rafmacalaba/data-use-mentions-extended huggingface dataset:
Supplemental Table 1. Internal consistency and interrater reliability of child development measures

||Obs.|No.<br>items<br>in<br>scale|Cronbach's<br>alpha|Interr|ater reliabilit|y N = 48|
|---|---|---|---|---|---|---|
|||||Percent<br>agreement|Cohen's<br>kappa|Krippendorff's<br>alpha|
|Receptive vocabulary|||||||
|English|505|34|0.569|0.958333|0.954023|0.998914|
|Swahili|505|31|0.757|0.895833|0.888424|0.996739|
|Luo|505|27|0.779|1|1|1|
|Expressive vocabulary|505|20|0.660|0.958333|0.949712|0.99934|
|Adapted MDAT|||||||
|Fine motor|495|32|0.935|0.9375|0.934005|0.999635|
|Language|495|28|0.901|0.875|0.864407|0.996617|

Notes. English, Swahili, and Luo receptive vocabulary were measured using separate assessments based on the British Picture Vocabulary Scale (BPVS). Expressive vocabulary was measured using a tool developed from the PPVT. The adapted MDAT is the Kenya adaptation of the Malawi Developmental Assessment Tool (MDAT).
which contain an administrative<br>seat; and communes<br>established as appropriate and without regard to size.<br>Economically active- all persons over 11. Women's<br>participation<br>-<br>65 percent versus 77 percent for men.|
|_Av_|This conception of the rural economic transformation<br>draws on a<br>wealth of antecedent views, most explicitly expressed by Johnston<br>and Kilby (1975), but also drawing on Anderson and Leiserson<br>(1980), Anthony et al. (1979) and Binswanger (1983), Byerlee and<br>Eicher (1974), Liedholm (1973), and Vyas and Nathai (1978).|
||African rural nonfarm entrepreneurs<br>commonly hire or apprentice 50<br>to 70 percent of their workers from outside the family (Aluko,<br>1972 and 1973; Malawi, 1980; Milimo and Fisseha, 1986; Mozambique,<br>1983; Rwanda, 1978; Tanzania, 1982; Wilcock and Chuta, 1982;<br>Williams and McClintock, 1981; all data reported in Haggblade,<br>Hazell and Brown, 1987, Table 13). In agriculture, nonfamily<br>labor usage averages closer to 15-20 percent of total farm<br>employment (Eicher and Baker, 1982; Byerlee, 1980; Anthony et al.,<br>1979; Cleave, 1974; Collier and Lal, 1986; Collier, Radwan and<br>Wargwe, 1986; Ghai and Radwan, 1983b; Natlon et al., 1979; Norman,<br>1972; Norman, Pryor and Gibbs, 1979; Oates, 1984; Robertson and<br>Hughs, 1978; Spencer and Byerlee, 1976; and Weinrich, 1975).<br>Combining these estimates with the labor force data in Table 1, we<br>estimate that about 20 percent of Africa
|Direct vs.|_Directly_|Firms where national or municipal governments hold 10|
|---|---|---|
|indirect|_owned_|percent or more stakes and report_directly_to a government|
|supervision|_SOEs_|agency or ministry, be it central or local.|
||_Indirectly_<br>_owned_<br>_SOEs_|Firms that are subsidiaries of other companies where national<br>or municipal governments hold 10 percent or more stakes.|

Source: World Bank staff analytical categorization based on the World Bank Global Businesses of the State (BOS) database, 2019. See World Bank (2022) for details.

## 3.3 Sector Taxonomy

**The paper also relies on a sector taxonomy that classifies industries based on the economic rationale that justifies an SOE presence** . The taxonomy was developed by Dall'Olio et al. (2022b) in conjunction with the BOS database. It classifies 4-digit NACE industries into three broad sectors (competitive, partially contestable, and natural monopoly) based on the economic rationale—the intrinsic features and associated market failures—that justifies SOE presence in an industry (see Table 2). Other NACE codes are excluded from the sector classification of SOEs because firms in those industries provide public goods (e.g., public administration and defense and activities of extraterritorial organizations). In contrast, others are characterized by externalities (e.g., education and human health activities). In these excluded sectors, government interventions are justified because entities in these services are either incapable of generating profits or not set up for market production. Of the 615 4-digit NACE industries, 511 are classified as competitive sectors, 41 as partially contestable sectors, 11 as natural monopoly sectors, and 52 industries are excluded (see Annex Table B1 for the list of partially contestable and natural monopoly sectors).<sup>7</sup>

Table 2: Sector Taxonomy Based on Economic Rationale Justifying SOE Presence

|**Sector
for a sector pair the shareholder and the affiliate belong in is higher on average.

# **2 Data**

In this paper we use a unique combination of data sets that allow us to explore the effects of PTA on firm ownership.

First, we obtain the data on firm ownership from the Bureau van Dijk’s ORBIS data set. Our main explanatory variable -PTAs- comes from the Deep Trade Agreement Dataset prepared by the World Bank, which also includes a detailed text analysis of every treaty’s content. Finally, we use World Input-Output Tables from the WIOD to obtain different measures of GVC organization.

## **2.1 Firm Ownership Data**

The ORBIS data set extensively compiles firm level data such as annual accounts and ownership structure for the period 2007-2018. For the purpose of this analysis the most relevant information is the ownership structure. In this data set a link is defined as an ownership relation of any kind (regardless of the share of ownership) between a parent firm located in country _j_ and sector _s_ and an affiliate located in country _i_ and sector _r_ .

To clean the data set, we drop the duplicated entries and also those observations with relevant information missing such as country or sector. Furthermore, we keep a panel of incumbents (observed during the full sample) and entrants (firms born during the sample).

Finally, we aggregate these data at the country-sector-to-country-sector level and fill in the 0s. In the process we create a new variable called number of connected firms ( _CFij,t_<sup>_rs_),thatcountsthenumberoffirmsincountry</sup><sup>_i_andsector</sup><sup>_r_thatare</sup> owned by firms from sector _s_ in country _j_ .

Note that given the number of countries (209) and sectors (38) this data set is huge. More concretely, we have 209 _×_ 209 _×_ 38 _×_ 38 = 63 million observations per year, which represent almost 600 million observations
any education, wages are closer to the 40<sup>th</sup> percentile in 2017.<sup>5</sup> The potential income gains from the public works program are substantial in the context of low employment, even if the wage rate is not very high compared to those who were employed in the public or private sector at that time. Recall that only 10 percent of women in our study have any labor income at baseline. This income came mostly from selfemployment. In this context, the potential income from public works is about 3 times higher than women’s average weekly earnings from self-employment activities. On the other hand, women’s potential earnings from public works participation are less significant when compared to their husbands’ labor income: both the hourly wage and hours offered to such women are lower than what their working husbands receive. For husbands, the weekly wage offered by the public works program is just over half of their mean labor income. In this sense, prospective public works income has the potential to increase household income from any source by 50 percent.

> 4 The International Labour Office (2015) details a variety of ways in which public works programs can be adapted to address the constraints that women face.

> 5 These computations are based on the EDAM 2017 and include reported earnings from self-employment and salary/wage jobs, with population weights applied.

9
# **3.4 Summary statistics**

Table 3 presents correlation coefficients between our regional enforcement capacity measure (inspectorate density) and key regional characteristics such as number of firms and gross regional product (GRP). The relatively high positive correlation (0.6) between enforcement and the regional unemployment rate stands out. The fact that regions with stronger enforcement capacity have higher rates of unemployment is consistent with theoretical predictions about the effect of stringent labor laws; however this is not a hypothesis we can convincingly test given the data and setting.

Regions with stronger enforcement capacity are somewhat less ‘industrial’: they have fewer firms, a smaller share of manufacturing and a larger share of services in total output. This is consistent with our claim that labor inspectorate density is determined by an ‘exogenous’ population based rule, as that rule would automatically leave more industrialized regions with lower inspectorate density (unless regional population size is correlated with industrialization).

Regions with stronger enforcement capacity are (weakly) less urbanized. Other attributes like regional GDP per capita and nominal wages are uncorrelated with enforcement capacity. Thus, with the exception of unemployment, urbanization and industrialization, regions with high and low inspectorate density look broadly comparable.

Table 4 summarizes the cleaned RUSLANA panel data by year, showing the number of firms, how many of those are “entering” RUSLANA that year, and the breakdown of firms by sectors in surges and slumps. We note, again, that “entry” into RUSLANA is not an accurate measure of firm entry into the market. Regarding surges and slumps, the data show that 2006‐2009 were volatile years. A large share of firms were in surging sectors in 2006 and 2007, and then slumping in 2008 and 2009; the latter reflects the onset of the global recession in those years.

Table 5 summarizes the panel of incumbent firms that we use for most of our analysis; the statistics for each year _t_ include only those firms that existed in the panel and have data in both
|
|Orissa (9% area)|_|
|Uttar Pradesh (Avadh & Agra)||
|**All-India**|2.1|

Sources: Sharma (1992a) for tenure systems; Fan, Hazell and Thorat (1998) for agricultural production data

# POLITICAL ECONOMY OF LAND REFORMS

Land was made a 'state subject' by the Government of India in 1935. As a result, under the Indian Constitution, land reform is the responsibility of individual states, although central guidance is offered at federal level. The nature of the legislation, the level of support or otherwise from existing or new institutional arrangements, and the degree of success in implementation have varied significantly from state to state. Annex 1 summarizes some key features of land 9
In addition, the annex supplies a description of many of the tools considered. It also includes information on the cost of implementation and appropriate software for some of the tools.

# **_2. Simulation Models of the Size Distribution of Income_**

The idea of simulating the distribution of income among individuals or households originated in the field of public finance because of the need to have reliable models for the detailed analysis of the incidence of a tax-benefit system. The basic idea is to model the distribution of household income and consumption _taking household behavior as exogenous_ , but fully accounting for applicable taxes and transfers (Davies 2004). To take full account of the diversity of the characteristics in a population, these simulation models require a nationally representative micro-data set. The fact that household behavior is exogenous means that these accounting models can predict only the first-round effects of a tax-benefit policy on inequality and poverty.

All the approaches reviewed in this section are various interpretations of the basic idea underlying tax-benefit simulation models. _POVCAL_ and _SimSIP Poverty_ illustrate the use of the Lorenz curve for poverty and distributional impact analysis. These two tools are particularly useful when only aggregate or grouped data are available. The second class of simulation tools considered under this heading relies on unit record data on the distribution of some money-metric measure of economic welfare at the individual or household level. Three approaches are examined. _PovStat_ uses per capita consumption as a measure of welfare. The second approach relies on _the envelope theorem_ and employs the _maximum value function_ to model the determinants of individual welfare (Chen and Ravallion 2004; Ravallion and Lokshin 2004). The last approach is based on a _reduced-form model of household income generation_ (Bourguignon and Ferreira 2005). Even though the envelope model and the income-generation models include some aspects of household behavior, these microsimulation models must be viewed as _statistical_ devices to the extent that they fail to fully account for market adjustment through endogenous prices or the adjustment of the behavior of agents from one equilibrium state to another (Ferreira and Leite 2003).
3%|2.6%|
|3<br>0.3%|0.1%|4.4%|3.4%|
|4<br>0.5%|0.3%|5.2%|4.2%|
|5<br>1.6%|0.9%|8.0%|5.6%|
|6<br>2.9%|2.0%|7.9%|7.3%|
|7<br>5.0%|3.8%|9.9%|8.2%|
|8<br>9.6%|8.2%|11.7%|11.2%|
|9<br>18.7%|17.4%|16.2%|18.3%|
|Richest<br>61.3%|67.3%|29.9%|37.7%|

_Source_ : Staff calculations using BLSS 2017.

# 6. Conclusion

The analysis presented in this paper provides insights into the distributional impact of Bhutan’s tax-benefit system. To our knowledge, this is the first comprehensive analysis undertaken on this issue. The CEQ methodology is used to provide an assessment of the impact of tax-benefit policies as of 2017 as the baseline and then is supplemented with simulations to estimate the impact of two major fiscal reforms in Bhutan: the personal income tax reform in 2020 and the introduction of the GST in 2022.

The results in this paper show that the fiscal system in Bhutan plays a modest yet positive role in reducing inequality, while it slightly increases poverty. Taxes and social spending combined contribute to a decrease in inequality in Bhutan, while leading to a small increase in poverty as of 2017 (before the major fiscal reforms). The increase in poverty is mainly due to the burden of indirect taxes and
Table 2: Average treatment effects of German acquisition on firm-level employment

||TWFE<br>(1)|Doubly robust<br>(2)|
|---|---|---|
|Average treatment efect (German acquisition)|0.075**<br>(0.035)|0.112**<br>(0.038)|
|Efect by treatment year|||
|2005|0.057**|0.059**|
||(0.026)|(0.028)|
|2006|0.043*|0.045|
||(0.026)|(0.030)|
|2007|0.040|0.068**|
||(0.033)|(0.029)|
|2008|0.087**|0.117**|
||(0.041)|(0.047)|
|2009|0.059|0.083*|
||(0.039)|(0.045)|
|2010|0.096*|0.140**|
||(0.052)|(0.056)|
|2011|0.117**|0.161***|
||(0.052)|(0.062)|
|2012|0.070|0.177**|
||(0.077)|(0.079)|

Notes: Authors’ calculations based on Orbis data. Results include only firms established before 2004. The doubly robust estimator of Sant’Anna and Zhao (2020) in column (2) has been implemented using pre-treatment levels of employment, age, region and sectoral dummies as covariates. *10% level, **5% level, and ***1% level.
.1 Estimates of the AVEs of Barriers Against FDI in Business Services. ........................ 22|
|4.2.2 Estimates of AVEs of Barriers to Cross-Border Trade in Services. ............................. 23|
|4.3 Estimates of the AVEs of the Costs of Time in Exporting and Importing. ......................... 23|
|4.4 Estimates of the AVEs for Non-Tariff Measures (NTMs) for the Regions of our Model .. 23|
|4.5 Tariff and Trade Data .......................................................................................................... 25|
|4.6 Social Accounting Matrices ................................................................................................ 25|
|4.7 Share of
# **4. Conclusion**

The face-to-face index discussed here, as well as the home-based work index by Dingel and Neiman (2020) can provide quick, data-driven metrics of vulnerability to the COVID-19 crisis. These two related measures complement each other well and should therefore be used in conjunction.

It goes without saying that any forecast of vulnerability is necessarily a second-best approach in the absence of real-time information on the impact for firms and workers. In fact, initial unadjusted unemployment claims in the United States indicate that layoffs have largely occurred in sectors with high F2F and HBW scores (U.S. Department of Labor, 2020). Another caveat to consider is that some firms may adjust their business model in response to the crisis. For example, a retail firm may pivot to primarily add value through remote delivery. Further, some niche markets may even experience positive demand effects as a result of COVID-19, which these measures are not designed to capture. Lastly, the F2F interactions measure captures one aspect of demand where a negative shock results directly from social distancing. We do not aim to capture other demand shocks that are transmitted through global value chains, oil prices and the like.

Our results show considerable heterogeneity in the possible effects across industries and occupations. As a result, COVID-19 can have negative distributional impacts, disproportionately affecting lowerpaid and female workers. In responding to job losses owing to the COVID-19 pandemic, governments should take this heterogeneity in the feasibility of doing home-based work and face-to-face interactions into account. The temporal dimension also matters. Once restrictions are lifted, continued support to industries will likely be most needed where face-to-face interactions matter. This could help the vulnerable tide over these difficult times. It will hopefully not be too long before the tide of COVID-19 turns too.

10
representative sample of our study population (all households in relatively small villages and all households living in a representative sample of segments of villages in larger villages). The census population formed the sampling frame for our study. The second dataset used in this study (referred to as ‘endline’), on a sample of households in our study area, was collected in August-September 2017, one year after the last SBM snapshot data was taken. This allows us to include a longer-term impact perspective. In October-December 2018, we also conducted a baseline survey of a sub-set of households in our study sample, which we use in robustness checks (not in the main analysis).

The surveys collected rich information on household socio-economic status, household toilet ownership (household self-reported and interviewer observed), toilet quality and usage, open defecation practice and self-reported subsidy receipts. Unlike the SBM data, the survey data contain information on subsidy receipts at _any_ point in time, not just since the launch of SBM. Importantly, the surveys collected information on the timing of toilet construction and subsidy receipts.

# **MFI and credit bureau data**

While the surveys also collect information on sanitation loan uptake and timing of loan receipt as well as borrowing from formal and informal sources beyond the partner MFI, we are able to get a more accurate measure on this outcome by linking our survey data to MFI and credit bureau administrative data, using the MFI’s client identifier. Regulations introduced by the Reserve Bank of India in 2011 require all microfinance institutions to report to a credit bureau on all loans outstanding for each client on a monthly

> 13http://swachhbharatmission.gov.in/SBMCMS/sbm-mis.htm

9
# Competition Makes IT Better: Evidence on When Firms Use IT More Effectively<sup>_∗_</sup>

Leonardo Iacovone<sup>_†_</sup> Mariana Pereira-L ́opez<sup>_‡_</sup> Marc Schiffbauer<sup>_§_</sup> JEL Classifications: E23, F14, L25, O33 Keywords: IT, productivity, competition, Mexico

> _∗_ ACKNOWLEDGEMENTS: We thank Gerardo Durand, Gerardo L ́opez, Sim ́on S ́anchez and Gabriel Romero from INEGI for their support with the data use in compliance with the confidentiality requirements set by the Mexican Laws. We also thank Indhira Vanessa Santos for helping us with the MODUTIH data and Martin Guzman for his support with Mexico’s trade data. Finally, thanks to Remi Jedwab, Bob Rijkers, Pablo Cotler, and the participants of the Seminars at Universidad Iberoamericana and Banco de Mexico, the World Bank’s ABCDE Conference 2015, and the LACEA Labor Network 4th Annual Meeting for their valuable comments. All remaining errors are naturally our own.

> _†_ World Bank, email: liacovone@worldbank.org (Corresponding Author)

> _‡_ World Bank and University Iberoamericana, email: mpereiralopez@worldbank.org

> _§_ World Bank, email: mschiffbauer@worldbank.org
N/A|N/A|

Source: UN COMTRADE via WITS and author’s calculations based on data from individual country EPZ authorities Given that many of the African zone programs were established only recently, could the low levels of exports simply be a function of time? Certainly, this is part of the story. Data on growth of exports during the period 2000 to 2008 do indicate that the African zones, by and large, grew relatively quickly (although in most cases from very small bases) during the decade. A closer look at the growth patterns, however, raises some concerns over many of the African zones programs. As can be seen in Table 2, many of the African programs experienced rapid growth during the period 2000 to 2004, but slower growth or even decline since then. This is partly attributable to the base on which they are growing in each of those periods. But there may also be a structural factor at play. This is competition from Asia in general, and specifically, garment sector competition in the post‐MFA environment. In the period 2000 to 2004, African EPZs (particularly Kenya and Lesotho) benefited enormously from trade preferences to the US market granted under the AGOA program. At the same time, they faced limited competition in the US market from Asian producers, who continued to confront quota restrictions until the Multifiber Agreement expired at the end of 2004. At least in the cases of Kenya and Lesotho, declining competitiveness in the aftermath of the MFA is a well‐documented source of export stagnation and employment losses since 2005.

More broadly, if we look at the typical growth path of successful economic zones, it appears that most of the African zones are failing to shift to the “exponential” growth path that typically occurs 17
robustness analysis capital income is also taken into account assuming its distribution over activities is equal to the labor income distribution.

More formally, data on exports is used for a particular country-year to derive a vector **_l_** of dimensions ( _i_ x 1) with typical element representing export income by workers in industry _i_ as (1) )<sup>−1</sup> , with **e** a vector of exports (ll= vv� (II−AA _i_ x 1),<sup>DD</sup> ee the domestic input coefficients matrix ( _i_ x _i_ ) and **I** an identity matrix ( _i_ x _i_ ) such that )<sup>−1</sup> is the Leontief inverse matrix, and the diagonal matrix of AA<sup>DD</sup> labor income to gross output ratios ( _i_ x _i_ ). The values of export activities are derived by multiplying (II−AA<sup>DD</sup> vv� export income by industry with the occupation income distribution for each industry from the activity data set. Appendix A1 provides a more detailed technical exposition. Data for all variables vary across countries as well as over time allowing for meaningful cross-country and temporal comparisons.

# **3. Export activities along the development path**

How do export baskets evolve in terms of activities as countries grow richer? Figure 1 shows the development of export activity over GDP per capita. It is estimated using a non-parametric LOWESS smoother on data for 59 countries over a time span of 20 years.<sup>7</sup> For parsimony, panel A aggregates the 455 activities into 5 broad groups: engineering, managerial, production, support, and other activities across all industries. Panel A suggests that at lower levels of economic development, production activities account for a major part of export income. The production share steadily declines when countries grow richer: from more than 50 percent of total