estimate Equation (2) using the Ecuador worker database and show the results in Appendix Figure E1 and Table E3. The negative coefficients in Panel A of Appendix
# **2. Data and descriptive statistics**

Our empirical analysis rests on industry‐specific and country‐specific data, spreading over 2002‐ 2010 and including 23 manufacturing sectors in five GCC countries. These are: Bahrain, Kuwait, Oman, Qatar and Saudi Arabia. The United Arab Emirates are left out because of the lack of industry‐specific data. Data on our dependent variable (annual growth in value added) come from the Industrial Statistics Database of the United Nations Industrial Development Organization (UNIDO). The manufacturing sectors are classified as two‐digit ISIC codes, which is the only available for our sample of countries. Value added data are deflated using the CPI indexes from the World Bank World Development Indicators. The variable _share in value added_ represents the value added of each sector as a percentage of the total valued added of the manufacturing sector in an economy in each year.

As a measure of external finance dependence for each industry, we use data from Rajan and Zingales (1998), in line with similar studies (Cetorelli and Gambera, 2001; Claessens and Laeven, 2005; Dell’Ariccia et al., 2008; Liu et al., 2014). Rajan and Zingales (1998) define external finance dependence as the fraction of capital expenditures not financed by cash flows from operations. The figures are based on US manufacturing firm‐level data during the 1980s. An important assumption underlying our approach is therefore that external finance dependence reflects technological features of an industry that are stable across time and space. Capital markets in the US are among the most advanced in the world, and firms face the least frictions in accessing finance. This suggests a valid and exogenous way to identify the extent of an industry’s external dependence elsewhere in the world. Thus, the degree of US firms’ dependence on external finance is good proxy for the demand of external funds on other countries (see Rajan and Zingales, 1998).<sup>1</sup> One potential
6: Combined investment climate measures**

Source: BEEPS II, PICS 2003 Serbia

# **Policy implications for entry, privatization and exit**

As presented above, the results from the international survey show that privately-owned firms, whether privatized or new private, have levels of TFP that are 30-38 percent higher than state-owned firms in the same country. Foreign ownership adds another 23-43 percent to TFP levels. Export orientation adds 22 percent to TFP (for manufacturing 15
headcount rate. These correlations are in line with our theoretical priors, although the association with the poverty rate is stronger than expected, given that no statistically significant correlation is found between poverty and income protection measures.<sup>5</sup> The magnitude of the elasticities between expenditure on job protection and the four economic outcomes is substantial. An increase in job protection expenditures of 1 percentage point of GDP is associated with an increase in GDP of about the same magnitude in 2021 (Table 3, column 2); an increase in employment of about 0.4 percent of the working-age population during 2020 (column 4) and an equivalent decrease in the inactivity rate in the same year (column 6).

These results suggest that preserving employment during 2020 by subsidizing firms’ wage bills brought levels of economic activity during 2021 closer to the pre-pandemic trend. The longer-term effects are unclear, as evidence from business enterprise surveys suggests that job protection measures were associated with less labor reallocation from low productivity firms to high productivity firms. This finding would imply a loss of efficiency in the economy in the longer run (Demirgüç-Kunt et al. 2022).

The share of the population with incomes under the $5.50 a day international poverty line is about 0.4 percentage points lower for every percentage point of GDP spent on job protection measures <u>(Table 3, column 8). From a cross-country perspective, this result suggests that preserving labor</u> earnings may have a stronger effect on short-term poverty reduction than direct income protection measures. However, income protection programs appear to be more effective in preventing extreme poverty in low-income settings during the pandemic (Bottan et al., 2021).

To assess the robustness of these results, we estimate a series of alternative specifications of equation (1), shown in <u>Table 4. In specification I, all dependent variables are expressed as the</u> difference between the values observed in 2020 and 2019. In specification II
possible to measure similar impacts of a policy on PM2.5 pollution at the city level using three different data sources. Thus, if there is data available from only one source, it is still possible to measure the impacts of a policy that affects pollution at a larger geographic level. Nevertheless, the ability to accurately measure the impact of a policy with any data source will depend on the type of policy and the spatial and temporal frequency of interest. For instance, if there is interest in evaluating the impact of an intervention that is spatially concentrated and is unlikely to affect air pollution globally in a city, the satellite data would not be adequate since it provides only one value for the city. Similarly, if policy makers are interested in the impact of a policy to reduce short-term peaks in air pollution, the satellite data would not be a good choice since it is not well aligned with data from regulatory-grade monitors at the hourly level. In these cases, it would be necessary to use data from air pollution monitors on the ground that collect data across space (whether low-cost or regulatory-grade), and the accuracy would depend on the density of the network.

Finally, the results up to this point suggest that the unadjusted PM2.5 measurements from the lowcost monitors give much lower magnitudes (and a lower coefficient) relative to the regulatory and satellite data, even as the effects of a policy measured in percent change is largely aligned across data sources. Therefore, we also evaluate the impact of the policy at the hourly level using the four different calibrations for the Purple Air Data: the two off-the-shelf calibrations from the literature (Barkjohn and Jaffe), and our two co-located calibrations based on PM2.5 measurements from regulatory-grade monitors and satellite estimates (Table 4). Both off-the-shelf calibrations estimate much smaller coefficients (treatment effect in levels), even as the treatment effect as a percent change stays around the same value for the Barkjohn calibration. The co-located calibrations result in a larger coefficient than the unadjusted data and