rafmacalaba/datause-encoder-data huggingface dataset:
evidence from Bown and Crowley (2013b, 2013c) for 18 high income and emerging economies over 1989-2010 finds that as more products’ applied MFN tariffs push up against the trade agreement constraints of WTO tariff binding commitments, governments implement new import protection by turning to instruments like temporary trade barriers.

# **4.3 Preferential tariff reductions and multilateral tariff reductions**

A separate stream of research examines potential interrelationships between a government’s preferential tariffs and its MFN tariffs offered under the WTO. These empirical studies have begun to inform understanding on whether and when preferential trade agreements can be ‘building blocks’ versus ‘stumbling blocks’ for multilateral cooperation in trade policy (Bhagwati, 1991). This has been an open empirical question given the conflicting results that arise from the theoretical literature.<sup>12</sup> The first major empirical study to examine this question used product-level data from the United States (Limão, 2006). It considered the impact of U.S. preferential trade agreements (PTAs) on the multilaterally negotiated MFN tariff cuts that the United States subsequently made under the Uruguay Round. Evidence suggests a stumbling block effect: i.e., U.S. multilateral tariff reductions were smaller for products imported under its PTAs relative to similar products imported only from PTA nonmembers. In a follow-up study of the European Union, Karacaovali and Limão (2008) provide related evidence indicating this stumbling block effect is not limited to just the United States and its PTAs. Their evidence indicates the EU reduced its multilateral tariffs on goods not imported under FTAs by almost twice as much as the tariffs imported under its PTAs.

Because the spread of PTAs is so pervasive, there are many opportunities to investigate the extent to which the stumbling block phenomenon extends to other settings. In one important study,

> 12 One fundamental economic efficiency concern arising from preferential tariffs is the trade diversion identified initially by Viner (1950). For excellent surveys of the theoretical literature on the incentives that can arise under preferential versus multilateral liberalization, see Freund and Ornelas (2010) and Panagariya (2000).

17
Gaston, N. and D. Trefler (1994). ``Protection, Trade and Wages: Evidence from U.S. Manufacturing,'' Industrial and Labor Market Relations Review, vol. 47, pp. 574-93.

Goldberg, P. and N. Pavcnik (2005). ``Trade, Wages, and the Political Economy of Trade Protection: Evidence from the Colombian Trade Reforms,'' Journal of International Economics, vol. 66, pp. 75-105.

Harrigan, J. and G. Barrows (2009). ``Testing the Theory of Trade Policy: Evidence from the Abrupt End of the Multifibre Arrangement,'' The Review of Economics and Statistics, 91(2): 282-294.

Harrison, A. and J. Scorse (2010). “Multinationals and Anti-sweatshop Activism,” American Economic Review, 100(1): 247–73.

Helpman, E. and O. Itskhoki (2010). “Labor Market Rigidities, Trade and Unemployment,” Review of Economic Studies.

Kambourov, G. (2009). ``Labour Market Regulations and the Sectoral Reallocation of Workers: The Case of Trade Reforms,'' Review of Economic Studies, 76(4), pp. 1321--1358.

Kruger, A. and L. Summers (1989). ``Efficiency Wages and the Inter-industry Wage Structure,'' Econometrica, vol. 56, pp. 259-93.

Magee, Stephen P. (1972). “The Welfare Effects of Restrictions on U.S. Trade,” Brookings Papers on Economic Activity, 3, pp 645-701.

Matusz, S.J., and D. Tarr (1999). “Adjusting to Trade Policy Reform,” Policy Research Working Paper No. 2142, World Bank, Washington, DC.

Menezes-Filho, N. and M. Muendler (2007). “Labor Reallocation in Response to Trade Reform,” CESifo Working Paper, March.

Morkre, Morris and David Tarr (1980). _Effects of United States Restrictions on Imports: Five Case Studies and Theory_ , Bureau of Economics Report to the Federal Trade Commission, Washington D.C.: U.S. Government Printing Office.

Monson, Terry (1978). “An Extension of Bale's Labor Displacement Cost Estimates,” Journal of International Economics, Volume 8, Issue 1,pp. 131-133.

Muendler, M. (2010). “Trade Reform, Employment Allocation and Worker Flows,” en G. Porto and B. Hoekman (2010).

Peluffo, C. (2010). “Costos de Movilidad Intersectorial del Empleo, Shocks Comerciales y Tecnológicos: Resultados para Argentina en Base a un Modelo Estructural,” Económica, vol. LVI, pp. 105-144.

Porto, G. and B. Hoekman (2010) (Eds). _Trade Adjustment Costs in Developing Countries: Impacts, Determinants and Policy Responses_ , CEPR and World Bank, Washington DC.

Richardson, D. (1982). “Trade Adjustment Assistance under the United States Trade act of 1974: An Analytical Examination and Worker Survey, in Import Competition and Response,” in Bhagwati, Jagdish (Eds.), University of Chicago Press, Chicago.

Takacs, Wendy and L. Alan Winters (1991). “Labour Adjustment Costs and British Footwear Protection,” Oxford Economic Papers, 43: 479-501.

31
# **References**

Atlas do Desenvolvimento Humano no Brasil (1998). IBGE.

Amadeo, E. and M. Neri, (1997). “Macroeconomic Policy and Poverty in Brazil.” Texto para Discussão No 383, PUC-Rio, Rio de Janeiro, dezembro de 1997.

- Barros, R., S. Firpo and R. Mendoça, (1999).  “Education and Equitable Economic Development.”  IPEA.

- Barros, R. and R. Mendoça, (1998). “O Impacto de três inovações instrucionais na Educação Brasileira” IPEA, Texto para Discussão No. 566.

Beltrão, Kaizô Iwakami, Sonoê Sugahara Pinheiro, Francisco Eduardo Barreto de Oliveira, (1999). “A População Rural e a Previdência Social no Brasil: Uma Análise Com Ênfase Nas Mudanças Constitucionais”, IPEA, mimeo Carrizosa, M.; N. Fiess and D. Verner (2001). “” Economic growth in Pernambuco and the Northeast Brazil”. World Bank mimeo.

Consultations with the poor, (1999). Also called “Voices of the Poor.” World Bank.

Deaton, A. (1997). “The Analysis of Household Surveys.” World Bank Book.

Delgado, Guilherme Costa, (1999). “Caso Brasil: Sistema de Previdência Social Rural, paper delivered at conference Experiências Exitosas de Combate à Pobreza Rural: Lições para Reorientação de Políticas”.

Dollar, D. and Kraay, A. (2000). “Growth IS good for the poor, International Monetary Fund Seminar Series.” World Bank, No. 2000-35:1-44.

41
**Table 2.** Results of attrition analysis

||(1)<br>Completed<br>treatment|(2)<br>Started<br>evaluation|(3)<br>Completed<br>evaluation|(4)<br>Started<br>survey|(5)<br>Completed<br>survey<br>(end)|
|---|---|---|---|---|---|
|Control|0.293***|0.274***|0.235***|0.240***|0.231***|
||(0.015)|(0.016)|(0.020)|(0.021)|(0.021)|
|Comprehensive Active|-0.045**|-0.039*|-0.022|-0.016|-0.013|
|Inoculation|(0.022)|(0.022)|(0.023)|(0.023)|(0.023)|
|Brief Active Inoculation|0.027|0.032|0.043*|0.044**|0.046**|
||(0.021)|(0.021)|(0.022)|(0.022)|(0.022)|
|Passive Inoculation|0.068***|0.072***|0.050**|0.054**|0.054**|
||(0.020)|(0.021)|(0.022)|(0.022)|(0.022)|
|Controls|No|No|No|No|No|
|Observations|4,150|4,150|4,150|4,150|4,150|
|R-squared|0.060|0.050|0.031|0.031|0.028|

_Note._ OLS estimates. Base comparison group is the placebo condition. Robust standard errors are shown in parenthesis. _*** p<_ 0.01 _, ** p<_ 0.05 _, * p<_ 0.1.

# **Discernment in Misinformation Judgements**

Before running analyses to test our hypotheses, we summarize the descriptive statistics for our three scores of misinformation judgements (see **Table 3** ). As expected, misleading headlines tended to be perceived as using misinformation tactics to a greater extent than non-misleading headlines. Violin plots depicting the full distribution of scores are also reported in **Appendix H.**

**Table 3.** Descriptive statistics of misinformation discernment scores by study arms

||Misinfo<br>Discer|rmation<br>nment|Misinfo<br>Judgem<br>Misle<br>Head|rmation<br>ents of<br>ading<br>lines|Misinfo<br>Judgem<br>Non-Mi<br>Head|rmation<br>ents of<br>sleading<br>lines|
|---|---|---|---|---|---|---|
||_M_|_SD_|_M_|_SD_|_M_|_SD_|
|Placebo|0.89|1.06|3.08|0.72|2.19|0.86|
|Control|0.96|1.06|3.11|0.72|2.15|0.85|
|Comprehensive Active Inoculation|1.25|1.03|3.22|0.69|1.98|0.84|
|Brief Active Inoculation|1.09|1.01|3.34|0.72|2.24|0.92|
|Passive Inoculation|0.90|1.05|3.14|0.69|2.23|0.85|

18
|**Table 4: Impact on Household Level Outcomes**<br>Sample|Control|ITT|LATE|
|---|---|---|---|
|Outcome<br>Size|Mean|Impact|Impact|
|Log Household Income<br>282|9.488|0.850***<br>(0.124)|1.149***<br>(0.138)|
|Log Household Income per capita<br>282|7.987|0.799***<br>(0.124)|1.080***<br>(0.132)|
|Log Household Expenditure<br>282|9.185|1.023***<br>(0.119)|1.383***<br>(0.122)|
|Log Expenditure per capita<br>282|7.684|0.972***<br>(0.119)|1.314***<br>(0.112)|
|Household Size<br>385|5.264|0.174|0.307|
|||(0.180)|(0.322)|
|Household Earned Income<br>385|6829|16595***<br>(1933)|29220***<br>(2419)|
|Remittances sent to Tonga<br>385|118|938***<br>(140)|1651***<br>(204)|
|Number of Vehicles<br>385|0.797|0.509***<br>(0.069)|0.896***<br>(0.129)|
|Household has a Cellphone<br>385|0.511|‐0.079<br>(0.069)|‐0.139<br>(0.119)|
|Household has a Computer<br>385|0.181|0.360***<br>(0.064)|0.633***<br>(0.128)|
|Household has a DVD player<br>385|0.104|0.161***<br>(0.038)|0.284***<br>(0.072)|
|Household has a Microwave<br>385|0.082|0.573***<br>(0.063)|1.008***<br>(0.081)|
|Household has a Washing Machine<br>385|0.385|0.355***<br>(0.069)|0.626***<br>(0.125)|
|Household has a Bank Account<br>282|0.986|0.020|0.027|
|||(0.016)|(0.021)|
|Household has an ATM card<br>282|0.986|0.020<br>(0.016)|0.027<br>(0.021)|
|Total Savings<br>282|428|5838***<br>(1020)|7892***<br>(1083)|
|Number of Foods in Diet<br>282|7.385|1.561***|2.110***|
|Notes:<br>Robust standard errors in parentheses, *, **, ***<br>10, 5, and 1 percent levels respectively.<br>All regressions include controls for age, gender,<br>and survey form (long or short).<br>Regressions weighted for population proportion|indicate si<br>island of b<br>s of compl|(0.249)<br>gnificance at<br>irth, ballot ye<br>iers and non‐|(0.306)<br>the<br>ars entered,<br>compliers.|
Country: the Dynamic Household Regional Product Dummy Model”, _Discussion Paper, ERU/2015-01, of Economic Research Unit, Indian Statistical Institute, Kolkata._ Chen. S. and M. Ravallion (2010), “The developing world is poorer than we thought, but no less successful in the fight against poverty”, _Quarterly Journal of Economics_ , 125 (4), 15771625.

Coondoo, D., A. Majumder and S. Chattopadhyay (2011), “Estimating Spatial Consumer Price Indices Through Engel Curve Analysis”, _Review of Income and Wealth_ , 57(1), 138-155.

Costa, D. (2001), “Estimating real income in the United States from 1888 to 1994: Correcting CPI Bias using Engel Curves”, _Journal of Political Economy_ , 109 (6), 1288- 1310.

Deaton, A and O. Dupriez (2011), “Purchasing Power Parity Exchange Rates for the Global Poor”, _American Economic Journal: Applied Economics_ , 3, 137-166.

Diewert, E. (2010), “New Methodological Developments for the International Comparison Program”, _Review of Income and Wealth_ , 56 (1), S11-S31.

Gibson, J, S. Stillman and T. Le (2008), “CPI Bias and real living standards in Russia during the transition”, _Journal of Development Economics_ , 87 (1), 140-160.

Hamadeh, N. and M. Mouyelo-Katoula (2014), “ _The Methodological Innovations, Main Results and Findings from the 2011 Round of the International Comparison Program_ ”, paper presented at the IARIW, 2014 Conference in Rotterdam. Copy of this paper is available at <u>http://www.iariw.org/papers/2014/HamadehPaper.pdf</u> Hamilton, B. (2001), “Using Engel’s Law to estimate CPI bias”, _American Economic Review_ , 91(3), 619-630.

Houthakker, H. (1987), “Engel’s Law”, in J. Eatwell, M. Milgate and P. Newman (eds.), _The New Palgrave Dictionary of Economics_ , Vol. 2 (London: McMillan), 143-144.

Inklaar, R. (2013), “Price levels and economic growth: making sense of the revisions to data on real incomes: a comment”, _Review of Income and Wealth_ , 59(4), 614-622.

33
# FIGURE 7: SHARE OF BANKING AND INSURANCE SERVICES IN VALUE ADDED: USSR AND OECD

<!-- Start of picture text --> 8%-<br>PRT AFT<br>6x-<br>AIWT<br>USA<br>ISL DEU swNeBSA 0<br>-<br>4Xc,- ND x<br>. 22 - a~~TW NOR DNK<br>~~~~~~~~~~~~~DEU<br>US8R US8RUC<br>2%-- IClA NLP I A<br>7 8 ~~~~~~~~~~~~~~~9 1 0<br>LOG OF PER CAPITA INCOME<br><!-- End of picture text -->
use unit values defined as the ratio of import value to import weight by country-productyear. Moreover, we measure the import share for a specific set of firms, those that are also exporters of commodities, identified based on a database of all firm-level export transactions recorded by customs authorities in the same 53 countries.

Natural resource dependence. We employ two measures capturing natural resource dependence: (i) the share of commodities in total merchandise exports from the World Development Indicators (World Bank, 2024a); and (ii) an index relying only on fluctuations in world commodity prices (Gruss and Kebhaj, 2019).<sup>6</sup> Means and standard deviations of these variables are reported in Table S2. Commodities and natural resources are defined broadly to include food (e.g., animal and vegetable oils and fats, beverages, fruits and vegetables, live animals); fuels (e.g., mineral fuels, lubricants, related materials); and ores and metals (e.g., crude fertilizers, metalliferous ores, non-ferrous metals).

Measure (i) captures both price and quantity variation in commodity exports, and is our preferred measure of natural resource dependence. In some specifications we separate this into exports of different types of commodities: food, fuels, and ores and metals. Measure (ii) captures only price variation. The advantage of this measure is that it is plausibly exogenous, since it excludes quantities determined by local production costs and world prices should not be affected by local exports as economies in our sample are small relative to the commodity markets they export to. A disadvantage of this measure is that price variation alone does not capture the dependence of an economy on natural resources for export revenue. In sum, there is a trade-off between ”quantity” (export share) and ”quality” (price index) of variation in natural resource dependence.

6For country c in year t this index is given by<sup>�K</sup> k=1<sup>log(Pk,t)ωc,k where Pk,t is the world price of commodity</sup> k in year t, and ωc,k is the weight given by the average value of commodity k’s exports as a share of GDP across the 1980-2020 period. The index is scaled for each country so 100 equals the price index in 2012.

8
Table 2: Round 1 characteristics - Households survey

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||ULGSP LGA|Control LGA|Dif|p-value|N|
|Household size|4.54|5.04|-0.51|0.00|2,998|
|Literate (all respondents)|0.84|0.75|0.10|0.00|2,998|
|Education: None|0.10|0.16|-0.06|0.00|2,998|
|Education:Less than complete Primary|0.13|0.20|-0.06|0.00|2,998|
|Education:Less than complete lower Secondary|0.50|0.53|-0.03|0.07|2,998|
|Education:Less than complete upper Secondary|0.23|0.10|0.12|0.00|2,998|
|Education:College degree|0.04|0.01|0.03|0.00|2,998|
|Sector of Work: Agriculture|0.47|0.76|-0.29|0.00|2,743|
|Sector of Work: Small Business|0.32|0.15|0.16|0.00|2,743|
|Sector of Work: Other|0.22|0.09|0.13|0.00|2,743|
|Owns a car|0.11|0.03|0.08|0.00|2,998|
|Owns TV|0.45|0.18|0.27|0.00|2,998|
|Owns computer|0.13|0.02|0.11|0.00|2,998|
|Owns video|0.39|0.15|0.24|0.00|2,998|
|Owns refrigerator|0.25|0.06|0.18|0.00|2,998|
|Owns stove|0.21|0.05|0.16|0.00|2,998|
|Owns radio|0.65|0.57|0.08|0.00|2,998|
|Owns phone|0.88|0.80|0.08|0.00|2,998|
|Asset Index (ICW)|0.25|-0.20|0.44|0.00|2,998|
|Government maintains roads well|0.62|0.47|0.15|0.00|2,987|
|Government maintains prices well|0.52|0.44|0.08|0.00|2,941|
|Government maintains health standards well|0.67|0.63|0.04|0.04|2,918|
|Government keeps community clean|0.70|0.66|0.04|0.04|2,932|
|Government manages use of land well|0.68|0.64|0.04|0.05|2,822|
|Easy access to building/business permit|0.41|0.34|0.08|0.00|1,661|
|Easy access to hhd services (water)|0.50|0.38|0.12|0.00|2,186|
|Easy access to waste collection|0.54|0.47|0.06|0.00|2,211|
|Easy access to place in primary school|0.93|0.93|0.01|0.37|2,728|
|Easy access to medical treatment|0.62|0.60|0.03|0.17|2,841|
|Most or all neighborhoods accessible by paved road|0.08|0.01|0.07|0.00|2,998|
|Waste collection at least sometimes|0.59|0.31|0.28|0.00|2,998|

_Note:_ This table presents average traits of household in ULGSP and control LGAs, and the difference between the means. P-value is reported for difference of means, clustered at LGA-level.

44
_Table A2.3 Weighted Intention‐to‐treat effects of the original intervention on main socio‐economic outcomes._

|(1)<br>ITT<br>(no controls)|(2)<br>ITT<br>(controls)|
|---|---|
|_Main outcomes – Socio‐economic_||
|Employment index<br>0.065|0.075|
|(0.285)|(0.211)|
|Consumption index<br>0.095|0.094|
|(0.146)|(0.146)|
|Savings, investment and debt<br>0.266***|0.265***|
|index<br>(0.000)|(0.000)|
|Business skills index<br>0.012|0.018|
|(0.814)|(0.744)|
|Observations<br>1523|1507|

_Note: Observations are weighted by their inverse likelihood to be in the final sample, based on who was easy to reach during the phone survey. All regression control for gender‐state fixed effects. Control variables of column (2) include all baseline controls that were significant determinants of attrition and of selection between receiving “training and grant” vs “training, but no grant”. In particular, these are age, marital status, employment status, business ownership, food consumption, non‐food consumption, formal bank account, formal loans, informal loans, education level, literacy level, numeracy level, household size, number of children, number of rooms, number of buildings at baseline, and exposure to conflict events between baseline and endline. P‐values are in parenthesis displayed below the estimated coefficients. * (**, ***) indicates statistical significance at the ten‐ percent (five‐percent, one‐percent) level._ 39
# **VI.  Concluding Comments**

This study has examined five quinquennial rounds of National Sample Survey data, covering the period 1983 to 2004/5, to enquire into the extent and speed of poverty decline in rural India during the 1990s, and to explore the inter-relationship between rural poverty, agricultural wage labor employment and the rural non-farm economy. Our main findings can be summarized as follows.

We suggest that while there is evidence of declining poverty between 1983 and 2004/5, the pace of reduction has been rather slow. Consistent with earlier studies we find evidence that employment over time in agricultural labor has not shown a clear declining trend, and we find that employment in agricultural labor is strongly correlated with low consumption levels. This finding is consistent with the notion that agricultural labor remains a “last resort” option for the rural population – as has often been suggested in the literature. Real wages have been rising over time (continuing a trend that started in the early 1970s), but the rate of increase appears to have slowed substantially between 1999/0 and 2004/5.

Consistent with other researchers we document a sizeable non-agricultural sector in rural India. While this sector appears to have grown in step with overall population growth, we suggest that the non-farm sector only modestly increased its share of total employment during the 1990s. This latter finding appears to be robust within NSS data. However, studies based on other data sources have suggested that growth in the non-farm sector has been more pronounced (Foster and Rosenzweig, 2003a, b)..

We suggest that non-farm employment comprises three sub-sectors: regular employment, casual employment and self-employment. We document that regular nonfarm employment is associated with high consumption levels, but show that those with low education levels, low social status, and low wealth are not well-represented in this sub-sector. Education, social status and wealth seem less relevant for employment in casual non-farm employment, although there is some evidence that lower levels of education are helpful in gaining access to casual non-farm employment. Selfemployment in the non-farm sector seems to be particularly heterogeneous, comprising both last resort as well as productive activities. On balance, involvement in this sub24
Quantity-targeting, categorical targeting, geographical targeting, and means-tested can all be used to allocate either consumption subsidies or connection subsidies. In the remainder of this article we evaluate the targeting performance of consumption and connection subsidies that use all of these targeting methods to determine subsidy eligibility and to distribute subsidy benefits.

# **Data, measurement, and conceptual framework**

## **_Data_**

The data on which this study is based come from a variety of published and unpublished studies of subsidy performance in individual cases. All of these studies evaluate subsidy performance using a combination of household survey and utility data. In a small number of cases, we have done our own analysis of household survey data in order to generate a new subsidy case or expand the analysis done in a previous study.

The final sample includes 45 cases of existing subsidy programs in water and electricity, respectively. The majority are quantity-based subsidies, reflecting the prevalence of this subsidy mechanism. A number of simulated subsidies were also included to illustrate the differential performance of alternative subsidy mechanisms. The final sample includes 45 electricity subsidies from 27 utilities, plus 32 water subsidy programs from 13 utilities. Most cases are delimited by utility service area. Water utilities tend to serve one urban area; thus, the water cases are primarily city cases. For electricity cases, the sample includes a mix of national, state, and municipal cases. Though by no means a representative sample, this group of subsidy cases is representative of a broad cross-section of subsidy types and utilities located in different geographical areas, which permits drawing more systematic conclusions than could have been drawn from individual case studies. A list of the cases and the original sources for the case material can be found in appendices 1 and 2.

## **_Measurement of targeting performance_**

The analysis of the distributional incidence of subsidies considers two dimensions of targeting performance: the beneficiary incidence of the subsidy and the benefit incidence. Beneficiary incidence is meant to address this question, “To what extent does the subsidy reach poor households?” The indicator used to measure how well utility subsidies target beneficiaries is the error of exclusion, defined as the percentage of poor households that do not receive the subsidy.

Benefit incidence addresses the question:, “How well does the subsidy instrument target benefits to the poor versus other households?” To measure the benefit incidence of the subsidy, a targeting performance 6