rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 4.5: Outage frequency and lightning strikes**

<!-- Start of picture text --> 250 250<br>200 200<br>150 150<br>100 100<br>50 50<br>induced outages 2000-2017 0 0 Number of lightning strikes<br>Average number of days of storm<br>January February March April May June July August September October November December<br><!-- End of picture text --> Source: _authors based on BREB electricity performance data and_ NASA Tropical Rainfall Measuring Mission Lightning Imaging Sensor (LIS)

**Figure 4.6: Time series for outages and lightnings.**

<!-- Start of picture text --> 400 16<br>350 14<br>300 12<br>250 10<br>200 8<br>150 6<br>100 4<br>50 2<br>0 0<br>Lightning count<br>Days with outages<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> _Source: authors based on BREB electricity performance data and_ NASA Tropical Rainfall Measuring Mission Lightning Imaging Sensor (LIS) To better understand the correlation between power outages (caused by natural shocks) and severe local convective storms, we further disaggregate the lightning data by Dewan et al. (2017) using seasonal maps for the period 1998–2014 (Appendix 5). Seasonal weights were assigned to divisions, depending on the average number of lightnings each of these divisions experienced over the considered period, thus allowing us to disaggregate the LIS lightning data to the division level. Table 4.3 presents these correlations. Overall, lightning strikes are highly correlated with power outages. The correlation is highest in the divisions of Dhaka (75%), Rajshahi (72%), and Chittagong (49%), which
use data from Jamaica to analyze the short- and medium-term impact of interventions in early childhood. In one study, a lottery was used to divide a sample of stunted children ages 9-24 months into four groups: The first study group received a food supplement of 1 kg. of milk-based formula provided weekly; a second study group received early childhood stimulation, specifically weekly home visits by social workers who demonstrated play with home-made toys and discussed parenting issues with mothers; the third study group received both the supplement and the stimulation; the fourth group served as a control group. In addition, data was collected on a sample of non-stunted children. Children in all of the study groups were then followed over time. Results after two years suggested that both the stimulation and nutritional supplement interventions had positive impacts on child development as measured by the Griffiths Mental Development Scales. The largest effects were found in the group that had received both interventions—after two years these 18
7|
|**Avg Labor Productivity in Woreda**|1,916|228,403|335,585|
|**Avg Markup Materials ACF Cobb-Douglas**|1,916|2.0|1.0|
|**Avg TFP ACF Cobb-Douglas w/o constant using phi**|1,916|-0.9|1.0|
|**Avg Wage of Firms in Woreda**|1,858|1,463,913|4,999,070|

Table 1 shows the descriptive statistics for the main variables from the manufacturing panel data, including the overall number of observations (woreda-years), and for each variable we list the mean as well as the standard deviation.

# **4. Estimation Strategy and Regression Results**

## **4.1 Difference-in-Differences and Triple Differences**

As explained earlier, Ethiopia adopted the 3G broadband network in 2008, thereby substantially improving mobile communication access. Taking advantage of the geographic variation of such shock, this study employs the difference-in-differences (DID) methodology in estimating the impact of enhanced local mobile access on firm performance. Specifically, the DID models compare firm performance across treated and untreated woredas before and after the adoption of 3G networks in 2008.

14
has at its disposal for producing and disseminating data. This could include the physical plant, the quantity and quality of computing facilities, and the number and skill level of workers, among other inputs. All of these are clearly relevant information for gauging the capacity of an NSS. However, it is also clear that implementability would once again be a problem, since gathering accurate, homogenous data on inputs would be a costly and drawn out task.

# **3.2.5. Variables and Aggregation**

Once the dimensions have been specified, attention turns to identifying the variables and selecting an appropriate aggregation method. The desiderata suggest that the variables in an index should be coherent with the concept being measured; they should be publicly available (i.e., drawn from NSSs, rather than from experts or other sources). The aggregation method should be selected with rigor in mind, including the axioms or properties that the method satisfies. At the same time, it should aim for simplicity to maximize general understanding and impact.

We turn next to discussing our proposed aggregation method. To help focus discussion on policy relevance and conceptual issues, we leave the more technical details to Appendix 1, Part A. We also offer a simple numerical example that further illustrates the ideas in this subsection in Appendix 1, Part B.

# _Basic Setup_

In the domain of statistical capacity, a variable is typically derived from a simple “yes-no” question concerning a normative guideline that a country’s NSS should meet. For example, in the MSC dimension, a relevant question is whether a modern system of national accounts is being used. In the CS dimension, a question might ask whether there has been a population census within the last ten years. Such goals and targets can be found in discussions of standards and best practices 15
in that year.

Let us also inspect time-trends in inequality. Figure 16 shows our estimates of the Gini coefficient for the years under consideration. Both approaches are found to produce qualitatively similar results.<sup>26</sup> We observe a slight moderation in consumption inequality in India since 2011. This could in part be attributed to the fact that top-income households are under-represented in household surveys (whether NSS or CPHS). Consequently, consumption inequality estimated from household survey data capture distributional changes for households that are in the bottom 95 percent, say, of the distribution. To the extent that the income or consumption growth since 2011 is largely concentrated in the top end of the distribution (Chancel and Piketty, 2019), our household survey-based estimates of consumption inequality will be downward biased.

Figure 14 in Appendix 4.3 reveals that the moderation of inequality has been larger in rural than urban areas. Since 2015, changes in rural inequality have been less pronounced than urban areas. Urban inequality dropped in 2018 which coincides with the year in which the rate of poverty reduction was its highest. Figure 15 in Appendix 4.4 shows that other measures, namely, poverty gap and mean-log deviation yield trends in poverty and inequality dynamics that are consistent with the main results.

Finally, in Figure 17, we connect our estimates of poverty and inequality for India over the last decade with estimates dating back to 1993. It can be seen that our estimates of headcount poverty preserve the long-term trend of poverty reduction that is observed in India over this period. By the same token, our estimates suggest that the current poverty rate is higher than the forecasts based on pass-through adjusted consumption growth from national accounts (under the assumption of distribution neutrality). For consumption inequality we observe a trend reversal around 2011 (see Figure 18). Inequality is estimated to have steadily increased between 1993 and 2011. By our estimates inequality has started to moderate after 2011.

# **5.2 Robustness analysis**

Our preferred specification
|
|LVA|-0.0001|-0.0007|0.1550|
|NLD|0.0054|0.0561|0.0961|
|NZL|-0.0025|-0.0188|0.1301|
|POL|0.0069|0.0562|0.1228|
|PRT|0.0039|0.0378|0.1026|
|SLV|0.0053|0.0478|0.1103|
|SVK|0.0010|0.0067|0.1520|
|SWE|0.0074|0.0631|0.1175|
|TUR|0.0010|0.0106|0.0988|
|**Average **|**0.0034**|**0.0328**|**0.1138**|

**Table 2: Effective VAT progressivity and redistribution in OECD countries**

Source: Thomas (2020)

# 2.3.3 Other taxes

Effective progressivity studies can also be undertaken for excise taxes (including health- and energyrelated taxes) where microdata is available on consumption by different households across products subject to excise taxation. Excise taxes are often found to be regressive, although this is not always the case (e.g., Flues and Thomas, 2015, find transport fuels in some OECD countries to have some progressive impact). Furthermore, beyond the pure fiscal impact, health- and energy-related excise taxes, through eliciting behavioral changes, can have broader progressive impacts (see, e.g., Fuchs et al., 2019); Heine and Black, 2019; Sassi et al., 2018).

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Gondim, F. S., Campos, M. O., Flores, T. R., França, G. V., & Medeiros, A. C. (2022). 2019 National Health Survey: progress in monitoring. _Epidemiologia e Serviços de Saúde_ , 31(Special Edtion), 1-3. doi:10.1590/S2237-9622202200001.especial Higgins, S., and Lustig, N. (2018). Allocating Taxes and Transfers and Constructing Income Concepts. Commitment to Equity Handbook: Estimating the Impact of Fiscal Policy on Inequality and Poverty, 219.

Higgins, S., and Pereira, C. (2014). The Effects of Brazil’s Taxation and Social Spending on the Distribution of Household Income. Public Finance Review, 42(3), 346–367. <u>https://doi.org/10.1177/1091142113501714</u> Higgins, S., Pereira, C and Cabrera, M. (2020). CEQ Master Workbook: Brazil (2008-2009), CEQ Data Center on Fiscal Redistribution (CEQ Institute, Tulane University). May 2020.

IBGE (2021). Nota técnica 03/2021. PNAD Contínua. Sobre a divulgação da Reponderação da PNAD Contínua em 2021.

IBGE (2022). Nota técnica 04/2022. PNAD Contínua. Sobre as Características gerais dos moradores em 2020 e 2021.

International Monetary Fund (2023). Government Finance Statistics Yearbook (GFSY) [Data set]. https://data.imf.org/?sk=a0867067-d23c-4ebc-ad23-d3b015045405 <mark>Kaufman, P. R., MacDonald, J. M., Lutz, S. M., & Smallwood, D. M. (1997).</mark> _<mark>Do the poor pay more for food? Item selection and price differences affect low-income household food costs</mark>_ <mark>(No. 1473-2016120710).</mark> <u>
# **8 Conclusions**

We conducted a correspondence study using an online job platform in Malaysia. We tested for ethnic discrimination, gender discrimination and the value of signaling soft skills in the labor market. Unlike many correspondence studies, the data allow us to observe different stages in the hiring process. We observe if the employer rejects an application, visits the profile of a candidate, number of times the profile is visited, if they contact them and if they offer an interview. Uniquely, we observe competition in the labor market on both the demand and supply sides.

We do not find evidence of gender discrimination in the hiring process. Malaysia’s observed differential wages and labor force participation rates by gender do not seem to be associated with discrimination or human capital accumulation. More research is needed to determine why women in the Malaysian labor market have lower employment rates and wages.

We find that Indian and Malay sounding name profiles are discriminated against in comparison to Chinese-sounding name profiles. There is discrimination along all the hiring process variables we observe. Malay and Indian candidates are 8 and 9 percentage points less likely to receive an interview offer relative to a Chinese candidate. Discrimination for both ethnicities is also present in other outcomes. They are at least 10 percentage points more likely to be rejected for the position they apply for, even when the profile was not thoroughly examined. Their profiles are visited less often and they are less likely to be contacted by the employer. Ethnic discrimination varies by company characteristics. Small companies seem to examine job applications of Malay and Indian sounding names more carefully, as measured by profile visits. However, this does not translate into higher contact rates or interview offers. Jobs that offer high salaries have fewer visits to the profiles of the discriminated groups. Companies with low average processing time reject more applications of Indian and Malay sounding names.

The results of this paper are consistent with the model proposed by Bartoˇs et al. (2016) where employers decide to put differential levels of effort to
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- The relatively slow growth in wages and manufacturing employment in Kalimantan in the context of government regulation of the timber and other industries, and the recent decline in exports in the plywood industry

- Factors accounting for the apparent dramatic reversal of employment and wages trends - especially in manufacturing - and their impact on household incomes and welfare in the poorer provinces of West and East Nusa Tenggara

These a just a few of the important regional labour market issues. To examine them satisfactorily, careful thought will need to be given to generating new data sources which enable researchers to draw the link between labour migration and other indicators of labour market performance.<sup>3 5</sup> Answers to all these questions have important policy implications. For example: possible efforts needed to support local agriculture and help solve regional employment and poverty problems; further deregulation required to assist employment growth in resource abundant and poorer Outer Island regions; and the role of poverty alleviation programs - such as the _Inpres Desa Tertinggal (IDT)_ - for local employment and incomes in regions where many people are moving out, and those left behind are being increasingly supported by remittances.

This is a forbidding but by no means exhaustive list of issues. One thing seems clear. Patterns of regional labour market change are very different in the 1990s than in **the** earlier years of the New Order. Innovative policies based on new national and regional patterns of development are becoming increasingly necessary for improvements in labour welfare as Indonesia approaches the year 2000.

> 35 Unfortunately, national data sets sources do not provide sufficient information to support analysis of the impact of labour migration on labour markets. Inter-provincial migration data are collected in the censuses and the intercensal surveys but these sources do not collect information on wages. The National Labour Force Survey collects data on wages but not on migration.
regulatory agencies for 24 countries. For African countries, staff numbers were clearly low (around 20) and in some cases under 10. However, Argentina, Brazil, Mexico, Malaysia and the Philippines all had over 140 staff, although there was still the question of whether they had adequate numbers of experienced regulatory professional staff (eg accountants, economists and lawyers)<sup>29</sup> .

The discussion of the whole topic was advanced considerably by Domah, Pollitt and Stern (2002) – henceforth DPS (2002). Using a purposed-designed postal and e-mail survey, information was collected for electricity regulators<sup>30</sup> in 60 countries on (a) numbers of staff and (b) numbers of professional staff. Data was also collected on a

> 29 Stern (2000) also reported staff numbers for 13 mainly developed country telecom regulators reported in Buckle (1999).  The staff numbers for telecom regulators did not indicate as serious a problem on staff numbers even for relatively low income countries like Jordan or Peru.

> 30 Including energy and multi-sectoral regulators.

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results support the idea that entry barriers are particularly important for unleveled sectors whereas conduct regulations are more important for neck‐and‐neck sectors. Additionally, the last two papers use TFP growth rather than levels which we are also able to compute as the log difference of TFP between two years. However, no meaningful results are obtained through this way.

# **4.2.3 Post‐Crises Performance**

Up till now we have selected our time frame to be 2006‐13 in an attempt to exploit more observations and regulatory variance present in the data. However, this time period covers the global financial crisis in which precisely many firm would feel forced to exit the market, although it is unlikely that outgoing firms would represent an equivalent drop of 60 percent which is the difference between our two data sets as previously explained. This is likely to be due to reporting issues. However, we nonetheless would like to check whether our results are robust to including firms as of 2008 only. Admittedly, this will be done by reducing only two years, but it may give us nonetheless extra evidence whether our results of conduct barriers are really the main policy area of concern. Furthermore, the OECD’s NMR point of measurement of regulation for quite some services sector is done in 2008 with previous year of measurement 2003. As such, by selecting our data as of 2006 till latest year available we may also have been picking up any inconsistent real regulatory variation at the time of collecting regulatory data before the year 2008.

26 Of note, although we are able to compute the technology gap at the firm, we nonetheless prefer to compute an average so as to have industry‐specific variation of technology gap in order to be consistent with Aghion _et al_ . (2005) and other papers. Moreover, both McMorrow _et al_ . (2010) and Bourlès _et al_ . (2013) do not work with firm‐ level data and therefore use industry‐specific TFP which automatically creates a technology gap
The model provides insights into the link between entry barriers, markups, and productivity. It mainly implies that reducing in entry barriers (e.g., through pro-competitive policies) reduces the dispersion in markups, which then generate less concentrated ICT markets. The link to sectoral and aggregate productivity depends on the degree of price dispersion and the elasticities of substitution.

# **4 Empirical Results**

The previous section highlighted the causal link between the regulatory environment, distortions and productivity. This section focuses on microeconomic evidence by mapping exogenous variations in competition regulations (the sectoral laws) and competition enforcement data to firm level data. We estimate the impact of regulations and enforcing regulations on productivity and markups for different ICT sectors (hardware, infrastructure, telecommunications and software).

The results show that competition enforcement by either Indecopi (the National Institute for the Defense of Free Competition and the Protection of Intellectual Property) or Osiptel (the Supervisory Body of Private Investment in Telecommunications) has a positive impact on revenue total factor productivity (TFPR). Assuming that we account for variations in markups correctly, this indicates that there is a real productivity improvement. Interestingly, and as Edmond et al. (2018) would have expected, this result holds mainly for the most productive firms, the leaders of the productivity distribution. It appears that middle-aged, but more productive firms benefit most from enforcement of competition regulations, however, inconsistent validation by robustness checks raises doubts about the significance of firm age. Markups seem to not be affected by competition enforcement cases.

When isolating each ICT branch, we observe sector-specific patterns.

## **4.1 Data Description**

Firm-level data from the Annual Economic Survey (EEA) is collected by the National Institute of Statistics (INE) for 2007-17. The EEA draws its sample from a directory of formal firms with annual sales above 150 Tax Units,<sup>3</sup> based on administrative tax records. The survey stratifies the sampling frame by economic activity and firm size
country. Second, by closely following the methodology of the two closest antecedent studies in developing countries (Hertz et al., 2007; Azam and Bhatt, 2015), we are able to rank countries in terms of intergenerational educational mobility. To the best of our knowledge, there exist no comparable estimates on these countries.<sup>2</sup> Third, unlike other studies in developing countries, the estimates presented in this study do not suffer from selection bias caused by imposing co-residence to construct parent and child pairs. Most of the studies on intergenerational mobility still rely on cohabitation to identify parent and child pairs. Using this method has two major consequences. First, using coresidents to identify parent and child pairs leads to a sample selection that biases the intergenerational elasticity downward. For instance, Francesconi and Nicoletti (2006) and Azam and Bhatt (2015) document a substantial bias in constructing father and son pairs in the United Kingdom and India, respectively. Second, coresidence over represents younger adults who are still living with parents, which restricts the analysis to an unrepresentative young population (Jalan and Murgai, 2007; Hnatkovska et al., 2013). The current study addresses this issue by using nationally representative data on educational attainment among adult individuals and their parents regardless of whether parents are alive or, if alive, reside in the same household.<sup>3</sup> Fourth, the existing evidence on the link between child and parental education by gender is largely unexplored. A handful of studies examine the intergenerational persistence of economic status between parents and daughters (see Grusky and DiPrete, 1990; Chadwick and Solon, 2002). In this study, we attempt to fill this gap in the literature and provide gender estimates (daughters-mothers, sons-mothers, daughters-fathers, and sons-fathers) of intergenerational educational persistence by five-year birth cohorts in each country.

The study uses two measures of intergenerational educational mobility: intergenerational elasticity and the partial correlation coefficient. The analysis shows the trends in intergenerational educational mobility across