rafmacalaba/datause-encoder-data huggingface dataset:
dataset. The methodology attempts several cuts of the data, randomly selected, separately between 2005 and 2011, and also non‐random holdout of each of four agro‐climactic regions of Ethiopia. Overall the model performs consistently across different `cuts’ of the data when the sample is randomly divided into training and testing data. The outcome variable (root‐mean‐squared error of the model) is not very different across either the models or the choice of training and testing dataset. The analysis also tests the support of non‐linearity in the drought impact, and we find that a quadratic model (a squared term in the regression) best fits the data. However, there is data paucity at higher levels of the shock (low probability but high impact events), so we cannot be certain that the model would accurately predict drought impact for e.g. 70% crop failure and above.

Based on these results, the paper concludes that the analysis demonstrates some level of external and internal validity of the derived relationships. However, several caveats remain. An attempt was also made to test the model on 2012 data from a different source (Ethiopian Rural Socioeconomic Survey), which has fewer observations, and a slightly different questionnaire, leading to difficulties in comparability of the main welfare measure. Further, the econometric question of identification is never completely solved when using cross‐section data, so a follow‐up robustness check with panel data would be extremely useful.

With these caveats in mind, the paper concludes that the derived relationships could form the basis of a ‘vulnerability module’ within a CAT risk model, and outlines how the model developed could be then applied to a CAT risk modeling framework. The impact of drought at various levels of severity on poverty is shown, and a suggestion is made that after some further robustness checks a full CAT risk model could be built. This would entail development and application of a stochastic model of rainfall that could be applied into the drought (LEAP) framework to produce values for actual evapotranspiration in the index calculation. The model would need to be compatible with the time and geographic resolution of the derivation of the vulnerability/damage functions in order for these to be applied. The final outcome would then link the damage functions with poverty outcomes in financial terms, for example the total poverty gap and associated fiscal burden.

3
PAM, beneficiaries are considered to have access to social security in the official poverty measurement indicator.

In terms of income, the composition of the total current per capita income is different between the total population and the 65 years old and older subgroup in the 2010-2014 period. While labor income is the single most important source of income for both groups, there is a huge difference in the proportion of total income that it represents for each group. For the total population, labor income represents around 80% of their total income while it represents only about 50% of total income for older adults. Income from transfers is much more important for the latter group, representing about 40%, while for the total population it only represents about 13%. Table 1 describes the poverty and labor participation characteristics of the population 65 years of age and older in Mexico. Table 2 describes other relevant demographic characteristics of the elderly in Mexico. Appendix D shows more sociodemographic and economic conditions data of the older adults.

_Table 1: Poverty and Labor Characteristics of Mexican Population 65 Years Old and Older (2010-2014)_

||2010||2012||201|4|
|---|---|---|---|---|---|---|
||Millions|%|Millions|%|Millions|%|
|**Extreme Poverty Measures**|||||||
|Multidimentional Poverty HC|0.9|11.0|0.8|9.7|0.8|8.5|
|Head Count Ratio|1.5|19.1|1.9|21.5|1.9|21.2|
|Poverty Gap Index|0.07|1|0.078||0.07|1|
|Poverty Severity Index|0.03|9|0.042||0.03|5|
||Millions|%|Millions|%|Millions|%|
|**Labor Condition**|||||||
|Labor Force|2.2|27.6|2.6|29.9|2.6|28.5|
|Employed|2.1|95.9|2.5|95.4|2.5|96.5|
|Unemployed|0.1|4.1|0.1|4.6|0.1|3.5|
|Non Labor Force|5.6|72.4|6.1|70.1|6.5|71.5|
|**Total**|7.8|100.0|8.6|100.0|9.1|100.0|
||Hours|%|Hours|%|Hours|%|
|**Use of Time**|||||||
|Work|11|17.5|11|19.2|11|18.2|
|Care-giving without pay|8|12.1|3|5.3|3|5.2|
|Housekeeping|16|24.0|14|24.2|15|25.0|
|Leisure|27|42.0|27|47.3|28|47.4|
|Others|3|4.4|2|4.0|2|4.2|
|**Total**|65|100.0|58|100.0|58|100.0|

Source: Based on authors’ calculations with the ENIGH-MCS 2010, 2012, and 2014.

criteria: 1. Out of the salaried economically active population, those who receive the benefits established by the Social Security Law; 2. Out of the independent or non-salaried economically active population, those who have access to medical services as a work benefit or by voluntarily participating in a social security program from a social security institution such as IMSS; 3. Any person who receives retirement or pension benefits, or who is related to someone who has access to social security; and 4. Any person of 65 years of age or older who receives a social pension (CONEVAL, 2014).

12
8 We assume that the chance of being caught increases with the share of exports going unreported, and that a penalty proportional to sales **Is** imposed once an exporter is caught. Thus revenue net of expected penalties are: - (6b) P2(l+xs)x0<sup>+ PF(1+4)(X</sup> Xo) Pxx7(x0<sup>/x), 7r<0.</sup> Maximizing (6b) with respe_t to **x0** for given total volume exported thus leads to a simple expression for the share of exports reported:

- (7) x./x g((l+xs)/(l+0))

Note thay due to overinvoicing and fictitlous exports, the expression in (7) can exceed one. (5) and (7) together fully determine supply of Turkish exports and the degree of overinvoicing, underinvoicing or faking export receipts altogether.

Finally demand for Turkish goods abroad. We 'issume foreigners have a nested CES utility function, with aggregrate foreign and home goods in level one and the foreign good split up over Turkish goods and other imported goods in level 2 (cf Kharas (1989) for a similar approach). This leads to standard demand functions:

- (8a) M*/A* h(Ph./PH.A*),

- (8b) xd/M k(PX/P*,M*M)

Inserting (8b) into (8a) and taking log-differences yields one of the two specifications used below: (9) dlog(xd) - c0 + o1dlog(p,/pm*) + c7 ydlog(Ph./PM*) 1 + a20 2 dlog(A') at are the elasticities corresponding to (8b) and **7V** the elasticities of equation (8a). "dlog' refers to logdifferences: dlog(x)-log(x)-log(x(-l)). The other specification used is (8b) directly. Market clearing requires (10) to hold: (10) X - Xd (10) determines an equilibrium value of P, for given values of P, PM and so on.

3.2 Econometric Results All equations are estimated using TSLS. The instruments used are listed in the appendix. Consider first the export -.:ply equation: (11) log(x/y) - 2.38 + 0.96*log(rpxy) & 0.81*log(rpxy(-l)) (5.26) (2.95) .-.76) + 0.66*log(rpxy(-2)) + 0.51*log(rpxy(-3)) (3.86) (1.56) R<sup>2</sup> - 0.68, TSLS, SHPL - 72-89
# **6.1 Productivity Regressions**

To explore the relationship between productivity and technology sophistication, we estimate variations of the following productivity regression: where the dependent variable is the log of sales per worker in establishment _j_ , _Kj_ is the log of the book value of capital per worker, _Hj_ is the percentage of workers in the establishment with a college degree, _Sj_ represents measures of technology sophistication in the establishment, _Xj_ is a vector of controls, _αc,s_ reflects various combinations of 2-digit sector and country dummies (typically not interacted), and _uj_ is classical measurement error.<sup>24</sup> Columns 1 to 3 of Table 8 report the estimates of specification (9) with technology sophistication measured by the average of _MAXj_ and _MOSTj_ , denoted by _Sj_ , and with both country and sector dummies (column 1), only sector dummies (column 2) and countryspecific sector dummies (column 3). In all three specifications we find a large and positive coefficient of _Sj_ in the productivity regression. In the baseline, with country and sector effects, an increase by one point in technology sophistication is associated with an increase in the (log) productivity of the establishment by roughly 0.5. The point estimate is roughly the same when allowing for country-specific 2-digit sector effect. However, the estimated coefficient of technology sophistication increases to 0.63 when excluding the country-fixed effects, suggesting that differences in technology sophistication are even more relevant to account for cross-country than within-country differences in productivity.

Since the establishment’s productivity is measured by its sales per worker, one may wonder whether the association between technology sophistication and productivity is driven by the association with establishments’ prices or with establishments’ output per worker. FAT does not contain information on the prices of the goods and services produced by each establishment. However, it collects information on the markup charged for the main good or service sold by the establishment.<sup>25</sup> We ascertain the relevance of markups in the relationship between technology sophistication and productivity by including the markup as a control

> 24The estimates are robust to measuring productivity as value added per worker, using the log of sales as dependent variable including the log of employment as a control, or to calibrating the coefficients of capital and labor to the average sectoral share of the compensation to employment and capital in total sales (e.g., De Loecker and Syverson (2021)).

> 25This information is collected for Croatia, Chile, Brazil, Georgia, Vietnam, India, Cambodia, Bangladesh, Senegal and Ethiopia. As a result, controlling for markups reduces the sample from 13046 to 8553 establishments. The point estimate of the coefficient of _Sj_ in the baseline specification for the subsample of establishments with markup information is 0.52, very similar to the point estimate in column 1.

27
Policy ReseaRch WoRking PaPeR 4400

# **Abstract**

The authors test the hypothesis that product standards harmonized to de facto international standards are less trade restrictive than ones that are not. To do this, the authors construct a new database of European Union (EU) product standards. The authors identify standards that are aligned with ISO standards (as a proxy for de facto international norms). The authors use a sample-selection gravity model to examine the impact of EU standards on African textiles and clothing exports, a sector of particular development interest. The authors find robust evidence that non-harmonized standards reduce African exports of these products. EU standards which are harmonized to ISO standards are less trade restricting. Our results suggest that efforts to promote African exports of manufactures may need to be complemented by measures to reduce the cost impacts of product standards, including international harmonization. In addition, efforts to harmonize national standards with international norms, including through the World Trade Organization Technical Barriers to Trade Agreement, promise concrete benefits through trade expansion.

This paper—a product of the Trade Team, Development Research Group—is part of .a broader project on trade facilitation and development supported through a Trust Fund of the U.K. Department for International Development. Compilation of the World Bank EU Standards Database (EUSDB) was supported by a Trust Fund of the U.S. Agency for International Development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted , witold.czubala@gmail.com, bshepherd@worldbank.org, or jswilson@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
20 _The cointegration hypothesis is not rejecteda even at 1% critical level._ 20 Moreover, we cannot reject the null that the (nornalized) cointegration vector is (1, -1), which implies that the series have exactly the same random walk (stochastic trend) component.

Next, we split the sample (two years from 7/29/94 to 7/12/96) in two: the first sub-sample goes from 7/29/94 to 7/28/95 (51 observations) and includes the period of the Mexican crisis; the second one goes from 7/28/95 to 7/12/96 (51 observations). We report below the results for the cointegration tests on the two sub-samples.

# _1) First sub-period (7/29/94 to 7/28/95):_

_Sample: 7/29/94 - 7/28/95 Included observations: 51_ _Test assumption: No deterministic trend in the data Series. MEXDISI_ **_POLDIS_** _Lags interval: I to I_ Likelihood 5 Percent I Percent Hypothesized Eigenvalue Ratio Critical Value Critical Value No. of CE(s) 0.343390 24.04490 19.96 24.60 None 0.049535 2.590984 9.24 12.97 At most I

## **L.R. test indicates I cointegrating equation(s) at 5% significance level**

Unnormalized Cointegrating Coefficients: MEXDISI POLDIS C 0.001156 -0.001715 0.561364 -1.78E-05 0.001175 -0.757838 Normalized Cointegrating Coefficients: I Cointegrating Equation(s) MEXDISI POLDIS C 1.000000 -1.483337 485.6730 (0.19940) (138.440) (Log likelihood -628.7797) 20 Note that we performed the tests for the lag length on a VAR in levels. The ECM is specified in first differences, so the lag length should be reduced by one.
arrangements) and supported the implementation of Free Trade Zones (FTZs) to facilitate export diversification and attract FDI in the energy sector as well as downstream manufacturing industries linked to energy-based projects. The Government was confident this would contribute to overcome obstacles and distortions in the rest of the country and eventually generate forward linkages to the development of manufacturing industries outside the energy sector. The Government has also tried to actively promote foreign investments and established the Trinidad and Tobago Country Branding and Investment Promotion in charge of the administration of existing Industrial Parks.

<!-- Start of picture text --> 20 Graph 4.4: FDI inflows and diversification in T&T 0.5 Graph 4.5: Inward Foreign Direct Investment by<br>sector<br>18 0.45 3000 (in millions of US Dollars) 0.35<br>16 0.4<br>14 0.35 2500 0.2849 0.3057 0.296 0.3<br>0.2631<br>0.248 0.25<br>12 0.3 2000 0.2382<br>0.2155<br>10 0.25 0.2<br>1500<br>8 0.2 0.15<br>1000<br>6 0.15 0.1<br>4 0.1 500 0.05<br>2 0.05<br>0 0<br>0 0<br>2004 2005 2006 2007 2008 2009 2010<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010<br>Non-Petroleum Industries<br>Petroleum Industries<br>FDI Inflows HH index<br>Herfindahl-Hirschman concentration index (HH)<br>Source: WITS and IMF-WEO Source: IMF Article IV (2012) and Central Bank of T&T<br><!-- End of picture text --> Given the high ratio of FDI to GDP in T&T, evidence from the lack of diversification outside the energy sector suggests that T&T was not able to fully maximize the potential benefits of the large FDI it received (see Figures 4.4 and 4.5). As Mohammed (2010) indicates, the additional benefits that a foreign investor can bring depend on whether that investor generates spillovers to the rest of the economy through employment, technology or innovation. This, at the same time, is strongly associated with the type of activity of the investing company and how it connects to other producers. The findings of Mohammed (2010) also show that FTZs have stronger spillover effects in countries where the local technology can be complementary to the FDI and weaker effects in countries with a wide technological gap. This may be a partial explanation of why FDI inflows have not substantially fostered diversification outside the energy sector in T&T. The country’s technological and human capital is indeed still below its potential outside of the oil and gas sectors. Linking FDI incentives to human and technological capital development, as well as finding various ways of engaging the domestic and foreign private sector in this mission, would enable T&T to better seize the benefits of FDI in terms of economic diversification.

Moreover, Mala and Ulabasoglu (2013) document heterogeneous effects of FDI on sectorial diversification. Using a large panel dataset, they arrive at the conclusion that in countries with higher technology, a stronger financial system, and higher efficiency wages, FDI tends to 17
Mavrotas, G., S.M. Murshed, and S. Torres (2011). ‘Natural Resource Dependence and Economic Performance in the 1970–2000 Period’. _Review of Development Economics_ , 15(1): 124–38.

Mehlum, H., K. Moene, and R. Torvik (2006). ‘Institutions and the Resource Curse’. _Economic Journal_ , 116(508): 1–20.

Michalopoulos, S., and E. Papaioannou (2013). ‘National Institutions and Subnational Development in Africa’. _Quarterly Journal of Economics_ , 129(1): 151–213.

Miguel, E., S. Satyanath, and E. Sergenti (2004). ‘Economic Shocks and Civil Conflict: An Instrumental Variables Approach’. _Journal of Political Economy_ , 112: 725–53.

Mveyange, A. (2015). ‘Night Lights and Regional Income Inequality in Africa’. Working Paper 2015/85. Helsinki: UNU-WIDER.

Papyrakis, E., and R. Gerlagh (2004). ‘The Resource Curse Hypothesis and its Transmission Channels’. _Journal of Comparative Economics_ , 32(1): 181–93.

Rabe-Hesketh, S., and A. Skrondal (2012). _Multilevel and Longitudinal Modeling Using Stata_ . 3<sup>rd</sup> Edn, Vol. 1. Texas: Stata Press.

Ren, J., Z. Chen, Q. Zhou, and H. Tang (2008). ‘Regional Yield Estimation for Winter Wheat with MODIS-NDVI Data in Shandong, China’. _International Journal of Applied Earth Observation and Geoinformation_ , 10(4): 403–13.

Ross, M.L. (2004). ‘What Do We Know About Natural Resources and Civil War?’ _Journal of Peace Research_ , 41(3): 337–56.

Ross, M.L. (2007). ‘How Mineral-Rich States Can Reduce Inequality’. In M. Humphreys, J. Sachs, and J.E. Stiglitz (eds), _Escaping the Resource Curse_ . New York: Cambridge University Press.

Ross, M.L., P. Lujala, and S.A. Rustad (2012). ‘Horizontal Inequality, Decentralizing the Distribution of Natural Resource Revenues, and Peace’. In P. Lujala and S.A. Rustad (eds), _High-Value Natural Resources and Post-Conflict Peacebuilding_ . London: Earthscan.

Sachs, J.D., and A.M. Warner (1995). ‘Natural Resource Abundance and Economic Growth’. Working Paper 5398. Cambridge, MA: NBER.

Sachs, J.D., and A.M. Warner (1997). ‘Natural Resource Abundance and Economic Growth’. Cambridge, MA: Center for International Development and Harvard Institute for International Development, Harvard University.

Sachs, J.D., and A.M. Warner (2001). ‘The Curse of Natural Resources’. _European Economic Review_ , 45(4–6): 827–38.

Salami, M.B. (2001). ‘Environmental Impact Assessment Policies, their Effectiveness or Otherwise for Mining Sector Environmental Management in Africa in Mining Development and Social Conflicts in Africa’. Accra: Third World Network-Africa.

21
<!-- Start of picture text --> Rural SC poverty and rural per-capita exependiture, 2009-10<br>80.0<br>70.0 Eihas<br>2 60.0 Chvhat.<br>=y250.0£4004E3 30.0 Shark &SpAssimOrissasp °2uPKameRa @¢tN Maha. # Haryana =<br>=z3 -20.0 WB AP, —_2“Punjab=* Kerala<br>+ Guj<br>10.0<br>0.0<br>0 200 «400 6s 800-1000 1200 1400 1600 1800-2000<br>Rural per-capita expenditure in current rupees in 2009-10<br>* Rural SC poverty —Expon, (Rural SC poverty)<br><!-- End of picture text --> <!-- Start of picture text --> Rural ST poverty and rural per-capita expenditure, 2009-10<br>50.0<br>70.0 * Chhat<br>2 600 = MP.<br>Lr==£2500g£40030.0 —Shatke——Assamoe\syp AP=Biche.¢Rajiu<br>=£2 20.0 = Kam.<br>-<br>10.0<br>0.0<br>0 200 400 600 800 1000 1200<br>Rural per-capita expenditure in current rupees in 2009-10<br>®TM ST Poverty Ratio | —Expon. (ST Poverty Ratio)<br><!-- End of picture text -->
<!-- Start of picture text --> EBITDA and EBITDA Margin Gross debt/EBITDA (x)<br>VND Billion 14,594<br>11,564 12,953 4% <94.26<br>3.74<br>LLL<br>2015 2016 2017 2015 2016 2017<br>Operating Cash Flow and CAPEX Debt-to-Equity Ratio (x)<br>VND Billion Debt/Equity Ratio < 3x<br>12,187 12,235 won nee nnn ean nen eeneee<br>to101, 2.66 255 746<br>2.47<br>2 2 2<br>(13,194) (21,288) (10,051) 2015 2016 2017 Dec-18<br><!-- End of picture text -->
<!-- Start of picture text --> Expenditure Levels and Volatility*<br>196 1- 2004<br><!-- End of picture text --> <!-- Start of picture text --> 30)<br>o<br>oo won none none ene rn ne nie one == gh oo eee roe gat + --- ---------<br>a A<br>Do °<br>o<br>BV panna annaannals<br>ran ° WEN S » ~ Wctence eeceeee nee temeannanees<br>= . “PAR 2<br>10 o o *, a =<br>EGt--, Berno re- rr *.BRT % ogerneCHH<br>ee aeo aA we THAa ae<br>cHL* HKG .<br>UK FRA JAP<br>oO eee? 50 EeICE<br>O 2 A 6 8 10<br>Level<br><!-- End of picture text --> ‘The lewelis the average growth rate of public spending and wolatility is the standard deviatien of the series,
Table 5: Robustness analysis: endogenous regressors

||I|II|III|
|---|---|---|---|
|Stage 1:||||
|Initiative on lagged three-year average trade growth|–|-0.1583***|-0.1583***|
|Stage 2: Regressand: ln(imports)||||
|Initiative on intra-regional trade /|1.4821***|1.5544*** /|1.5010***|
|Probability of the initiative on intra-regional trade||5.6690***||
|(INI / INI_PROB)|(0.021)|(0.021)|(0.020)|
|Initiative on imports from the ROW|-0.0193*|0.3020***|0.0618***|
|(INI_M)|(0.010)|(0.010)|(0.010)|
|Initiative on exports to the ROW|1.1464***|1.3365***|1.07***|
|(INI_E)|(0.010)|(0.009)|(0.010)|
|RTA on intra-regional trade|1.8908***|2.1206***|1.6474***|
|(RTA)|(0.013)|(0.012)|(0.013)|
|RTA on imports from the ROW|0.0911***|-0.0450***|-0.0471***|
|(RTA_M)|(0.006)|(0.006)|(0.006)|
|RTA on exports to the ROW|-0.0091|-0.0919***|-0.0787***|
|(RTA_E)|(0.006)|(0.006)|(0.006)|
|Generalized residual|–|–|6.4712***|
|Number of observations|1804781|1804781|1804781|
|Log likelihood|-4598521|-4566748|-4536913|

27
<!-- Start of picture text --> on 30%<br>a<br>O 25%<br>&<br>& 20%<br>Cc<br>o<br>SG 15%<br>oO<br>a<br>o 10%<br>oO<br>GS 5%<br>o<br>o<br>= 0%<br>c ise 9° S < Re & = O ® o)<br>re}E 2 =§ &jg 7)e© 8 2E€ §€ x6 §= &s<br>2 < g ag 2 N = 5N=N<br>=<br><!-- End of picture text --> m@ Trade expansion Trade stagnation <!-- Start of picture text --> © ° .O& .O ( 2O & @ @ ®<br>xr SF FF ff SF Sf S S SL &<br>eS Vv A? we Ss ee oe ¥<br>VF Ke x Ys zy 49 s<br>ie) we of<br>|<br><!-- End of picture text -->