rafmacalaba/datause-encoder-data huggingface dataset:
Chart I.

tnvosta.nt and GroWth in Sub-Seharcn Africa. 1968-1988 <!-- Start of picture text --> 0. 4<br>0. 3<br>0 BWA<br>0.12<br>0.11I<br>0.10<br>0.09<br>0. 0-<br>0.07<br>0.0O<br>KN(<br>4~ & 0.Oa 9 .04 - - _ _ _ . R*4 .. 0 a _ -- G NCA tlv OWS O sa s S P GAS<br>Ms<br>0.03 - E, C I TGO<br>0.02 O.82~~~~~~E-- - 'KA aa S3X N \;s 9 0 9 ° ca<br>0.01 -<br>SSA 0 1<br>-0.01<br>-0.02- -<br>0.00<br>0.20<br>0.40<br>Irt11sftnen/CDP at current pru-es<br>Abbroviations a<br>SSA Sub-Saharan Africa<br>SSA 1990 SSA oxcl. NCA Sub-Sahsr&a Africa Target for sub-Saharan excluding Africa to achiove in Nigeria 1990<br>SSA 2000 Target for sub-Sabaran Africa to achieve in 2000<br>1 DEN Benin 21 M1I alawi<br>I A Botswana 23 I. MaLi<br>3 4 SWO SUR Durkina Burundi Paso 23 24 MIT M S Mauritania HaunitLus<br>6 5 7 CAP CMi CPV Caeroon Cape Verde Central African Republic 23 26 27 NGA hO2 NZR Niger MosambiqueNigeria<br>8 TCD Chad 26 REA Rwanda<br>11 12 10 9 COX COO CIV ETH Comoros Congo Coto D'tvoire Ethiopia 32 29 30 31 SEN SYC SLZ SOM SeychellesSierra Somalia Senegal Leone<br>13 GAB Gabon 33 SDN Sudan<br>14 CMI Gambia 36 SWZ Swaziland<br>15 GH4 Ghana 35 TZA Tanzaaia<br>16 17 18 GNU ZEN LSO Kenya Guinea-Bissau Lecotho 37 38 36 TGO UGA ZAR TogoUganda Zaire<br>19 LBR Liberia 39 ZHJ Zambia<br>20 MDG Madagascar 40 ZWe Zimbabwe<br>Notes: -- -- Both indicators. least variables square were The parameters method defined using as the of the the arithmetical averages regression of the averages 40 countriee line were of yearlycalcuLated as observations.by<br>Sources: World Tablas. 1989-90 Edition, Prom the data files of the World<br>Bank. The Johns Hopkins University Proes, Baltimore and London.<br>1990. both book and diskette versions.<br><!-- End of picture text --> Sub-Saharan Africa, PrQM Crisis to Sustainable Growth. A Long-Term Parspective Study, The World Bank, VWahington. D.C. 1989.

20
Table F.2. List Experiment

||List Experiment<br>(1)|
|---|---|
|_β_1=I(Treatment)_×_I(List Treatment)|0.176<br>(0.112)|
|_β_2=I(Treatment)|-0.059<br>(0.080)|
|List Treatment Diff. Effect=_β_1+_β_2|0.117<br>(0.078)|
|R-squared|0.050|
|Observations|1,040|

_Notes:_ The table depicts the results of the listing experiment randomly assigned to all participants. The respondents were asked: how many of these individuals would you not want to have as neighbors?. “[Please respond how many, not which of them].” The options were: “a. Abusive people”; “b. People in poverty”; “c. People who profess a religion different from yours”; “d. Venezuelan migrants.” Option d was the statement assigned randomly so that only half the participants got this statement. The variable List Treatment is an indicator variable [=1] if the respondent received option d on the questionnaire. This table reports OLS estimates from equation xx. All the columns control for sex (female and male), two age groups (21–28 and 29+) and three economic strata (high, medium, and low). *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.

Table F.3. Heterogeneous Effects on Voting Intentions by Social Desirability Score

||Vote intention<br>in 2023|Vote intention<br>in 2026|
|---|---|---|
||Mayoral<br>elections|Presidential<br>elections|
||(1)|(2)|
|_β_1=I(Treat)_×_I(High Soc. Desirability)|0.057**|0.049**|
||(0.023)|(0.022)|
|_β_2=I(Treatment)|-0.025|-0.025|
||(0.023)|(0.022)|
|Social Desirability Diff. Effect=_β_1+_β_2|0.032|0.024|
||(0.033)|(0.032)|
|R-squared|0.024|0.018|
|Observations|1,040|1,040|

_Notes:_ This table reports an OLS estimate interacting the treatment variable with the Social Desirability Score (SDS). The Social Desirability Score (SDS) is a measure of the individual’s propensity to report socially desirable answers. High SDS refers to having an above-median score among all participants. Dependent variables in Columns (i)–(ii) are indicator variables [=1] if the respondent had the intention to vote in the next mayoral election in 2023 and in the next presidential election in 2026. All the columns control for sex (female and male), two age groups (21–28 and 29+) and three economic strata (high, medium, and low). *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.

90
profits data and Indonesian school enrolment data, and most difference for the LEAPS math test score and height z-score data.

# **4. Simulation results**

Appendix 3<sup>16</sup> reports the full set of simulation results for all four data sets for 30, 100, and 300 observations. We summarize the results of these simulations in this section, organizing their discussion around several central questions that a researcher may have when performing a randomized assignment. We start by addressing the following core question:

# **4.1 Which methods do better in terms of achieving balance and avoiding extremes?**

We first compare the relative performance of the different methods in achieving balance between the treatment and control groups in terms of baseline levels of the outcome variable. Table 3 shows the average difference in baseline means, the 95<sup>th</sup> percentile of the difference in means (a measure of the degree of imbalance possible at the extremes), and the percentage of simulations where a t-test for difference in means between the treatment and control has a p-value less than 0.10. We present these results for a sample size of 100, with results for the other sample sizes contained in Appendix 3. Figures 1a through 6c graphically summarize the results, plotting the densities of the differences in average outcome variables for all three sample sizes: 30, 100, and 300 observations.

Table 3 shows that the mean difference in baseline means is very close to zero for all methods – on average all methods of randomizing lead to balance. However, Table 3 and the figures also show that stratification, matching, and especially the minmax t-stat method have much less extreme differences in baseline outcomes, while the big stick method only results in narrow improvements in balance over a single random draw. For example, in the Mexican labor income data with a sample of 100, the 95<sup>th</sup> percentile of the difference in baseline mean income between the treatment and control groups is 0.384 standard deviations (s.d.) with a pure random draw, 0.332 s.d. under the big stick method, 0.304 s.d. when stratifying on 4 variables, 0.099 s.d. with pair-wise greedy matching, and

> 16 Appendix 3 is available on the author’s website.

- 17 -
**Figure 9. Degree of diversification, 2004**

<!-- Start of picture text --> Vietnam<br><!-- End of picture text --> <!-- Start of picture text --> Kazakhstan<br><!-- End of picture text --> <!-- Start of picture text --> Cambodia<br><!-- End of picture text --> <!-- Start of picture text --> Uzbekistan Ghana<br><!-- End of picture text --> <!-- Start of picture text --> Uruguay<br><!-- End of picture text --> <!-- Start of picture text --> Herfindahl Index Hirschman Index<br><!-- End of picture text --> _Source: Database on export diversification (PRMED). Note: The higher the Herfindahl and Hirshman indices, the lower the degree of diversification._

**Figure 10. Degree of export diversification, Mongolia**

<!-- Start of picture text --> 0.35 100%<br>90%<br>0.30<br>80%<br>0.25 70%<br>60%<br>0.20<br>50%<br>0.15<br>40%<br>0.10 30%<br>20%<br>0.05<br>10%<br>0.00 0%<br>1990 1995 2000 2004<br>Herfindahl Index Export share of five largest exports<br>% of exports<br>Herfindahl Index<br><!-- End of picture text --> Source: Authors’ estimates.

# **_3.3.3 Are coordination failures a binding constraint to private sector growth?_**

Effective coordination with domestic and international partners is necessary in order to create a climate for firm “innovation” on a sustained basis which involves coordination when designing and implementing laws and regulations, economic policies, sector, resource use and trade strategies. Characteristic for a climate conducive to “innovation” are services that are taken for granted by entrepreneurs in developed countries. These include access to logistics transport networks and other types of infrastructure, marketing, 25
# **3.4.2 Exposure to increased cost of imported inputs**

Alongside potential protection of US industries, tariffs may have the additional negative impact of increasing the cost of inputs to these or other US industries. We calculate this exposure by taking the output measure from above and, using US input-output tables, weighting it by the share this ‘input’ industry makes up in all of the ‘output’ industry’s inputs.<sup>12</sup> Specifically, the measure is calculated as follows: = <u>1</u> input tariff exposure _kt_ L _k_ 2015 � _j_<sup>_Sjk_</sup> � _cp∈j_<sup>US imports</sup> _pc_ 2016<sup>_×_US tariff</sup><sup>_pct_</sup> (3) where _k_ is the output sector, _j_ is the input sector (6-digit BEA level), and _Sjk_ is the inputs from sector _j_ as a share of total inputs used by sector _k_ sourced from input-output tables. In other words, this variable captures the cost of tariffs on inputs per worker employed in the output industry.

Figure 3 provides a map of the input tariff exposure measures once weighted by employment composition as discussed in Section 3.3. As for the output tariffs, the map shows a concentration in the East with lower exposure in the Great Plains and West, but with a different pattern of variation within these regions.

# **3.4.3 Exposure to retaliatory tariffs**

In response to the imposition of US tariffs, a number of countries implemented retaliatory tariffs on US exports to their countries (see Table 1 for an overview). A sectoral exposure measure can be constructed as for output tariffs, but using foreign tariffs and US exports instead of imports: where US exports _pc_ 2016 is the value of US exports of product _p_ (HS-6) to destination country _c_ in 2016, and Foreign tariff _pct_ is the tariff levied by country _c_ on its imports of product _p_ from the US in month _t_ .

Figure 4 provides a map of the export tariff exposure measures once weighted by employ-

> 12Here US Bureau of Economic Analysis (BEA) input-output tables are used, as in Amiti et al. (2019), along with concordances between NAICS and BEA industry codes.

18
Adjusting to Transitory Shocks: Worker Impact, Firm Channels, and (Lack of) Income Support Ana Margarida Fernandes<sup>_∗_</sup> Joana Silva<sup>_†_</sup> Originally published in the Policy Research Working Paper Series on _May 2023._ This version is updated on _March 2024._ To obtain the originally published version, please email prwp@worldbank.org.

**JEL classifications:** F14, F16, J24, J46, L25.

**Keywords:** employment, wages, firms, exports, foreign shocks, global financial crisis.

> _∗_ Ana Margarida Fernandes is a Lead Economist in the Trade and International Integration Unit of the Development Research Group at the World Bank, Washington DC, USA (email: afernandes@worldbank.org).

> _†_ Joana Silva is a Senior Research Economist at the World Bank and Associate Professor at Catolica Lisbon, Lisbon, Portugal (email: joana.silva@worldbank.org).

We are grateful to Erhan Artuc, Fernanda Brollo, Isabel Horta Correia, Marcio Cruz, Rafael Dix-Carneiro, Romain Duval, Raquel Fernandez, Penny Goldberg, Alaka Holla, Beata Javorcik, Filippo di Mauro, Naercio Menezes-Filho, Julian Messina, Sara Moreira, Nina Pavcnik, Jose Perez, Martin Rama, Pedro Raposo, Hugo Reis, Raymond Robertson, Norbert Schady, Peter Schott, Andres Rodriguez-Clare, Rui Silva, Carlos Vegh, Guillermo Vuletin, and participants in several seminars for helpful comments. We thank Sebastian Melo, Natalia Camelo, Daniel Quintana, Leandro Veloso, Martim Leit ̃ao, Catarina Gaspar, and Glaucia Ferreira for their exceptional research assistance. Finally, we thank the Ministry of Employment of Brazil for access to the Rela ̧c ̃ao Anual de Informa ̧c ̃oes Sociais (RAIS) data, the Ministry of Economics of Brazil for access to SECEX data, the National Institute of Statistics of Ecuador (INEC) for access to the Ecuadorian social security records (IESS), and Esteban Ferro for access to Ecuador’s business registry data. The authors are particularly grateful to Herlon Brandao, Roberto Castillo, Rosangela Farias,Natalia Garzon, Karla Marra, Jociany Monteiro, Paulo Oliveira, and Diana Zambonino for their warm hospitality and unconditional help with these administrative data. Financial support from the Office of the Chief Economist for Latin American and the Caribbean (LCRCE) at the World Bank is gratefully acknowledged. This paper was also partly supported by the Umbrella Facility for Trade trust fund and the Strategic Research Partnership on Economic Development. 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 of Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the countries they represent. All errors are our responsibility.
|**Table 6.5: Years of Effective E**<br>**Population, 19**<br>**1**|**ducation, T**<br>**81 and 1999**<br>**981**|**otal and I**<br> <br>|**ndigent**<br>**1999**|
|---|---|---|---|
|**Total**|**Indigent**|**Total**|**Indigent**|
|Brazil<br>4.46|2.19|6.41|3.78|
|Maranhão<br>2.57|1.72|4.28|2.88|
|Piauí<br>2.43|1.56|4.14|2.78|
|Ceará<br>2.84|1.61|4.75|3.15|
|Rio Grande do Norte<br>3.48|1.95|5.57|3.69|
|Paraíba<br>3.37|2.03|5.46|3.06|
|Pernambuco<br>3.47|2.05|5.36|3.46|
|Alagoas<br>2.61|1.6|4.67|2.83|
|Sergipe<br>3.02|1.64|5.42|3.46|
|**Bahia**<br>**3.15**|**1.86**|**4.69**|**3.09**|

Indig: Below indigence poverty line. _Note:_ Effective education evaluated as years of completed education. _Source:_ Calculations based on PNAD 1981 and 1999.

Students in rural areas lag behind their urban counterparts. Tremendous strides have been made in improving access of the poor to basic education. However, inequalities remain between rural and urban dwellers. The primary school students in rural areas attained 0.8 years less education than their urban peers, but the gap is closing as it came down from 1.3 in 1992. The rural poor tend to lag behind their urban counterparts in their access to services because of their initially lower starting point and greater improvements in service delivery in urban areas.

<!-- Start of picture text --> Figure 6.1: Average Years of Schooling<br>by Decade of Birth, Bahia<br>8<br>6<br>4<br>2<br>0<br>1920s 1930s 1940s 1950s 1960s 1970s<br>Decade of Birth<br>White Black*<br>Source: Calculations based on PNAD 2001 .<br>* “Black” includes mulattos too.<br><!-- End of picture text --> Blacks and mulattos have accumulated less human capital than whites. Figure 6.1 shows clearly that average years of education have increased for both groups but the educational gap is not closing. The gap is largest for the cohort born in the 1950 (2.7 years) and smallest for the cohort born in the 1920s (1 year). Unfortunately, the gap between blacks and whites born in the 1970s is slightly larger (1.17 year) than 32
3 using a regression model with specific functional form and distributional assumptions. The estimated frontier permits the comparison of actual output or spending to the corresponding point on the frontier to measure inefficiency. Both of these methods have pros and cons, and both share some weaknesses. Our approach tries to take the attractive features of each while trying to avoid their shared shortcomings.

We borrow two ideas from DEA and two from SFA. We focus on overall efficiency— this is straightforward in SFA but not typically done in DEA where the focus is on technical inefficiency.<sup>2</sup> We think policymakers will typically want to look at overall inefficiency not just technical inefficiency. On the other hand, our approach is more similar to DEA in that it envelopes the data through the use of nonparametric methods. This gets round the criticism of SFA that its results are dependent on the functional form assumed, and allows for multiple efficient units; in contrast, SFA typically produces at most a few efficient units and quite possibly just one—this will almost certainly to be the case in the classic panel-data formulation where inefficiency is modeled as a time-invariant fixed effect (Schmidt and Sickles 1984). Our approach imposes fewer assumptions than DEA, however: DEA imposes assumptions about the shape of isoquants, but we impose no assumptions about the cost curve. Finally, our approach is closer to SFA in that we make some allowance for statistical noise and measurement error. In its traditional and commonest form DEA does not allow for measurement error or statistical noise.<sup>3</sup> SFA, by contrast, does allow for noise and measurement error, but this comes at the expense of an arbitrary and untestable assumption that enables inefficiency to be distinguished from random shocks and measurement error.

In addition to borrowing the attractive features of DEA and SFA, we also try to avoid their shared weaknesses. Both assess the efficiency of a unit by comparing the unit‟s output or spending to that of a hypothetical unit rather than that of a real-life one. This use of hypothetical comparators makes the exercise untransparent to a policymaker and introduces a large element of „make believe‟. A policymaker in an inefficient country, or the head of an inefficient school board, can quite reasonably dismiss claims of inefficiency when the comparison is with a fictitious unit. And even if the policymaker or head of school district is keen to learn how to improve their performance, there is no better-performing country or better-performing school

> 2 Allocative inefficiency _can_ be estimated in a DEA, but has to be done explicitly, and the analyst needs to have input prices for all the inputs (Coelli 1996).

> 3  Recent advances in DEA research have introduced bootstrapping in DEA to get around this (see Simar and Wilson 2000).
markets in our data. If strong correlations exist between the climate shocks and market prices, the exclusion restriction would be clearly violated.

# **IV. Results**

Tables 1-4 examine the descriptive linkages between production, climate, and nutrition. In Tables 1-3, households are separated into degree day and rainfall deviation quartiles where deviations are with respect to the historical mean of the climate variable. Those households in the first quartile experienced larger negative deviations (e.g. below average rainfall, fewer degree days) while those in the fourth experienced larger positive deviations (e.g. higher rainfall and more degree days). Increased degree days have negative effects on crop yields and agricultural income for a variety of crops (Hatfield et al. (2008), Schlenker and Lobell (2010)).

Table 1 provides descriptive estimates for total production across degree day and rainfall shock quartiles. For both degree day and rainfall shocks, an inverted-U relationship is observed whereby agricultural revenue is highest when there is a small deviation from average weather but smallest when there are large positive or negative shocks. Negative rainfall shocks and positive degree day shocks appear to have the greatest effect on agricultural revenue. Table 1 suggests there is a strong relationship between degree day and rainfall variability and the type of crops harvested by the household. Grains and flours as well as pulses, nuts, and seeds were more likely to be harvested by households that experienced above average temperatures (degree days) and below average rainfall during the planting season. This may be because grains and pulses are generally more drought and heat tolerant than other crops. For all other crop groups, the reverse descriptive trend is observed. The middle panels of Table 1 present the shares of total production in terms of both agricultural revenue and the calories produced of each crop group.

15
# **Acronyms and Abbreviations**

- ATR Average Time to Re-fixing

- CIR Cox, Ingersoll and Ross Model

- CKLS Chan, Karolyi, Longstaff and Sanders Model

- CPI Consumer Price Index DM Decision Maker

- GDP Gross Domestic Product

- GUI Graphical User Interface

- IMF International Monetary Fund

- OECD Organization for Economic Cooperation and Development PDM Public Debt Management

- TDSM Turkish Debt Simulation Model

- TRY Turkish Lira

- USD US Dollar

- VaR Value at Risk

3
the bottom panel presents them in contract terms, that is, the number of fields (plots) under a given contract. Both panels are in percentages of rentedin land. The use of fixed rent has increased from 45% to 58% as a percentage of the total acreage rented in, over the course of twenty years, while sharecropping arrangements have decreased from 45% to 31%. The pattern is similar, albeit less striking, in contract terms. Fixed rent arrangements have increased from 35% to 49% while sharecropping arrangements have decreased from 49% to 38%.<sup>14</sup> We further split up fixed rent by fixed monetary rent (rent paid in money), and fixed crop output (rent paid in produce), a distinction we explore in the analysis.

We focus our analysis on the acreage terms data. Using acreage terms instead of contract terms gives us a more accurate representation as to the amount of land, and by extension, the effect on input decision-making, and regional yields.

Due to the presence of other contract forms, the total percentages in Table 1 (that is, fixed rent plus sharecropping) do not add up to 100%. In Appendix Figure A1 we display the percentage of contracts under various contract types, including: ‘for fixed money’, ‘for fixed produce’, ‘for service contract’, ‘for share of produce together with other terms’, ‘under usufructuary mortgage’, ‘from relatives under no specified terms’, and ‘under other terms’. Fixed and sharecropping represent the majority of the rental contracts. Again, one notes the increase in the various fixed terms (as opposed to sharing terms).

Table 1 presents data from the kharif (rainy) season. The trends observed are similar for the rabi (dry) season (see Appendix Table A1). Although crop types and irrigation levels tend to differ across seasons, contractual forms may be similar across them because multi-season and multi-year contracts are increasingly common. In Appendix Figure A2 we indicate the duration of the contract: 75% are of at least one agricultural year. As the kharif season is the main season, we focus our analysis on this season. We consider this and other aspects of formality in Table 1, that is, whether the field is

> 14As a comparison point, in 1982, 45% of land rented in acreage was under sharecropping and 17% was under a fixed rent arrangement.

13