rafmacalaba/datause-encoder-data huggingface dataset:
# **1. INTRODUCTION**

At the World Food Summit in 1996, leaders from 183 countries committed to halving the number of people living in hunger, a commitment they renewed in 2009. Halving the proportion of hungry people between 1990 and 2015 is also part of the Millennium Development Goals. Yet there were still 870 million hungry people worldwide in 2010-2012, down only slightly from 1 billion in 1990-92, according to the flagship publication of the Food and Agriculture Organization (FAO) of the United Nations -- _The State of Food Insecurity in the World_ . Over the same period, the slow fall in the proportion of hungry people, from 19% to 13% (FAO 2012), suggests that the goal of halving the hunger rate will not be met on time.

The FAO estimates of the hunger rate combine aggregate food balance sheets for every country with survey estimates of the variance in calorie availability. The population falling below calorie requirements is computed from the combination of the total food balance and its variance. This method, which we will refer to in shorthand notation as the FBS-CV method, has been heavily criticized by the international research community (Svedberg 1999, de Haen et al. 2011). As an alternative, some researchers have been advocating for calculating hunger numbers directly from the food quantity data in household surveys (Smith and Subandoro 2007, Fiedler et al. 2012b). Given the expansion of such surveys in lowincome countries, this is increasingly feasible. From 1990 onward, there are at least 760 nationally representative household consumption expenditure surveys (HCES) available for 129 developing countries.<sup>1</sup> These HCES are already being used to monitor global poverty trends (Chen and Ravallion 2010) and hold the promise of allowing global hunger counts to be derived from them too. We will call this approach to calculating hunger the HCES-direct method. Of note is that neither the FBS-CV nor the HCES-direct method actually measures what individuals ate (as in a food intake survey) or ask about their perceptions of hunger (Thompson and Byek 1994, Radimer et al. 1990).

The FBS-CV and HCES-direct methods both rely on household surveys. In the case of the FBS-CV method, the second moment of the calorie distribution comes from the surveys, while the HCES-direct method relies on the surveys for all moments. Yet, the design of HCES varies over several key dimensions, such as the method of data capture (diary versus recall questionnaires), the level of respondent (individual versus household), the reference period for which consumption is reported (anywhere from 24 hours to one year), and the degree of commodity detail (from less than 20 items to over 400 items). This

> 1 We use the generic term HCES to refer to a range of household survey efforts to capture total household consumption expenditures. This can include surveys described as household budget surveys, living standards surveys, or others.

2
better-off families, they remit less as their family rely less on remittances for their livelihood.

As for the economic condition of recipient countries, the economic growth rate of home country does not seem to have any effect on remittance flows. This is consistent with some of the existing literature (e.g. Buch and Kuckulenz, 2004). This is likely to be due to the fact that the two countervailing forces described above – high economic growth may reduce the incentives to migrate and hence less remittances, but it can also induce migrants to invest more at home – cancel out.

Based on the IV estimation results (Table 5), as we would expect, the size of the domestic economy measured as the logarithm of GDP is found to positively affect the overall flow of remittances, but it is negatively correlated with remittances relative to GDP. In other words, the smaller domestic economy has relatively limited opportunities for economic activities, and migrants have to remit more to support their families at home. The effect of the level of economic development expressed as the logarithm of GDP per capita is negative and significant with the exception where we normalize remittances by population. This implies that migrants from poorer countries remit a greater amount of money to their families at home as would be expected.

One of caveats of our analysis, however, is that our data on migration only includes the migrants in OECD countries. As a consequence, for those countries that send their migrants to non-OECD countries, to the Gulf countries for example, the number of migrants is certainly underestimated. In order to assess its effect, we repeated some of the IV estimation based on the sample excluding Bangladesh, India, Pakistan, the Philippines, and Sri Lanka which are thought to be sending a relatively large number of migrants to non-OECD countries. The results are presented in Table 6. The results do not change significantly with the exception of the coefficient on the migration level in one specification (column 5 in Table 6) that loses its significance when estimated on the subsample. Hence the robustness of our findings is generally supported.

11
Rodrik. Dani, 2008b. “Normalizing Industrial Policy.” Commission on Growth and Development, Working Paper No. 3.

Rosenbaum, Paul R. 2010. _Observational Studies_ . New York: Springer.

Shleifer, Andrei, 1997. “Government in transition.” _European Economic Review_ 41(3-5): 385-410.

Shleifer, A., 1998. State versus private ownership. _Journal of Economic Perspectives_ 12(4), 133-150.

Shleifer, Andrei and Vishny, Robert, 1994. “Politicians and Firms.” _Quarterly J. Econ._ 109, 4:995-1025.

Song, Lina, and Du, He, 1990. “The Role of Township Governments in Rural Industrialization.” In William Byrd and Qingsong Lin, Eds., _China's Rural Industry: Structure, Development, and Reform_ , Oxford: Oxford University Press, 1990.

- Stigler, George. 1971. “The Theory of Economic Regulation.” _Bell Journal of Economics and Management Science_ 2, 3-21.

- Stiglitz, Joseph, 1996. “Some Lessons from the East Asian Miracle,” _The World Bank Research Observer_ ,  11 (2): 151-77.

- Stiglitz, Joseph, 2003. “Globalization and the Economic Role of the State in the New Millennium,” _Industrial and Corporate Change_ , 12(1): 3-26.

- Stiglitz, J.E. and Yusuf S. (eds) 2001. Rethinking the East Asia miracle. Oxford University Press, New York.

- Wade, R. 1990. Governing the market: economic theory and the role of government in East Asian industrialization. Princeton University Press, Princeton.

- Walder, Andrew, 1995a. “China's transitional economy: interpreting its significance.” _China Quarterly_ 35: 963-979.

- Walder, Andrew, 1995b. “Local Governments as Industrial Firms: An Organizational Analysis of China's Transitional Economy,” _American Journal of Sociology,_ 101( 2): 263-301.

- Weingast, Barry R., Qian, Yingyi, and Montinola, Gabriella, 1995. “Federalism, Chinese Style: The Political Basis for Economic Success in China,” _World Politics_ 48 (1): 50-81.

Wong, Christine, “Fiscal Reform and Local Industrialization.” _Modern China_ , 18, 2:197-227, June 1992.

- Wong, Christine P., 1997. _Financing local government in the People's Republic of China_ . New York, Oxford University Press.

- World Bank. 1993. _The East Asian Miracle: Economic Growth and Public Policy._ Oxford University Press, Oxford.

- World Bank. 2008. _Finance for All? Policies and Pitfalls in Expanding Access._ Washington DC: The World Bank.

30
**Table 9: Networks and export intensity, Heckman selection model**

|Dependent variable:|exp|ort particip|ation|ln|export reve|nue|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|ln firm labor productivity|0.0016*** <br>(0.0002)|0.0016*** <br>(0.0002)|0.0016***<br>(0.0002)|0.4025*** <br>(0.0553)|0.4029*** <br>(0.0558)|0.4241***<br>(0.0294)|
|ln GDP|0.0023*** <br>(0.0006)|0.0023*** <br>(0.0006)|0.0019***<br>(0.0006)|0.1590*** <br>(0.0579)|0.1594*** <br>(0.0584)|0.1847***<br>(0.0511)|
|ln GDP per capita|0.0026*** <br>(0.0008)|0.0025*** <br>(0.0008)|0.0037***<br>(0.0006)|-0.0375<br>(0.1238)|-0.0358<br>(0.1240)|0.0143<br>(0.0916)|
|ln distance|-0.0062***|-0.0062***|-0.0058***|-0.6126***|-0.6132***|-0.6902***|
||(0.0013)|(0.0013)|(0.0013)|(0.1502)|(0.1510)|(0.0838)|
|EU member|0.0004<br>(0.0013)|0.0006<br>(0.0016)|0.0016<br>(0.0015)|1.0212*** <br>(0.1390)|1.0219*** <br>(0.1388)|1.0445***<br>(0.1470)|
|common currency|0.0025<br>(0.0017)|0.0025<br>(0.0016)|0.0014<br>(0.0013)|0.0643<br>(0.1270)|0.0645<br>(0.1276)|0.0821<br>(0.1367)|
|landlocked|-0.0017<br>(0.0015)|-0.0016<br>(0.0015)|-0.0023<br>(0.0016)|-0.8521*** <br>(0.1177)|-0.8538*** <br>(0.1172)|-0.8807***<br>(0.1007)|
|common language|0.1508*<br>(0.0779)|0.1547**<br>(0.0751)|0.1243*<br>(0.0717)|0.3722<br>(0.5530)|0.3798<br>(0.5581)|0.6990<br>(0.4425)|
|ln emigrants|0.0005**<br>(0.0002)|0.0005**<br>(0.0002)|0.0007***<br>(0.0002)|0.0645**<br>(0.0257)|0.0647**<br>(0.0255)|0.0736***<br>(0.0227)|
|ln number of procedures|-0.0042**<br>(0.0018)|-0.0038**<br>(0.0017)|||||
|ln number of days||-0.0004<br>(0.0009)|||||
|reg. costs (proc. & days)|||-0.0005<br>(0.0014)||||
|Observations|2350338|2350338|2350338|2350338|2350338|2350338|
|Firms|14782|14782|14782|14782|14782|14782|
|Destinations|159|159|159|159|159|159|
|R2|0.25|0.25|0.25||||
|Notes: Columns (1) to (3) rep<br>Loglikelihood|ort results fro|m the Probit|selection eq|uation. Marg<br>-297017.8|inal effects<br>-297005|and pseudo R<br>-297929.5|

<u>Notes: Columns (1) to (3) report results from the Probit selection equation. Marginal effects and pseudo R2</u> reported. Columns (4) to (6) report the corresponding maximum likelihood estimates from a Heckman selection model. Robust standard errors clustered by importing country are in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%.

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# but lost: 3 contenders per GP

Lists were provided to the survey teams of all incumbent Mukhiyas to be interviewed, and a random selection (with replacement) of 3 Ward members and 3 candidates from among those who contested the previous GP elections of 2016.<sup>8</sup> The targeted sample size of GP politicians is thus 1778 (7 each from 254 GPs) respondents.

- Block level:

- Elected head of block-level government (Panchayat Samiti Chairperson)

- Elected member of Panchayat Samiti: targeted to be the member who is on a committee related to public health

- Member of Legislative Assembly (MLA, Bihar state) elected from the MLA constituencies in our study area: approximately 1 per block (57 MLAs across the 64 blocks of the study area)

The survey firm was responsible for identifying the block-level politicians targeted to be interviewed. The targeted sample size of Block-Panchayat (Panchayat Samiti) elected members’ is 128 respondents (2 each from 64 blocks). The 57 MLAs across the 64 blocks of the study area were also identified by the survey firm. However, because of problems of reaching politicians at a time that was close to the 2019 elections in India, the survey firm was able to complete interviews with only 39 MLAs (of the targeted 57) , and with 119 Panchayat Samiti members (of the targeted 128).

- District level:

- Member of Parliament (MP, national-level) elected from the MP constituencies in our study area: approximately 1 per district

- Elected head of district-level government (Zilla Parishad Chairperson)

- 8Across Bihar, on average, 12 candidates per GP contested the Mukhiya elections in 2016.

13
|||The V<br>Value of Total|alue and Dest<br>All|ination<br>of<br>(l981<br>Share<br>|Table I<br>Individu<br>, 1983 and<br>of All E<br>|al<br>Caribbean<br>Count<br>1985)<br>xports Destined<br>F|ryls<br>Exports<br> <br>or:<br>|||
|---|---|---|---|---|---|---|---|---|---|
|Cibb||<br>|<br>|of<br>|hich||Centrally|All|of which|
|arean Country|Year|Exports (S ill)|Developed|EEC(10)|Japan|United States|<br>Planned|Developing|Latin America/Carlbbean|
|Antigua and Barbuda|1981<br>1983|13.3<br>197|3.5<br>|3.1<br>|0.3<br>|**0.0**<br>|**0.0**|**96.5**|**64.5**|
||<br>|.<br>|28.8<br>|8.2<br>|0.2<br>|15.1|0.0|71,2|59.1|
||1965|7.2|1.9|0.7|0.9|0.0|0.0|98.1|91.3|
|Bahamas<br>1/|1981<br>1983|1,860.0<br>39954|93.0<br>|4.1<br>|1.1<br>|23.4<br>|0.0|7.0|4.5|
||<br>1985|,.<br>|51.4<br>|2.4<br>|0.4<br>|46.4<br>|0.4|48.2|4.1|
|||1,069.4|91.2|17.8|5.7|61.0|0.0|8.8|4.2|
|Barbados|1981<br>|147.7<br>|63.4<br>|13.6<br>|-|44.7|0.0|36.6|33.5|
||1983<br>1985|289.2<br>|77.4<br>|5.9<br>|0.0<br>|70.2<br>|0.0|22.6|21.3|
|||214.9|95.9|10.6|0.1|83.0|-|4e1|2.6|
|BelIze|1981<br>|110.9<br>|75.0<br>|29.1<br>|1.1<br>|41.2|0.0|25.0|22.6<br>1|
||1983<br>1985|77.7<br>855|71.8<br>|26.8<br>|1.1<br>|34.9<br>|0.0|28.2|23.0|
|||.|87.1|23.7|1,6|57.7|0.0|12.9|10.8|
|Doilnican<br>Republic|1981<br>1983|991.1<br>|86.9<br>|3.4<br>|0.8<br>|76.8<br>|0.0|13.1|7.2|
||<br>1985|1,084.2<br>7430|88.4<br>|3.7<br>|1.4<br>|78.9<br>|0.0|11.6|7.0|
|||.|95.9|4.0|1.3|86.2|0.0|4.1|.4|
|Douinica|1981<br>1983|18.7<br>|53.8<br>|52.2<br>|0.0|1.5<br>|0.0|46.2|43.3|
||<br>|27.5<br>|54.0<br>|51.1<br>|-|.2.7|0.0|46.0|17.2|
||1985|31.6|83.5|75.7|7.7|0.0|0.0|16.5|7.3|
|Grenada|1981<br>1983|20.1<br>|na<br>|na<br>|na<br>|_na_<br>|na|na|na|
||<br>|21.7<br>|61.9<br>|55.0|0.1|0.0|0.0|38.1|28.0|
||1985|20.3|69.1|60.9|0.5|0.0|0.0|30.9|25.9|
|Guyana|1981<br>1983|346.2<br>1890|65.6<br>|36.7<br>|1.2<br>|19.3<br>|1.0|33.4|25.4|
||<br>|.<br>|66.8<br>|30.4<br>|4.8|24,4|0.0|33.2|29.5|
||1985|236.0|85.6|47.5|5.6|22.8|0.0|14.4|12.0|
respective organizations. Several of our other sources provided by commercial risk rating agencies and commercial survey organizations have only been available for a fee. In the interests of greater transparency, these organizations have kindly agreed to allow us to report their proprietary data in the form in which it enters our governance indicators. As discussed in detail in Appendix A and B, we in some cases use a simple average of multiple questions from the same source as an ingredient in our governance indicators. On our website we report either the individual question, or the average of individual questions, from each source that enters into our governance indicators. All the individual variables have been rescaled to run from zero to one, with higher values indicating better outcomes.

The only data sources we have not been able to obtain permission to publicize fully are the World Bank's Country Policy and Institutional Assessment, and the corresponding internal assessments produced by the African Development Bank and the Asian Development Bank. We do note however that starting in 2002 the World Bank has begun publishing limited information on its CPIA assessments on its external website. For the years 2002-2004 the overall CPIA ratings are reported by quintile for the low-income countries eligible to borrow from the International Development Association (IDA), the soft-loan window of the World Bank. For the data covering 2005, the individual country scores for the IDA allocation factor, a rating that reflects the CPIA as well as other considerations, has now become publicly available. The African Development Bank's CPIA ratings are also publicly available by quintile only since 2004, and the Asian Development Bank has committed to publicly disclosing its ratings starting in 2005.<sup>5</sup> Finally, we have this year slightly altered our presentation of the aggregate indicators in order to make them more comparable over time. In our aggregation procedure, we assume that the distribution of governance across countries follows a normal distribution in each period, with mean zero and standard deviation of one. This is simply a convenient choice of units for governance, and would be entirely innocuous were it not for two considerations. First, as we have discussed in our previous work, it is

> 5 For the African Development Bank see <u>http://www.afdb.org/pls/portal/docs/PAGE/ADB_ADMIN_PG/DOCUMENTS/OPERATIONSINFOR MATION/AFDB_2004_RATINGS_FINAL.PDF, and for the Asian Development Bank see http://www.adb.org/Documents/Reports/ADF/2005-adb-cpa.pdf.</u>

8
exports should not only depend on downstream trade costs (export distance), but also on upstream trade costs (import distance), and the interaction of both. Using China’s bilateral processing trade data, we have found empirical support for this complex impact of trade costs on intra-GPN trade.

A key theoretical prediction by Yi (2003) is that intra-GPN trade should be more sensitive to changes in trade costs than regular trade since vertical specialization leads to products crossing borders many more times before reaching the final consumer. Rubin and Tal (2008) and Rubin (2009) have built on this theory to conjecture that rising oil prices will increase trade costs, thus leading to a major slowdown in the growth of world trade and especially intra-GPN trade. To test this conjecture, we have used the panel data structure of the processing trade data to analyze the sensibility of intra-GPN trade to changes in oil prices. We have found evidence that China’s processing exports indeed have become more sensitive to both upstream and downstream trade costs in times of rising oil prices. Furthermore, in line with Yi’s (2003) theoretical prediction, China’s processing exports have been found to be more sensitive to oil price movements than non-processing exports.

Finally, we have used the processing trade data to analyze the role of GPNs in the large trade collapse during the recent Great Recession. In line with the predictions of many trade economists, we found that the Great Recession led to a disproportionate drop in China’s processing exports compared to non-processing exports, thus suggesting that GPNs played a key role in the trade decline. Part of this was a compositional effect. The sectors that contributed most to China’s exports collapse were those where processing exports were more prevalent. However, even within highly disaggregated industries, we have found that processing exports consistently dropped more than non-processing exports _,_ thus suggesting that intra-GPN trade is more sensitive to business cycle fluctuations than regular trade. Furthermore, in line with the bullwhip effect, we showed that in virtually all industries, the drop in demand for China’s processing exports led to a magnified drop in processing imports, thus feeding the trade collapse.

Our analysis provides new insights into the success of China’s export-oriented growth strategy. To a large extent, prior studies have attributed China’s dramatic exports rise to 34
**Table 4.3. Results for roots and lowland livelihood zone**

|||**Soil moistur**|**e**||**NDVI**|||**SPEI**||**Tot**|**al seasonal rai**|**nfall**||**WRSI**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|(13)|(14)|(15)|
|**Landed h**|**ouseholds**|||||||||||||||
|Hazard|**0.152*****|**0.092*****|**0.136*****|**0.069*****|**0.092*****|**0.091*****|**0.082*****|**0.063***|0.027|**0.069*****|**0.036***|0.022|0.019|**-1.337*****|-3.865|
||**[8.123]**|**[3.668]**|**[4.314]**|**[4.269]**|**[4.214]**|**[3.038]**|**[2.634]**|**[1.785]**|[0.527]|**[3.406]**|**[1.766]**|[0.744]|[1.360]|**[-2.927]**|[-0.237]|
|Hazard<sup>2</sup>||**-0.07*****|**-0.149*****||0.022|0.023||-0.017|0.013||**-0.041*****|**-0.036*****||**0.007*****|0.033|
|||**[-2.801]**|**[-4.316]**||[1.496]|[1.419]||[-0.715]|[0.379]||**[-3.894]**|**[-3.504]**||**[2.967]**|[0.196]|
|Hazard<sup>3</sup>|||**-0.051*****|||0.001|||0.023|||0.005|||-0.000|
||||**[-2.668]**|||[0.092]|||[0.999]|||[0.839]|||[-0.154]|
|**Landless**|**households**|||||||||||||||
|Hazard|**0.166*****|**0.154*****|**0.165*****|0.036|0.013|-0.001|0.027|0.112|0.022|0.021|0.015|-0.010|**0.050****|**-2.006****|11.437|
||**[3.698]**|**[3.391]**|**[2.970]**|[0.971]|[0.236]|[-0.016]|[0.442]|[1.254]|[0.209]|[0.543]|[0.429]|[-0.148]|**[2.281]**|**[-2.266]**|[0.441]|
|Hazard<sup>2</sup>||-0.032|-0.037||-0.018|0.005||0.069|**0.167***||-0.008|-0.010||**0.011****|-0.126|
|||[-0.579]|[-0.571]||[-0.486]|[0.097]||[1.264]|**[1.677]**||[-0.323]|[-0.370]||**[2.335]**|[-0.476]|
|Hazard<sup>3</sup>|||-0.007|||0.014|||0.077|||0.010|||0.000|
||||[-0.157]|||[0.466]|||[1.373]|||[0.600]|||[0.514]|
|Obs.|5,705|5,705|5,705|5,705|5,705|5,705|5,705|5,705|5,705|5,705|5,705|5,705|2,206|2,206|2,206|
|R-sq|0.175|0.179|0.182|0.157|0.158|0.158|0.154|0.155|0.157|0.156|0.160|0.160|0.107|0.115|0.115|

_Source:_ World Bank.

_Note:_ NDVI = Normalized Difference Vegetation Index; SPEI = Standardized Precipitation Evapotranspiration Index; WRSI = Water Requirement Satisfaction Index. Results are from a weighted regression such that each household is assigned its survey-specific population weight. One country, Nigeria, is included for years 2011, 2013, and 2016. Dependent variable is the log of per capita (or adult equivalent) consumption. Other variables included and not shown are education of household head, gender of household head, whether the household location is remote, whether the household was surveyed during the lean season, survey year, country, country x survey year, and zone fixed effects. Robust t-statistics are shown in parenthesis. Significance level: * = 10 percent, ** = 5 percent, *** = 1 percent. When variable is significant the coefficient and standard error are in bold font.

20
are taken from the Bureau of Labor Statistics export price series data. The results are qualitatively similar to the results in the paper: while we see little or no wage effects within manufacturing, occupational exposure to import price declines has affected workers across sectors. In particular, we find that a 1 unit increase in the price index for imports raises a worker’s real wage by .031 percent. We find no significant effect of our export price series on wages. For the wage impact of offshoring, our results are similar in sign and magnitude to those presented in the paper. This suggests that our core results are robust to our use of quantities for imports and exports. Since our data on quantity of imports and exports is more comprehensive than the price data we have for this period, we proceed in our other results with our quantity measures.

C. Imports and Trends in Wage Premiums by Occupation In Table A5, the most affected workers were shoe machine operators, for whom occupation-specific import penetration increased from 37.2 percent in 1983 to 77.4 percent in 2002. For these workers, the coefficient on import penetration in column (5) of Table 3, which is -.27, implies that their real wages fell by nearly 11 percent as a result of competition from trade. The contrasting experiences of workers in textiles and apparel related sectors compared to many service sector employees such as teachers helps to explain why some parts of the U.S. economy have been deeply affected by globalization while others have not. On average, occupation-specific import and export shares only increased from an average of 2 to 4 percent during the 1983 through 2002 period, in large part because of the importance of services and the lack of global competition in service occupations. Consequently, the average effect of an increase from .02 to .04 for occupation-specific import competition is quite small, equal to .02 x .27, which is a fall in real wages of half a percent. However, what Table A5 makes clear is that some groups of occupations experienced significant wage declines as a consequence of rising (occupation-specific) import competition. Since occupation-specific import penetration is correlated with offshore employment in low wage countries, this provides insight into the occupations most affected by offshoring as well.