rafmacalaba/datause-encoder-data huggingface dataset:
Comparison of group‐shares across NSS rounds, as shown in Figure 6, indicates that uneducated rural males (young and old) are shifting out of agriculture most rapidly.

<!-- Start of picture text --> 1993‐94 2011‐12<br>100% 100%<br>75% 75%<br>50% 50%<br>25%<br>25%<br>0%<br>0%<br>Rural Urban Rural Urban<br>Agri.+ Forestry+Fishing Construction Mining, Manufacturing and Utilities Professional Services<br>Figure 6: Industry share of employment by demographic group<br>Educated Old Male Educated Old Male<br>Educated Old Male Educated Old Male<br>Educated Young Male Uneducated Old Male Educated Old Female Educated Young Male Uneducated Old Male Educated Old Female Educated Young Male Uneducated Old Male Educated Old Female Educated Young Male Uneducated Old Male Educated Old Female<br>Uneducated Young Male Educated Young Female Uneducated Old Female Uneducated Young Male Educated Young Female Uneducated Old Female Uneducated Young Male Educated Young Female Uneducated Old Female Uneducated Young Male Educated Young Female Uneducated Old Female<br>Uneducated Young Female Uneducated Young Female Uneducated Young Female Uneducated Young Female<br><!-- End of picture text -->

# **B. All‐India decomposition**

SDI metrics at the national level are reported under, respectively, the second, third and fourth major column headings of Table 1. So, why did the wages of educated workers decline _relative_ to the wages of uneducated workers in rural India? First off, there was a substantial increase in relative supply of educated workers, especially for females, spread rather evenly across the two sub‐periods. Meanwhile, relative demand for educated workers fell, especially for males. And, finally, there were modest declines in the institutions index for educated relative to uneducated workers. In other words, uneducated workers moved out of (low‐paid) agricultural labor faster than educated workers. Similar, but less pronounced, patterns are seen for educated vs. uneducated workers in urban India (rows 6 and 7).

In Table 2, we compute urban vs. rural SDI changes. Focusing on unskilled labor, we see that shifts in relative supply were not a decisive factor behind the wage gains of uneducated workers vis‐a‐vis the educated. There were, however, big drops in relative demand for 11
that lasted about 10 quarters in the U.S. from 2007:Q3-2009:Q4 (Berger and Bouwman, 2013).<sup>1</sup> It also begat an economic crisis known as the Great Recession, that lasted 18 months from December 2007 to June 2009 according to the National Bureau of Economic Research (NBER). The crisis started with the toxic mix that often brings about financial crises – lowered credit standards, misunderstood financial innovations, failure to diversify, and excessive leverage.

The GFC involved a lowering of credit standards during a boom, which often occurs, as lenders lose institutional memory of past credit lessons as discussed above. In this case, the banks expanded credit availability at relatively low interest rates to subprime mortgage borrowers. This practice appears to have been based on a faulty assumption that housing prices would almost surely continue to rapidly rise, so that the subprime borrowers would be able to refinance without necessarily having sufficient income or other means to make the regular mortgage payments.

The misunderstood financial innovations included the mortgage-backed securities (MBS) and other structured finance products built on subprime mortgages. The future payment flows from these mortgages were often sliced and diced multiple times to extract additional highly rated securities that were difficult to value. These securities caused massive capital losses and illiquidity when the housing price boom that underlaid the value of these securities ended, creating losses, uncertainty, and market freezes.

The lack of diversification occurred in many financial entities, including commercial banks and investment banks. Many, if not most U.S. commercial banks had very significant portions of their loan portfolios in residential and commercial real estate, and some also packaged and held MBS and other securities based on mortgages. Some investment banks also engaged actively in

> 1 These are unofficial dates for the U.S. as established in one research paper, as we are unaware of any official dating of financial crises.  Other researchers also date the financial crisis in the U.S. as ending in June 2009 when the recession ended.  Some distinguish the European Sovereign Debt Crisis that continued for several years as a separate crisis, while others think of it as part of the GFC.

8
Analysis ot the Structural Change in Japanese Nontariff Barrier Protection: 1966-1986 1

# Table 8

||Imports C<br>Type I|overed by 1<br>NTBs 2/<br>T|986 Nontariff<br>ype I * 11|Barriers<br>NTBs2/|Imports Affect<br>1966 NT|ed by 1966en<br>Os|d1986 Type<br>1986|Iand IINTBs 2/<br>NTBs|
|---|---|---|---|---|---|---|---|---|
|Product groupI/|**S**<br>ofimports<br>covered|Value ot<br>imports<br>covered|**S**<br>of imports<br>covered|Value of<br>imports<br>covered|S<br>of imports<br>affected|Valueotf<br>imports<br>affected|of import<br>affected|Valueof<br>imports<br>affected|
|||(S million)||(Smillion)||(Smillion)||**(S**million)|
|AllProducts|14.4|18,946|36.9|48.857|31.4|3,648 3/|43.5|57,535|
|ofwhich:|||||||||
|Foodstuffs|53.6|8.639|98.9|15,940|72.7|1,652|99.5|16,037|
|Agricultural<br>Raw Miaterials|4.2|394|57.4|5,380|0.0|0|58.8|5,511|
|Fuels|7.6|5,485|21.4|15,447|33.4|748|27.9|20,139|
|Ores and Metals|3.5|263|28.8|2,165|2.2|52|31.3|2,353|
|Manufactures|15.4|4,165|36.7|9,925|47.9|1,196|49.9|13,495|
|of which:|||||||||
|IronandSteel|0.0|0|0.0|0|0.0|0|0.0|0|
|Chemicals|28.5|1,744|95.2|5,826|60.4|369|99.8|6,107|
|Electrical<br>Machinery|0.3|87|2.6|75|58.0|III|19.3|561|
|Nonelectrical<br>Machinery|4.4|169|4.4|169|72.0|446|18.9|658|
|Transport Equipment|17.3|472|17.3|472|65.0|104|37.7|1,028|
|Textiles<br>andClothing|31.9|1,087|32.1|1,094|38.9|46|43.1|1,469|

1/ See Tables 2 and 3 for the SIIC groups included in each product category. 2/ See Table t for a listing of Type I and Type 11 nontariff barriers. 3/ This is equivalent to $12.4 billion measured in terms of 1986 prices.

TABLE NOTE: The actual 1986 Japanese NTB trade coverage ratios for total imports have been decomposed into trade coverage ratios for some of thnmore important types of nontarift barriers. These ratios (shown in parentheses) are as follows. tariff quotas (0.4); variable impo levies (0.1); antidusping and countervailing duties (0.0); reference import prices (0.0); minimum import prices (0.0); "voluntary price restraints (0.0); special import taxes (0.0); import authorization requirements (0.0); non-automatic import licensing (10.2); quantitative restrictions other than _WA_ or textile quotas (4.3); "voluntary" export restraints on volumes (0.0); tfA restrictions 40.0); other quantitative textile restrictions 40.0). Trade coverage ratios for other Type II NTBs were not calculated.
<!-- Start of picture text --> WPS3255<br><!-- End of picture text -->

# **Trade, Regulations, and Growth**

Bineswaree Bolaky Caroline Freund* University of Maryland The World Bank

# March 2004

Abstract: Trade does not stimulate growth in economies with excessive regulation. We examine the effect of openness on growth using cross-country regressions on data from more than 100 countries. Results from levels regressions imply that increased openness is, if anything, associated with a lower standard of living in heavily-regulated economies. Growth regressions confirm that the effect of increased trade on growth is absent in highly regulated countries. Once we control for the effect of domestic regulation, the evidence that trade positively affects growth is stronger than has been found in previous studies. Excessive regulations restrict growth because resources are prevented from moving into the most productive sectors and to the most efficient firms within sectors. In addition, in highly regulated economies, increased trade is more likely to occur in the wrong goods—i.e. goods where comparative advantage does not lie. The results imply that regulatory reform enhances the benefits of trade liberalization.

> *  We are grateful to Mary Amiti, Simeon Djankov, Jose Garcia-Montalvo, Hiau Looi Kee, Aart Kraay, Daniel Lederman, Philip Levy, Marcelo Olarreaga and participants at the World Bank seminar for comments on an earlier draft.  Correspondence: The World Bank, 1818 H Street, NW, Washington DC, 20433, email: <u>cfreund@worldbank.org , tel: 202-473-7102.</u>
# Pl Planning, and Rn_sch

# Public Economics

Can tax policies be designed to encourage economic growth in developing countries? One view holds that by providing the govemment with a stable source of funding and reducing the current account deficit, tax revenues encourage long-term growth. In this view, the economic distortions aggravated by tax rates are slight in comparison to such institutional constraints as price controls, foreign exchange allocations, and trade quotas.

The other view is that high marginal tax rates constrain long-temi economic development by discouraging business expansion, investment, and foreign trade. The contention is that the benefits of a carefully designed, moderate tax structure exceed the costs of budget deficits or spending cuts.

This paper tests these v:.ews by measuring the effect of government spending and taxation on output growth. In theory, higher tax rates shift investment and employment to sectors with low - or even negative - tax rates, such as import-substitution or underground sectors. The lower returns to investment and labor in these sectors mean that the economy wiU generally record lower growth rates.

Data from 31 African countries show the medium- and long-term effects of fiscal policies on growth during 1965-73 and 1974-82. Govemment investments for the earlier period were sufficiently productive to justify the distortions imposed by the relatively high tax rates necessary to finance them. By 1974-82, however, the return on govenmment investments had fallen to almost zero, suggesting that the burden of personal and corporate taxes led to a contraction in growth. Although taxes on imports did not affect out-ut directly, such taxes reduce investment and thereby indirectly curtail grwth. On balance, sales and excise taxes had the most moderate effects on growth and investment.

In sum, a balanced increase in govenmment spending financed by sales and excise taxes, or by a shift from personal and corporate taxes to consumption taxes, can increase growth rates.

This paper is a product of the Public Economics Division, Country Economics Department. Copies are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Ann Bhalla, room N10077, extension 60359.

'Me PPR Working Paper Series dissenitnates thie fdings of work under way in the Banees Policy, Plawnngk and Reseach Complex. An objective of the series is to get thiese findings out quickly, even if presentations are less than fully polished.| The findings, interpretations, and conclusions in these papers do not necessarily Tepresent official policy of the Bank.l

Copyright 0 1988 by the Inwteraonal Bank for Reconstruction and Development[Me World Bank
- Deaton, Angus (2005). Measuring poverty in a growing world (or measuring growth in a poor world). _Review of Economics and Statistics_ , **87,** number 1, 1–19.

- Deaton, Angus and Dreze, Jean (2002). Poverty and inequality in india: A reexamination. _Economic and Political Weekly_ 3729–3748.

- Deaton, Angus and Kozel, Valerie (2005). Data and dogma: The great indian poverty debate. _World Bank Research Observer_ , **20,** number 2, 177–199.

- Desai, Sonalde, Banerji, Manjistha, Barik, Debasis, Tiwari, Dinesh and Sharma, Om Prakash (2020). A glass half full: Changes in standards of living since 2012. India Human Development Survey Data Brief 2020-01. IHDS.

- Deshpande, Ashwini (2020). The covid-19 pandemic and gendered division of paid and unpaid work: Evidence from india. Discussion Paper No. 13815. IZA.

- Dhingra, S. and Ghatak, M. (2021). How has covid-19 affected indias economy? _Economics Observatory_ , **30** .

- Douidich, Mohamed, Ezzrari, Abdeljaouad, Van der Weide, Roy and Verme, Paolo (2016). Estimating quarterly poverty rates using labor force surveys: A primer. _World Bank Economic Review_ , **30,** number 3, 475–500.

- Dreze, J. and Somanchi, A. (2021). View: New barometer of indias economy fails to reflect deprivations of poor households. _The Economic Times_ J, **une 21** .

- Dreze, Jean and Khera, Reetika (2017). Recent social security initiatives in india. _World Development_ , **98,** 555–572.

- Dreze, Jean and Sen, Amartya (2012). Putting growth in its place. _Economy Perspective_ .

- Edochie, Ifeanyi Nzegwu, Freije-Rodriguez, Samuel, Lakner, Christoph, Herrera, Laura Moreno, Newhouse, David, Roy, Sutirtha Sinha and Yonzan, Nishant (2022). What do we know about poverty in india in 2017/18? Policy Research Working Paper 9931. The World Bank.

- Elbers, C., Lanjouw, J. and Lanjouw, P. (2003). Micro–level estimation of poverty and inequality. _Econometrica_ , **71,** number 1, 355–364.

- Elvidge, Christopher D, Baugh, Kimberly, Zhizhin, Mikhail, Hsu, Feng Chi and Ghosh, Tilottama (2017). Viirs night-time lights. _International Journal of Remote Sensing_ , **38,** number 21, 5860–5879.

66
13 Figure 3. Export Diversification and Real GDP Per Capita <!-- Start of picture text --> 0 20000 40000 60000<br>Real GDP Per Capita<br>All Countries Nonparametric Fit<br>Quadratic Fit<br>6<br>5<br>4<br>Theil Index 3<br>2<br>1<br><!-- End of picture text --> _Sources_ : Penn World Table 7.0, UN COMTRADE, and author calculations. _Note:_ Each observation denotes one country-year combination. Real GDP Per Capita bases on 2000 constant U.S. dollars.

Figure 4. Export diversification by Region, 1960–2010: Extensive Margin <!-- Start of picture text --> 1960 1970 1980 1990 2000 2010<br>Year<br>Frontier Asia Emerging Asia<br>Sub-Saharan Africa Western Europe<br>Latin America & Caribbean Middle East & North Africa<br>4<br>3<br>2<br>Theil Extensive<br>1<br>0<br><!-- End of picture text --> _Sources_ : UN COMTRADE, and author calculations.
Figure 8: Average annual district-level GDP growth in Rwanda, 20002012 <!-- Start of picture text --> 12.0%<br>10.0%<br>8.0%<br>6.0%<br>4.0%<br>2.0%<br>0.0%<br><!-- End of picture text --> Notes: Based on long‐differenced specification between 2000 and 2012; Only nonzero lights data for 16 districts

# **5. Application to Kenya and Rwanda: GDP Levels**

In this section we estimate subnational GDP levels for 2013 based on the spatial distribution of night lights and the rural population. Following Ghosh and others (2010), we assume that the within-country distribution of nightlights (across the subnational units) only proxies the withincountry distribution of secondary- and tertiary sector economic activity (given that agriculture is largely subsistence and unlit). As such, the spatial distribution of nightlights within a country is used only to distribute the part of estimated GDP resulting from industrial and service activities. The part of estimated GDP accounted for by agriculture is distributed across the subnational units in proportion to their share of rural population.<sup>6</sup> Our methodology consists of three steps. In the first step, we estimate national GDP based on specification (1). The estimated GDP is divided into two parts based on the share of agriculture in the economy (based on actual national accounts data): Agricultural GDP and industrial and services GDP. In the second step, we distribute industrial and services GDP among the subnational units in proportion to their share of national nightlights. In the third step, we distribute agricultural GDP among the subnational units in proportion to their share of the rural population (we assume that urban populations do not produce agricultural output). Finally, we sum estimated industrial and services GDP and estimated agricultural GDP to arrive at an estimate of subnational-level GDP.

> 6 This is in contrast to Ghosh and others (2010) who use the Landscan data to distribute agricultural GDP among sub-units.

16
*

-

-

-

> investment (e.g. Taylor, 19S8; Serven and Solimano, 1992; and Mosley, 1992); others (e.g. Benjamin, 1992) assert the more conventional view of crowding out that government ~~~~expenditure willincrease recourse to deficit financing and therefore reduce credit availability to the private sector (either through quantity rationing or changes in the real interest rate). The general decline in absorption at the outset of stabilization or adjustment will (often through expectationary mechanisms) reduce investment. The issue is clearly an empirical one, and both the type and financing of investment will be important in determining the outcome in any particular cuse.

the role of financial l iberalization: the McKinnon-Shaw view that liberalizing interest rates will stimulate investment (by increasing savings availability) has found a number of - critics, for reasons including the following (i) higher interest rates alter only the composition not the level of savings (see review by Fry (1988: Chapter 6) and argument by Morisset (1993) on. portfolio effects of higher interest rates); (ii) in a standard structuralist model (e.g. Taylor, 1983) higher interest rates push up costs and prices because of fixed working capital requiremnents, but do nothing for investmnent; and (iii) the interest rate may go "too high" for productive investors (Roe, 1982 and Rittenberg, 1991).

the role of uncertainty has been stressed by several writers, investors must be confident that the reforms will remain in place (the incentive structure will not change) if they are **to** invest. This aspect is one of the most difficult to model.

exchange rate policy is policy is a further important determinant of investmnent; although there is some ambiguity, most channels (e.g. contractionary impact and higher cost of capital goods) suggest a reduction in investmwot as a result of a devaluation.

In addition to these relationships the possibility of increaing the efficiency of investment (through financial liberalization, removal of other administrative controls etc.) need also be considered. Evidence from returns to World Bank supported projects show the returns to be higher in adjusting economies (World Bank, 1992.: 33).

(c) The current account Rather than estimate the rcsponse of the current account to the ad-option of an adjustment program it is more enlightening **to** consider how imports and exports respond to changes in 79
# Indiaenous Territories and Tronical Forest Manaaement in Latin America

Shelton H. Davis & Alaka WallAbstract Indigenous peoples have received much attention as potential resource managers of threatened tropical forest ecosystems. Using data from Latin America, this article argues that fundamental changes need to take place in the legal recognition and demarcation of indigenous territories in order for this potential to be fulfilled. A comparison is made between different national land tenure models for forest-dwelling indiganous peoples and a model proposed by Latin American indigenous organizations. This comparison suggests that not only do indigenous peoples need to be provided with some degree of control over their territories and resources, but there needs to be a new type of partnership among indigenous peoples, the scientific community, national governmments and international development agencies for the management of tropical forests.

The past decade has witnessed a proliferation of research and writing on the extent and ecological implications of tropical deforestation in Latin America. Current estimates are that the region's tropical forests are being cleared at a rate of O.5 percent annually in South America and 1.6 percent annually in Central America. While research and scientific discussions continue, Shelton H. Davis is Senior Sociologist in the Environment Department at the World Bank in Washington, D.C.. Alaka Wali is Associate Professor of Anthropology at the University of Maryland in College Park, Maryland.
Ma, Yue, Robert Fairlie, Prashant Loyalka, and Scott Rozelle. 2020. “Isolating the ‘Tech’ from EdTech: Experimental Evidence on Computer-Assisted Learning in China.” <mark>Maloney, William F., and Carlos A. Molina. 2019. “Is Automation Labor-Displacing in the Developing Countries, Too?: Robots, Polarization, and Jobs.” World Bank, Washington, DC.</mark> Maddison, Angus. 2001. _The World Economy: A Millennial Perspective_ . Paris: Organisation for Economic Co-operation and Development.

Marrano, Mauro G., Jonathan Haskel, and Gavin Wallis. 2009. “What Happened to the Knowledge Economy? ICT, Intangible Investment, and Britain’s Productivity Record Revisited.” _Review of Income and Wealth_ 55 (3): 686–716.

Marrocu, Emanuela, Raffaele Paci, and Marco Pontis. 2012. “Intangible Capital and Firms’ Productivity.” _Industrial and Corporate Change_ 21 (2): 377–402.

Martins-Neto, Antonio, Nanditha Mathew, Pierre Mohnen, and Tania Treibich. 2024. “Is There Job Polarization in Developing Economies?” _World Bank Research Observer_ 39 (2): 259–88.

Mascagni, Giulia, Andualem T. Mengistu, and Firew B. Woldeyes. 2021. “Can ICTs Increase Tax Compliance? Evidence on Taxpayer Responses to Technological Innovation in Ethiopia.” _Journal of Economic Behavior and Organization_ 189: 172–93.

Mascagni, Giulia, Denis Mukama, and Fabrizio Santoro. 2019. “An Analysis of Discrepancies in Taxpayers’ VAT Declarations in Rwanda.” Working Paper 92, International Centre for Tax and Development, Brighton, United Kingdom.

Mascagni, Giulia, and Christopher Nell. 2022. “Tax Compliance in Rwanda: Evidence from a Message Field Experiment.” _Economic Development and Cultural Change_ 70 (2): 587–623.

Maskara, Pankaj Kumar, Emre Kuvvet, and Gengxuan Chen. 2021. “The Role of P2P Platforms in Enhancing Financial Inclusion in the United States: An Analysis of Peer-to-Peer Lending across the Rural–Urban Divide.” _Financial Management_ 50 (3): 747–74.

McCullough, Jeffrey S., Stephen T. Parente, and Robert Town. 2016. “Health Information Technology and Patient Outcomes: The Role of Information and Labor Coordination.” _RAND Journal of Economics_ 47 (1): 207–36.

Melnik, Mikhail I., and James Alm. 2002. “Does a Seller’s eCommerce Reputation Matter? Evidence from eBay Auctions.” _Journal of Industrial Economics_ 50 (3): 337–49.

Michaels, Guy, Ashwini Natraj, and John Van Reenen. 2014. “Has ICT Polarized Skill Demand? Evidence from Eleven Countries over Twenty-five Years.” _The Review of Economics and Statistics_ 96 (1): 60–77.

37
indicators. In addition, expanding the surveys would allow more in-depth analyses. For example, more information could expose the social impacts of migration and remittances or of missing a father or both parents. Furthermore, domestic violence and street children are left out of most analyses, but youth who experience domestic violence or live in the streets are in obvious need of help and guidance; further research and data collection on these groups are invited. Moreover, substance abuse and its extent require far more investigation in order to understand the impact on social and economic aspects. It is important to examine and better understand issues associated with taboos, such as drugs and domestic violence, in order to significantly improve the future for Haitian youth.

Estimating the cost of not investing in youth would be a great challenge, but could also be extremely useful to convince policy makers to invest in children and youth, for example, through prevention programs (see appendix) to avoid negative outcomes rather than being forced to invest in attention programs when failure is directly observed in the forms of crime and violence. The appendix delineates a framework for analyzing and helping youth at risk and outlines a range of possible programs to improve the situation and create opportunities for youth.

34