rafmacalaba/datause-encoder-data huggingface dataset:
Table 3 Effects of stabilizing copper and coffee pricer. !961-1986

|||Coef|ficients of var|iation|percentages|
|---|---|---|---|---|---|
|Country|Average<br>export share<br>(1)|Revenue<br>unstabilized<br>(2)|Revenue<br> <br>stabilized<br>(3)|Exports<br>unstabilized<br>(4)|Exports<br>stabilized<br>(5)|
|||Cop|per|||
|aCile|S0|18|17|12|8|
|Zaire|39|16|16|31|24|
|Zambia|88|18|16|18|12|
|Papua New<br>Guinea|20|33|13|14|13|
|||Cof|fee|||
|Bazil|22|22|22|17|16|
|Colombia|43|19|15|10|10|

Source: World Bank data

- Notes: (1) is avege share of exports in total export revenue (2) is the CV of deviations from 5-yr MA export revenue

- (3) is the CV of deviations from 5-yr export revenue valuing the exports at prices stabilized at their 5-yr MA level

- (4) is the CV of deviations from 5-yr MA total export revenue (5) is the CV of deviations from 5-yr MA total export revenue valuing the exports at prices stabilized at their 5-yr MA level

All export revenues were deflated by the Index of Manufacturing Unit Value VMUV).

48
74

# Figure A6: Dynamic Ban Effects: Deseasonalized Imports

<!-- Start of picture text --> −4 −3 −2 −1 0 1 2 3 4 5 6 7 8 9 10 11 12 −4 −3 −2 −1 0 1 2 3 4 5 6 7 8 9 10 11 12<br>Month relative to period when treatment first changes (t=0) Month relative to period when treatment first changes (t=0)<br>(a) Extensive Margin (b) Intensive Margin<br>−4 −3 −2 −1 0 1 2 3 4 5 6 7 8 9 10 11 12 −4 −3 −2 −1 0 1 2 3 4 5 6 7 8 9 10 11 12<br>Month relative to period when treatment first changes (t=0) Month relative to period when treatment first changes (t=0)<br>(c) Extensive Margin, fertilizer only (d) Intensive Margin, fertilizer only<br>.4 4<br>.3 2<br>.2<br>.1 0<br>0 −2<br>−.1<br>−.2 −4<br>−.3 −6<br>Effect on Pr(imports>0) −.4 Effect on log(imports)<br>−.5 −8<br>−.6<br>−10<br>−.7<br>.6<br>6<br>.4<br>4<br>.2<br>2<br>0 0<br>−.2 −2<br>−4<br>Effect on Pr(imports>0) −.4 Effect on log(imports) −6<br>−.6<br>−8<br>−.8 −10<br><!-- End of picture text --> _Notes_ : The charts show De Chaisemartin and d’Haultfoeuille (2022) estimates of the effects of import bans on both the extensive and intensive margins of (deseasonalized) imports, relative to the last month before the ban takes effect, using an HS8 product-month level panel. The sample covers the January 2019-September 2022 period (but only coefficients in January 2021-May 2022 are reported) and excludes non-fertilizer HS8 products whose imports were ever banned. In panels A6c and A6d, the sample also excludes all non-fertilizer products, even the ones who were never banned. For each month of the year, we compute the (log of the) product’s historical average import value and the historical average probability of nonzero imports for that month in 2017-2019; then, for every product-month we subtract the relevant historical average from log import value and from the indicator of nonzero imports, thus obtaining deseasonalized versions of these two outcome variables. The treatment variable is a set of dummies _ban_<sup>_τ_</sup> _ct_<sup>indicating whether fertilizer product</sup><sup>_c_was first banned</sup><sup>_τ_months before month</sup><sup>_t_,and the not-yet-treated products serve as the</sup> control group. The y-axis shows the effects of import bans on the (deseasonalized) probability of nonzero imports (panels A6a and A6c) or on the (deseasonalized) log value of imports (panels A6b and A6d), where negative values correspond to decreases in probabilities/values. The p-values of the joint significance tests for the placebo estimators are 0.71 (panel A6a), 0.68 (panel A6b), 0.35 (panel A6c), and 0.85 (panel A6d).
**Table 8: Networks and export intensity, OLS and Tobit**

|Dependent variable:|||ln expor|t revenue||||
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|ln firm labor productivity|0.3808***<br>(0.0387)|||1.9873***<br>(0.2012)||1.9951***<br>(0.2009)||
|ln firm employment||0.3310***<br>(0.0231)|||4.1915***<br>(0.3596)||4.1879***<br>(0.3573)|
|ln GDP|0.1310***|0.1414***|0.1914***|2.8406***|2.7758***|2.8132***|2.7494***|
||(0.0347)|(0.0377)|(0.0459)|(0.5817)|(0.5958)|(0.5733)|(0.5875)|
|ln GDP per capita|-0.0727*|-0.0745*|0.0046|1.5500***|1.5132***|1.5341***|1.4977***|
||(0.0381)|(0.0405)|(0.0395)|(0.5922)|(0.5851)|(0.5869)|(0.5801)|
|ln distance|-0.5246*** <br>(0.0871)|-0.5943*** <br>(0.0996)|-0.6477*** <br>(0.0891)|-6.9170*** <br>(0.9276)|-6.9301*** <br>(0.9195)|-6.8611*** <br>(0.9244)|-6.8748***<br>(0.9167)|
|EU member|0.9288***|0.769***|0.7166***|4.3223***|3.9470**|4.3025***|3.9276**|
||(0.1379)|(0.1459)|(0.1445)|(1.6598)|(1.6444)|(1.6505)|(1.6357)|
|common currency|0.0896|0.1278|0.0171|1.1312|1.1824|1.1789|1.2301|
||(0.1364)|(0.1401)|(0.1351)|(1.2395)|(1.2415)|(1.2363)|(1.2389)|
|landlocked|-0.8273***|-0.8543***|-0.7335***|-4.0660|-4.0295|-4.0425|-4.0078|
||(0.1124)|(0.1171)|(0.1200)|(2.5010)|(2.5175)|(2.4721)|(2.4891)|
|common language|0.0119|0.3332|0.9917**|25.4061***|25.6086***|25.2408***|25.4474***|
||(0.2204)|(0.256)|(0.3998)|(5.5898)|(5.5996)|(5.5485)|(5.5589)|
|ln emigrants|0.0454**|0.0517***|0.0802***|0.8546***|0.8464***|0.8512***|0.8429***|
||(0.0178)|(0.0188)|(0.0205)|(0.2456)|(0.2499)|(0.2436)|(0.2478)|
|Estimator|OLS|OLS|OLS|Tobit|Tobit|Tobit|Tobit|
|Firm fixed-effects|No|No|Yes|No|No|No|No|
|Observations|52053|52053|52053|2852926|2852926|2852926|2852926|
|Firms|14782|14782|14782|14782|14782|14782|14782|
|R2|0.18|0.2|0.51|0.15|0.18|0.15|0.18|
|Destinations|193|193|193|193|193|193|193|

<u>Notes: In columns (1) to (3), the estimation method is OLS and only non-zero export flows are used in the estimation. In</u> columns (3) and (4) the estimation method is Tobit and the dependent variable is left-censored at the minimum firm-level export value observed in each destination. In columns (6) and (7) the estimation method is Tobit and the dependent variable is left-censored at zero. Marginal effects reported. Robust standard errors clustered by importing country are in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%.

7
Table 2: Average Slopes of the Informality Engel Curves

|**Specifcation:**|**Ma**|**in**|**Geog**|**raphy**||**Product**|**Codes**||**All**|
|---|---|---|---|---|---|---|---|---|---|
|Avg. of 31 Countries|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|(Negative of) Slope|9.8|10.6|9.2|8.5|6.9|6.3|6.1|5.4|4.3|
|Confdence Interval|[9.2,10.4]|[9.9,11.2]|[8.5,9.9]|[7.7,9.2]|[6.2,7.4]|[5.7,6.7]|[5.5,6.5]|[4.8,5.7]|[3.7,4.7]|
|# of p-values_<_0.05<br>|31|31|31|30|30|29|30|29|28|
|_R_<sup>2 </sup>adjusted|0.19|0.21|0.25|0.41|0.43|0.51|0.51|0.50|0.54|
|Household Characteristics||X|X|X|X|X|X|X|X|
|Urban/Rural|||X|||||||
|Survey Blocks||||X|||||X|
|Food Products|||||X|||||
|COICOP 2-dig||||||X||||
|COICOP 3-dig|||||||X|||
|COICOP 4-dig||||||||X|X|

This table shows the (negative) average slope of the Informality Engel Curves across countries for different specifications. In column 1, we report the slopes that are estimated from the following regression: _Share In f ormalip_ = _β_ 0 + _β_ 1 _ln_ ( _expenditurei_ ) + _εip_ where _Share In f ormalip_ is the share of household _i_ ’s informal expenditure on product _p_ . We weigh each observation using household survey weights and the expenditure share of the product. Average of lower and upper bound of 95% confidence intervals in brackets, from robust standard errors. In column 2, we augment this regression with controls for household characteristics (household size, age, gender, education of head). In column 3 (4), we instead add fixed effects for urban/rural (survey enumeration blocks). In column 5, we instead add fixed effects for food versus non-food products. In column 6/7/8, we instead add fixed effects for product codes at 2nd/3rd/4th level of the COICOP classification. In column 9, we add household characteristics, as well as fixed effects for survey blocks and COICOP-4.

47
# **<mark>Table 9: Agricultural Production</mark>**

<mark>Coefficients, standard errors in parentheses</mark>

||Pooled|Mean|Impac|t byRoun|d(OLS)|Pooled|Impact|byRoun|d(SAR)|
|---|---|---|---|---|---|---|---|---|---|
||N|R3|R1|R2|R3|Impact|R1|R2|R3|
|**_Panel A: Plots Controlled by Women_**|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Total value of harvest in the last major season [in Cedis]|1,400|93.2|-48.8*<br>(28.3)|-54.2*<br>(29.1)|-70.4**<br>(31.0)|-57.1***<br>(17.3)|-28.6<br>(27.0)|-24.4<br>(27.8)|-42.6<br>(29.5)|
|Total value of harvest per ha in the last major season [in|1,276|217|-102|12.3|-63.2|-53.1|-6.56|8.20|-15.0|
|Cedis]|||(63.2)|(66.8)|(65.9)|(38.3)|(9.69)|(9.96)|(11.6)|
|**_Panel B: Plots Controlled by Men_**||||||||||
|Total value of harvest in the last major season [in Cedis]|1,672|466|-69.2|-71.4|-44.1|-62.1|-55.2|-60.5|-28.3|
||||(74.2)|(75.5)|(78.3)|(44.5)|(73.0)|(74.3)|(77.0)|
|Total value of harvest per ha in the last major season [in|1,599|1,239|22.3|-103|12.8|-22.0|89.8|-87.8|267|
|Cedis]|||(70.7)|(73.2)|(74.7)|(42.8)|(170)|(173.1)|(228)|

Notes: Standard errors are reported in parenthesis and are clustered at the individual level. *** denotes significance at 1%, ** at 5% and * at 10%. Standard errors are clustered by respondent and are shown in brackets. All outcome variables in Panel A (B) are measured conditional on the existence of a female (male) plot. Control variables include household structure, household size, number of females in the household, number of household members under five years of age, dummy variable for household head being male, age of household head, dummy for whether household head has ever been to school, three dummies for the predominant chieftaincy institution in the community, number of sampled households situated around respondents’ house within a 200m radius (household density), distance between the house of the respondent and the main road demarcation.

31
_Table A3. Parametric values in India in 2019 for 1/θ when the poverty lines is $2.15 (2017 PPPs) per day. (1/θ =Number of years spent in monetary poverty yielding the same well-being loss as one year of life lost)_

|**y0 ($ a day)**|**0.5**|**0.75**|**1.0**|**1.25**|
|---|---|---|---|---|
|**ɛ (CRRA)**|||||
|**2.5**|7.8|4.1|2.6|1.8|
|**2.0**|4.7|3.0|2.1|1.6|
|**1.5**|3.0|2.2|1.7|1.4|
|**1.0**|2.1|1.7|1.5|1.3|

Note: According to the PIP platform, in India in 2019, mean consumption is 5.13 $/day (2017 PPPs). For the $2.15 a day line, mean consumption among the poor is $1.75 a day and mean consumption among the non-poor is $5.51 a day. The utility function considered is CES.

# **Appendix 3: Imputation model for nutritious outcomes**

Given the lack of available information on individual health in the Household Living Conditions Survey (NLSS), the main source of data used in this study, a related survey, the Nigeria's Demographic Health Survey (DHS) 2018, was exploited to infer information on the nutritional status of individuals. The general approach used was to build a model from DHS data, from which the nutritional status of individuals interviewed in the NLSS were inferred, in order to reproduce the representative distribution of the nutritional status in DHS. We succinctly describe in this Appendix the construction of this model.

## ⮚ **_Nutritional statuses_**

The nutritional status of individuals was first defined considering the age and sex of the individuals. Three main categories were thus retained: children, defined as all individuals between 0 and 5 years of age inclusive, and adult men and women, i.e., those over 5 years of age.

The nutritional status of children was measured on the basis of the height for age z score - number of standard deviations of the actual height of a child from the median height of the children of his/her age as determined from the standard sample - from which three standard classes were identified:

- -2 < HAZ Normal (Well nourished)

- -3 < HAZ < -2 Moderately Stunted (Moderately Malnourished)

- `o` HAZ < -3 Severely Stunted (Severely Malnourished)

Stunting is a condition that results from chronic or recurrent undernutrition, usually associated with poverty, poor maternal health and nutrition, frequent illness and/or inappropriate feeding and care in early life. Stunting prevents children from reaching their physical and cognitive potential. (WHO) As for the nutritional status of adults, it has been defined on the basis of the body mass index (BMI), which is calculated according to height and body mass, and which makes it possible to estimate the corpulence of a person. Three categories have been formed from the indicator:

- BMI < 18.5 Undernourished

- BMI >= 18.5 & BMI < 30 Normal (Well nourished)

- BMI > 30 Obese

43
relationship is mostly flat in Nigeria and is not monotonic in Tanzania. Because the per kilogram costs of fertilizer and price for which outputs can be sold are not necessarily indicative of the full incentive to use inputs, we expect that some part of these two anomalous relationships is a function of the missing portions of cost (e.g., transport cost) and crop response to fertilizer application. Moreover, the included prices are not household-specific, which are better correlated with household input use rates.

Figure 5: Local linear non-parametric regression of conditional fertilizer use per hectare by fertilizer/output price ratio <!-- Start of picture text --> Malawi (maize) Niger (millet)<br>Local polynomial smooth Local polynomial smooth<br>80<br>350 mean median mean median<br>300 60<br>250 40<br>200 20<br>150<br>0<br>3 3.5 4 4.5 5 5.5 1 1.5 2 2.5 3<br>input/output price ratio input/output price ratio<br>95% CI lpoly smooth 95% CI lpoly smooth<br>kernel = epanechnikov, degree = 1, bandwidth = .17, pwidth = .26 kernel = epanechnikov, degree = 1, bandwidth = .28, pwidth = .42<br>Nigeria (maize) Tanzania (maize)<br>Local polynomial smooth Local polynomial smooth<br>600<br>150<br>median mean<br>400<br>200 100<br>0<br>median mean 50<br>-200<br>1 2 3 4 5 3 3.5 4 4.5 5<br>input/output price ratio input/output price ratio<br>95% CI lpoly smooth 95% CI lpoly smooth<br>kernel = epanechnikov, degree = 1, bandwidth = .23, pwidth = .34 kernel = epanechnikov, degree = 1, bandwidth = .2, pwidth = .3<br>household average kg/ha inorganic fertilizer household average kg/ha inorganic fertilizer<br>household average kg/ha inorganic fertilizer household average kg/ha inorganic fertilizer<br><!-- End of picture text --> Note: Only households with fertilizer use included, and application rates are total, not nutrient, values. All prices are median prices at levels of geographic aggregation where at least 10 household-level observations exist. In Nigeria, we limit the fertilizer use rates to those below the 90<sup>th</sup> percentile and price ratios to those below 5 given a lot of noise in the data. Ethiopia is excluded because input prices are only observed in the community survey with mostly missing values. Uganda is excluded due to very little fertilizer use alongside very few observed prices of fertilizer.

# <u>Input market accessibility</u>

Not only does the market price of the input matter, but so do the other costs associated with its procurement. Some of the costs can be observed, like transport costs, while others cannot, like the transactions costs associated with finding an input retailer and possibly negotiating prices. While many measures of market accessibility exist (Chamberlin and Jayne 2013), most research focuses on the transport cost and distance when considering input markets specifically. For 41
_Figure 2: Number of PTAs covering each WTO+ provision_ <!-- Start of picture text --> WTO+<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>Included Legally enforceable<br>Number of agreements<br><!-- End of picture text --> Only a few WTO‐X provisions, such as competition policy, movement of capital, investment rules and IPR, are included and legally enforceable in a relevant number of trade agreements (see Figure 3). Competition policies are included in more than 200 PTAs and are enforceable in 185. Movement of capital and investment are included in more than 150 PTAs but legal enforceability differs: almost all provisions on movement of capital are legally enforceable (141) while fewer PTAs have legally enforceable investment provisions (109). IPR is also enforceable in only 111 PTAs. All other WTO‐X provisions are legally enforceable in less than 1⁄4 of PTAs.<sup>18</sup> In this set, the policy areas that are most often covered and legally enforceable are environmental laws, visa and asylum disciplines and labor market regulations.

_Figure 3: Number of PTAs covering each WTO‐X provision_ <!-- Start of picture text --> 200<br>150<br>100<br>50<br>0<br>Included Legally enforceable<br>Number of agreements<br>Investment IPR Visa and Asylum Agriculture Energy Statistics SME Political Dialogue Social Matters Taxation Anti‐Corruption Data Protection Approximation of... Health Illicit Drugs Human Rights Audio Visual Mining Terrorism Nuclear Safety Civil Protection<br>Competition Policy Movement of Capital Environmental Laws Information Society Regional Cooperation Labour Market Regulation Research and Technology Economic Policy Dialogue Industrial Cooperation Education and Training Public Administration Consumer Protection Cultural Cooperation Financial Assistance Innovation Policies Money Laundering Illegal Immigration<br><!-- End of picture text -->

> 18 The only exception is the visa and asylum provision that is enforceable in 74 PTAs.

11
<!-- Start of picture text --> +.<br>oQ<br>2<br>@<br>5 “0<br>“=.<br>oO<br>-2 0 2 4 6<br>Log hourly wages in October 2012 prices<br>— Informal —— Formal<br>——— Public ———_ NGO<br><!-- End of picture text --> <!-- Start of picture text --> Sectors of work<br>Agriculture ea<br>Public Sector & NGO —<br>Private Informal —e— °<br>GenderMale oe<br>Education. groups<br>Primary Education —e—e<br>Secondary Education —<br>Higher education —e—<br>4 -5 t) 5 1 15<br>Point estimate and 95% Cl<br>———_ 2007 —— 2012<br><!-- End of picture text -->
**Table 4 Labor Participation and the** **_Maquila_ Contribution, 1991–2006 (percent)**

|_Item_|_1991_|_1995_|_2001_|_2006_|
|---|---|---|---|---|
|Working-age population (15–65)|51.7|52.1|53.4|56.1|
|Of which: Active population|59.4|61.0|60.8|58.3|
|Of which: Employed|81.1|76.3|78.5|75.1|
|Men in_maquila_|0.5|0.9|1.9|2.7|
|Women in_maquila_|1.1|2.4|3.5|3.1|
|Underemployed|14.4|19.4|17.1|22.6|
|Unemployed|4.5|4.3|4.3|2.3|

_Source_ : Authors’ computation based on data from EPHPM.

# III. Methodology

The trade reforms introduced in Honduras during the 1990s could be seen as an external shock redistributing resources to the _maquila_ sector. Redistribution of productive factors is given through price mechanisms (that is, increases in labor demand in the _maquila_ sector causes a rise in the relative wages of labor, an increase in labor participation, or both in that sector). The poverty impact of the changes brought about by trade reform can be analyzed using the empirical framework developed in this section.<sup>7</sup> Household _h_ is defined as poor if its per capita household income (or expenditure), _yh_ , is below a predetermined poverty line, _z_ . At the national level, poverty indices can take into account the proportion, depth, and severity of poverty. These three aspects of poverty (the poverty headcount, the poverty gap, and the distance from the poverty line) are estimated using the poverty measures developed by Foster, Greer, and Thorbecke (1984): where _N_ is the total population and α is a parameter that penalizes the differences between the income of the poor and the poverty line. Let us define the income of household _h_ as the sum of incomes of all household members derived from various sources: 7. A similar methodology, with an application to Mexico, can be found in Artecona and Cunningham (2002) and De Hoyos (2005 and 2006).

8
Policy ReseaRch WoRking PaPeR 4910

# **Abstract**

This paper examines the impacts of disasters on dynamic human capital production using panel data from Bangladesh, Ethiopia, and Malawi. The empirical results show that the accumulation of biological human capital prior to disasters helps children maintain investments in the post-disaster period. Biological human capital formed in early childhood (long-term nutritional status) plays a role of insurance with resilience to disasters by protecting schooling investment and outcomes, although disasters have negative impacts on investment. In Bangladesh, children with more biological human capital are less affected by the adverse effects of floods, and the rate of investment increases with the initial human capital stock in the post-disaster recovery process. In Ethiopia and Malawi, where droughts are rather frequent, exposure to highly frequent droughts in some cases reduces schooling investment but the negative impacts are larger among children embodying less biological human capital. Asset holdings prior to the disasters, especially the household’s stock of intellectual human capital, also helps maintain schooling investments at least to the same degree as the stock of human capital accumulated in children prior to the disasters.

This paper—a product of the Global Facility for Disaster Reduction and Recovery Unit, Sustainable Development Network Vice Presidency—is part of a larger effort in the network to disseminate the emerging findings of the forthcoming joint World Bank-UN Assessment of the Economics of Disaster Risk Reduction. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The team leader—Apurva Sanghi—can be contacted at "asanghi@worldbank. org", and the author of this paper at "f.yamauchi@cgiar.org". We thank Apurva Sanghi and participants at the World Bank for useful comments.

_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