rafmacalaba/data-use-mentions-extended huggingface dataset:
- Gopalan, C., B. V. R. Sastri, and S. C. Balasubramanian. 2004. _The Nutritive Value of Indian Foods_ . Indian Council of Medical Research, Hyderabad: National Institute of Nutrition.

- Gupta, A. D. 2013. “Veblen preferences and falling calorie consumpton in india: Theory and evidence.” _Working Paper_ .

- Henry, C. 2005. “Basal metabolic rate studies in humans: measurement and development of new equations.” _Public Health Nutrition_ .

- Indian Council of Medical Research. 2009. “Nutrient requirements and recommended dietary allowances for indians.” _National Institute of Nutrition_ .

- Krishnaveni, G., S. Veena, R. Kuriyan, R. Kishore, A. Wills, M. Nalinakshi, S. Kehoe, C. Fall, and A. Kurpad. 2009. “Relationship between physical activity measured using accelerometers and energy expenditure measured using doubly labelled water in indian children.” _European Journal of Clinical Nutrition_ .

- National Sample Survey Organization. 2000. “Choice of reference period for consumption data.” _NSSO Report 447_ .

- NSSO. 2013. “Nutritional intake in india, 2011-2012.” _Report No. 560_ .

- Patnaik, U. 2010. “On some fatal fallacies.” _Economic and Political Weekly_ 45(47): 81–87.

- Shetty, P., M. Soares, and M. Sheela. 1986. “Basal metabolic rates of south indian males.” _Report of FAO_ .

- Smith, L. C. 2015. “The great indian calorie debate: Explaining rising undernourishment during india’s rapid economic growth.” _Food Policy_ 50: 53–67.

- Smith, L. C., O. Dupriez, and N. Troubat. 2014. “Assessment of the reliability and relevance of the food data collected in national household consumption and expenditure surveys.” _International Household Survey Network Working Paper 8_ .

- St. Jeor, S. T., and
.58|4.36|12.41|4.89|4.23|2.16|12.31|5.84|
|Timor|1.16|2.24|n.a.|n.a.|2.73|1.26|n.a.|n.a.|
|Turkey|0.93|5.20|10.94|5.17|3.78|0.48|10.80|5.31|
|Uruguay|4.27|6.81|9.43|2.30|6.68|2.39|9.18|2.17|
|USA|3.44|3.27|10.78|5.56|4.87|2.09|10.35|6.17|

Source: Authors’ elaboration based on survey data. Notes: Women and men aged 15 to 64. In Argentina, the starting age is 18.

<u>Table 2. Mean hours per day by educational level, type of activity and sex</u>

|||<br> <br>Wom|<br>en|<br> <br>Primar|<br>ylevel|<br> <br>M|en||
|---|---|---|---|---|---|---|---|---|
||Market<br>work|<br>Unpaid<br>domestic<br>work|Personal<br>care|<br>Leisure,<br>social and<br>study|Market<br>work|<br>Unpaid<br>domestic<br>work|Personal<br>care|<br>Leisure,<br>social and<br>
comprehensive understanding of tax evasion. This paper is the first study linking administrative and survey data to investigate the issue of envelope wages and measure tax compliance in Romania.

**In the case of Romania, the following data sources were available for this study: administrative tax records, the EU-LFS (European Union Labor Force Survey), and the EU-SILC (European Union Statistics on Income and Living Conditions).** The administrative tax records collected by the Romanian Ministry of Public Finance included a full sample of monthly tax records for all employees from 2020 to 2021. The EU-LFS Additionally, the records for self-employed individuals were available for 2019 and 2020.<sup>2</sup> and EU-SILC, household survey data, were also available for this study. The EU-SILC data covered the years 2020-2021, which included the income years 2019 and 2020. The EU-LFS data covered the years 2019-2021. The EU-LFS and EU-SILC data were also valuable as they allowed for comparing estimates of reported and true incomes. Using a combination of administrative records and survey data, this study can better understand tax evasion in Romania.

**The administrative records are an essential and rich data source, providing detailed information on tax payments and potential estimates of reported income.** Brockemyer (2019) discusses the types of administrative data used in empirical economic research. She also debates the upsides and downsides of administrative tax data.  Our data is a tax register covering all employees who received labor income in 2020 – 2021. The value of income is reported by the third party – the employer. The tax administration extracted the de-identified data under the formal agreement. The tax administration also provided a detailed and comprehensive description of the tax forms and variables in the dataset. Tax administrative records cover the entire formal sector, providing a more comprehensive sample than survey data. The data provided detailed information on labor income and COVID-19 support programs. The monthly number of taxpayers varies between 6.9 million and 7.4 million. This data is less prone to
stabilization and other market reforms, and their open economy variable may be picking up these other effects as well. But they argue that trade liberalization is the _sine qua non_ of the overall reform process, because other interventions such as state subsidies often are unsustainable in an open economy. Although the estimates of the gains from trade liberalization based on crosscountry growth regressions are controversial, there are a few reasons to believe that the gains are very substantial. Frankel and Romer (1999) have shown that adjusting for the simultaneity bias in cross country regression studies such as Sachs and Warner, does not reduce the estimated impact of openness on growth. Further, the significant literature, starting with Coe and Helpman (1995), on the productivity gains from imported intermediate is largely unchallenged at this time. Finally, the cross-country growth regressions focus on trade in goods, so the estimates of the gains from openness to FDI in services should further contribute to the gains.

Since trade policy is inherently controversial, policy-makers wonder why they undertake the exercise if so little is at stake. CRTS models without services, by missing the productivity gains form an open trade regime and FDI regime in services, are missing most of what is important.

# **Literature**

Francois and Hoekman, in this volume, provide a review of the numerical general equilibrium modeling of services. I therefore focus here on the papers most closely related to the models in this paper. There have been a number of theoretical papers modeling foreign direct investment liberalization in services including (Markusen (1989; 1990), Francois (1990a; 1990b) and Markusen and Venables (1998). The modeling here incorporates a key idea of Markusen, namely, that when multinationals engage in FDI, they bring technology or expertise to the local 8
036)|0.506<br>(0.039)|0.0110|
|Women are partly at fault if harassed on public space|0.217<br>(0.031)|0.199<br>(0.030)|0.675|

_Notes_ : Unit of observation is one respondent. Survey modules were randomly allocated to subsets of platform survey participants to minimize survey length. The number of women respondents for each question varies between 250 and 303. The number of men is between 235 and 302. Sampling weights applied. Robust standard errors in parentheses.<sup>1</sup> Women were asked about their past experiences, while men were asked about their women relatives.

Table A7: Test for order effects in on screen presentation of public / reserved space

||Dependen<br>Chose rese<br>(1)|t variable:<br>rved space<br>(2)|
|---|---|---|
|Reserved space shown frst|0.005<br>(0.010)|-0.008<br>(0.020)|
|Positive opportunity cost||0.014<br>(0.021)|
|Reserved space shown frst _×_ Positive opportunity cost||0.013<br>(0.022)|
|Constant|0.068***|0.054***|
||(0.011)|(0.018)|
|Observations|6001|6001|
|Riders|273|273|

_Note:_ Unit of observation is one ride. Sample includes revealed preference rides of riders recruited in the first wave. Observations weighted by the inverse of the number of rides taken by the individual rider. Standard errors in parentheses, clustered at rider level.
States displays a much more modest and stable level of segmentation compared to India at the establishment level. Ideally, researchers in other countries with administrative data of workforce gender can also provide comparison points.

# **_Correlates of Female Activity in India_**

Table 6a presents our first district level regression results that estimate the correlates of female participation in economic activity and gender segmentation, measured as the average over the period 2001-2010. The estimations in Tables 6a and 6b use information on 421 districts in India, are weighted by log district population and report robust standard errors. Table 6a suggests that a higher female-to-male ratio in population is highly correlated with heightened activity among females, including higher segmentation in female-led plants and lower segmentation in male-led plants. A larger demographic dividend, measured as the ratio of working age population relative to non-working age population, is also associated with increased activity and lower segmentation, especially in male-led plants. In the case of services sector, however, a higher working age population is significantly correlated with higher segmentation in female-led plants. Another difference between unorganized manufacturing and services is in the role of technology. Interestingly, the proportion of plants using the Internet in the services sector is associated with larger female employment share and lower segmentation in both the male-led and the female-led establishments. Comparably, we find null effects of Internet usage among plants on female based activity or segmentation in unorganized manufacturing. Besides these, most other district level traits are not found to be correlated with either the participation of women in manufacturing and services or gender segmentation. The result continues to hold across a wide range of specifications and sample restrictions (e.g., young plants, high-wage plants and so on).

Table 6b repeats the set-up in Table 6a using the change in female activity over the period covering 2001 to 2010 as the dependent variable. In addition to the listed covariates, these regressions also control for the initial value of the dependent variable. The most interesting feature of this table is that the state of electronic infrastructure and technology
of transformation at each level. The model assumes product differentiation by region of origin through the Armington assumption. This assumption is embedded in the model through a twolevel nested constant elasticity of substitution (CES) structure. At the first level the aggregate Armington demand is allocated between goods produced domestically and aggregate imports. At the second level, aggregate import is further disaggregated across trading partners. The degree of product differentiation depends upon the magnitude of the substitution elasticity at each level.

The closure rules used in the model include: (a) savings of households are endogenous and are affected by demographic factors through a saving function; (b) government revenues are endogenous but government expenditures are a fixed share of nominal gross domestic product; (c) government balance is fixed through a uniform shift in the household direct tax; (d) investment is savings driven; and (e) current account is exogenous, thus foreign savings is fixed. Changes in foreign trade are balanced through changes in the real exchange rate.

There are three factors that drive the dynamics of the model: (a) exogenous growth in population and labor (based on the population projections of the United Nations); (b) capital accumulation (based on capital stock at previous period, investment in the current period and capital depreciation); (c) factor productivity/efficiency parameters which are spread almost throughout the model.

# **2.2 The GIDD Model**

The second model is the Global Income Distribution Dynamics, or the GIDD model (Bussolo, de Hoyos, and Medvedev, 2008), which utilizes the results of the CGE to simulate the effects on global income distribution and poverty. The GIDD model uses household survey data of 116 countries, which represent about 90 percent of the world population. The GIDD model projects household survey data using three sets of ex-ante macroeconomic information: (a) changes in demographic composition which consist of projection of population by age and by educational attainment; (b) movement of labor between agriculture and
29|1.89|1.88|10.19|42.81|
|Pakistan|1.16|2.28|-|-|8.11|4.09|6.57|4.58|1.48|1.10|
|Sri Lanka|7.55|12.84|17.57|9.89|-|-|6.52|0.24|0.66|16.63|
|Bangladesh|15.94|21.86|16.34|9.71|4.95|0.94|-|-|2.82|9.72|
|Nepal|13.22|35.03|1.22|3.57|0.25|1.70|26.19|18.45|-|-|
|Maldives|6.67|2.41|2.51|2.80|44.00|40.00|N/A|N/A|N/A|N/A|
|Bhutan|0.00|16.34|0.22|0.54|N/A|N/A|2.32|4.36|N/A|9.04|
|US|1.02|1.26|0.84|1.3|1.75|2.17|2.06|2.15|1.58|1.55|
|UK|0.98|1.05|1.16|1.23|0.89|1.75|1.12|1.66|0.90|0.21|

Note: Calculations are based on data from the IMF Direction of Trade Statistics While the South Asian countries demonstrate TIIs (not normalized) greater than unity, some relatively increasing and some decreasing as seen
The role of foreign policy variables, e.g., national security/defense relationships or the extent to which countries seek to give preferential treatment to (some) developing countries or former colonies is another potential research question. A feature of the implementation of export restrictions in the EU was that actors could request exemptions based on such considerations. But presumably there was also lobbying by countries, firms and interest groups (NGOs) for selective removal of measures. An assessment of the impact of trade measures on developing countries using the data set could be an input into such more granular political economy research questions.

**Table 5: Import liberalization conditional on export controls** (medical products)

|**Table 5: Import liberalization co**<br>Dependent variable:<br>|**nditional on export controls**(medical products)<br>I<br>I lllllll|
|---|---|
|EI lrllrllr<br>|<br>0.54***|
|<br>|<br>(0.18)|
|<br>ll(1 +TARj)|0.59**<br>(0.21)|
|<br>Constant|-1.69**|
||(0.83)|
|Observations|108|
|R-squared|0.59|

**_Note._** Regression includes controls for government effectiveness; revealed comparative advantage; population; GDP weighted geographic distance to global markets; number of COVID-19 cases and import dependence. Robust standard errors, clustered by country, in parentheses. Levels of significance: *10%, **5%, ***1%.

# **_4.2 Policy impact_**

A second set of potential questions revolves around the quantification of the effects of different trade policy strategies. Natural outcome variables varying at the country, sector and time level include trade flows,
this condition in an eligible province cannot benefit from the 6 additional points subsidy if any other firm or branch under the same establishment with this firm has any outstanding debts or administrative fines. Importantly, the amount implied by the 5 points reduction in the disability, old age, and death insurance is calculated from the actual wage reported to the Social Security Institution, whereas the amount of reduction through the 6 additional points is calculated from the minimum wage irrespective of the actual wage of the employee.

# **4. Data**

## 4.1. Description of the administrative data set and data construction

The Social Security Institution (SSI) hosts all data related to formal employment in Turkey. To formally register workers under the SSI, each firm must first register as a formal employer under SSI, which then uses the employer identification number to track employers. SSI also assigns separate identification numbers for each worker registered under the employer, who can be linked to their employers by matching the employer and employee identification numbers.

SSI collects detailed information about formal employers and employees in various databases and we rely on two different anonymized databases provided by SSI in this study. The first one is the registry database that includes all relevant information on employers, including date of establishment, location, sector as well as other details. The declaration database is the main database that covers information on all registered workers on a monthly basis, and includes details such as occupation, location, gross wage and days worked in that month, and any subsidy that the individual benefits from.

SSI provided a random sample of the full database for the current analysis, which includes a 30 percent sample of the universe of all employers that ever benefitted from the 5-point subsidy, irrespective of the duration they benefitted from it. Once these establishments were randomly selected, the SSI staff then tracked all relevant variables for all time periods available in the database for that employer. In practice, though, information prior to 2004 was scattered and unreliable, and we decided to
listed to GDP is 0.29, ranging from a value very close to zero to 2.68. We chose to scale credit listed by GDP instead of the outstanding volume of credit the country since this yields more observations. One caveat with this variable is that reliable information on volume of credit listed in the credit bureau is only available for 55 out of 104 countries that have a credit bureau. We therefore use an alternative measure of credit bureau coverage, as reported by the Doing Business team and defined as: the total number of individuals plus firms listed in the credit bureau divided by the adult population of a country. For consistency, we also calculate this Doing Business measure of credit bureau coverage as the 2005 through 2010 average. As shown in table 1, this variable is available for 102 countries and has an average of 0.347.

Figure 2 compares both measures of credit bureau coverage across country income groups. The Doing Business measure indicates higher coverage in middle and high income countries compared to the credit listed measure. In low income countries, on the other hand, the ratio of credit listed to GDP is 0.41 while the coverage of individuals plus firms divided by the adult population is only 0.02. This large discrepancy suggests that in low income countries a small fraction of individuals or firms hold very large loan volumes.

We also constructed indices measuring (i) the extent of information distributed on each borrower by the credit bureau and (ii) the transparency of the credit bureau. The _information index_ is constructed as follows: the Doing Business survey asks a series of questions capturing what type of information credit bureaus distribute on borrowers, loans, and the repayment history of borrowers. The questions have a yes (=1) or no (=0) response. The index is constructed by adding all the questions with an affirmative response and dividing by the number of total questions. The summary statistics in table 1 show that the values of the index range from 0 to 0.
# **1. Introduction**

The COVID-19 pandemic has brought devastating economic impacts to low- and middle-income countries. The containment measures implemented by the governments to prevent the spread of the virus, such as the orders of lockdowns, the closure of non-essential businesses, and social distancing, have resulted in employment and income loss among people with limited coping strategies. The number of global extreme poor—who live on less than US$1.9 per day in 2011 PPP terms—is projected to have increased for the first time during the last two decades (World Bank 2020a). A recent study of harmonized household phone surveys in 34 developing countries highlights the immediate impacts of the pandemic: 36 percent of respondents stopped working because of the pandemic and 64 percent of respondents reported income loss (Bundervoet, Davalos, and Garcia 2021). Moreover, COVID-19 exacerbated existing inequalities and those who were disadvantaged before the pandemic, such as women, youth, and low-skilled workers, are experiencing even greater challenges.

Moreover, the impacts of the COVID-19 pandemic on household incomes and welfare have been spatially uneven. It has been widely observed that the pandemic more severely affected urban households—many of whom are informal, self-employed, or casual workers—in many low- and medium-income countries (Bundervoet, Davalos, and Garcia 2021). However, there are other important and less analyzed spatial factors that contributed to heterogeneous COVID-19 impacts on urban households’ employment and incomes. Households in cities and towns with a larger population size and higher population density may have suffered from larger income losses, because of higher contagion risks and the government’s implementation of tighter restrictions on people’s movement and business operations. In addition, the mobility shocks induced by the COVID-19 pandemic probably had larger effects on households in larger and denser cities, as their agglomeration economies are hinged on connectivity and accessibility. Within a large city, workers in poorly connected neighborhoods in the suburbs and peri-urban areas may have found it difficult to access jobs and operate their
one if the respondent has savings, _cash_ takes the value one if the respondent has savings but only saves in cash, _formal savings_ measures whether the respondent has any savings excluding cash; _bank savings_ captures whether the respondent has savings in a savings deposit or current account; _contractual savings_ shows whether the respondent saves using a life insurance or pension fund; _capital market savings_ measures whether the respondent has stocks, mutual funds or bonds; finally, _>1 formal savings_ indicates whether the respondent holds more than one of the saving instruments excluding cash. We consider cash as an indicator of informal savings as the amounts are saved outside the formal financial system.

As the question in Table 1 shows, in contrast to household wealth surveys, the surveys contain information on the existence of savings and assets but not on amounts. Thus, percentages reflect participation rates only and not amounts invested in the respective assets. A further difference in comparison to household wealth surveys is that the questionnaire focuses on individuals rather than households. However, the questionnaire partly accounts for this issue by asking whether individuals hold financial assets alone or together with their partner. Also, in contrast to wealth surveys, we do not impute missing values but assume that non-response is random. For household income, we take this into account by including a dummy variable for those respondents who refuse to answer the question on income.

# **3.3. Household savings are low in Central, Eastern and Southeastern Europe**

There are substantial heterogeneities between countries in the percentage of individuals with savings – with the highest percentage of savers in a European Union member state (Czech Republic) followed by a candidate for accession (FYR Macedonia) (Figure 1). In the European 6
The spleen rates reported in Rustomjee (1944) show the e¤ect of altitude clearly; the average spleen rate for 1938-41 was 2.5 percent for areas above 3000 feet and the corresponding ...gure for the areas below 1500 feet was 43.7 percent. The water body, especially stagnant water, on the other hand is very suitable breeding ground for anopheles mosquitos. We interact district level malaria estimate (Gabaldon’s “endemicity index” based on the enlarged spleen rates from Table 4, page 34 in Newman (1965)) with the elevation and inland water to create instruments for the proportion of LDO land in a DSD.<sup>20</sup> Since the e¤ect of higher elevation

> 19 Note that even though the partial correlation between the district level malaria and LDO incidence seems strong, it is not enough for identi...cation. Because, to achieve identi...cations, what is important is the power of malaria variations across districts in explaining DSD level LDO incidence after controlling for all other regressors including the district ...xed e¤ects.

> 20 Gabaldon’s endemicity index is the lowest spleen rate recorded in the previous ...ve years, divided by 5. Gabaldon (1949) suggests that an endemic index of over 10 indicates highly severe malaria, and an index of less than 3 very low endemicity.

17