rafmacalaba/data-use-mentions-extended huggingface dataset:
A detailed analysis of progressivity (or of the broader concept of redistribution), requires a welfare ranking of individuals or households (or at least of a set of disaggregated groupings of individuals or households), and for tax liabilities of each individual or household (or group) to be ascertained. This can be achieved for certain taxes where: (a) the economic incidence of the tax can be determined with at least a reasonable degree of confidence; and (b) the necessary data is available distinguishing rich from poor among those that the economic incidence of the tax falls on.

The most notable example is the personal income tax (PIT), as tax liabilities can be linked to taxpayers at different income levels. Most simply this can be done through analysis of the underlying PIT rate schedule, or in a more complex manner through use of household income or consumption survey data (potentially augmented with tax return data), and simulation of tax liabilities. Furthermore, there is broad consensus that the incidence of the PIT falls predominantly, if not entirely, on the individuals earning the income. Consequently, the majority of empirical analyses of tax progressivity focus on the PIT. This attention is unsurprising given that the PIT is the primary tax instrument used in most countries to achieve distributional objectives. Even so, there are significant data limitations associated with PIT analyses, such as the underreporting of top incomes in survey data, tax avoidance and tax evasion, which affect the accuracy of microdata-based analyses.<sup>2</sup> Detailed microdata-based analyses are also highly resource intensive and often suffer from significant time lags.

For countries with comprehensive systems (where labor and capital income are taxed together under the PIT), PIT analyses can capture taxes imposed on labor and personal capital income. In schedular systems (where personal capital income is taxed separately, and often at lower rates than labor income) it is more challenging to capture personal capital income. Use of tax return data can often aid the capture of capital income taxes
<!-- Start of picture text --> Figure 12: Rates of Increase per Year of Age in COVID-19 Mortality<br>vs. Hospital Beds per 1000 People by Country<br><!-- End of picture text --> <!-- Start of picture text --> CHE<br>CAN<br>DNK<br>AUS<br>SWE<br>ESP<br>ITA DEU KOR<br>PRTNOR HUN<br>FRA<br>JPN<br>CHL<br>ARG<br>PHL<br>BRA<br>COLMYS<br>AFG PER<br>PAK<br>MEX<br>0 5 10 15<br>Hospital beds per 1,000 people<br>Figure 13: Rates of Increase per Year of Age in COVID-19 Mortality<br>vs. Doctors per 1000 People by Country<br>CHE<br>CAN<br>NLD<br>DNK<br>AUS<br>SWE<br>ESP<br>KOR ITADEU<br>NOR HUN PRT<br>FRA<br>JPN<br>CHL<br>ARG<br>PHL<br>BRA<br>MYS COL<br>ZAF<br>AFG PER<br>PAK<br>MEX<br>0 1 2 3 4 5<br>Medical doctors per 1,000 people<br>.15<br>.1<br>Rate of increase in COVID-19 mortality rate<br>.05<br>.15<br>.1<br>Rate of increase in COVID-19 mortality rate<br>.05<br><!-- End of picture text --> Note: Hospital bed and medical doctor data are from World Development Indicators. LMICs are shown in red and HMICs in blue.

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Figure 2 Trends in Tariffs and in Wage Premium <!-- Start of picture text -->
1.5<br>1<br>.5<br>0<br>1974 1976 1982 1989 1991 1994 2001<br>Skilled wage prem ium Average tariff<br><!-- End of picture text -->

Note: own calculation based on historical trade data and labor surveys (EPH). Tariff: average tariff across all 3-digit ISIC sectors, weighted by employment in each sector. Skilled wage premium: coefficients on the skilled dummy in different earnings regressions per year.

we ran separate regressions of wages on the skill dummy for one survey in each year, controlling for age, age squared, gender and marital status. Notice that we do not include trade related variables at this point.

Figure 2 reveals the breaks in trade liberalization trends and the breaks in the wage inequality trends that we exploit in this paper. The broken line corresponds to the evolution of the average tariff during the period 1974-2001. The figure clearly shows the initial high protection on the early 1970s, the liberalization of the late 1970s, the stagnation of tariffs during the 1980s, and the last episode of liberalization of the 1990s.

The solid line in Figure 2 depicts the skill premium.<sup>5</sup> There is a sharp increase in the differential between wages of skilled and unskilled individuals between 1974 and 1982, coinciding with the first wave of trade policy reforms. While individuals with college education earned roughly 60% more than their unskilled counterparts in 1974, the difference grew to about 120% in 1982. Between 1982 and 1989, when trade liberalization lost momentum, the skill premium decreased markedly. In contrast, the skill premium resumed

> 5Note that we report the estimated coefficient directly —i.e., without the standard exponential transformation (e<sup>coefficient</sup> − 1).

7
distributes evenly inside the prefecture.

> 32 Because the data come from various sources, the modern variables center around years 2000 to 2010, but are not of the same year. See Appendix B for data sources.

> 33 For most variables, the jurisdiction is based on the Qing Dynasty.

> 34 - In defining Taiping prefectures, our main source of data is " 太平天国地理志 华强 " (Taiping Kingdom MapsHuang Qiang).  To implement, we first coded Taiping-influenced area at the county level using the Qing 1893 administration boundaries, then converted it in to Qing 1820 administration boundaries (between 1820 and 1893). In our research we thus use the administration boundary of prefectures and provinces in the year 1820.

23
# **4.2 Approach 2**

## _Model_

Approach 2 uses a single predictor to impute NSS-type consumption into the CPHS, namely observed CPHS consumption, a variable that is arguably highly predictive of NSS-type consumption, but which is entirely ignored in approach 1. In other words, in this approach we will convert the observed CPHS consumption into NSS-type consumption. Let CPHS-consumption expenditure for household _i_ be denoted by _xi_ . Section 3.2 establishes the following stylized facts: (a) The variance of NSS log consumption is higher than the variance of CPHS log consumption. The re-calibration of the survey weights has reduced this gap in the second moment, but some gap still remains, and (b) CPHS log-consumption is near normally distributed, while NSS log consumption shows a more marked deviation from normality. Specifically, the third moment of NSS log consumption is approximately twice the third moment of CPHS log consumption. (A similar ordering applies to the fourth moment.) To accommodates the above-mentioned stylized facts, consider a model where CPHS log consumption is described as a linear combination of NSS log consumption and a normally distributed error term: where _εi_ is an independent identically distributed error term with mean zero and unit variance. In practice we do not observe _yi_ and _xi_ for the same household _i_ given that the two measures of consumption come from different cross-sectional surveys with their own samples of households that cannot be linked. Accordingly, the model that describes the relationship between the two cannot be estimated using standard regression analysis (which is the reason why observed CPHS consumption was excluded as a predictor in approach 1). Instead, the parameters _a_ , _b_ , and _σ_ will be estimated using method of moments.

A minimum of three moment conditions will be required. The first three moments of the log consumption distribution are natural candidates. The mean and variance of both sides of eq. (2) solve: where _μq_ and _σq_<sup>2evaluate the mean and variance of the variable</sup><sup>
The main caveats and directions for improvement are as follows. First, the data are collected through a survey of regulators, and thus data availability and quality are based on the data collection processes of the regulators. Although SME financing data are generally not among the standard data collected by regulators, many regulators were able to provide the data. However, this indicates a potential avenue for improvement. As regulators improve their processes for collecting data, harmonizing the SME definition is crucial. Although financial institutions likely have information on the size of firms in terms of number of employees and/or sales volume at the time of loan application, they may not keep this information in their records. Thus, the only way for them to report on SME lending is based on the size of the loans. Unfortunately, in most cases, there is no unified definition of an SME, even within one country, due to this multiplicity of criteria.

Second, the data on SME lending are only based on those collected through regulated financial institutions, and in most cases, through commercial banks only. This severely underestimates SME lending volume, as one would expect that a sizeable amount of SME financing is done through unregulated and informal institutions. In some cases, even consumer loans could end up being used for commercial purposes. By going through secondary regulators, financial institutions, and firms themselves, the current data initiative could be strengthened.

14
of picture text --> 60<br>40<br>20<br>0<br>-20<br>-40<br>-60<br>-80<br>retail & recreation transit stations workplaces<br><!-- End of picture text --> Source: Authors’ calculations based on Google COVID-19 Community Mobility Trends (as of March 2022).

Following its mid-March 2020 emergency declaration, the GoE undertook immediate precautionary measures aimed to contain the spread of the virus and prevent and mitigate crisis impacts. Measures implemented included nighttime curfews, restrictions on gatherings and domestic and international travel, and decrees facilitating home-based work for civil servants. Educational institutions, including schools and universities, were closed, as were religious establishments. Sports and recreational activities were also suspended, and restaurants were shut. Businesses and shops were required to close by 5 p.m., in line with the national curfew, and were fully shuttered on weekends. Some villages and cities with a high number of COVID-19 cases were completely isolated. A national campaign for 4
</sup> —granularity of the model—data and then these quarterly paths are fed into the cash-flow engine module of TDSM in order to estimate cost and risk profiles of alternative debt strategies.

# **4.3 Cash-Flow Engine**

The cash-flow engine is where computations are carried out with regard to the borrowing requirement, the bonds issued, the payments and finally the output metrics, given the scenario paths. The central feature of the engine is the debt-stock matrix. This is a matrix where each row represents a different financing instrument (i.e., Treasury bill, coupon bond, floating-rate note, etc.) and each column represents a different attribute of the corresponding financing

> 13 Using MS Excel platform to transfer simulation paths to Matlab enables users to create their scenarios manually (deterministic scenarios) and feed them easily to the Cash Flow Engine.

> 14 Although, TDSM performs mainly on quarterly granularity, financial scenario generator is based on monthly historical data and subsequently provides monthly forecasts. Since historical financial data was restricted to the beginning of 2002, this approach enables to extend the length of data set.

27
mal and informal sectors, sheds light on previous studies, which consistently found that premiums for the formal sector were greater for prices than for rents. We show that the difference between the two premiums precisely reflects the risk faced by investors.

Consistent with previous studies, we find the difference in rents between the formal and informal sectors to be important. Once observed characteristics of the dwellings and of the neighborhood are controlled for, there is still a 35 percent gap between the two sectors. This figure is comparable, although larger, than those found by Jimenez (1984) and Friedman, Jimenez and Mayo (1988) for the Philippines. This difference may be explained only by unobservable characteristics of the dwellings, as well as by the different ways in which housing characteristics interact in the formal and informal sectors. However, since the variables we use in our hedonic regressions are similar to those used in previous papers, one can suppose that this high premium reflects idiosyncratic conditions of the rental market in the informal sector in Pune. Exploiting datasets from other cities in India could give us indications whether this high premium is particular to Pune city, or if it reflects conditions prevailing in the rental markets throughout India. Further empirical studies are needed to better understand what drives those differences across countries or regions.

We believe that our very simple investment model can be extended in many fruitful ways. An obvious extension consists in going beyond our literary discussion in section 4 to consider endogenous land prices. Other insights could be obtained by introducing heterogeneity in the degree of uncertainty on informal investment (that is more categories of informal land), or in the opportunity cost of capital (to reflect the difference between private households with no or limited access to alternative portfolio choices and firms with potentially high returns on financial investments). Consider the latter case. If one finds that firms (supposed to have higher opportunity cost of capital) only invest in the formal sector, this would mean that returns in the informal sector are not high enough to compensate for
information problems have been the main factors driving the process of foreign bank entry in developing countries.<sup>11</sup> Another recent strand of that literature emphasizes that the physical and cultural proximity between a parent bank‘s home country and the host country of its affiliate helps to ameliorate information problems and therefore affect its commitment to (and activities within) that host market (Claessens and Van Horen, 2008 a,b; 2009).

Wide variation in the participation levels of foreign banks at the end of the 1990s can be viewed as a reflection of the depth of commitment of foreign banks to those host countries.<sup>12</sup> This leads to the following hypotheses regarding the effect of the depth of foreign banks‘ commitment to a country on the relative severity of the crisis for firms in the ECA region:

**_Hypothesis 4._** _In host countries where foreign banks have a high level of commitment, as reflected in their initial participation level, we expect that reported financial constraints at the time of the crisis would be less severe than in countries with low initial levels of foreign bank participation._

**_Hypothesis 5._** _We expect the increase in the severity of financial constraints at the time of the crisis to be less for firms in countries with high initial levels of foreign bank participation._

It remains unclear, however, whether the current crisis, which started in developed countries, would deepen or lessen foreign banks‘ commitment to these countries for the reasons described above. Therefore, our regressions provide an implicit test of those conjectures.

# **III. Data**

This paper uses data from two enterprise surveys. The first is the Business Environment and Enterprise Performance Surveys (BEEPS), which were conducted in most countries in Eastern

> 11 For a summary of this literature see Cull and Martinez Peria (2010).

> 12 A simple regression shows that the share of banking sector assets held by foreign-owned banks in 1999 is
-|
||**Treated**|**Control**|**Percent**<br>**Bias**|**P-Value**|
|Age of Head of Household|45.934|46.046|-0.9|0.759|
|SC or ST Household(Y/N)|0.791|0.784|1.6|0.574|
|DependencyRatio|0.649|0.644|0.7|0.81|
|Status of Primary School in Village in 2012|0.967|0.966|0.5|0.849|
|Education Level of Household Head|3.824|3.916|-2.2|0.476|
|Household Type Kutcha(Y/N)- SECC 2013|0.567|0.549|3.7|0.22|
|Household Owns Telephone/Mobile Phone -<br>SECC 2013|0.402|0.404|-0.6|0.854|
|Any Member of Household has a Salaried<br>Job-SECC 2013|0.053|0.061|-3.9|0.298|
|Household Owns Any Land-SECC 2013|0.459|0.467|-1.7|0.58|
|Percentage of Irrigated Land from Total<br>Land in the Household-SECC 2013|5.894|6.502|-3.2|0.235|
|Household Income Main Source: Cultivation<br>-SECC 2013|0.398|0.399|-0.3|0.931|
|Household Income Main Source: Non-<br>Agricultural Own Account Enterprise - SECC<br>2013|0.006|0.007|-2|0.567|

> 10 In the absence of poverty rates data at the village
Using two panel data sets at the sectoral level, our empirical strategy relates GVC participation as a buyer (backward GVC links) and seller (forward GVC links) to domestic value added. The measures are based on the Organisation for Economic Co-operation and Development’s (OECD) Inter-Country InputOutput (ICIO) data set as well as the World Input Output database (WIOD). Both databases cover a wide range of industrialized and developing countries, manufacturing and services sectors, and years. Related streams of literature have focused on the productivity gains from international trade and investment at the sector or firm level, including the role of exporting (Bernard and Jensen 1995 and following studies), importing inputs (see, for example, early contributions by Görg and Hanley 2003 or Mann 2003), and FDI (see, for horizontal spillovers, Haddad and Harrison 1993; for vertical spillovers, Javorcik 2004a). To the best of our knowledge, Kummritz (2016) is the only other study that looked into the effects of GVC integration on economic upgrading based on international input-output measures.

The type of GVC integration is a main determinant for which policies matter. Javorcik’s (2004a) seminal study on spillovers from foreign direct investment (FDI) introduced measures of backward and forward FDI links based on input-output data. The concept of backward and forward links has been used widely in the literature of FDI and, increasingly, in international trade. Overall, the literature suggests that backward and forward FDI links have a differential effect on economic upgrading (see, for example, Javorcik 2004a; Havranek and Irsova 2011), and similar results can be expected for different types of GVC integration (Kummritz 2016). Integrating as a buyer into GVCs requires a focus on building the infrastructure and strengthening connectivity to global firms in order to import world-class inputs. While GVC participation as a seller also hinges on a similar policy framework, the focus now shifts more towards increasing productivity in order to be more competitive in the global marketplace. Following Kummritz (
extremely accurate. For instance, the industrialized countries imported USD 310 billion worth of machinery and transport equipment in 1980, whereas their yearly payments for technology and technical and management services in the early 1980s only amounted to approximately USD 10 billion. Concurrently, the developing countries' imports of machinery and transport equipment from dleveloped countries reached USD 129 billion, and their payments of royalties, fees, and remuneration for technical services totaled about USD 2.5 billion. There are no data on the overall importance of joint ventures, but, for comparison, it can be noted that the industrialized countries' inward stock of foreign investment was estimated at approximately USD 401 billion in 1983, while the corresponding figure for developing countries was USD 138 billion (UNCTC, 1988, p. 25).' There are also some data available to describe the extent of MNC participation in licensing and gcoods trade. These data are interesting because they confirm that MINCs are the main sources of these types of technology, but also because they indirectly introduce foreign direct investment into the picture. MNCs control technology supplies by virtue of their ownership of proprietary technologies, but they also account for a significant share of the demand, via their foreign affiliates. This is most apparent for the transfers of "disembodied" technology that are captured by data on trade in royalties, licenses, and patent rights. Over 80 per cent of the registered payments to the United States for technology sales during the 1970-1985 period were made by foreigni affiliates of US firms (Grosse, 1989). More than 90 per cent of the technology payments from developing countries to Germany in the early 1980s, and over 60 percent of the payments to Japan, originated from their own foreign affiliates (UNCTC, 1988, p. 177).

The intra-firm character of the technology transfers that take place through trade in capital equipment and other products is less apparent, but still distinguishable. What we know about MNC involvement from statistics on goods trade is that between 70 and 80 per cent of the goods
1985, p. 2), that “the legal tradition relates the legal system to the culture of which it is a partial expression,” then the origin of a country's legal framework may rely on characteristics of its informal institutions. Thus, unequal legal treatment of women may be the product of a conservative culture of patriarchy and the consolidation of power in men (Cherif, 2010). We capture the cultural dimensions that reflect differences in gender by using an index provided by the World Atlas of Linguistic Structures that indicates the intensity of gender marking in language ( _Language_ ). Grammatical gender marking has been associated with a wide range of women’s economic and social outcomes (see Mavisakalyan and Weber (2018) for a complete survey). Moreover, language’s grammatical structure offers the advantage of being a stable feature inherited from the distant past, therefore ensuring the respect of the exclusion restriction.

17
**Figure 34: Root Mean Square Error Before and After FH Approach (Poverty Headcount Ratio $5.5/day - Left; Average per-capita consumption 2011 PPP – Right)**

<!-- Start of picture text --> 0.12 4<br>3.5<br>0.1<br>3<br>0.08<br>2.5<br>0.06 2<br>1.5<br>0.04<br>1<br>0.02<br>0.5<br>0 0<br>Standard Error Fay‐Herriot Standard Error Fay‐Herriot<br>Standard Error No Correction Standard Error No Correction<br><!-- End of picture text --> An additional robustness check estimates FH models for each country individually, comparing the results with those using pooled data across countries. The results presented in figure (35) show that this makes little substantive difference in either the point estimates or in the root mean square error for within-sample districts. There is a very strong relationship between the estimates regardless of the pooled vs. individual approach. The relationship remains strong but weakens somewhat with respect to out-of-sample districts (figure 35 - right). Using the pooled approach likely improves on the outof-sample prediction: this method improves the training of the model with more data than is available at the national level.

**Figure 35: Comparison of Individual Models vs. Pooled Model (Only in sample - Left; Including out of sample - Right)**

<!-- Start of picture text --> 1 1<br>0.9 R2 = 0.9852 0.9 R2 = 0.9216<br>0.8 0.8<br>0.7 0.7<br>0.6 0.6<br>0.5 0.5<br>0.4 0.