rafmacalaba/data-use-mentions-extended huggingface dataset:
.6 percent of all services as inputs, while the services sector absorbs around 63 percent of this total input use from services. However, we are interested whether our results are stable when taking out services firms so that we are left with the sole situation in which services only go into the downstream manufacturing sector.

The results of this exercise are reported in Table 9 for both our preferred TFP variables from Ackerberg and Hsieh and Klenow respectively. In this table columns (1) through (12) show that actually this time it is not conduct regulations that seem to matter most, but entry barriers. This is visible by the negatively and significant outcome on entry barriers only as the conduct regulatory barriers remain insignificant. Note that when using EU‐wide input coefficient, these results become somewhat weaker although still significant. Furthermore, in columns (10) and (12) the FDI restrictions become again significant with a negative and sizable coefficient sign. Surprised by these results, we repeat the manufacturing‐only sample for using the Hsieh and Klenow TFP measure and are confirmed with this outcome in case of using the country‐specific input‐output tables. No significant results were found when using EU‐wide tables for the Hsieh and Klenow TFP measure in the right‐hand panel.

This is a somewhat surprising result and could point to two explanations. One is that including the manufacturing sector only in the analysis could create biased results in the sense that for some economies this sector count for a relatively minor share compared to the firms active in the services sector. An alternative explanation, and which is more likely, is that the results indicate that different types of regulatory restrictions matter for different types of economies: economies which are more reliant on manufacturing firms experience entry barriers as the main regulatory barrier at stake. Yet, when a country is moving more and more into services, the relative importance between conduct and entry barriers changes accordingly with regulations on the operations of the firm being the main issue of 21
) level data.

The already influential work of Dingel and Neiman (2020)—DN2020 from now on—uses the Occupational Information Network (O*NET) surveys containing information about whether the occupation requires working outdoors, using specialized equipment, contact with the public and so forth, to assess if an occupation can be done at home. They assume that if the occupation requires at least one of such tasks, then it cannot be done at home.<sup>2</sup> These occupation-level

> 1 A related strand of literature examines  labor market vulnerability to COVID-19 by examining which jobs are deemed essential across countries (see e.g. Tomer and Kane, 2020, del Rio-Chanona et al., 2020, Fassani and Massa, 2020), and which ones require extensive face-to-face interaction (Avdiu and Nayar, 2020). Kahn et al. (2020) use data from unemployment insurance claims and vacancies to assess the impacts of COVID-19 in the United States. 2 There are other studies that also use O*NET to estimate the jobs that can be done from home. For example, Leibovici, Santacreu, and Famiglietti (2020) focus on whether the occupation requires physical proximity to other people and

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1365 in 2007 prices (which are $1731/1.27) This suggests that the costs of exporting a container in Armenia in 2010 are .854 times the costs in 2007 ($1365/1600). Finally, we estimate that the border costs of exporting to the EU from Armenia in 2010 are 8.9 percent (which is 10.4% * .854) of export revenues. This means that for each dollar of revenue from export sales to the EU in 2010, Armenian exporters lose 8.9 cents to border costs.

Similarly for imports, the cost of importing a container into Armenia was $1880 in 2007, but had risen to $2096 in 2010. Converting the $2096 estimate for 2010 to 2007 prices, we estimate the cost of importing a container into Armenia in 2010 are $1652 in 2007 prices (which are $2096/1.27) This suggests that the costs of exporting a container in Armenia in 2010 are .88 ($1652/1880) times the costs in 2007. We estimate that the border costs of importing from the EU to Armenia in 2010 are 9.1 percent (which is 10.4% * .88) of import revenues.

Given the focus of the EU on institutional development for trade facilitation, a deep and comprehensive free trade agreement with the EU is likely to reduce these costs for exports to the EU. To estimate the extent of a reduction in these costs, we note that the border costs of exporting by the transition countries that are now members of the EU, again from the Doing Business survey. The border costs of exporting a container in 2010 are as follows: Bulgaria, $1551; Czech Republic, $1060; Estonia, $730; Hungary, $1225; Latvia, $600; Lithuania, $870; Poland, $884; Romania, $1275; Slovak Republic, $1445; and Slovenia, $1075. All have lower border costs of exporting in 2010
**Table 3. Ex-ante evaluation of the impact of minimum wages: Baseline simulation results (an increase to 250 euros)**

||Nopolicy|Short-run|Long-run|
|---|---|---|---|
|Employment rate, %|28.80|28.65|28.58|
|||[28.61; 28.68]|[28.54; 28.60]|
|Unemployment rate, %|29.59|29.97|30.15|
|||[29.90; 30.05]|[30.07; 30.24]|
|Youth unemployment rate, %|55.41|56.13|56.48|
|||[55.89; 56.40]|[56.15; 56.78]|
|Share of youth NEETs, %|30.11|30.28|30.34|
|||[30.24; 30.34]|[30.28; 30.41]|
|Change in real earnings, %||0.24|0.03|
|||[0.15;0.33]|[-0.08;0.12]|

_Note: Kosovo LFS 2018 is used. Wages are imputed as described in Section 2. Short-run (long-run) labor demand elasticity values are set at -0.5 (-0.8) for skilled labor and -0.7 (-1.0) for unskilled labor. Only employees with wages between the old and the proposed minimum wage are assumed to be affected. Aged 15-64. 95 percent confidence intervals are in brackets._

**Figure 13. Simulated reduction in earning inequality as a result of minimum wage increase**

<!-- Start of picture text --> 2.47 2.56<br>
|-0.005|161|0.722|
||<br>Timeliness<br>-0.663**|0.056***|-0.051|0.123***|-0.009|-0.196**|161|0.657|

Notes: Estimates are robust to heteroskedasticity. The WEF based dependent variables range from 1 (worst outcome) to 7 (best outcome). PPP GDP per capita (2005 prices) and population are obtained from World Development Indicators. Internal distances are obtained from CEPII. The World Bank Doing Business (Trading on Time) indices are constructed as follows: The underlying variables (export and import costs, time and documents) are first standardized (mean 1 and variance 1). The simple average of these is then converted to a 1 to 7 scale (1 corresponds to worst outcome, 7 to best outcome). The underlying trading times, documents and costs are the most recent values available from the World Bank Doing Business Indicators (accessed 1 June 2010). The World Economic Forum Enabling Trade Indices are obtained from the WEF Global Enabling Trade Report (2009). Values for Botswana are obtained from the 2010 edition. Area measured using internal distance of country, which is calculated as _dii_  .067 _area_ /  (see Head and Mayer (2002). * p<.1; ** p<.05; *** p<.001 In Table 8, the explanatory variables are in columns and each row presents a regression for a different measure of trade costs. The simple regression fits the data reasonably well with 60 to over 80 percent of the variation in the composite indicators

> 14 The ETI and LPI are indicators of the various institutional, infrastructural and regulatory or policy constraints to the free flow of goods over borders. The WEF Enabling Trade Index, for example, is an index of barriers to trade related to market access, border
the social insurance package and the earnings offered in job B are orthogonal to respondents’ observable and unobservable characteristics.

Prob (A) is the likelihood of the respondent _i_ preferring Job A, the baseline scenario B is the randomly allocated hypothetical job description _j_ from 1 to 4 to individual _i_ εε is the error term

## **5 Results**

# a. Descriptive Statistics

The survey data show that over 40% of respondents contribute to private health insurance (44.8%) and private retirement schemes (46.9%), while over 25% contribute to EPF (26.6%) and SOCSO (27.3%). The coverage of respondents by EPF and SOCSO is almost equivalent to that of the coverage of all employed persons (which includes formal and informal workers), estimated at about 30% (World Bank 2020). Moreover, the data also suggests that a relatively large share of respondents are already covered by private schemes. It is important to note that 44.9% of the respondents do not have other jobs outside of freelancing. Hence, they may have access to insurance or retirement savings through their other job(s).

There is a mismatch between what freelancers deem as very important, and what they currently have in their jobs – especially when it comes to protection. For instance, 68.9% of respondents stated that it is very important for them to have insurance for workplace injuries, 66% stated that it is very important for them to have pension income upon retirement, and 57.2% stated that it is very important for them to have health insurance. At the same time, 59.9%, 62.7%, and 57.8% reported that they do not have access to these in their current jobs. These findings suggest that there is a desire among gig workers for protection that they are not currently accessing through their current jobs.

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invested increasing amounts in countries with whom they were already connected at the beginning of the sample. The value of investment in the North-to-South, South-to-North, and Southto-South blocks combined represented about 4 percent of world GDP for portfolio investment and

> 1 The data on international reserves are not bilateral, but we estimate the destination of international reserve holdings at the country-to-region level. This limitation is not a serious problem because virtually all reserves are held in assets issued by a few countries in the North.

2
standardized index of local service provision constructed using the 2014 _Podes_ survey. The 2014 index has the same components except garbage collection, which was not collected in 2014, and a different coding for street lighting, which was categorized into two groups in 2014 ( _no lights_ , _any lights_ ), compared to three groups in 2021 ( _no lights_ , _some lights_ , and _many lights_ ). The dots are conditional means of each outcome across binned intervals of the margin of victory of the best-ranked challenger on each side of the RD threshold, with 95% confidence intervals in solid gray lines.

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# **3. Data**

The data for our analysis come from two primary data sets. The first is a database compiled by the team that includes two of this paper’s authors (Demirgüç-Kunt, Lokshin, and Torre 2022). It provides estimates of the social protection measures the government implemented in response to the COVID-19 pandemic in 2020 and 2021 in 154 countries. The data come from the Global Database on Social Protection Responses to COVID-19 (Gentilini and others 2021), budget data from official documents (including IMF Article IV revisions and other international organizations’ related documents), government websites, and news sources. For inclusion in this data set, the key criterion is that social protection spending must have been part of a pandemic response package. In some cases, this spending was part of a new program, in others, it represented the expansion of an existing program. To the extent feasible, we estimated the budget corresponding to expanding existing programs (for example, the cost of extending the eligibility period of unemployment benefits).<sup>1</sup> Social spending programs can be broadly divided into income protection and job protection - policies <u>(Demirgüç Kunt, Lokshin, and Torre 2022). Income protection policies include social</u> assistance measures such as cash transfers, food, and other in-kind transfers; waivers and reductions of utility and financial obligations; some labor market policies, such as public works programs and activation measures; and social insurance policies, such as pensions, unemployment benefits, and out-of-work support. These policies seek to directly increase incomes of beneficiaries. Job protection policies include policies aimed at job retention, such as wage subsidies and other indirect subsidies, including waivers of or reductions in social insurance contributions. These policies represent a more indirect way of supporting individuals’ welfare by preserving their employment.

The two main dependent variables in our analysis are the _size_ of spending on social assistance and income protection measures and the _share_ of this spending in the total
|Macro returns to years of schooling 36.9%, or each year of<br>schooling is statistically significantly associated with a long-run<br>growth rate thatis 0.58 percentage pointshigher|Hanushek and<br>Woessmann (2008)|
|Controlling for physical capital stock, the rate of return to the<br>average year of schooling is 12.1%|Barro and Lee (2010)|

Such a function has also been applied in a macro context where _Y_ refers per capita income or output per worker in country _i_ and _S_ to the mean years of schooling of the population or the work force. The enrolment ratio at different levels of education has also been used in lieu of years of schooling as an independent variable. Table 4 gives typical results of application of such function fitted to pooled cross-country-time data.

Another methodological line of estimating the contribution of education to economic growth has been instigated by the work of Solow (1956, 1957) who added technical change ( _T_ ) as an independent variable in an aggregate production function, along with the traditional factors of production physical capital ( _Kp_ ) and labor input ( _L_ ) in order to explain output ( _Y_ ). Omitting time subscripts for expository simplicity, the Solow function takes the form (3) _Y = f (Kp, L, T)_ Schultz (1961a,b) challenged Solow‘s formulation and added education as a determining factor of income. A stream of empirical research followed along two alternative measures of the education input: "Schultz-type" and "Denison-type" growth accounting.
such as county-level cities.<sup>11</sup> However, the available sub-national economic statistics are much more limited for India. For example, annual data on Gross District Domestic Product are reported by the Planning Commission, but exclude some big states like Gujarat, and for many states the data end in 2005/06 and so miss the recent period of rapid poverty reduction. The Population Census for 1991 and 2011 roughly lines up with the 50<sup>th</sup> and 66<sup>th</sup> or 68<sup>th</sup> round NSS data but not with the 61<sup>st</sup> round, and does not inform about economic growth. The lack of agglomeration-level boundaries, like the Metropolitan Statistical Areas (MSA) in other countries, also means that India’s census data correspond to administrative rather than to economic definitions of cities.

> 11 Deng et al (2010) are an example of studying patterns of urban change in China using these spatially detailed GDP data.

9
2008, and Demombynes et al, 2007, for more details). Failing to take account of spatial correlation in the disturbances would result in underestimated standard errors in the final poverty estimates (Tarozzi and Deaton, 2009). Different models are estimated for each region and the specifications include census mean variables and other aggregate level variables in order to capture latent cluster-level effects. All regressions are estimated with household weights and with parsimonious specifications to be cautious about overfitting. Heteroskedasticity is also modeled in the household-specific part of the residual.

> 7 These surveys are stratified at the region or state level, as well as for rural and urban areas. Within each region there are further levels of stratification, and also clustering. At the final level, a small number of households (a cluster) are randomly selected from a census enumeration area.
10 km of the GQ network.

The first-stage relationships are quite strong. IV Route 1, which does not connect Bangalore directly, has a first-stage elasticity of 0.43 (0.05) and an associated F-statistic of 74.5. IV Route 2, which treats Bangalore as a connection point, has a first-stage elasticity of 0.54 (0.05) and an associated F-statistic of 138.1.

Panel B presents the second-stage results. The IV specifications generally confirm the OLS findings. Column 1 shows a modest growth in the estimated impact of proximity to 11 Banerjee et al. (2012) provide an early application and discussion of the straight-line IV approach, and Khanna (2014) offers a recent application to India. Faber (2014) provides an important extension to this methodology. Faber (2014) uses data on local land characteristics and their impact on construction costs to define a minimum-cost way of connecting 54 key cities that were to be linked by the development of China's highway network.

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- _Openness of the Economy_ : is shown by restrictions on foreign trade, foreign exchange mechanism, foreign investment and capital markets. We use a measure from the Fraser Institute, which compares the actual size of trade sector compared to the expected size.

- _Structure of the Economy and Markets:_ are proxied by production and allocation of resources via government mandates rather than private market (price controls, banking sector, state ownership of enterprises, competitive environment. A composite index is formed by using data from the Fraser Institute on state ownership of enterprises, price controls, and interest rate controls.

All variables are re-scaled such that high numbers are associated with more government involvement in the economy.

# _2. Institutions of Accountability_

- _Internal Control_ **:** A survey question from the World Development Report 1997 reflects access to other officials or to superiors to get the correct treatment.

- _Judiciary_ **_:_** Several indices are available to measure the effectiveness of the judiciary. However, most of them, given the way they are measured, have elements endogenous with corruption. We use an index from the Fraser Institute, which is the least susceptible to simultaneity bias.

- _Democratic Institutions_ : Since it is almost impossible to make a distinction between political institutions and civil society, we combine these two variables under one title: institutions of democracy. A composite measure for democratic accountability provided is constructed using three indices of the Freedom House. These indices are political rights, civil rights, and freedom of the media.

All variables are re-scaled such that high numbers are associated with more effective institutions.

14
VoL 29_

- xv London Economics research

- **xvi** Lee & Anas (1992)

- **xvii** Lee and Anas (1992) p.<sup>10 8 8</sup>

- **xviii** Economist 12 August (1995)

- **xix** ESMAP (1991)

- xx ESMAP (1998)

- xx. London Economics research London Economics research

- **xxiii** London Economics research

- **xxivi** London Economics research

- **xxv** London Economics research

- xxvi London Economics research
et des Comptes Publics – Agence pour l’Informatique Financière <u>de l’Etat.</u> In many countries this type of flexibility is de facto already in place as they use manual or excel based systems, allowing them to capture and control spending ex-post but without the integration between the payment process and the recording process. There is thus little guarantee of the integrity of the expenditure data. An example of this is Malawi, where the lack of integration has undermined confidence in the system entirely (World Bank 2016a). With the increased penetration of the banking sector, an innovative approach integrating spending, recording, and the FMIS is possible, which would facilitate the same level of flexibility used in a manual system, while capturing all transactions and thereby guaranteeing good expenditure information. Using such an approach therefore would allow a shift toward the eastern quadrants where spending units have adequate flexibility, whist maintaining financial accountability. Such an approach will implicitly also lead to greater facility autonomy and should be recognized through an explicit delegation of power.

11
; and could have reported the harvest condition as fresh or dry – following the prevailing practices in household and farm surveys and the approach as part of the Malawi IHS. Consequently, the recall respondents reported production for 74 percent of cassava plots in 50-kilogram bags; 19 percent in kilograms; and 3 percent in 70-kilogram bags. The production on the remaining plots were recorded in pails and pieces (each differentiated as small, medium or large), and in _gondolos_ (non-standard, cylindrical baskets that can vary in size and that are often attached to bicycles to carry produce). To convert these non-standard units to kilograms, we used the regional conversion factors readily available from the Malawi IHS

> 23 The harvest that was weighed could have also been expressed in terms of non-standard measurement units used in the recall arms, as such the kilogram equivalences obtained for the non-standard measurement units for fresh cassava harvest could have been used as ancillary data for gauging the sensitivity of the recall-based production estimates that would have been reported in non-standard units. In the end, most of the cassava harvests in the diaries ended up being expressed in 50-kilogram bags (alongside their objective weights in kilograms), as the diary households relied on the sacks that were distributed to them.

> 24 To address any potential for differences arising from the diary reporting at the household-level and recall reporting at the plot-level, the analysis was run on the subsample of households cultivating only one cassava plot and the relationships between the four treatment arms remained the same.

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Figure B.7: Number of municipalities: commitment <!-- Start of picture text --> CE MG<br>180 840<br>175 820<br>170<br>800<br>2009 2011 2013 2015 2017 2019 2021 2014 2015 2016 2017 2018 2019 2020 2021<br>PB PR<br>223 400<br>221 395<br>219<br>390<br>216<br>385<br>213<br>380<br>210 375<br>2009 2011 2013 2015 2017 2019 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>RS SP<br>500 645<br>400 640<br>635<br>300<br>630<br>200<br>625<br>100<br>620<br>2010 2012 2014 2016 2018 2020 2008 2010 2012 2014 2016 2018 2020<br>Year Year<br>Municipalities from our data Municipalities from Siconfi<br>Municipalities<br>Municipalities<br>Municipalities<br><!-- End of picture text -->

**Notes:** This figure compares the number of municipalities in our dataset and the number of municipalities in SICONFI in commitment data.

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