rafmacalaba/data-use-mentions-extended huggingface dataset:
fund was established by the public-owned banks. In line with the transformation to EC Deposit Guarantee Directive, the official binding statutory deposit protection has been limited to 90% of EUR 20,000 for commercial banks, which co-exists with the voluntary funds by various banking associations. In the official and voluntary deposit protection schemes, coverage amounts are calculated as per depositor. _Source_ : Own survey of deposit insurers.

**Gibraltar.** ( _The Deposit Guarantee Scheme Ordinance_ ) The deposit insurance scheme in Gibraltar was established in 1998 in line with the insurance arrangements in the EU via directive 94/19/EC. It is jointly administered and privately funded, where the membership is mandatory. There is no permanent fund in place. The banks make ex-post contribution to the fund and pay administrative expenses on a regular basis. The coverage is the lesser 90% of all qualifying deposits or 18,000 British pounds (or Sterling equivalent of EUR 20,000, whichever is the greater).

_Sources_ : Own survey of deposit insurers, Garcia (1999), Gibraltar Deposit Guarantee Board (1998).

**Greece.** ( _Hellenic Deposit Guarantee Fund, Law 2832/2000_ ) The deposit guarantee scheme of Greece was established in 1995 by Law 2324/95 which was later amended by 2832/2000. It is administered jointly. The board has eight members;  three members from the Bank of Greece, five members from the Hellenic Bank Association with a participant from the Ministry of Finance. If the fund resources are not sufficient to meet the depositors’ claims, members may be called upon to pay an additional contribution that can not exceed 300% of the last annual contribution. The premiums paid by the members are determined by the following brackets: 0.5 million GRD – 1.25%, 51-250 million GRD – 1.20%, 251-750 million GRD – 1.175%, 7511750 million GRD – 0.205%, 1751 million GRD and above – 0.025%. Inter-bank
vary across countries, the use of a balanced panel imposes implicit restrictions. Luckily, the novel estimator developed by Guimaraes and Portugal (2010) is able to estimate highdimensional fixed effects in an iterative procedure on an unbalanced panel.

One might also worry about the linearity of the estimating equations. However, the nature of our application is to only look at very local changes. The level of exports and the extent of export market participation is mostly captured by the rich set of double interaction dummies. In particular, the specification includes a country-product interaction dummy that fixes the average export performance. We then only estimate how this average performance is influenced at the margin by small changes in market access. Hence, the linearization that Santos Silva and Tenreyro (2006) concern themselves with is of a secondary importance here. Similarly, the use of a non-linear model for export market participation, e.g. a logit or probit model, is of lesser value here. The change in market access will only influence the probability of participation marginally and the included fixed effects will make sure the predicted probabilities are bounded away from zero and one.

# **5. Data**

## **5.1 Sources**

We perform the analysis on a detailed dataset that combines trade data with information on market access for imports into the European Union and the United States of around 5,000 products under various preferential schemes.

The trade data consists of bilateral import and export transaction values taken from BACI which is the World trade database developed by the CEPII at a 6-digit level of product disaggregation, classified according to the Harmonized System (HS) 1992 nomenclature, for more than 200 countries from 1995 to 2016.<sup>17</sup> An advantage of using BACI rather than the underlying information from UN Comtrade is that the same trade flow, which can be reported differently by the exporter and importer, has been reconciled in order to have a single statistic on each directional bilateral relationship. BACI only reports positive trade
purged of any transitory variation. Standard errors on the measured and underlying Ginis are 0.02 or less for Russia and 0.01 or less for Poland. Differences between measured and underlying Ginis are highly statistically significant in all cases.

We apply this methodology to the income and expenditure distribution in Russia and Poland. For the purpose of living standards, we define any component of income or expenditure that is expected to disappear within 12 months as transitory. These transitory components are the economic equivalent of the idiosyncratic choice of what to wear on any given day. The underlying component is the component of income or expenditure that is expected to last at least 12 months (perhaps longer, but this cannot be inferred from the data). One can interpret this component as the income or expenditure that you Our own calculations, using the Tarki Panel for 1994-1997, yielded a 11.3% drop in Hungary's Gini for income per equivalent adult when income was averaged over 4 years. The equivalence scale equals household size raised to the power of 0.75.

18
making the partition explicit to the enumerator or demanding the execution of complex skip patterns. Furthermore, advanced CAPI technology can be used to generate the questionnaire automatically based on the assignment of the household to an optional module. While enumerators should be made aware that different households will be asked for different items, administering a rapid questionnaire does not require any additional training of enumerators beyond the standard skills for consumption questionnaires.

Conversely, the analysis of data using the rapid approach requires high analytical capacity, something that is usually lacking in developing countries. While the general concept of the assignment of optional consumption modules to households can usually be explained to local partners, poverty analysis based on a bootstrapped sample of the consumption distribution can potentially be too demanding for local capacity. However, even standard poverty analysis often surpasses the limits of local capacity, especially in conflict or post-conflict settings. Therefore, capacity building tends to focus on data collection skills with the long-term perspective of creating data analysis capacity. In addition, the rapid approach might be the only possibility to create poverty estimates in certain areas. For example in the case of Somalia, the rapid approach limited overall questionnaire administering time to less than 60 minutes for more than 90 percent of households as required by security considerations for enumerators (Pape and Mistiaen 2018; Pape and Wollburg 2019).

The rapid approach administers different consumption modules to different households. In theory, this can create a response bias if households report differently on a consumed item depending on the type 16
industry classification) at the 4-digit level, sales revenue, total fixed assets, raw material input expenditure and the number of employees. All monetary variables are in units of national currency (i.e. thousand rupiah) and deflated at the industry-year level with the wholesale price index. We also observe the nominal average wage within a plant, computed as the ratio of total wage bill costs over the number of employees. This data set provides reliable information on the plant’s location at kapubaten or district-level (administrative III level). Although our analysis uses data from 2000-2015, district boundaries are made consistent with the first available year of this data set, that is, 1996. This helps control for changes in the administrative boundaries over time (e.g. district split-ups or mergers).

For countries considered in our study, plant-level product information is available with the varying Harmonized System (HS). In order to ensure consistency across the sample, this information on product-level details is concorded to relevant year, such that product codes are comparable over time. For each detailed product we observe the quantity produced, its value, and the unit in which its quantity is measured, e.g. kg, liter, etc. Product specific unit values are calculated by dividing total values over total quantities for each product. To ensure unit prices are comparable, we define products as unique product-unit combinations.

After merging manufacturing census data with the geospatial variables, the establishment-level data has complete information on 302,871 observations with 34,366 establishments engaged in 136 4-digit industries located across 291 districts and metros over our sample period 2000-2015. Similarly, the establishment-product level data contains the same number of establishments, and 7,826 products.

# A.2: Data on local demographic and geospatial variables

_Demographic variables:_ For population density and instruments used in IV strategy we construct geospatial variables and population characteristics for spatial units chosen in each country. Population and population density (population/land (km sq.) variables were
rounds may serve as a credible source of data for analyzing the program’s effects on girls’ academic achievement.<sup>27</sup>

> 27 A caveat: Greater secondary school enrollment induced by the program may change the average composition of students, if, for example, the program attracts girls from lower down in the ability distribution to enroll in school. As a result, using exam score data only on students—instead of on children irrespective of their school enrollment status, such as through testing at home—can produce a downward bias in the potential program effect on girls’ academic achievement.

24
bad times. For example, insuring oneself by selling risk to others (through, say, credit default swaps) can actually raise systemic fragility through interconnected risk and lead to contagion and accentuated downward spirals in bad times, when default risk becomes highly correlated and the value of collateral collapses. Or else rising participation along the intensive dimension (the same intermediaries engaging in more transactions and becoming financial giants) can boost social moral hazard by vastly increasing the social costs of individual institution failures through the too-big-to-fail or too-interconnected-to-fail syndromes.

The dark side has yet a third mode in which the successful reduction of agency or participation frictions leads to problems of collective cognition. The bonanza associated with FD feeds a collective mood of optimism that puts the system on a disequilibrium path. This is typically amplified by financial innovations (for example, securitization) that unleash bouts of exuberance, even if the full implications of the innovations are not well understood. The mood swings first accentuate the upswing; once the market sentiment switches and euphoria turns into despair, they worsen the collapse.<sup>15</sup>

# **3.         The empirics of financial development**

# **_a. Data and methodology_**

We now proceed to conduct a simple empirical analysis of aggregate FD indicators. Our aim is to illustrate the above conceptual framework by showing that the order of appearance of various financial activities and the path they follow as they develop validate, at least broadly, the linkages and dynamics described in the previous section. We measure domestic financial development based on a battery of indicators that include:<sup>16</sup>

- Key components of commercial banks’ operations (deposit and non-deposit funding, credit to the private sector, claims on other domestic financial institutions);

- Insurance companies premia (life and non-life);

- Mutual funds and pension funds assets;

- Public and private debt securities (domestic and external capitalization);

- Domestic equity market capitalization.

- Domestic equity market turnover

- The share of fee-based
of the likelihood of being a treated municipality as a function of these characteristics, and taking the predicted probabilities from this estimation which form the propensity score.

The common support is the range of overlap of the distributions of the propensity score for the treatment and the control groups. In our case this ranges from 0.008 to 1. Restricting the untreated municipalities to this range of common supports eliminates 410 municipalities that are not very similar to the treated municipalities. Column 3 of Table 1, and the other plotted line in Figure 2 are then for the sample of 357 municipalities within this common support. We see this group has more registrations on average and a larger population size than the full set of untreated municipalities, but is still significantly different in size and in pre-reform trend.

We therefore further restrict the sample by using nearest neighbor matching. This selects (with replacement) the control municipality that is the closest match to each treated municipality in terms of the propensity score. This results in the use of only 29 of the 767 non-treated municipalities. The fourth column of Table 1 shows that this control group is much more similar to the treatment group. In particular, there is no significant difference in the average number of registrations or their variability, population size averages 50,000 compared to 56,800 for the treated municipalities, and there is only a marginally significant difference in the share of industry in municipal GDP. Moreover, although we only matched on pre-2011 data, the level and variability of registration rates are not significantly different over the eight pre-reform months of 2011, which provides a nice falsification test. Figure 3 plots the registration data for this nearest neighbor control group. We see it is much closer in level to the treatment municipalities, and displays similar trends. We cannot reject a common linear time trend, although do reject a common non-parametric time trend over the 44 pre-intervention months. This appears to be driven by a difference in January 2011, and so the last row of Table 1 shows that we cannot
countries _and_ transitions to capitalism having made experience more obsolete in exCommunist countries which are now majoritarily middle-income countries.<sup>34</sup>

In Web Appx. Table A.16, we examine the Spearman rank-order correlation between the returns and log per capita GDP (PPP; constant 2011 USD; for the mean year in the data for each country). The baseline correlation is 0.38 (N = 145). Including decadal cohort fixed effects, using returns based on the 0-25 bins, and employing country populations (2017) as weights increases the value to 0.60 (specification 1). If

> 34Since most low-income countries are in sub-Saharan Africa and to ensure that our results are not driven by a lower quality of surveys from the region, we verify that the patterns that we obtain for subSaharan Africa in I2D2 are consistent with the patterns observed when using other global databases such as the _World Development Indicators_ database of the World Bank (see Web Appx. Section A.1).
World (1948-2014)<br>CEPII Gravity Database 2010 and UN Comtrade 2015||
|C38 <br>**st o**|Trade and Importer GDP per capita Kenya and Rest of the World (2014)<br>CEPII Gravity Database 2010 and UN Comtrade 2015<br>**f Tables**||
|1|Estimates of Kenya’s Trade Flows (1948-2014). Dependent variable: Kenya’s exports|14|
|2|Estimates of China’s Trade Flows (1948-2014). Dependent variable: China’s exports .|16|
|3|FDI Projects and related employment (2007-2011)<br>. . . . . . . . . . . . . . . . . . . .|29|
|4|Export Processing Zones sector contribution in 2012 (%) . . . . . . . . . . . . . . . . .|41|
|C5|Estimates of Kenya’s Trade Flows (1948-2014). Dependent variable: Kenya’s exports|50|
|C6|Estimates of China’s Trade Flows (1948-2014). Dependent variable: China’s exports .|53|
|C7<br>C8|Estimates of China’s Trade Flows (1948-2014). Dependent variable: China’s exports .<br>Estimates of Kenya’s Trade Flows (1948-2014). Dependent variable: Kenya’s exports|56<br>59|

# **List of Tables**
on Finance Yearbook of China 2019. _Note:_ The figure shows the composition of government revenue as a percentage of GDP, aggregated by income group. OECD countries form a separate group. Data by revenue type are from 2020 when available or the most recent available year back to 2015. The sample includes 155 economies. CIT = corporate income tax; GDP = gross domestic product; HICs = high-income countries; LICs = low-income countries; LMICs = lower-middle-income countries; OECD = Organization for Economic Cooperation and Development; PIT = personal income tax; CSS = contribution to social security; UMICs = upper-middle-income countries; VAT = value added tax.

Indirect taxes are generally regressive, as the burden of these taxes falls disproportionately on the lower deciles of the income distribution. Indirect taxes are applied on the level of consumption and, because poorer households spend a larger share of their income on consumption (compared to richer ones), indirect taxes paid account for a greater share of their incomes too. Even though informality of purchases plausibly shields some of the lower income 23
*|-0.243|-0.857**|191|
|btwn CL and district administrator (CL survey)|(0.911)|(0.283)|(0.301)|(0.341)||
|CommunityMobili|zation - Increase|d Pressure from|Below|||
|No. community meetings organized|5.323|0.337|-1.511|-1.848|193|
|by CL (CL survey)|(7.643)|(1.231)|(1.262)|(1.430)||
|HH’s knowledge test score (0-1)|0.597|-0.005|0.001|0.006|2151|
|(HH survey)|(0.228)|(0.014)|(0.013)|(0.016)||

Table displays standard errors in parentheses. *, ** and *** indicate significance at the 90, 95 and 99 percent confidence levels respectively.

35
791|12,912|10,575|13,550|
|_Source_: Authors‘ calculatio<br>Standard Deviation<br>1,870|ns.<br>846|1,172|2,541|2,885|3,925|2,770|3,047|2,784|2,792|

_Source_ : Authors‘ calculations.

Our aim is to highlight these issues of measurement as the next generation of growth is being fueled by services and academics and policymakers should to be aware of these issues when they think about new strategies for economic development. While we acknowledge that the data on service exports is far from perfect, we would still argue that it is good enough to get a first picture of service export sophistication dynamics and its relation to growth, which is where we turn our attention now.

Figure 8 shows the evolution of _Service EXPY_ over time for selected countries. The following two figures show log _Service EXPY_ against log GDP per capita. The relationship is positive. In order to look at the evolution of service export sophistication over time, we contrast the level correlation in 1992 and 2007 in Figures 9 and 10 respectively. For both time periods, the scales have been kept constant as we want to bring the attention to the shifting paradigm of service exports globally. India and China started at similar income levels in 1992 but India jumped up by 2007. In accordance to Kaldor‘s law, as the world gets richer, services become an important driver of the global economy. In Figure 11 we show S _ervice EXPY_ against _Goods EXPY_ . The

> 10 For example, India‘s export of computer information services from 1990 till later in 1990‘s was 0. With the ICT boom, India‘s share of Computer & Information services rose to 0.43. This is almost half of Indian service exports. With PRODY for Computer & Information services (CIS) rising faster, a higher share of exporting CIS resulted in
visa fees). A reasonable assumption for the out-of-pocket cost of moving (and returning) is US$4,000, covering the costs of obtaining a visa as well as travel and relocation costs.<sup>10</sup> In addition, Clemens et al. (2019) provide a useful clue to the plausible inner range of values for migration costs by comparing the wages in the mainland US of migrants

> 7See, for example, Beaudry et al. (2018), who find a wage elasticity of labor demand of -1 based on the variations across cities and industries over 40 years.

> 8The methods and results using this method are described in Passel and Cohn (2018).

> 9Capps et al. (2018) question the Fazel-Zarandi et al. (2018) estimates. Rodilitz and Kaplan (2021) find similar results to Fazel-Zarandi et al. (2018) using data from Mexican surveys of prior illegal migrants to the US.

> 10We took an approximate amount of US$1,700 for processing of an H1B visa.

12
percent for girls and 64 percent for boys. The regressivity of tertiary education spending may be due to high primary and secondary dropout rates and low completion rates that depress tertiary enrollment. This could be more important for girls and children from poor households, for whom progression beyond primary education has long been difficult.

<!-- Start of picture text --> Figure 7a: Concentration of In-kind Figure 7b: Concentration of In-kind<br>Transfers, Men Transfers, Women<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>Cumulative proportion of the population Cumulative proportion of the population<br>Market income Primary Market income Primary<br>Secondary Tertiary Secondary Tertiary<br>In-kind education In-kind health In-kind education In-kind health<br>Population share Population share<br>Source : Authors’ calculations; ESS 2018/19 data. Source : Authors’ calculations; ESS 2018/19 data.<br>Cumulative proportion of in-kind transfers Cumulative proportion of in-kind transfers<br><!-- End of picture text -->

# _Health_

Health spending is for both genders progressive (Figure 3), though more so for women than for men. The health benefits for the poorest women and men are relatively high as a share of their market incomes (Figure 7a and 7b). However, for both men and women health spending is not propoor: about 9 percent is concentrated in the poorest decile, but the top decile takes 13 percent. The relative progressivity of health spending could be due to the recent increases in almost all neighborhoods of health extension agents, who help ensure that basic health services are available to individuals, especially women.

Figures 8a and 8b summarize the progressivity and pro-poorness of government transfers (direct and in-kind transfers plus subsidies),
children in the household as a control (please see online appendix Table A.5).

The evidence above is also consistent with other available studies on the nonfarm sector and rural industries (TVEs) in rural China. The income gap between the farm and nonfarm households was mitigated in the early years of reform by two factors: the household responsibility reform increased farmer’s income, and, in many cases, people employed on the farm were paid wages similar to the wages paid to workers in the township village enterprises (TVEs), the growing TVE sector in effect subsidizing the agricultural employment (Peng, 1998). This also reflects in part the lingering effects of policies during the cultural revolution that were successful in eliminating any significant differences between the peasants and non-peasants in rural China (Hannum et al. (2008)).

# <u>Rural India</u>

In contrast to China, there were no restrictions on rural-urban migration in India during the study period. A substantial body of independent evidence on occupational mobility in rural India suggests strong intergenerational persistence in farm/nonfarm occupations (Reddy (2015), Motiram and Singh (2012), Azam (2015), Hnatvoska et al. (2013)). Hnatvoska et al. (2013) show that there is strong persistence in rural occupations both in 1983 and 2004-2005; the son of a farmer is highly likely to be a farmer himself. Using the IHDS (2005) survey, Motiram and Singh (2012) also provide similar evidence. The fact that there was significant persistence in farm/nonfarm occupations implies that the expected returns to investing in children’s education are likely to differ across farm and nonfarm households. But whether the intercept in the nonfarm households would be higher or lower depends partly on the expected relative returns to education in the children’s generation, i.e., whether _R_<sup>_cn_</sup> _> R_<sup>_cf_</sup> or _R_<sup