rafmacalaba/data-use-mentions-extended huggingface dataset:
the ten-steps income variable contained in the World and European values surveys) or the Gini WIDER (imported from the UNU-WIDER database). They also concord if we use the Gini WIDER constructed with different income measures or the Gini WIDER constructed only with disposable income and they concord if we use the larger or smaller samples. The standard model provides consistent evidence of a negative association between life satisfaction and income inequality.

It is important to note, however, that the Gini is highly collinear with other independent variables used in the model. This is visible in all four equations considered as indicated by the high levels of the Variance Inflation Factor (VIF) reported on the bottom of Table 1.<sup>16</sup> When we tested for collinearity of the Gini with other variables, we found that this is due to GDP per capita and to most countries and years dummies included into the model. We found a large and significant correlation between the two Gini and GDP per capita (Pearson correlation coefficient of +0.6 for the Gini WVS and +0.5 for the Gini WIDER) and we also found these correlations to be high with most country and

> 15Note that we are not trying to replicate Alesina et al. results, we simply use the same form of equation as also used in other contributions and with different data. Our purpose is to test this general form of equation under different specifications.

> 16The VIF is estimated as 1/(1-R2) from an OLS regression where the dependent variable is the Gini and the independent variables are all other regressors used in the equations. This is perhaps the most popular test for collinearity. A VIF equal to one indicates no collinearity while values higher than one indicate higher degrees of collinearity. Values of five or more are generally considered as indicators of high levels of multicollinearity.

11
Indeed, the productivity gap between firms that are not exporting but will export in the following year and those that never export is economically small (2 percent, column 4) and statistically insignificant. Instead, the difference between future exporters and always exporters is both economically and statistically significant (22 percent, column 6). The fact that firms’ productivity increases as they move from the ‘future exporters’ class to the ‘always exporters’ class adds a kernel of truth to the assertion that the estimated TFPR in this paper is capturing efficiency rather than mark-ups. With high effective rates of protection in Pakistan (see for example, World Bank, 2020), increasing export orientation is likely to increase the extent of competition the firm faces.

Table 6 – Average productivity by export status

|Y|Did not export|Export in the following|Export over the|Gap|Gap|Gap|
|---|---|---|---|---|---|---|
|ear|over entire period|year|entire period|(2) – (1)|(3) – (1)|(3) – (2)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|2012|2.25|2.12|2.50|-0.13|0.25***|0.38***|

18 The share of exporting firms in the universe of firms above five employees is estimated at 14 percent, according to the latest World Bank Enterprise Survey for Pakistan. In 2017, the last year of our sample, there were a total of 14,000 exporting firms according to records from the Federal Board of Revenues.

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# **Appendix E: Impact of harmonization of Armenian standards with the European Union**

In order to assess the National Quality Infrastructure<sup>35</sup> of Armenia, the World Bank, in collaboration with the EU Advisory Group in Armenia and the Armenian-European Policy and Legal Advice Centre (AEPLAC), conducted enterprise surveys and visited various National Quality Infrastructure institutions in October 2009 and February 2010. Their study, World Bank (2010), shows that the National Quality Infrastructure is very poorly developed in Armenia, imposes significant costs on Armenian enterprises, and will have to be significantly improved to meet the requirements of a Deep and Comprehensive Free Trade Agreement with the European Union. The study documents several problems. Among them are the following: (i) Laboratories in Armenia have poorly trained personnel, poor equipment and produce results that are subject to much skepticism; only one-quarter are accredited. Armenian enterprises complain about a shortage of certification bodies and testing laboratories. The Armenian legislative requirement that certification bodies must have their own testing laboratories limits the development of certification facilities. This is not a requirement in the EU.

(ii) There is no real industrial calibration system in place in Armenia.

(iii) “Armenian certificates are not recognized abroad, including in Russia. This is one of the main obstacles for Armenian companies wishing to export. This is directly linked to the quasinonexistence of internationally accredited certification bodies in Armenia, the lack of international recognition of the national accreditation system and the lack of international traceability in the national metrology system. Among the surveyed enterprises, the evaluation of NQI services varied from “very good” to “incompetent and corrupt”, but the majority expressed dissatisfaction with the NQI system.” World Bank (2010) (iv) “The mandatory nature of certain standards differs from the international practice of voluntary standards.’ This is a significant problem since mandatory requirements for products not subject to these requirements in export markets, limit that capacity to adapt to market needs in addition to
Given the significant difference between the scope and hence the survey methodologies between the SWIFT on which the PPMT is designed and the CLCI’s verification field-test survey to which the PPMT is applied, the ranking method is more appropriate; a small reduction in successful referral rates compared to a much greater number of successful referrals and a slightly larger number of disappointed referrals is arguably preferable.

_Table 4: Share of humanitarian beneficiary households (extremely vulnerable groups) that are referred for government support by the PPMT model_

|||GOI social assist<br>|ance sim<br>|ulatedpro<br>|gram cov<br>|erage<br>||
|---|---|---|---|---|---|---|---|
|PPMT cases|Referral<br>confidence<br>level|Referral<br>poverty line<br>(log)|Btm<br>15%|Btm<br>18%|Btm<br>25%|Btm<br>30%|Btm<br>35%|

8 Ideally, one should use the rate based on the targeting criteria for the government’s program that is specific to the group or region that the data represents. Therefore, the ideal rate for this field-experiment data would be the poverty rate among the IDPs and returnees in the Northern governorates for a MoLSA program targeting households below the poverty line. Unfortunately, we do not have such information.

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Furthermore, it will tend to offer a more limited range of non-financial control variables and correlates<sup>12</sup> , and as such the range of research questions that can be answered will be constrained and the reliability of those that can may be somewhat compromised.

The purpose for which the data is being collected will also color the choice between stand-alone and embedded surveys, and will also, of course, influence the questions that are to be included. If the focus is purely on market research aimed at understanding the scale and determinants of the demand for different financial services, a shorter (albeit more specialized) list of questions will likely suffice than if the focus is on the influence of different forms of financial market access on household poverty, or on the determinants of household access.

> 10 Justifying and quantifying this and other assertions about the relative cost of different modes of data collection is beyond the scope of the present paper, but should be an early priority. 11 When grossed-up to national totals, household surveys of financial asset holdings systematically understate the reality, mainly attributable to non-response bias (richer households refusing to participate) and to respondents falsely reporting zero holdings of certain assets).  Interestingly, where respondents reported non-zero holdings it seems (from circumstances in which independent verification was possible) that their reporting errors are unbiased (Ferber, 1965).

12 For instance, LSMS surveys ask hundred of questions and typically report in detail on health, education and employment status of each household member as well as on income and expenditure patterns. Most also ask about migration, fertility, agricultural activities and anthropometry (personal height, weight etc.).

10
special assessment taxes provide revenue to municipalities based on the increase in land value from public improvements. The main challenge countries have faced in implementing these is the calculation of the increased value due to an infrastructure project. For their implementation to be viable these instruments require institutions for valuation and price data collection and public dissemination. A simplified version of these has successfully been implemented in Bogotá, Colombia, where levies are not estimated parcel by parcel but rather linked to a citywide fee. Its success can be partly attributed to large efforts to update and maintain a comprehensive cadastral database and increased participation of the citizens as overseers of the process.

-Development and impact fees and TIFs have been mostly used in developed countries. Their successful implementation requires strong institutions that are non-existent in many developing counties. These instruments need a strong regulatory authority to enforce the collection of the fees and guarantee that they are used only for the purpose for which they are defined. Clear definition of property rights is essential. Further, up to date information on property values and a clearly defined methodology for the estimation of the impact of projects on land and property values is also a pre-requisite for their successful implementation. TIFs also require a welldeveloped property tax regime. If a strong property tax system is not in place, their im <mark>p</mark> lementation ma <mark>y p</mark> rove to be difficult.

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the equator) spanning -65° to 75° in latitude and -180° to 180° in longitude.<sup>21</sup> To derive the city-level nighttime light intensity, we first clip the DMSP-OLS nighttime lights data by China’s municipal administrative boundaries, and calculate an area-weighted average of lights across pixels within a municipality for each of our sample years.

# **3.2 Biographical database for Chinese leaders**

The data on city officials are from the China Political Elite Database (CPED), constructed by Jiang (2018) and Jiang and Zhang (2020). The database consists of more than 4,000 Chinese leaders, covering all city leaders in office since 2000 and all provincial and national leaders since the mid1990s. Information for these officials is drawn from their biographies through authoritative internet sources. For each official, the database contains basic demographic characteristics as well as the time, place, organization and rank of every job assignment listed in his curriculum vitae. We match each city-year spell in our dataset with a party secretary and a mayor. In turnover years when multiple officials held the same post, we keep the official who held the position at the end of the year. Panel B of Table 1 shows the summary statistics for the city party secretaries.<sup>22</sup> Our database contains 1,041 city PSs in office between 2000 and 2013. Among the city PSs, 130 served in more than one city (mostly two); 4.2% are female, and 6% are ethnic minorities. Their average tenure is 3.9 years, with only 25% staying in the office for more than 5 years. Their mean age when assuming office is 50.6 years old. More than 68% of them are from their native provinces, and around 7% serve in cities that are their native cities.<sup>23</sup> We define promotion and demotion significantly more comprehensively than the literature. The
to construct an instrumental variable for tourism and apply it to a cross‐section of countries for the period 1980–2002. They find that tourism specialization is positively correlated with economic growth, with a one standard‐deviation increase in the share of tourism in exports leading to a 0.5 percentage point in additional annual GDP growth. Similarly, Sequeira and Nunes (2008) use panel data methods and find that tourism has a positive impact on economic growth. More recently, Faber and Gaubert (2019), using microdata for Mexico, find that tourism results in “large and significant” economic gains — employment, GDP — in tourist destinations relative to other regions. Their identification is based on measures of tourism attractiveness that result from local natural and cultural characteristics. In their simplest estimation results, the authors find that a 10 percent increase in tourism revenues leads a 2.5 percent increase in relative employment and a 2 percent increase in population. They also find sizable Page **4** of **34**
have to cut their consumption. Household micro data support this view.

The structure of household expenditure does not differ significantly across the population, highlighting the low level of inequality in Belarus (Figure 22). Only the richest 10<sup>th</sup> decile stands out, as it spends a significantly lower share than the population on average on food and much more on savings, investments and the purchase of personal cars (Shymanovich, Chubrik, 2019). The bottom decile spends a higher share of income on food, as well as basic services such as utilities, public transportation and communication services. Differences between the 2<sup>nd</sup> and 9<sup>th</sup> deciles are not very large, but they increase with disposable income. The main difference is related to the falling share of food in households’ expenditures with growing household incomes. We focus our analysis of the consumption behavior during the crisis on the expenditures of the 1<sup>st</sup> and 10<sup>th</sup> deciles. Other deciles we will aggregate into three groups, representing 2<sup>nd</sup> and 3<sup>rd</sup> deciles, from 4<sup>th</sup> to 7<sup>th</sup> deciles, and 8<sup>th</sup> and 9<sup>th</sup> deciles.

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**Figure 9: Share of employment by formality status, 2016-2023 (%)**

<!-- Start of picture text --> 100<br>22.8 23.7 24.0 25.0 23.2 23.1 23.3<br>80<br>60<br>40 77.2 76.3 76.0 75.0 76.8 76.9 76.7<br>20<br>0<br>2016 2017 2018 2019 2021 2022 2023<br>Informal Formal<br>Share of employment (%)<br><!-- End of picture text --> Source: Author’s calculations using Sakernas data

**Figure 11: Share of employment by formality and sector, 2023 (%)**

<!-- Start of picture text --> 100 3.7<br>28.0 31.7<br>80<br>60<br>96.3<br>40<br>72.0 68.3<br>20<br>0<br>Agriculture Industry Services<br>Informal Formal<br>Share of employment (%)<br><!-- End of picture text --> Source: Author’s calculations using Sakernas data

**Figure 10: Share of employment by formality and age group, 2023 (%)**

<!-- Start of picture text --> 100.0 3.1<br>11.4<br>26.7 23.2<br>33.5<br>80.0<br>60.0<br>96.9<br>88.7<br>40.0 73.3 76.8<br>66.5<br>20.0<br>0.0<br>15-24 25-34 35-54 55-64 65+
the database by Asonuma and Trebesch (2016). Since 1975, their data identifies a total of 192 debt restructurings with 74 developing and emerging countries. To assess the scope of debt relief, we add information from Cruces and Trebesch (2013) and from the updated data set of Asonuma et al. (2017) and Asonuma and Trebesch (2016). Specifically, we use their dummy variable on whether debt restructurings involved face value debt reductions in contrast to agreements that only reschedule debt payments. Figure A4 shows the resulting data and depicts the number of countries that completed a debt restructuring with private creditors in any given year since 1975.

> 12 Data on principal and interest arrears from the World Bank’s International Debt Statistics offers corroborative evidence: In several countries that have large bilateral debts to China, arrears to official creditors have been rising sharply, although no credit events or restructuring agreements have been made public.

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of global GDP.

Fourth, policies could help reverse these trends and boost global growth. For advanced economies, labor market reforms (especially targeting pension systems) may have the highest potential to stem the decline in potential growth. Among EMDEs, in particular, education, health, and labor market reforms could significantly increase potential growth. A combination of these policies could lift potential growth in EMDEs over the next decade by 0.8percentage point, and more in EAP and ECA.

To sustain higher potential growth, countries need to reform labor and product markets, strengthen human and physical capital and build conducive environments for business and households to invest. The onus is particularly on the largest emerging markets and advanced economies, whose growth momentum generates spillovers for other EMDEs.

This paper draws on a comprehensive database that estimates potential growth using all standard approaches for up to 181 countries for 1980-2017 (extending to 2027 for 80 countries) based on Kilic Celik et al. (forthcoming). For clarity, the remainder of the paper presents only results using a production function approach for 30 advanced economies and 50 emerging market and developing economies for 1998-2027 that together

> 4 Other studies have investigated the link between actual growth or productivity growth and structural reforms, focusing on the near-term benefits (Prati, Onorato, and Papageorgiou 2013), productivity effects (Dabla-Norris, Ho, and Kyobe 2015; Adler et al. 2017) or a sample consisting of mostly advanced economies (Banerji et al. 2017; IMF 2015 and 2016b).

3
to jobs for women. Hence, we control for the annual real growth rate of real GDP per capita. The data source for the variable is World Development Indicators, World Bank; and for the quality of democracy proxied by the “Polity” index taken from the Polity IV database.

There are several ways in which more education for all (men and women) and especially for women may improve women’s labor market outcomes. For example, improved access to education for women is likely to add to their skills and also create awareness among them about available opportunities in the labor markets; it raises the cost to women of staying home; more educated women in society helps to change social attitudes towards women’s work, making women’s labor market activity socially more acceptable; (World Bank 2011, Chioda et al. 2011, Contrareras and Plaza 2010, Morrison et al. 2007). More education overall (among men and women) can also result in a more favorable attitude towards women’s work and their rights. Thus, we control for two measures related to access to education: gross enrollment rate in primary education in the country, and the gender parity index for gross enrollment rate in primary education. The data source for the variable is WDI, World Bank.

# **4. Estimation results**

4.1 Base regression results Our baseline regression results are provided in Table 1. For all the specifications considered, the relationship between female workers and exports is large, positive and statistically significant at the 1 percent level. The estimated coefficient value of exports ranges between 0.160 and 0.191. Thus, for each percentage point increase in exports, the associated increase in female workers equals 0.160 to 0.191 percentage points.

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of ageing, education and migration shocks. Section 3 describes the construction of our data set and provides summary statistics of the labor force, migrant data, and changes in their age and educational composition. This section also presents the discussion of the main parameter values used in the analysis. Section 4 discusses the simulated wage effects of ageing, educational changes and migration, using our model and the range of parameters available from the literature. Section 5 examines the consequences of relaxing some of the assumptions we make. Section 6 concludes the paper.

# 2 The analytic model

We construct a static aggregate model that endogenizes the effect of the changes in the education, age, immigrant and emigrant composition of the labor force on the wage levels of native (non ~~-~~ migrant) citizens. Workers differ by their migration status, educational attainment (i.e. skills), and age group and have heterogeneous labor force participation preferences. In terms of migration status, we distinguish between the nativ ~~e-~~ born and the foreig ~~n-~~ born workers, which are denoted by M = (N,F). In terms of educational attainment, we follow the literature in labor economics (Freeman, 1979; Katz and Murphy, 5
**Figure 11: Differences between HAI for CAR and control countries and their respective synthetic controls., 2009-17**

<!-- Start of picture text --> We keep all placebo countries<br>20<br>15<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>-20<br>1990 92 94 96 98 2000 02 04 06 08 10 12 14<br>Seleka Occupation<br>Year<br>No pre-HAI's MSPE five times higher than CAR's<br>10<br>5<br>0<br>-5<br>-10<br>1990 92 94 96 98 2000 02 04 06 08 10 12 14<br>Seleka Occupation<br>Year<br>Gap Reduced human asset index<br>Gap Reduced human asset index<br><!-- End of picture text --> _Source: The reduced HAI series are derived from Feindouno and Goujon (2019)_ (28) _, from 1990-2014. Note: The dark dash line indicates the gap between actual and synthetic light series for CAR and the gray lines indicates the gap between actual and synthetic light series for control countries (normalized to 0 in 2012). The dark dash horizontal_ 23
the outcomes in each region.

# **3 Spatial Data and Calibration**

The information needed to calibrate the model comes from traditional data sources, such as surveys and geo-coded information on the transport network. It includes state- and districtlevel data on transport costs, land size, labor force, and wages.

The labor force includes the working-age population (people 15 and older). All wages are monthly and expressed in dollars. Data on labor force and wages in India come from the 2011 Periodic Labor Force Survey; data on Bangladesh come from the 2010 Labor Force Survey.

> 1 ˆ _<u>x</u>_<sup>_′_</sup> That is, _x_ = _x_<sup>,where</sup><sup>_x′_isthecounterfactualvalueand</sup><sup>_x_istheinitialvalueofvariable</sup><sup>_x_.</sup>

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suitability for the job based on characteristics that we do not necessarily observe.<sup>20</sup> As such,

> based on CFPA data. That said, our regressions do rely on explanatory variables that can be seen as proxies for those concepts. For example, the revenue derived from a loan is tied closely to the likelihood that it is repaid. At the most basic level, loan officers’ selection of applicants that are highly likely to be creditworthy contributes to high repayment rates among loan recipients. Within the pool of applicants deemed creditworthy, the interest rate and maturity of the loan further account for potential risk and provide additional information about the amount and timing of repayment. Regarding costs, many of the administrative costs incurred to support CFPA lending are pooled across loan officers, and thus it is not possible to ascribe them to an individual officer or a loan. Officers’ salaries would be easier to allocate, but we lack the necessary data. We argue, however, that these salaries are likely to fall within a relatively tight band and, in any event, the loan officer fixed effects regressions help account for any differences in salary costs across officers.

> 20 As a robustness check, we also ran regressions that used county fixed effects, and found results very similar to

19