rafmacalaba/data-use-mentions-extended huggingface dataset:
# **7 Conclusions**

This paper sheds light on the determinants of labor market power by examining how changes in product market regulation affect wage markdowns in a large sample of Indonesian manufacturing plants.

The empirical analysis leverages a new dataset on investment restrictions to quantify product market regulation, as well as highly granular information on values and quantities of products and inputs used by plants to estimate the markdown.

In line with the predictions of a simple oligopsony model, we document empirically a novel externality of product market regulation in the labor market. Specifically, we find that imposing investment restrictions in a product market through the Negative Investment List increases the markdown of plants operating in that market by 25%. The evidence suggests that the effect is driven by the negative impact of product market regulation on firm entry—particularly foreign.

These findings can have important policy implications. By creating a wedge between the marginal product of labor and the wage, product market regulation increases employers’ labor market power and distorts factor allocation. This can result in lower aggregate output and employment, but also higher inequality through a negative impact on the labor share.

Our analysis suggests that pro-competitive product market policies can mitigate labor market distortions. This provides evidence-based support for the policies advocated in Naidu et al. (2018) and Marinescu et al. (2021), which propose to extend antitrust measures from product to labor markets. Any change in industry concentration, due for instance to mergers and acquisitions, should also be evaluated in terms of its impact on concentration in the labor market.

29
the end of the cross-sectional survey (January-March 2021, see timeline 4.3). This study ensured that the campaign ads were distributed equally to both solid and non-solid dwellers (balancing also across gender, caste, education, as well as the treatment assignment of their district in the MNM ad campaign). In other words, by maintaining exposure to the campaign ads constant, this experiment offers a direct assessment of ad effectiveness on both types of households.

Specifically, after completing the cross-sectional survey of the broader trial, respondents were asked if they would be open to being contacted again within 1 to 3 months. To enhance statistical power, we also recruited a new group of participants using the same Facebook Ads and methodology of the longitudinal and cross-sectional samples. Those who agreed to take part in this study were randomly placed into either a treatment or control group, ensuring that each dwelling type was equally represented in each group. We used Facebook remarketing tool to show ads in the Facebook

> 36We expect ad recall to be larger among younger respondents, since they spend more time on social media and thus have more exposure to ads. We also expect larger recall rates among respondents with higher levels of concern about getting malaria, since they would pay more attention to malaria-related ads if these appeared on their feeds.

29
<br>1.5<br>1<br>0.5<br>0<br>KUR Other loans KUR Other loans<br>Median Average<br><!-- End of picture text --> Source: For KUR loans, survey of KUR borrowers; For other loans, 2023 World Bank Enterprise Surveys This requirement for substantial collateral limited the inclusivity of KUR loans and in particular hindered women’s access, since female entrepreneurs in Indonesia are less likely than their male counterparts to own buildings or land that can serve as collateral (World Bank 2016). Qualitative interviews revealed that lack of collateral was a principal challenge women faced in accessing KUR,<sup>29</sup> and quantitative data show

> 29  Another barrier to women’s access to KUR may be the assessment of their husband’s credit history. Some women report their KUR application was denied because their husbands had outstanding loans or had previously defaulted on a loan.

**20**
Table 2 gives an overview of data collection activities and timing of the preschool construction. Our analysis is based on three main waves of data collection: a baseline data collection in May—July 2016, a midline survey in April—June 2017 , and an endline survey in May—July 2018.<sup>16</sup> With 82% of CPS completed for the first school entry in October 2016<sup>17</sup> and 91% of CPS constructed at endline, Table 2 confirms the construction rolled out as planned. Nevertheless, and despite our effort to ensure that the baseline survey preceded CPS construction, a completed CPS building was already available at baseline in 17% of the treatment group villages. It is challenging to conduct an experiment like this with school construction. On the one hand, fielding the baseline too early (well before any construction) would have increased the risk (in case of construction delay) that our baseline sampled children would have been too old to attend the newly built preschools.<sup>18</sup> On the other hand, fielding the baseline too late would have resulted in baseline measures that were arguably already affected by the program. We discuss the implications of the slight overlap between baseline survey and construction below.

During baseline data-collection in 2016, we sampled up to 26 eligible households per village, using an adapted version of the EPI walk,<sup>19</sup> which guarantees representativity of households with children of preschool age.<sup>20</sup> Eligible households include at least one child between 24 and 59 months old at baseline. Children were therefore between three and five years old at midline and between four and six years old at endline. We identified 7053 eligible households at baseline. Using the same sampling method, we added extra households at midline in villages where the number of eligible households at baseline was below 10 (53 were added in total).

# **3.2 Data*
firm-level attributes during 2018 that the literature uses to predict default rates (Glennon and Nigro, 2005; Crawford et al., 2018).<sup>26</sup> This vector contains five real economic variables reported to the tax authorities: net worth, value added per worker (a proxy for productivity), age, wage bill (a proxy for labor intensity), and sales (a proxy for size). It also includes two financial variables collected by the financial supervisory agency: debt outstanding and loan spread. The spread is the difference between the weighted average

> 23This study was developed within the scope of the research agenda conducted by the Central Bank of Chile in the economic and financial affairs of its competence. The Central Bank of Chile has access to anonymized information from various public and private entities through collaboration agreements signed with these institutions. The information contained in the databases of the Chilean tax authority is of a tax nature originating in self-declarations of taxpayers presented to the authority; therefore, the veracity of the data is not its responsibility.

> 24We build different samples of these datasets depending on the analysis. The different samples are described in Appendix A and Appendix Table 1.

> 25In Appendix Table 2, we estimate firm defaults in 2020 using firm characteristics from 2019. The estimated coefficients are very similar to those in our main analysis, confirming the robustness of the probit model estimated using 2019 data. Also, the explanatory power (R-squared) remains virtually unchanged, indicating that the model is similarly informative across years.

> 26The results hold if we use firm-level data during 2016–2019.

11
- Nelson, A. (2008). _Travel time to major cities: A global map of Accessibility_ , Global Environment Monitoring Unit edn. Joint Research Centre of the European Commission, Ispra Italy.

- Nhu, C. and Noy, I. (2020). Measuring the impact of insurance on urban earthquake recovery using nightlights. _Journal of Economic Geography_ , **20,** 857–877.

- NSO (2012). Third integrated household survey 2010-2011 - final report. Technical Report. Malawi National Statistical Office (NSO) and The World Bank.

- Nunn, N. and Puga, D. (2012). Ruggedness: The blessing of bad geography in africa. _Review of Economics and Statistics_ , **94,** number 1, 20–36.

- Perez-Heydrich, C., Warren, J. L., Burgert, C. R. and Emch, M. (2013). Guidelines on the use of DHS GPS data edn. ICF International.

- Pinkovskiy, M. and Sala-i Martin, X. (2016). Lights, camera ... income! illuminating the national accounts-household surveys debate. _The Quarterly Journal of Economics_ , **131,** number 2, 579–631.

- Pokhriyal, N. and Jacques, D. C. (2017). Combining disparate data sources for improved poverty prediction and mapping. _Proceedings of the National Academy of Sciences_ , **114,** E9783–E9792.

- Pratesi, M. and Salvati, N. (2008). Small area estimation: the eblup estimator based on spatially correlated random area effects. _Statistical Methods and Applications_ , **17,** 113–141.

- Ravallion, M. (2018). Inequality and globalization: A review essay. _Journal of Economic Literature_ , **56,** number 2, 620–642.

- Roberts, M., Blankespoor, B., Deuskar, C. and Stewart,
compensation when calculating public-private sector pay differentials, an issue to which the literature pays insufficient attention.

When self-selection is taken into account, pay differentials lose significance when we focus on the whole sample. However, private sector premium is still present for workers with the highest level of education. Results from the Oaxaca – Blinder decomposition show that this differential is driven by the higher returns offered by the formal privates sector to those workers that achieved a higher level of education.

Finally, the analysis allows investigating the issue of wage differentials from the gender perspective. Results show that female workers earn a lower salary in Peru and that the gender penalty is more significant in the formal private sector than in the public sector.

16
# **References**

- Acosta-Henao, M., A. Fern ́andez, P. Gomez-Gonzalez, and S. Kalemli-Ozcan.<sup> ̈</sup> 2024. Firm financing during sudden stops: Can governments substitute markets? NBER, Working Paper 33283.

- Acurio, B., R. Pardo, J.-L. Peydro, and J. Pozo. 2023. The impact of REACTIVA on the real economy and on bank risk-taking. Central Bank of Peru, Working Paper 2023/005.

- Akcigit, U., U. Seven, I. Yarba, and F. Yilmaz. 2021. Firm-level impact of credit guarantees: Evidence from Turkish credit guarantee fund. Central Bank of the Republic of Turkey, Working Paper 21/10.

- Albagli, E., A. Fern ́andez, J. Guerra-Salas, F. Huneeus, and P. Mu ̃noz. 2023. Anatomy of firms’ margins of adjustment: Evidence from the COVID pandemic. Central Bank of Chile, Working Paper 981.

- Altavilla, C., A. Ellul, M. Pagano, A. Polo, and T. Vlassopoulos. 2025. Loan guarantees, bank lending and credit risk reallocation. _Journal of Financial Economics 172_ , 104137.

- Amiram, D. and D. Rabetti. 2020. The relevance of relationship lending in times of crisis. SSRN, 3701587.

- Anderson, J., F. Papadia, and N. V ́eron. 2023. COVID-19 credit support programs in europe’s five largest economies. Bruegel, Working Paper 41282.

- Autor, D., D. Cho, L. D. Crane, M. Goldar, B. Lutz, J. Montes, W. B. Peterman, D. Ratner, D. Villar, and A. Yildirmaz. 2022. The $800 billion paycheck protection program: Where did the money go and why did it go there? _Journal of Economic Perspectives 36_ (2
# **References**

<mark>Amin, M. (2024). H</mark> ow Does Competition from Informal Firms Impact Research and Development by Formal Manufacturing Small and Medium Enterprises in the Developing and Emerging Economies? Forthcoming _Kyklos_ .

<mark>Amin, M. (2023). Does Competition from Informal Firms Hurt Job Creation by Formal Manufacturing SMEs in Developing and Emerging Countries? Evidence Using Firm-level Survey Data.</mark> _<mark>Small Business Economics</mark>_ <mark>60: 1659–1681. https://doi.org/10.1007/s11187-022-00672-z</mark> Amin, M. and Y. C. Soh (2021). Does Greater Regulatory Burden Lead to Higher Corruption: Evidence Using Firm-level Survey Data for Developing Countries. _World Bank Economic Review_ 35(3): 812-828. https://doi.org/10.1093/wber/lhaa007 Anderson, S., J. Daly, and M. Johnson (1999). Why Firms Seek ISO 9000 Certification: Regulatory Compliance or Competitive Advantage? _Production and Operations Management_ 8: 28–43.

Aterido, R., M. Hallward-Driemeier, and C. Pages (2011). Big Constraints to Small Firms’ Growth? Business Environment and Employment Growth Across Firms. _Economic Development and Cultural Change_ 59 (3): 609-647.

<mark>Avenyo, E.K., M. Konte, and P. Mohnen (2021). Product Innovation and Informal Market Competition in Sub-Saharan Africa.</mark> _<mark>Journal of Evolutionary Economics</mark>_ <mark>31: 605–637.</mark> Backus, Matthew (2020). Why Is Productivity Correlated with Competition? _Econometrica_ 88(6): 2415-2444.

Bayati, A., and A. Taghavi (2007). The Impacts of Acquiring ISO 9000 Certification on the Performance of SMEs in Tehran. _The TQM Magazine_
Economic Review 95: 437-449.

Bloom, David E., and Killingsworth, Mark R. (1985), “Correcting for Truncation Bias caused by a Latent Truncation Variable”, Journal of Econometrics, 1985, pp. 131-135.

Bowles, Samuel (1972), “Schooling and inequality from generation to generation”, Journal of Political Economy 80: 219-251.

Chetty, R, and N. Hendren, P. Kline, and E. Saez, (2014). “Where is the land of Opportunity? The Geography of Intergenerational Mobility in the United States,” The Quarterly Journal of Economics, Oxford University Press, vol. 129(4), pages 1553-1623.

Cohen, A (1991), _Truncated and Censored Samples: Theory and Applications_ , CRC Press.

Corak, M and A. Heisz (1999), “The Intergenerational Earnings and Income Mobility of Canadian Men: Evidence from Longitudinal Income Tax Data, Journal of Human Resources. Volume 34, Number 3 (Summer), pages 504-533.

Deaton, A (1997), _The analysis of household surveys: A microeconometric approach to development policy_ . Oxford University Press.

Emran, M. Shahe and F. Shilpi (2011). “Intergenerational Occupational Mobility in Rural Economy: Evidence from Nepal and Vietnam”, Journal of Human Resources, issue 2, 2011.

Emran, M. Shahe and F. Shilpi (2015). “Gender, Geography and Generations : Intergenerational Educational Mobility in Post-reform India”, World Development, Vol. 72, 362-380.

Emran, M. Shahe and Yan Sun (2015), Magical Transition? Intergenerational Educational and Occupational Mobility in Rural China: 1988-2002, World Bank Policy Research Working Paper 7316.

Greene, W (2012), Limited Dependent Variables - Truncation, Censoring, and Sample Selection, Chapter 19, _Econometric Analysis_ , Pearson.

Hausman, J and D. Wise (1977), “Social Experimentation, Truncated Distribution, and Efficient Estimation”, Econometrica, May 1977.
Following the notation in Section 3, the block-level treatment indicator is denoted by _Tg ∈{_ 0 _,_ 1 _,_ 2 _,_ 3 _}_ with distribution P[ _Tg_ = _t_ ] = _qt_ for _t_ = 0 _,_ 1 _,_ 2 _,_ 3 where _Tg_ = 0 indicates the pure control blocks, _Tg_ = 1 indicates the blocks with 20% treated, _Tg_ = 2 indicates blocks with 50% treated, and _Tg_ = 3 indicates blocks with 80% treated. The account-level treatment indicator is _Dig ∈{_ 0 _,_ 1 _}_ .

We use an independent within-cluster treatment assignment and constant within-cluster treatment probabilities. In the absence of data from a pilot experiment, we assume equal moments across assignments _σ_<sup>2=</sup><sup>_σ_2</sup><sup>_μg_(</sup><sup>_d, t_)</sup><sup>_−μn_(</sup><sup>_d, t_)=</sup><sup>_μg_(0</sup><sup>_,_0)</sup><sup>_−μn_(0</sup><sup>_,_0)and</sup><sup>_ρg_(</sup><sup>_d, t_)=</sup><sup>_ρg_(0</sup><sup>_,_0)forall</sup><sup>_g, d, t_.We</sup> _g_<sup>(</sup><sup>_d, t_)</sup> _g_<sup>(0</sup><sup>_,_0),</sup> further assume that the intracluster correlation is constant across clusters. We then impute all these magnitudes based on our baseline data. The parameters of interest are the difference
, 2015; Demanega _et al._ , 2021). In addition to their use by individuals and businesses, the recognition that air pollution in urban areas is hyperlocal led to initiatives in highincome settings to complement networks of regulatory-grade monitors with low-cost monitors to provide air pollution data at a fine spatial scale. For instance, London set up a dense network of low-cost monitors across the city and several cities in California mapped pollution street by street using sensors attached to Google Street View cars (Gil-Alana _et al._ , 2020; Mittal, 2020).

The ability to capture variation in pollution at a granular spatial level is crucial for low-income settings where the prevalence of pollution is high and where the technology for capturing the origin of the pollution is limited (Hajat _et al._ , 2015; Kamigauti _et al._ , 2024). Therefore, the team set up a network of low-cost monitors in Dakar to measure pollution at a higher spatial granularity. We selected to use the Purple Air PA-II-SD, which, at the time the study began in 2019, had one of the highest correlations with regulatory monitors (R-squared of 0.93 to 0.97) (South Coast Air Quality Management District, 2024).<sup>7</sup> Additionally, its accuracy has been tested under atmospheric conditions when exposed to a variety of pollutants and PM2.5 concentrations and, unlike monitors from other providers with a similar technology, PM2.5 measures captured by PA-II monitors are not found to be affected by changes in temperature and relative humidity, making them more reliable (Ardon-Dryer _et al._ , 2020). From February 2020 until 2023, the team progressively deployed PA-II monitors across 28 locations in the Dakar region (Figure 1). The team also used data from an additional PA-II monitor set up by the Ecole

> 6Only 0.17% of PM2.5 values were above 500.

> 7At the time when the decision was made, in 2019, only PA-II classic monitors were available
B. Hazard Data

**Table B.1. Information on hazard data**

||**ERA5**|**CHIRPS**|**SPEI**|**WRSI**|**SWI10**|**NDVI**|
|---|---|---|---|---|---|---|
|**Hazard**<br>**measure**|Precipitation<br>(modeled)|Precipitation|Evapotranspiration|Availability of water for<br>crops|Soil moisture|Vegetation<br>(greenness)|
|Full name|European Centre for<br>Medium-Range<br>Weather Forecasts<br>(ECMWF) Reanalysis<br>5th Generation|Climate Hazards Group<br>InfraRed Precipitation<br>with Station data|Standardized<br>Precipitation-<br>Evapotranspiration<br>Index|Water Requirement<br>Satisfaction Index|Average of Soil Water<br>Index over a 10-day<br>period|Normalized<br>Difference Vegetation<br>Index|

37
- 12 -

While this research has been influential, both in academic and policy circles, it has also been criticized by Neumark and Wascher, among others.<sup>15</sup> They have questioned the quality of the establishment phone data, measurement of the minimum wage variable, the adequacy of control groups, and the failure to consider longer-term effects. In any event, Neumark and Wascher (2007) determine from their extensive review of the minimum wage literature that the weight of the evidence still strongly supports the negative employment conclusion, with magnitudes similar to the consensus estimates of Brown, Gilroy, and Kohen (1983). They conclude that this finding is consistent when researchers focus on less-skilled groups and use aggregate data, as opposed to data for small areas or specific industries.<sup>16</sup> Going beyond purely technical considerations, many economists who question the Card-Krueger-Katz findings are, at a more fundamental level, skeptical about the idea that induced increases in the price of unskilled labor could result in anything but decreased demand for that labor (e.g., Deere, Murphy, and Welch 1995). In any event, there is no question that the Card-Krueger-Katz research raised questions about the validity of different labor market models<sup>17</sup> and left economists more divided about employment effects of minimum wages.<sup>18</sup> These debates notwithstanding, what conclusions can be drawn from the ever-expanding international literature? On balance, the evidence suggests that an increase in the minimum wage is most likely to result in a modest negative employment effect though this is not always the case. For example, Brown (1999: 2154), in a frequently cited review, concludes that “the minimum wage effect is small (and zero is often hard to reject)”. In its reexamination of the Job Strategy, the OECD (2006) came to a similar conclusion, basing its policy advice on “the considerable number of studies [that]
.01|2|5|6|
|p10|5|5|7|7|
|p25|6|6|9|8|
|p50|9|7|11|11|
|p75|12|9|17|22|
|p90|19|12|26|27|
|p95|25|13|32|29|
|p99|40|17|48|43|
|max|48|19|48|43|

In summary, our results show that all three global technologies (Sentinel-5P, MODIS, Google Traffic), when combined with commonly-available weather data, provide statistically-robust and conservative PM2.5 predictions that identify hazardous conditions with high likelihood. At the same time, some global technologies perform better than others in our analysis. In the higher 15
and the East Asia and the Pacific region experienced widening income inequality over the past decade, while data on the Sub-Saharan Africa region and the South Asia region show almost no change in inequality over the same period. Data on the Middle East and North Africa region reveal a modest decline during the 2000s, but the sample in this region only includes five countries, and data collection there is rather infrequent. The authors conclude, then, that the narrowing in income inequality in Latin America in the 2000s has been remarkable.

To analyze regional trends in several parts of this study, we use unweighted averages of the countryspecific trends for which data are available (unless otherwise indicated). This means that each analysis presented will be the average of the same indicator at the country level. Since not all the countries have information for all the years, a simple linear interpolation is conducted to have balanced panel data. If data are missing at the beginning or at the end of each country series, they are replaced by the value of the nearest year on which information is available. However, excluding these interpolations and extrapolations does not change qualitatively the results of the long-term trends.

Our analysis of the Latin America region (figure 2), which is based on comprehensive harmonized household survey data (the SEDLAC database), reaches a conclusion similar to the conclusions of previous studies, showing that the total income Gini coefficient in the region rose moderately during the 1990s (Londoño and Székely 2000), dropped significantly during the 2000s (Gasparini et al. 2009; López-Calva and Lustig 2010), and experienced a slowdown and possible stagnation during the early 2010s (Cord et al. 2016; Gasparini et al. 2016). Although it may still be too early to claim with any certainty that stagnation has occurred in the regional trends in income inequality, both regional aggregates in figure 2 represent evidence of a slower pace in inequality reduction since 2010, more marked in the case of the weighted average because of the upward inequality in Mexico and the
**Table 3:** Delivery of information to parent association

||(1)<br>Overview|(2)<br>Role of<br>parents|(3)<br>Community<br>resources|(4)<br>Child<br>development|(5)<br>Action<br>plans|
|---|---|---|---|---|---|
|_Information ex_|_periment_|||||
|Treatment|0.936***|0.910***|0.936***|0.949***|0.936***|
||(0.028)|(0.033)|(0.028)|(0.025)|(0.028)|
|Control mean|0.000|0.000|0.000|0.000|0.000|
|Observations|174|174|174|174|174|

Notes: Robust standard errors in parentheses. Data source: parent surveys. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01 29