rafmacalaba/datause-encoder-data huggingface dataset:
estimating the impact of price changes on each household‘s real income by multiplying the price change experienced by the household by the quantity of the good produced and by the negative of the quantity consumed by that household (see Deaton, 1989 for a justification of this approach). The first modification involves allowing households to substitute away from commodities whose price rises. We do this by introducing a Constant Difference of Elasticities (CDE) demand system parameters (Hanoch, 1975), estimated for 112 countries and regions following the methodology of Liu, Surry, Dimaranan and Hertel (1997), to allow us to capture the second-order impacts of price changes on households‘ real incomes through changes in the volume of each good consumed. A second modification is to omit modeling the effects of commodity price changes on wage rates on the grounds that commodity price changes appear to take some time to affect wage rates in developing countries (Ravallion 1990). As in Ivanic and Martin (2008)—but with more justification given the speed of the 2010 price increase—we ignore the potential second-order effects of price changes on incomes through increases in output of products whose prices have risen.

Our measures of the poverty headcount and the poverty gap index follow the definition of Foster, Greer and Thorbecke (1984). Hence, our poverty headcount reflects the number of people whose daily expenditure is below the defined poverty-line income while the poverty gap measures the average expenditure shortfall of the poor as a share of the poverty-line income for the entire population.

# **Data**

In this study, we use household survey information on household food production, sales and consumption of thirty-eight food and agricultural commodities for twenty-eight developing countries (Appendix table 1). This is an increase from twenty commodities and ten countryperiods in the 2008 study. The agricultural commodities identified in the surveys include not only basic staples (e.g. wheat, maize and rice), but also various types of animal products (e.g. poultry, eggs, pork, beef) and a number of commodities that are important to the poor in a range of developing countries (e.g. sorghum, groundnuts, soybeans). The full list of agricultural commodities included is presented in Appendix table 2. The breadth of the product coverage is important given that the food price increases on this occasion included far more than the staple grains that were the main focus of the 2008 food price crisis.

6
As consanguinity may be linked to a wide range of human development indicators, we use a unified framework to lay out the decision to enter into consanguineous marriages and potential effects on child development. We then explore the relationship between consanguinity and two important child development outcomes: child cognitive ability and stature. The paper makes use of a unique household survey from Pakistan that includes information on the marriage patterns of all household members as well as their parents (regardless of whether they are still alive or present in the household). Human development outcomes include height and weight measurements for all household members age 5 and above, cognitive tests administered to children and adults, and a set of questions useful for modeling the marriage decisions of the parents of household members, including grandparent land ownership status and mortality.

The next section briefly reviews motivations for marriage to a relative, most frequently a first or second cousin, and existing evidence on human development impacts. Section 3 presents a modified version of Becker’s marriage model that is used to motivate the empirical analysis. Data sources are described by Section 4, followed by a discussion of empirical results and conclusions.

# **2. Consanguineous Marriages**

# **2.1 Why Marry a Relative?**

High levels of consanguinity in Pakistan have been well documented. Using the 1990/91 Pakistan Demographic and Health Survey, Hussain and Bittles (1998) estimate that 60 percent of marriages are consanguineous unions, and that the incidence of consanguinity had remained unchanged over the previous three to four decades.<sup>2</sup> The decision to marry a relative may be driven by cultural or economic factors or both.

In some societies, there is a belief that compatibility between husband and wife, as well as between the bride and the rest of the household, will be maximized by marriages among kin (Khlat et al. 1986). Further, in societies in which violence against women is rampant, concerns over the safety and welfare of females may influence the decision to enter a consanguineous marriage.<sup>3</sup> Further, beliefs about the potential negative effects on child outcomes of marriages among kin would likely influence the prevalence of such

> 2This is consistent with the trends observed in other developing countries: Givens and Hirschman (1994), for example, find that there was a modest increase in marriages between cousins in the Islamic Republic of Iran from the 1940s to the 1970s.

> 3As far as we know, this hypothesis remains untested.

3
38 trade is already duty-free under pre-MOU FTA. Therefore the effects of various scenarios of bilateral liberalization are only estimated for the dyads: Albania-FYR Macedonia and AlbaniaCroatia.

The model has been run for four different scenarios, which reproduce policy recommendations already suggested in various publications (e.g., Daskalov et al. 2000, EWI 2000). The policy variants examined are as follows (Table 14):

- _Scenariol:_ It assumes a full-fledged FTA between Albania and FYR Macedonia and Albania and Croatia with their respective MFN rates at applied levels in 2000.

- _Scenario 2:_ It adds to Scenario I the assumption that Albania, Croatia and FYR Macedonia adopt the EU MFN tariff schedule on industrial products as their MFN tariff schedules with one caveat. Wherever the EU tariff rate is higher than the current NIFN tariff applied by one of these countries, the lower tariff rate is used. It also assumes that FTAs that these countries had in 2000 are in effect.<sup>5</sup> "

- a _Scenario 3:_ It assumes that three SEE-economies adopt a uniform tariff of 10 percent on their MFN imports and, as above in Scenario 2, their respective preferential partners in 2000 have a duty free access.

- _Scenario 4:_ It examines the effects of 'zeroing' tariff rates on imports of industrial products from the EU while retaining their 2000 tariff arrangements on other imports.

Considering assumptions and the quality of foreign trade data, the estimates summarized in Table 14 should be treated with a great deal of caution. They are not counterintuitive, however. In fact, they fully corroborate expectations derived- from a standard analysis of geographically discriminatory barriers imposed by FTAs.<sup>52</sup> The model tends to exaggerate the impact of FTA on trade flows, because of the assumptions about perfectly competitive and integrated markets, and full substitutability and homogeneity of products. The latter in particular tend to overestimate both the potential for trade creation and trade diversion and therefore net change in welfare. Moreover, the effects are larger for Albania, as they stem from two rather than one FTA, as is the case of Croatia and FYR Macedonia.

> I in terms of the modeling exercise, this means that imports coming from preferential partners are not subject to tariffs. This does not apply to Albania, which-as of January 2002-has no FTA. In<sup>the caseof</sup> Croatia, duty-free treatment applies to imports from Bosnia and Herzegovina, FYR Macedonia,<sup>Hungary</sup> and Slovenia. For FYR Macedonia, duty-free access applies to imports from Bulgaria, Croatia,<sup>FRY,</sup> Slovenia and Turkey.

> 52 Although based on different methodology, they are similar to results obtained by Green (1999).
Figure 13: An increase in the progressivity of the wealth tax in 2019 <!-- Start of picture text --> Effective Tax<br>Rate (%)<br>2.25% 2013-15 2016<br>Amnesty Amnesty<br>Top 0.01%<br>p99.95-p99.99<br>p99.9-p99.95<br>1.25% p99.5-p99.9<br>0.75%<br>p99-p99.5<br>0.5%<br>0.25%<br>0<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br><!-- End of picture text --> _Notes:_ this figure compares the wealth tax liability expressed as a share of total (taxable and non-taxable) assets by the wealthiest 1% of adults (aged 20 and above) separately by bins of increasing assets. The progressivity of the wealth tax increased in 2019 when Argentina taxed offshore assets at higher rates. _Source:_ authors’ calculations using data from AFIP statistical yearbooks.

Table 1: Domestic and foreign assets in 2014, 2015, and 2016

|||**Dom**|**estic assets**|||**Fore**|**ign assets**||
|---|---|---|---|---|---|---|---|---|
||**2015**<br>US$|**2015–14**<br>_%_∆_(pre)_|**2016–15**<br>_%_∆_(post)_|**2016–15**<br>_% of total_∆|**2015**<br>US$|**2015–14**<br>_%_∆_(pre)_|**2016–15**<br>_%_∆_(post)_|**2016–15**<br>_% of total_∆|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Deposits and currencies|20,336|10%|1%|0%|4,736|16%|344%|23%|
|Stocks and investments|1,020|1%|76%|1%|9,297|13%|366%|48%|
|Real estate|37,230|-7%|24%|13%|792|20%|1044%|12%|
|Vehicles|13,264|4%|-10%|-2%|17|21%|230%|0%|
|Real rights|174|14%|71%|0%|9|13%|341%|0%|
|Equity of companies|10,685|-9%|1%|0%|758|43%|-|-1%|
|Accounts receivable|7,659|-6%|38%|4%|1,042|23%|116%|2%|
|Other assets|7,552|-3%|10%|1%|2,221|13%|-17%|-1%|
|**Total assets**|**97,920**|**-2%**|**13%**|**17%**|**18,872**|**8%**|**311%**|**83%**|

_Notes:_ this table shows changes in foreign and domestic assets before and after Argentina’s 2016 tax amnesty. First, columns (1) and (5) report the total amount of domestic and foreign assets declared by wealth taxpayers in 2015. Columns (2) and (6) report the percentage change before the amnesty between 2015 and 2014, while columns (3) and (7) report the post-amnesty change between 2016 and 2015. Lastly, columns (4) and (8) express these differences relative to the total change in reported domestic and foreign assets between 2015 and 2016. Each row corresponds to a different type of asset. The last row shows the aggregate across categories. _Source:_ authors’ calculations using data from AFIP statistical yearbooks.

36
- <mark>UN (United Nations). 2005. “Designing Household Survey Samples: Practical Guidelines.” Department of Economic and Social Affairs, Statistics Division, United Nations, New York.</mark>

<mark>______. 2015. “World Urbanization Prospects: The 2014 Revision.” Department of Economic and Social Affairs, Population Division, United Nations, New York.</mark>

- <mark>Veenhoven, Ruut. 2004. “Happiness as a Public Policy Aim: The Greatest Happiness Principle.” In</mark> _<mark>Positive Psychology in Practice</mark>_ <mark>, edited by P. A. Linley and S. Joseph. Hoboken, N.J.: John Wiley.</mark>

- <mark>Vogel, Kathryn Baragwanath, Ran Goldblatt, Gordon H. Hanson, and Amit K. Khandelwal. 2018. “Detecting Urban Markets with Satellite Imagery: An Application to India.” NBER Working Paper No. w24796, National Bureau of Economic Research, Cambridge, MA.</mark>

- <mark>Wooldridge, Jeffrey. 2002.</mark> _<mark>Econometric Analysis of Cross Section and Panel Data</mark>_ <mark>. Cambridge, MA: MIT Press.</mark>

- <mark>World Bank. 2013.</mark> _<mark>Urbanization beyond Municipal Boundaries: Nurturing Metropolitan Economies and Connecting Peri‐urban Areas in India</mark>_ <mark>. Washington, DC: World Bank.</mark>

- <mark>______. 2015.</mark> _<mark>Leveraging Urbanization in South Asia: Managing Spatial Transformation for Prosperity and Livability</mark>_ <mark>. Washington, DC: World Bank.</mark>

- <mark>______. 2016. World Development Indicators (database). Washington, DC. http://data.world bank.org/data‐catalog/world‐developmentindicators.</mark>

- <mark>Yu, Le, and Peng Gong. 2012."Google Earth as a Virtual Globe Tool for Earth Science Applications at the Global Scale: Progress and Perspectives."</mark> _<mark>International Journal of Remote Sensing</mark>_ <mark>33 (12): 3966‐86.</mark>

- <mark>Zhou, Y., S. J. Smith, C. D. Elvidge, K.  Zhao, A. Thomson, and M. Imhoff. 2014. “A Cluster‐Based Method to Map Urban Area from DMSP/OLS Nightlights.”</mark> _<mark>Remote Sensing of Environment</mark>_ <mark>147: 173–85.</mark>

32
Alesina, A., Giuliano, P. (2007) The power of the family, IZA DP, No. 2750 Alesina, A., Giuliano, P. (2009) Family ties and political participation, Harvard Institute of Economic Research. Discussion Paper No. 2171 Alesina, A., La Ferrara, E. (2002) Who trusts others?, Journal of Public Economics 85(2), 207-234 Banfield, C.B. (1958) Moral basis of a backward society. New York : Free Press Barro, R.J., McCleary, R.M. (2003) Religion and economic growth, American Sociological Review 68(5), 760-781 Bartolini, S., Bilancini, E., Pugno, M. (2008). Did the decline in social capital depress Americans’ happiness?. Quaderni del Dipartimento di Economia Politica, n.540, University of Siena.

Becchetti, L., Pelloni A., Rossetti, F. (2008) Relational goods, sociability, and happiness, Kyklos 61(3), 343363 Beugelsdijk, S., Smulders, S. (2009) Bonding and bridging social capital and economic growth. Discussion Paper No.27, University of Tilburg Beugelsdijk, S., de Groot, H.L.F., van Schaik, A.B.T.M. (2004) Trust and economic growth: a robustness analysis, Oxford Economic Papers 56(1), 118-134.

Bjornskov, C. (2008) Social capital and happiness in the United States. Applied Research Quality Life 3, 43-62 Bjornskov, C. (2006) The multiple facets of social capital. European Journal of Political Economy, 22, 22-40 Bourdieu, P. (1986) The forms of capital. In: John G. Richardson (ed.): Handbook of Theory and Research for the Sociology of Education. New York: Greenwood Press, 241-258.

Bruni, L., Stanca, L. (2008) Watching alone, Journal of Economic Behavior and Organization 65(3-4), 506-528 Clark, A. (2005) What makes a good job? Evidence from OECD countries. In Bazen, S., Lucifora, C., Salverda W. (Eds.), Job quality and Employer Behaviour, New York: Palgrave McMillan Clark, A., Lelkes, O. (2009) Let us pray: religious interactions in life satisfaction, Working paper no. 2009/1, Paris School of Economics Costa, D.L., Kahn, M.E. (2003) Understanding the decline in social capital, 19521998. Kyklos 56:17.46.

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**-**

**--** --

Page No.

# TABLE OF CONTENTS

|I.|Introduction<br>. . . . .|. .|. . . . . .|. . . . . . . . . .<br>1|
|---|---|---|---|---|
|II.|Corporate Debt Subsidies|and|Investment|Behavior<br>. . . . .<br>3|
|III.|The Hypothesis<br>. . . .|. .|. . . . . .|. . . . . . . . . .<br>3|
|IV.|Data and Measurements .|. .|. . . . . .|. . . <sup>. . . . . . .</sup><br>12|
|V.|Empirical Results . . .|. .|. . . . . .|. . . <sup>. . . . . . .</sup><br>18|
|VI.|Conclusions . . . . . .|. .|. . . . . .|. . . . . . . . <sup>. .</sup><br>27|
|Refere|nces.||.|..<br>X<br>so|
||List|of|Figures and|Tables|
|Figure|1 . . . . . . . . . .|.<br>.|. . .<br>. . .|. . . . . . . . . .<br>6|
|Figure|2 .6||||
|Table|1 . . . . . . . . . . .|. .|. . . . . .|. <sup>. . . . . . . . .</sup><br>14|
|Figure|3 . . . . . . . . . .|. .|. . . . . .|. <sup>. . . . . . . . .</sup><br>16|
|Table|2 . . . . . . . . . . .|. .|. . . . . .|<sup>. . . . . . . . . .</sup><br>17|
|Table|3 . . . . . . . . . . .|. .|. . . . . .|. <sup>. . . . . . . . .</sup><br>20|
|Table|4 . . . . . . . . . . .|. .|. . . . . .|. <sup>. . . . . . . . .</sup><br>21|
|Table|5 . . . . . . . . . . .|. .|. . . . . .|<sup>. . . . . . . . . .</sup><br>24|
|Table|6 . . . . . . . . . . .|. .|. . . . . .|. <sup>. . . . . . . . .</sup><br>25|
|Table|7 .26||||
In Columns (2) [respectively, Column (5)], we also add controls for the log of the initial GVO per capita, and initial population size, in 1952 [respectively, in 1978]. In Columns (3) and (6) we further control for some measures of the county geography and the distance to Xiamen. Finally, in columns (7) and (8), we trim the tail 5% of the dependent variable to ensure that our results are not driven by outliers.

The results are remarkably robust. Let us discuss Columns (3) and (6) where we include all the controls of initial economic condition, population and geography variables. Focusing on the 1952-1998 annual growth rate, we find that relative to FA3 counties, YRD counties exhibited an advantage of 0.91 percentage points per annum. In Column (7), we find that YRD advantage stays at 0.77 percentage points per annum even after we trim 5% of the tails. Focusing on the post-reform period of 1978-1998, the advantage of the YRD counties is estimated to be more than 2.1 percentage points per annum, and this advantage does not shrink much as we trim the 5% outliers. Table 6 also shows that initial income is associated with lower growth rate, which is consistent with conditional convergence hypothesis; that having a higher share of plain area is associated with higher growth and a closer proximity to Xiamen (and Taiwan) is associated with higher growth rate.14 We now turn to local accountability, proxied by the presence or absence of guerrilla forces in the county as of May 1948. In counties of strong guerrilla presence, the local grassroots were more organized, and thus they were more able to act as a force to be reckoned with for the survival of the local leaders. As a result, and as modelled earlier, the local leaders in counties with guerrilla presence are likely to adopt more pro-local economic policies.

Otherwise similar to in Table 7, in Table 7 we add the dummy of local Guerrilla presence and study the separate effects of YRD and guerrilla presence on local growth rates. We find that both YRD and Guerrilla dummies have statistically significant and positive effects on annual growth rates. Focusing on the results in columns (3) and (6), where we include the richest set of controls with the full sample, we find that YRD counties had a 0.86 percentage point higher average annual growth rate in 1952-1998 (or 0.6 SD), and 1.929 percentage points (or 0.5 SDs) in 1978-1998, than FA3 counties; and the effect of Guerrilla presence is also strong though slightly less pronounced: by 0.52 percentage point (or 0.35 SDs) in 1952-1998, and 1.52 percentage points (or 0.4 SDs) in 1978-1998. The results remain statistically significant and quantitatively similar when we trim 5% of tail counties.

> 14Reducing the distance to Xiamen by one SD (90KM) is associated with an increase in growth rate by 0.9 percentage point.

28
8 while the operational deficit of the consolidated public sector was negligible.

It follows from the abo"e discussion that a correct calculation of the government's revenue from money creation requires a careful examination of the structure of the financial system and of the regulations on reserve requirements.

We now turn to the Argentine case. On June 1, 1977 a financial reform introduced a fractional reserve banking system and liberalized interest rates. The central bank paid interest on the required reserves on time deposits to compensate anrks for the cost of these "immobilized' funds. At the same time, it charged conmercial banks interest on the fraction of the stock of demand deposits (on which banks did not pay interest) that they were able to lend. In other words, the central bank taxed away the seigniorage levied by commercial banks on demand deposits, while it compensated them for the required reserves on time deposits.<sup>4</sup> Given that the interest rate paid and charged on reserves was roughly the same, the inflation tax (in steady state) was given by (8) rtax - f(cc + rddd + rtdt) + i(l-rd)dd - irtdt 4 This system.of taxes and subsidies **WV:b** recorded through the Monetary Regulation Account (MRA, in Spanish Cuenta de Rebalacion Monetaria). Two reasons were invoked for the creation of the MRA in June, 1977: (a) Paying interest on the legal reserves required for time deposits was a mechanism designed to eliminate the distortionary effect of a high legal reserve requirement on interest rates. (b) Taking away the inflation tax on commercial banks demand deposits provided an instrument to avoid an 'unfair' advantage of the latter over other financial institutions (financieras and savings and loans associatiors), that were not allowed to accept demand deposits. For a complete description of the Monetary Regulation Account, see En&ayos Economicos, No 31, September 1984.
|Business Development<br>Past 3 months % of firms that:|||
|---|---|---|
|Visited a competitor to see what products/services they offer and at what price|49.25<br>(0.50)|-0.03<br>(0,05)|
|Asked your existing customers whether there are any other products/services they|70.15|-0.03|
|would like you to offer|(0.46)|(0.05)|
|Used a special offer to attract customers|41.79<br>(0.50)|-0.05<br>(0.05)|
|Done any form of advertising|26.12<br>(0.44)|-0.03<br>(0.04)|
|Attempted to negotiate with a supplier for lower prices|46.27<br>(0.50)|-0.05<br>(0.05)|
|% of firmst that:|||
|Have a record-keeping system (written or computerized) which allows you to know|67.91|-0.04|
|the quantity of supplies or inventory you have on hand|(0.47)|0.05|
|Keep written or computerized records relating to your enterprise|74.63<br>(0.44)|-0.03<br>(0,05)|
|Record every purchase and sale made by the enterprise|72.93<br>(0.45)|0.02<br>(0.04)|
|Regularly use your records to know whether sales of a particular product are|62.69|0.02|
|increasing or decreasing from one month to another|(0.49)|(0.05)|
|Worked out the cost to you of each main product or service you sell|78.20<br>(0.41)|0.00<br>(0.04)|
|Know which goods or services you make the most profit per unit in selling|80.60<br>(0.40)|0.05<br>(0.04)|
|Have a written budget, which tells you how much you have to pay each month for|65.91|0.01|
|rent, electricity, and other indirect costs of the enterprise?|(0.48)|(0.05)|
|% of firmst that:<br>Financial Practices|||
|Have target sale for next year|17.91<br>(0.38)|0.00<br>(0.04)|
|% of firmst that prepare the following at least annually:|||
|Profit and loss statement|62.69<br>(0.49)|-0.02<br>(0.05)|
|Statement of cash flow|58.96<br>(0.49)|-0.01<br>(0.05)|
|Balance sheet|64.18<br>(0.48)|-0.05<br>(0.05)|
|Income/Expenditure sheet|67.91<br>(0.47)|0.04<br>(0.05)|
|Hours spent looking after dependents|14.88<br>(10.54)|1.68<br>(1.41)|
|Hours spent doing household chores|7.29<br>(9.64)|5.56***<br>(1.28)|
|Hours spent practicing hobbies and personal pursuits|14.34<br>(9.12)|-1.72*<br>(1.02)|
|note: .01 - ***; .05 - **; .1 - *;|||

19