rafmacalaba/datause-encoder-data huggingface dataset:
documented (e.g., Bown, 2010), this China phenomenon is pervasive across policy-imposing economies. Next, in specification (2), when we substitute the macroeconomic determinants at the second lag (quarter _t-2_ ) for the first lag, there is a reduction to the estimated size differential (away from 1) of the IRRs on the US unemployment rate change and the foreign trading partner’s real GDP growth compared to the baseline. Finally, specification (3) confirms for the US a finding in the full sample of data (Table 2 column 5). Limiting the dependent variable to antidumping yields an insignificant estimate of the relationship between real bilateral US dollar fluctuations and new import protection.

Consider next the Table 3 results for the European Union, which we have noted are estimated on a shorter time series of data (1999:Q1-2008:Q3) to coincide with the introduction of the euro in 1999. Estimates of the baseline EU model in column (4) are also in line with the theory. When we substitute the second lag for the first lag of the macroeconomic determinants in specification (5), the estimated IRR for the change in EU unemployment rate drops considerably, and it is no longer statistically significant. There is no qualitative change to the other key determinants. Finally, when the dependent variable is defined as antidumping _alone_ in specification (6), there is no statistically significant evidence of a relationship between macroeconomic shocks and import protection. This specification notably omits an extensive set of import restrictions on steel products through the EU’s global safeguards policy in 2002.

Figure 3 provides information on the magnitudes of the estimated IRRs for the US and EU samples of data. The US panel is based on specifications (1) and (2), and the EU panel is based on specifications (4) and (5). The figure follows the approach of Figure 2 by presenting the percent increase in the count of imported products per trading partner per quarter subject to new TTBs that are associated with a one standard deviation change in each macroeconomic determinant. For the United States, a one standard deviation appreciation of the dollar is 16 percent (see Table 1) and is associated with a 21 (99) percent increase in imported products being subject to TTBs per trading partner per quarter, relative to the model’s estimates in specification (1) (specification (2), respectively) at the means of the data. For the EU, a one standard deviation appreciation of the euro is 13 percent and it leads to 49 (85) percent more protection according to specification (4) (specification (5), respectively). A one standard deviation increase in the domestic unemployment rate leads to 41 (65) percent more protection for the United States, and 26 (221) percent more protection for the EU . Finally, a one standard deviation decline in trading partner real GDP growth is 17
nearest health facility would be one option (see, for instance, Schmidt and Kedir [31] who estimate travel distance to nearest major urban area in Ethiopia, using GIS data). Access to food as proxied through wealth is another example, as is mothers’ education. The two latter examples are not great targeting metrics, as you need to observe each household, but due to their consistent high correlation to undernutrition, they are also included to gauge the SAE results merits.

To investigate if imputed woreda levels of stunting and underweight are a useful targeting metric, an OLS regression of z-scores is estimated in the EMDHS with addition of the SAE estimates. The regression includes the following potential targeting metrics as control variables: a) access to health services approximated by households utilizing any pre- or post-natal services [32], as physical access to health care could be used as a targeting mechanism through GIS analysis of traveling distance; b) access to clean water approximated through households utilizing protected water source; c) lack of access to toilet facilities; d) access to food and other necessities, and or knowledge approximated by wealth measured by the EMDHS asset index; and e)mothers’ education. Note that the alternative targeting mechanisms are allowed to vary for each household, which should give them a comparative advantage in correlation to the z-score, compared to imputed undernutrition levels at woreda level, which are averages across thousands of children.

Use of protected water sources, lack of access to toilet facilities, utilization of health services for both stunting (in height for age regressions) and underweight (in weight for age regressions) have the expected signs, with lack of access to toilets and access to health facilities being significantly related to the z-score (column 1 and 5, Table 2). Controlling for wealth and mothers’ education, however, render these variables insignificant with only wealth and mothers’ education remaining significant (columns 2,3,6 and 7, Table 2). Similar patterns have been observed in Ethiopia before, based on other data sets [30]. SAE results in addition to these variables are highly significant, indicating that additional information is found in the estimated undernutrition levels at the woreda level that cannot be found in the already included alternative targeting metrics at the household level. Further, when the estimated woreda levels of stunting and underweight are included in the household level regression, the r-square 11
accounted for 75 percent of the growth in exports destined to the region and to the rest of the world between 1990 and 1998.<sup>19</sup> The data suggest that Pakistan and India have experienced limited consistency in terms their dynamic exports to the rest of the world also being exported to the region. Evidence from all other SACs shows weaker coherence. The disparity is extreme for Bangladesh and Nepal. For example, not a single product that accounted for 75 percent of Nepal’s growth in exports to the rest of the world is represented in the growth of its regional exports; whilst, in case of Bangladesh, only 1 percent of those goods exported to ROW are represented in intra-regional trade. The extent to which the relative share of regional trade can be increased depends on the extent to which countries’ dynamic exports are represented in regional trade. In this context prospects for greater regional exports for Bangladesh, Nepal, and Sri Lanka, in particular, do not appear to be encouraging. The increase in intra-regional trade in recent years, as spectacular as it may seem, materialized within the existing exports that are not among these countries’ dynamic segments.

A relatively high level of concentration in exports for Nepal confirms that the country has a narrow export base compared with the other countries. For example, the raw data in annex Table A3 show that two products at the SITC 4-digit category accounted for 75 percent of Nepal’s export growth to the ROW. Export growth of Bangladesh and Pakistan too are relatively concentrated. Sri Lanka export growth is more diversified. India, in contrast, compares well with newly industrial countries; its greater dispersion of exports confirms its broad export structure.

**Table 7: Divergence and Concentration Summary**

|Divergence of Exports - Regional exports<br>that match exports to the ROW as a<br>share ofthelatter *|**India**<br>18%|**Pakistan**<br>22%|**Bangladesh**<br>1%|**Sri Lanka**<br>10%|**Nepal**<br>0%|
|---|---|---|---|---|---|

19 The products ranked on growth levels are chosen as they better reflect the dynamic products than mere trade values. The cut-off point of 75 percent was chosen to exclude marginal products that may not be reported regularly. The SITC 4- digit product classification is the highest level of disaggregation for which the comparisons can be carried out because consistent reporting of data is inaccessible at further disaggregated levels, such as the SITC or Harmonized 6 to 8-digit level.

25
Almost without exception, economic studies of labor migration in less developed countries (LDCs) focus on the potential contributions that migration may make to the absolute in zie of the relevant migration unit (the individual, the fam.Ly, or the household). In contrast, Stark (1984) has hypothesized that rural-to-urban migration might be undertaken primarily to improve an individual's or a household's comparative income position with respect to that of other individuals or households in the relevant reference group (for example, the village).

In a recent study Stark and Taylor (1989) found empirical evidence that the initial relative deprivation of households in their village reference group plays a significant role in migration from Mexico to the United States. Controlling for initial absolute income and the expected income gains from migration, these authors showed that the propensity of households to participate in international migration is directly related to the households' initial relative deprivation.

In this paper we expand this earlier work by addressing the role of absolute income versus relative deprivation incentives for internal and international migration in LDC households, taking into account continuities across some labor markets and discontinuities across others. The rationale for the analysis is threefold. First, there are fairly strong reasons to expec'. that the role of relative deprivation will differ between international migration and migration within a country, as we explain below. Second, sharp discontinuities in the returns to human capital between home- and host-country labor markets may affect the ability of households that differ in their human capital endowments to achieve gains in their income positions through
# **7. Conclusion**

This paper reconciled the Prebisch‐Singer hypothesis with the popular view that income growth in emerging economies has been the key driver of food price movements. In particular, the paper extended the literature on the income‐food price relationship in three ways. First, it tested the hypothesis by examining the effect of income on the ToT, as originally envisaged by Kindleberger, rather than relating ToT with a time trend, which is the common practice in the literature. Second, in addition to income, it accounted for key sectoral and macroeconomic fundamentals that are expected to influence commodity prices. Third, it examined whether the primary channel of the effect of income on ToT is food commodity prices or manufacturing prices.

The paper employed a reduced‐form price determination model, applied to 1960‐ 2014 annual data for five food commodities (maize, soybeans, wheat, rice, palm oil) and cotton. Using panel Autoregressive Distributed Models, we show that income has a negative and highly significant effect on the terms of trade in the long run. This finding is consistent with both Engel’s Law and Kindleberger’s thesis, the predecessor of the Prebisch‐Singer hypothesis. Moreover, results indicate that income’s impact operates mainly through the manufacturing price channel (the deflator).

Other key drivers of food prices in the short run are energy costs, physical stocks, and monetary conditions. Crude oil and crop conditions are important drivers as well. A depreciating U.S. dollar positively affects nominal food prices and ToT. Finally, the effect of interest rates on the ToT are muted.

From a methodological perspective, the literature review revealed that the research on the Prebisch‐Singer hypothesis applies mostly to the behavior of primary commodity prices, not manufacturing prices. In view of the differences in the way food and industrial commodities respond to fundamentals, as well as the fact that the impact of income on the ToT of food commodity prices operates primarily through the manufacturing price channel, there is a need to broaden the scope of analytical approaches on commodity price developments to more accurately reflect their drivers. Such approaches should at least focus on the relationship between income and manufacturing prices as well as measurement aspects of the latter.

_15_
use the whole-country distributions in the case of China, India and Indonesia (where we previously used separate rural/urban distributions). In the case of China, we now use income surveys where we previously used consumption surveys.<sup>49</sup> We use a Pareto imputation to split the top decile into smaller quantiles. We choose to split the top 10% into P90-P95, P95-P99 and P99-P100. The resulting data set thus consists of 12 (uneven) fractile groups per country (which are weighted by population in the analysis). The implicit assumption is that the top decile of our database follows a continuous Pareto distribution. Let Hi be the cumulative population share of individuals with incomes greater or equal to yi, e.g. these might be the top 10%. Let Si be the share of total income received by this group. Atkinson (2007) shows that for the Pareto distribution, the relative share of two top groups is given by We can re-arrange this to compute the Pareto coefficient a We use the top 10% and top 20% shares to compute the Pareto coefficient a for every countryyear observation.<sup>50</sup> Next we compute the top 1% and top 5% shares by using this estimate of a and solving equation (1) for Si. Then we can easily construct the new quantile groups. P99-P100 is simply the top 1%. P95-P99 is the top 5% share minus the top 1% share. Finally, P90-P95 is the top 10% share minus the top 5% share. For each country-year, we thus have 12 income fractiles.

The validity of our results obviously rests on the parametric assumptions. Our choice of functional form is relatively standard in the literature, where it is argued that top tails are approximately Pareto. Furthermore, the estimation is relatively flexible, since we estimate a different Pareto constant for each country-year observation.

> 49 For the latest benchmark year, PovcalNet does not have a consumption survey, so we used an income survey from the WYD.

> 50 Atkinson (2007) uses the top 5% and top 10% shares. Hlasny and Verme (2013) use the top 10% and top 1%. The World Top Incomes Database (Alvaredo et al., 2013) uses top 0.1% and top 1% in most cases.

22
Policy **Research** Workiiro Paper **Series**

|**Tifle**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|Y;.PS2289Exchange Rate Overvaluation and<br>and Trade Protection:Lessons<br>from Experience|Howard J. Shatz<br>David G. Tarr|February 2000|L. Tabada<br>36896|
|WNPS2290 Decentratlzation and CorrupUion:<br>Evidence across Countries|Raymond Fisman<br>Roberta Gatti|February 2000|E. Khine<br>37471|
|WPS2291 Incentives for Pollution Control:<br>Regulation and Public Disclosure|Jerome Foulon<br>Paul Lanoie<br>Benoit Laplante|February 2000|Y. D'Souza<br>31449|
|WPS2292 Dividing the Spoils Pensions,<br>Privatization, and Reform<br>in Russia's Transition|Ethan B. Kapstein<br>Branko Milanovic|Mlarch 2000|P. Sader<br>33902|
|WIPS2293 Should Capital Flows be Regulated?<br>A Look at the issues and Policies|Roumeen Islam|March 2000|R. Islam<br>32628|
|WPS2294 Reforming the Urban Water System<br>in Santiago, Chile|Mary M. Shirley<br>L. Colin Xu<br>Ana Maria Zuluaga|March 2000|P. Sintim-Aboagye<br>38526|
|WVPS2295Resolving Bank Failures in Argentina:<br>Recent Developmenis and Issues|Augusto de la Torre|March 2000|M. De Loayza<br>38902|
|WPS2296 An Ecological and Historical<br>Perspective on Agricultural<br>Development in Southeast Asia|Yujiro Hayami|March 2000|P. Kokila<br>33716|
|\WVPS2297 Sources of Ethnic inequality<br>in Vietnam|Dominique van de Walle<br>Dileni Gunewardena|March 2000|H. Sladovich<br>37698|
|\V'PS2298 Fiscal Def,cds, Mcrietarv Reforn-<br>and Inflation SabiliZation in Romania|Nina Budina<br>Sweder van Wiinbergen|March 2000|N. Budina<br>82045|
|k%VPS2299Household Savings in Tranisition<br>Econormes|Cevdet Denizer<br>Holger C. Wolf<br>Yvonne Ying|March 2000|A. Cubukcu<br>38449|
|WVPS2300 Single Mothers in Pussia:%ousehold<br>Strategies'orCoping vhith Poverty|Michael Lokshin<br>Kathleen Nlullan Harris<br>Barry Popkin|March 2000|P. Sader<br>33902|
|WvPS231 identifying Welifare Effects from<br>Subiective Questions|Martin Ravallion<br>Michael Lokshin|March 2000|P. Sader<br>33902|
-7-

# Table 3: hemo Distrbutlon by Funtiotnal Classification, **bidl** 1075/76

|Income<br>Level|Farming|Wages|Salary|Business|Rent,<br>Dlvidends,<br>Transfers|Total|
|---|---|---|---|---|---|---|
|(Rs/Household)|||(Per|cent)|||
|Rural Areas|||||||
|Less than 3,600|40.1|45.0|2.3|6.0|6.3|100|
|3,601- 7,500|58.5|16.2|11.7|7.0|6.8|100|
|7,501-15,000|64.5|2.1|18.8|8.4|6.2|100|
|15.001-30,000|74.5|.2|10.0|9.8|5.5|100|
|Over 30,000|40.5|0|7.5|38.8|13.2|100|
|Urban**Areas**|||||||
|Less than 3,600|4.7|54.6|16.4|17.3|7.0|100|
|3,601- 7,500|5.0|15.6|50.5|21.1|7.8|100|
|7,501-15,000|4.7|2.1|56.5|26.9|9.8|100|
|15,001-30,000|3.8|.1|57.5|29.4|9.0|100|
|Over 30.000|6.4|0|41.0|44.1|8.5|100|

- a/ Includes farm and nonfarm wages together. Nonfarm wages account for roughly 40 percent of rural wage. and 95 percent of urban wages.

## Source: NCAER, 1980.
the models are mainly due to four important factors. First, in many areas households are fairly homogeneous in terms of observable characteristics even if there consumption varies relatively more. Second, a large number of potential correlates are simply not observable using standard closed-questionnaire data collection methods. Third, some potentially good predictors had to be discarded at the first stage because their distributions did not appear to be identical in the Census and in the EIBEP. Finally, many variables do not account for quality and are only dichotomised.

The implementation of the above procedure and the computation of the welfare indicators in the Census has been greatly eased thanks to PovMap, a software especially written to implement the methodology (we used the version developed by Zhao, 2005).

# **_2.2. Results_**

Parameter estimates from the EIBEP regressions were applied to the Census data to compute a series of poverty indicators: the headcount ratio (P0), the poverty gap index (P1), and the squared poverty gap index (P2). In addition, inequality measures were estimated as well, including the Gini Index, the mean log deviation and the Theil index.

Table 2 presents estimated poverty measures for each stratum and compares them with measures obtained in the EIBEP. For each stratum and poverty indicators, the equality of the EIBEP and Census-based indicators cannot be rejected at the 95% confidence interval. This suggests that the estimates from the Census are reliable, at least at that level.

The usefulness of the poverty map consists in using estimates at a low disaggregated level. But in order to make an “objective” judgement on the precision of the estimates obtained at such low levels, it is useful to compute the coefficients of variation of the poverty measures for all three administrative levels used in Guinea as well as for the headcount index estimates obtained with the EIBEP. Figure 1 presents the coefficients of variation of the 9
<!-- Start of picture text --> 11.6<br>11.4 |<br>~<br>@<br>4<br>® 11.2 |<br>=<br>o<br>3<br>(6) l<br>ts<br>e 1<br>iS I<br>5<br>“ ”<br>10.8 I<br>— Control<br>10.6 I —— Treatment<br>2019 2020 2021 2022 2023<br><!-- End of picture text -->
|||**Table 3: S**<br>|**plit sample re**<br>|**sults for the**<br>|**level of labor**|**productivity**|<br>**fi**||||
|---|---|---|---|---|---|---|---|---|---|---|
|||**SM**|**s (<100 wor**|**ers)**|||**Large r**|**ms (>=100**|**workers)**||
|Dependent variable: Labor<br>Productivity (logs)|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|**(9)**|**(10)**|
|Ethnic Fractionalization|-2.171***|-1.244***|-1.155***|-1.099***|-1.056***|-1.816***|-0.673|-0.665|-0.540|-0.354|
||(0.412)|(0.336)|(0.336)|(0.328)|(0.331)|(0.564)|(0.449)|(0.443)|(0.425)|(0.399)|
|GDP per capita (logs)||0.605***|0.580***|0.579***|0.546***||0.751***|0.708***|0.690***|0.628***|
|||(0.091)|(0.087)|(0.087)|(0.114)||(0.145)|(0.143)|(0.138)|(0.180)|
|Age of firm (logs)|||0.152**|0.168***|0.168***|||0.349***|0.332***|0.368***|
||||(0.059)|(0.061)|(0.060)|||(0.090)|(0.087)|(0.086)|
|Manager experience (years,|||0.037|0.011|0.006|||-0.006|-0.025|-0.065|
|logs)|||(0.056)|(0.056)|(0.054)|||(0.101)|(0.096)|(0.086)|
|Exports (proportion of|||0.505***|0.457***|0.487***|||0.348**|0.282*|0.418**|
|sales)|||(0.119)|(0.112)|(0.108)|||(0.159)|(0.154)|(0.170)|
|Foreign Ownership Y:1 N:0|||0.348***|0.331***|0.332***|||0.157|0.128|0.100|
||||(0.106)|(0.105)|(0.107)|||(0.185)|(0.177)|(0.169)|
|Firm is Financially|||-0.295***|-0.256***|-0.258***|||-0.357*|-0.280|-0.312|
|Constrained Y:1 N:0|||(0.079)|(0.079)|(0.074)|||(0.197)|(0.193)|(0.189)|
|Female Top Manager Y:1||||-0.248***|-0.228***||||-0.219|-0.158|
|N:0||||(0.074)|(0.072)||||(0.144)|(0.149)|
|Firm Provides Training Y:1||||0.002***|0.002***||||0.001|0.001|
|N:0||||(0.001)|(0.001)||||(0.001)|(0.001)|
|Courts Obstacle Y:1 N:0||||-0.009|-0.001||||0.068|0.102|
|||||(0.067)|(0.061)||||(0.117)|(0.109)|
|Total hours of power||||-0.001|-0.001||||-0.001|-0.001|
|outages||||(0.001)|(0.001)||||(0.001)|(0.001)|
|Firm Purchased Fixed<br>Assets Y:1 N:0||||0.140**|0.158**||||0.371***|0.390***|
|||||(0.066)|(0.068)||||(0.112)|(0.114)|
|Time Tax||||-0.000|0.000||||-0.003|-0.003|

48
# Figure 6. Risks to EMDE growth prospects: Policy uncertainty and trade tensions

# A. Global policy uncertainty

<!-- Start of picture text --> Index, 6-month moving average<br>Global policy uncertainty<br>275<br>1997-2018 average<br>225<br>175<br>125<br>75<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019<br><!-- End of picture text -->

# C. Import tariffs

B. Impact of euro area policy uncertainty on investment in ECA <!-- Start of picture text --> Percentage points<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>-2.0<br>1 2 3 4 5 6 7 8<br>Quarter<br><!-- End of picture text --> D. Goods trade, container shipping, and export orders <!-- Start of picture text --> Percent Percent, year-on-year Index, 50+=expansion<br>30 China's tariff on US exports Goods trade<br>US tariff on Chinese exports Container shipping<br>25<br>8 New export orders (RHS) 58<br>20<br>6 56<br>15<br>4 54<br>10<br>2 52<br>5<br>0 50<br>0<br>-2 48<br>Jan-16 Apr-16 Jul-16 Oct-16 Jan-17 Apr-17 Jul-17 Oct-17 Jan-18 Apr-18 Jul-18 Oct-18 Jan-19 Apr-19 Jul-19<br>Jan-18 Feb-18 Mar-18 Apr-18 May-18 Jul-18 Jul-18 Aug-18 Sep-18 Nov-18 Jan-19 Feb-19 May-19 Sep-19 Oct-19 Dec-19<br><!-- End of picture text --> Source: CPB Bureau for Economic Policy Analysis, China Ministry of Finance, Davis (2016), Freund et. al. (2018), Haver Analytics, Institute of Shipping Economics and Logistics, International Trade Centre, United States Trade Representative, World Bank.

A. See Davis (2016) for details. Last observation is October 2019.

B. Vector autoregressions are used for estimation on a sample of aggregate EMDE variables for 1998Q1-2016Q2. The model includes the Economic Policy Uncertainty for the euro area, emerging market stock price (euro area) index, emerging market bond index, aggregate real output and investment growth in six ECA countries, with G7 real GDP growth, U.S. 10-year bond yields, and MSCI World Index as exogenous regressors and estimated with two lags.

C. Trade-weighted average tariffs computed from product-level tariff and trade data, weighted by U.S. exports to the world and China's exports to the world in 2017.

D. Figure shows three-month moving averages. New export orders measured by Purchasing Managers’ Index (PMI). PMI readings above 50 indicate expansion in economic activity; readings below 50 indicate contraction. Last observation is June 2019 for goods trade, July 2019 for container shipping, and August 2019 for new export orders.