rafmacalaba/datause-encoder-data huggingface dataset:
(2020)<sup>14</sup> , the indirect standardization was calculated using 2020 both-sex mortality from the United States as the standard mortality schedule 5Mxs by three 5-year age groups consisting of ages 85-89, 90-94, and 95-99. We define the share of each population _k_ for each group cx,k such that 1 = ∑x cy,k, summing up over the three 5-year age groups and where the shares for the United States age structure are referred to by cx,R. The adjustment for population _k_ is as follows: These adjustment factors were multiplied by the observed age-specific COVID-19 mortality rates (and separately the estimated age-specific excess mortality rates) for the open interval 85+.

The coefficients, βi , are the rates of increase per year of age in probability of death. This calculation was performed first defining D(x, i) in terms of official COVID-19 counts by age for each country for which data was available. The calculation was also performed for each country defining D(x, i) using excess mortality estimates by age as described in the previous section.<sup>3</sup> These results provide 1) the slope of the COVID-19 age-mortality curve according to official counts, and 2) the slope of the age-mortality curve for excess deaths.

# **Results**

The left side of Figure 2 shows the age distribution of officially recorded COVID-19 deaths in 2020 by country along with the age distribution of excess deaths. According to official reports , on average across high-income countries, just 11 percent of deaths were among those under age 65.<sup>4</sup> In contrast, people under 65 constituted on average 40 percent of official COVID-19 deaths in upper-middle-income countries and 54 percent of deaths in lower-middle-income countries. The same pattern is observed using the distribution of excess deaths by age, shown on the right side of Figure 2. In high-income countries, those under 65 are also 11 percent of excess deaths. For the upper-middle-income countries for which data is available (Peru, Colombia, and

> 3 In cases for which estimated excess deaths were zero or less, the value was coded as 0.001 in order to calculate logarithms.

> 4 Note that all countries in Figure 2 from Chile downward with the exception of Turkey on the figure are classified as high-income countries. Turkey is classified as an upper-middle-income country. Bangladesh, India, the Philippines, Jordan, and Indonesia are lower-middle-income countries. World Bank classifications are based on GNI calculated by the Atlas method while the figure uses GNI calculated with purchasing power parity exchange rates.

5
economies.<sup>6</sup> Aggregate or macroeconomic indicators based on average household indebtedness, however, mask the likely concentration of borrowing among selected households, including among those that are more vulnerable or less able to service their debt in the event of an economic slowdown. Debt holding could vary significantly across household income groups, across age and other demographic groups. To answer the above questions on the welfare and stability implications of increasing household indebtedness, micro-level data are therefore necessary.<sup>7</sup> This paper (i) documents the recent rapid increase in access to consumer and mortgage credit by households in the new EU member countries, (ii) compares use of mortgage credit by households of different characteristics across old and new EU member countries, and (iii) assesses whether mortgage holding can result in a financial burden, at least during the period preceding the current global financial crisis. We use aggregate data as well as household-level data in our analysis. Specifically, we use aggregate lending data across European countries to document recent trends and data from the EUStatistics on Income and Living Conditions (EU-SILC) to explore benefits and costs of mortgage holding on the household level across countries.

The development of mortgage markets in the transition economies has been part of the overall process of financial deepening in the region. While financing for the purchase of houses was previously available in some Central European countries, such as Hungary or Slovenia, this was not allocated on a market-basis (Roym 2008). However, even before 1990, there was a wide variation in tenure status. According to Struyk (1996), 84% of houses in Bulgaria were privately owned, but only

> 6 See Gyntelberg et al. (2007), IMF (2006: 61) and BIS (2007) on the need for micro data in assessing financial stability.

> 7 To date, most micro-level studies focus on one country, such as Żochowski and Zajączkowski (2007) on Poland, NBS (2006) on the Slovak Republic, CNB (2009) on the Czech Republic, Beer and Schulz (2007) on Austria, Holló (2007) on Hungary. These studies do _not_ assess the drivers of household debt holding and, because they are country specific, do not explore cross-country differences in household indebtedness. In addition, a recent study of Bosnia and Herzegovina (Chen and Chivakul 2008) looks at household credit market participation (self-reported desire to borrow) and credit constraint (whether refused a loan). Even in more advanced economies outside Central Europe, analyses of micro data on household debt have been few and very recent. See for example BIS (2007) and Dynan and Kohn (2007). Crook (2006) reports that most studies using survey data focus on the US, though there are some limited recent work on the UK and Italy.

4
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- Dina Pomeranz and José Vila-Belda. Taking state-capacity research to the field: Insights from collaborations with tax authorities. _Annual Review of Economics_ , 11:755–781, 2019.

- Joel Slemrod. Tax compliance and enforcement. _Journal of Economic Literature_ , 57(4):904–54, 2019.

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- Sandra Sequeira and Simeon Djankov. Corruption and firm behavior: Evidence from african ports. _Journal of International Economics_ , 94(2):277–294, 2014.

- Sandra Sequeira. Corruption, trade costs, and gains from tariff liberalization: Evidence from southern africa. _American Economic Review_ , 106(10):3029–63, 2016.

- Bob Rijkers, Leila Baghdadi, and Gael Raballand. Political connections and tariff evasion evidence from tunisia. _The World Bank Economic Review_ , 31(2):459–482, 2017.

- Ludvig Wier. Tax-motivated transfer mispricing in south africa: Direct evidence using transaction data. _Journal of Public Economics_ , 184:104153, 2020.

- Claudio Ferraz and Frederico Finan. Exposing corrupt politicians: the effects of brazil’s publicly released audits on electoral outcomes. _The Quarterly Journal of Economics_ , 123(2):703–745, 2008.

- Paul Niehaus and Sandip Sukhtankar. Corruption dynamics: The golden goose effect. _American Economic Journal: Economic Policy_ , 5(4):230–269, 2013.

- Rachid Laajaj, Marcela Eslava, and Tidiane Kinda. The costs of bureaucracy and corruption at customs: Evidence from the computerization of imports in colombia. _Documento CEDE_ , (2019-08), 2019.

- Lorenzo Casaburi, Michael Kremer, and Ravindra Ramrattan. Crony capitalism, collective action, and ict: Evidence from kenyan contract farming. Working paper, 2019.

- David Atkin and Amit Khandelwal. How distortions alter the impacts of international trade in developing countries. Working Paper 26230, National Bureau of Economic Research, 2019.

- Abhijit Banerjee, Rema Hanna, and Sendhil Mullainatha. Corruption. In Robert Gibbons and John Roberts, editors,

35
# Tamil Nadu

Figure 9a: Annual Percentage Change in Stamp Duty Revenues and Rates Figure 9b: Stamp Duty Revenues and Rates <!-- Start of picture text --> Tamil Nadu<br>40<br>30<br>20<br>10<br>0<br>-10<br>Year<br>Tax Revenue Stamp Rate<br>1980-81 1985-86 1990-91 1995-96 2000-01<br>Annual % Change<br><!-- End of picture text --> <!-- Start of picture text --> Tamil Nadu<br>120000 13.5%<br>100000 13.0%<br>12.5%<br>80000<br>12.0%<br>60000<br>11.5%<br>40000<br>11.0%<br>20000 10.5%<br>0 10.0%<br>Year<br>Tax Revenue Stamp Rate<br>1980-81 1985-86 1990-91 1995-96 2000-01<br>Rs. Lakhs Rate (%)<br><!-- End of picture text -->

# Uttar Pradesh

Figure 10a: Annual Percentage Change in Stamp Duty Revenues and Rates

## **Uttar Pradesh**

<!-- Start of picture text --> 700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>-100<br>Year<br>Tax Revenue Stamp Rate<br>1980-81 1985-86 1990-91 1995-96 2000-01<br>Annual % Change<br><!-- End of picture text --> Figure 10b: Stamp Duty Revenues and Rates <!-- Start of picture text --> Uttar Pradesh<br>120000 16.0%<br>100000 14.0%<br>12.0%<br>80000<br>10.0%<br>60000 8.0%<br>6.0%<br>40000<br>4.0%<br>20000 2.0%<br>0 0.0%<br>Year<br>Tax Revenue Stamp Rate<br>1980-81 1985-86 1990-91 1995-96 2000-01<br>Rs. Lakhs Rate (%)<br><!-- End of picture text --> 76
economies might trickle down and support the population. In particular, the results emphasize the ambiguous impacts that resource-led growth has had on poverty in other settings (e.g., Savoia and Sen 2021); these results build on the analysis in Pandey and Howes (2022) demonstrating how access to health has changed over the course of the resource boom in PNG, and extend the well-being indicators analyzed beyond health indicators over a portion of the resource boom; and the results comparing well-being in PNG to much of the rest of the world extend previous work that has shown similar disparities for either a subset of indicators or comparisons to a subset of the countries analyzed here (e.g., Howes and Mambon 2021; UNDP and OPHI 2022; Howes forthcoming; etc.).

The paper is structured as follows. Section 2 provides background information on the resource boom in PNG and some of the other significant changes that have occurred since the last official poverty estimate; Section 3 describes the data used in the analysis and the construction of the monetary and multidimensional poverty measures; Section 4 presents estimates of how well-being indicators have changed between 2009 and 20162018 for PNG and other comparison regions; Section 5 illustrates the disparity between economic activity and well-being in PNG relative to the rest of the world; and section 6 concludes.

# **Section 2. Background on Resource Growth and Welfare Measurement in Papua New Guinea**

As discussed in the Introduction, the resource sector is one of the largest contributors to the PNG economy. Although many economic statistics are imprecisely measured given the severe data deprivation in the country,<sup>4</sup> the biggest sectors of the economy have significant exports to other countries, and exports are much more precisely measured than many other economic statistics (e.g., Howes et al. 2022). Based on this information, the key contributors to overall economic activity include mining for gold, copper, cobalt, and zinc; drilling for natural gas and oil; and the production of timber from logging (e.g.,

> 4As mentioned in the Introduction, the last Household Income and Expenditure Survey was conducted in 2009. Economic statistics depend in a large part on the information from these surveys, and much of the basis for GDP estimates in the country are based on likely outdated information (e.g., World Bank 2023).

5
# 3. <u>Case studies</u>

Bertaud reviews case studies for India, South Korea, China and Thailand to examine how urban land development evolved in the face of rapid urbanization and income growth. The successful cases exhibit considerable variety, and no one country did everything ‘right’. However, in the cases where households experienced rapid improvements in housing quality and quantity assembled a few key ingredients are always present as part of a consistent and mutually reinforcing strategy. They are: primary infrastructure investments; supportive land use regulations; efficient taxation, fares and fees; and modest supply and demand side subsidies.

# 4. <u>The research agenda</u>

Against this backdrop Bertaud calls for a research agenda for urban land and housing that focuses on

- specific regulations and their quantitative impact on land and housing prices and consumption in specific urban locations, rather than the common academic approach that involves cross‐city and cross‐country analysis relying on broad dummy variables to estimate the impact of regulation in a generic form;

- urban land and housing affordability which considers “(1) removing supply regulatory constraints, and (2) subsidizing demand for the poor for safe water, health and education”; (Bertaud, 2010, p. 40)

- the existing urban land and housing stock as much as on the conversion of land to urban use and the supply of new housing.

In addition, Bertaud proposes four specific operational research tasks:

- <u>Land use audits: few cities maintain an inventory of land use; land use audits</u> would collect spatial information on land use, on land and floor supply and consumption, and on land values, disaggregated by types of land tenure and land utilization.

- <u>Regulatory audits: cities generally have a slew of land use regulations that</u> have haphazardly grown over decades; the regulatory audit would assess the status of all existing land regulations, assess their mutual consistency and their implied costs and benefits, and project the application of regulations onto citywide maps to test the spatial impact of regulations.

- <u>Study of the affordability thresholds set by regulations and the limits between the formal and informal land markets: This would involve the</u> establishment of the consumption curve relating household income to land and floor area consumption; calculate the affordability threshold between the formal and informal markets, identify the regulatory and/or productivity barriers that prevent low‐income households from gaining access to the formal market; and project the number of beneficiaries and their income if regulatory or productivity barriers were removed.

- <u>Study of the costs and benefits of limiting FAR: As described, above FARs are</u> frequently set by regulations at maximum levels substantially below market

16
This is the idea that is in fact embedded in the UNICEF framework of nutrition (UNICEF, 1990) that emphasizes the important roles that food, environmental health, and care play in whether children are stunted. Figure 1 summarizes this framework visually.

**Figure 1: The UNICEF Framework for Nutrition**

<!-- Start of picture text --> Food security/adequacy<br>(Access, availability,<br>utilization)<br><!-- End of picture text --> <!-- Start of picture text --> Care<br>(Pre and post-natal<br>care, breastfeeding<br>practices,<br>complementary food<br>in the diet etc.)<br><!-- End of picture text --> <!-- Start of picture text --> Health<br>(Susceptibility to<br>diseases, drinking<br>water, sanitation, etc.)<br><!-- End of picture text --> <!-- Start of picture text --> Nutritional Outcomes<br><!-- End of picture text --> Our empirical strategy derives directly from this framework and consists mainly of running a series of regressions of the following form: where _NSih_ is the nutritional outcome, measured by height-for-age or weight-for-age z-score, of child _i_ in household _h_ ; _FSh_ is a one of the nine measures of food security considered in this analysis, and _Xih_ is a vector of control variables, which includes individual, household, demographic, socio-economic, and environmental characteristics, and ε _ih_ is the random disturbance term. Here, β1 is our parameter of interest, which represents the relationship between food security and nutritional outcome. Since our primary interest is in comparing the strength of the association across the various measures of food 7
33

> 5fuel, y or p evehicles +<sup>5</sup> VMTs - mpg*

4 Wheaton estimates these elasticities (both income and gas price elasticities) for a 25 country sample and a 42 country sample, the latter containing a number of developing countries.

The results of the model may not be useful or reliable because they rely on old (1972) data. As the data requirements are fairly modest, however, the paper could be updated fairly easily. Explanatory variables used in the regressions include, in addition to the dependent variables, the percentage of the population in urban areas, the size of the country, the prices of gasoline and autos, and per capita income. Obtaining data on fleet efficiency and auto prices was somewhat complicated and the procedures employed questionable; better ones could surely be devised.

The models in this class do not estimate welfare effects of alternative policies, but they could be used for that purpose, as could any model that provided demand functions for a commodity. However, it permits only a limited set of the "broad" policy instruments to be modeled, linking these instruments to some targets of interest. For instance, the effects on fuel demand of gasoline taxes, policies affecting vehicle prices, and even policies affecting income can be modeled. Policies affecting modal choice and congestion are beyond the reach of this model, particularly where the entire country is a unit of observation. This general approach to modeling is useful as a firstcut in explaining fuel demand (and from there, emissions), and could be a useful source of data to include in a more elaborate model. In this regard, versions of this approach applied cross-sectionally to cities with more recent
33

**Table 3. Global Welfare Reduction Indexes, by Commodity, 1960–2004 (percent)**

||**1960-64**|**1965-69**|**1970-74**|**1975-79**|**1980-84**|**1985-89**|**1990-94**|**1995-99**|**2000-04**|
|---|---|---|---|---|---|---|---|---|---|
|**Grains and tubers**|**47**|**50**|**48**|**52**|**50**|**97**|**89**|**64**|**62**|
|Rice|66|65|86|75|75|150|152|116|141|
|Wheat|42|45|36|30|30|59|47|29|20|
|Maize|29|29|23|29|30|48|29|21|20|
|Cassava|na|na|23|9|11|16|10|14|10|
|Barley|60|57|46|48|32|97|87|45|33|
|Sorghum|137|89|90|76|52|56|54|39|39|
|Millet|68|66|34|21|32|59|126|73|31|
|Oat|55|74|67|108|41|67|70|33|31|
|**Oilseeds**|**9**|**16**|**16**|**20**|**28**|**37**|**34**|**24**|**24**|
|Soybean|4|6|10|16|28|31|27|24|25|
|Groundnut|29|27|52|41|38|50|50|43|43|
|Palmoil|21|29|36|22|23|26|55|28|15|
|Rapeseed|21|32|19|9|18|64|48|15|26|
|Sunflower|15|11|16|25|37|58|40|21|19|
|Sesame|48|60|62|65|56|44|47|45|38|
|**Tropical crops**|**53**|**90**|**46**|**46**|**48**|**60**|**56**|**50**|**55**|
|Sugar|157|224|58|68|72|99|76|77|87|
|Cotton|21|46|47|32|29|39|38|34|45|
|Coconut|29|24|12|14|19|24|38|27|12|
|Coffee|23|32|35|44|50|38|31|22|15|
|Rubber|37|39|19|25|25|20|21|26|11|
|Tea|43|41|32|41|39|36|35|32|30|
|Cocoa|28|47|42|58|51|38|36|36|38|
|**Livestock products**|**78**|**80**|**74**|**88**|**85**|**84**|**66**|**53**|**50**|
|Pigmeat|54|79|66|59|70|42|33|27|28|
|Milk|162|161|149|218|182|191|111|83|73|
|Beef|51|43|42|47|66|93|76|72|68|
|Poultry|44|43|54|48|50|48|54|46|45|
|Egg|52|48|39|32|28|39|36|36|26|
|Sheepmeat|95|129|159|190|123|107|75|41|31|
|Wool|0|0|6|7|11|7|10|8|6|
|**All of the above**<br>**28 commodities**|**62**|**65**|**57**|**63**|**63**|**82**|**71**|**55**|**52**|

Source: Authors’ calculations based on NRA and CTE estimates in Anderson and Valenzuela (2008).
Expenditures (PCE) Chain-type Price Index, as there are no ocial statelevel price data.<sup>14</sup> As state-level growth can be extremely volatile, especially for small states, I drop outliers from all specications where the annualized growth rate of per capita labor income growth or GDP is more than 20% in absolute value. Standard errors are cluster-robust (clustered at the state level), which allows for heteroskedasticity and arbitrary serial correlation. See Appendix 1 for descriptive statistics and additional information on data sources and construction.

2.3.1. Cumulative Multipliers. As an extension in Section 3.6, I estimate dynamic cumulative multipliers several quarters after the transfer is paid. The cumulative multipliers _Ch_ are dened as the cumulative change in an income variable relative to the cumulative spending on the transfers over a horizon of h quarters beyond the impact quarter ( _h_ + 1 quarters in total). I use two specications, both of which collapse to Equation 2 for _h_ = 0 .

For permanent transfers, I use a direct projections specication (Jordà 2005), which has been used by Miyamoto, Nguyen, and Sergeyev (2018) and others to estimate cumulative multipliers.<sup>15</sup> The estimated equation is: where<sup>�</sup><sup>_h_</sup> _j_ =0<sup>(</sup><sup>_Yi,t_+</sup><sup>_j −Yi,t−_1)</sup><sup>_/Yi,t−_1is the sum of</sup><sup>_h_+1growth rates over horizons</sup> up to _h_ periods (left side) and<sup>�</sup><sup>_h_</sup> _j_ =0<sup>(</sup><sup>_tri,t_+</sup><sup>_j−tri,t−_1)</sup><sup>_/Yi,t−_1(rightside)isthe</sup> sum of changes in scaled transfers over the same period. The cumulative multiplier can be estimated directly as _Ch_ = _βh_ .

For one-o transfers, I use a distributed lag (DL) specication, which adds extra lags of the transfer variable ∆ _tri,t−j/Yi,t−j−_ 1 to the right hand side of

> 14While the BEA does produce estimates of state-level GDP deators, these variables use national prices for dierent industries and are weighted using state-specic industry weights, which do not capture the price eects from local demand shocks. See Appendix 3.1 for robustness tests using constructed state-level ination indices.

> 15 Kraay (2014) uses a similar specication (in changes) but takes the sum after estimation. Ramey and Zubairy (2018) apply the same specication but estimate in detrended levels.

13