rafmacalaba/datause-encoder-data huggingface dataset:
strategies in such environments. Third, leveraging diversification of crop production and income sources as resilience strategies is effective only for rural households with a large family workforce (Brock and Coulibaly, 1999; Toulmin and Gueye, 2003; Lawson et al., 2015; Banda et al., 2016). Fourth, in drought-prone village economies, there may therefore be a vested interest in the preservation of traditional institutions that promote a large family, such as polygyny, to harness household size and composition to the effectiveness of resilience strategies against drought. If this is indeed the case, then communities where polygyny is highly prevalent should be more resilient to drought shocks compared to those where monogamy is highly prevalent. In other words, diversification of crop production and income sources in the aftermath of a drought should be more successful in polygynous than in monogamous communities.

To identify the resilience effect of polygyny, we regress households’ crop yields on the interaction of polygyny with current and lag droughts using data on Mali. Several challenges confront our identification of the causal effect of the interaction of drought and polygyny. First, there is the potential presence of spatial correlation in the error term, due to the of both and to be clustered in tendency drought polygyny space. Although droughts can happen randomly over time, they often tend to be clustered across neighboring geographic units (Hsiang et al., 2011). Likewise, polygyny, as a cultural practice, is often clustered in geographic units that share similar cultural values. Second, there is the potential serial correlation of our drought series within each geographic unit. To account for these two sources of correlation, we follow Conley (1999); Hsiang et al. (2011) in adjusting standard errors for both serial and spatial correlation. Third, there is the potential presence of spatial correlation due to omitted weather variables. We account for this problem by constructing a measure of drought during the agricultural season, using the Standardized Precipitation-Evapotranspiration Index (Vicente-Serrano et al., 2010; Miao and Popp, 2014; Harari and Ferrara, 2018). The final issue is the potential presence of unobserved time-constant and time-varying factors that can influence the relationship between our covariates of interest and crop yields. We account for this problem by controlling for enumeration area (EA) fixed effects, and time-variant household-level and community-level observables. Additionally, identification of the resilience effect of polygyny is obtained under the assumption that, upon controlling for EA fixed effects and over a relatively short time ( _i.e._ , 3 years), differences across Mali’s communes in the 3
# 3. -NO-DBEL

In the previous section I discussed<sup>the role played by</sup> demand factors in determining<sup>the effects of a reduction in</sup> the minimum wage on informal<sup>sectorearnings.</sup> In this section, a model of a dual economy<sup>is built in order to</sup> discuss the role of demand factors<sup>in an analytical fashion.</sup> The standard approach looks exclusively at the allocational effects of changes<sup>in the minimum wage, and the</sup> changes in informal sector earnings<sup>associated with them.</sup> Many economists disagree with<sup>the theory of employment</sup> underlying the standard approach.<sup>In fact, the existence of</sup> a negative relationship between<sup>wages and employment, crucial</sup> in the neoclassical approach, is<sup>not an undisputed truth.2</sup> Here, however, I will not<sup>dispute the exis-ence of a</sup> negatively sloped labor demand<sup>function. The possibility of</sup> a reduction in the minimum<sup>wage reducing informal sector</sup> earnings will not be associated<sup>with an un-conventional labor</sup> demand function.

I will concentrate my attention<sup>on a case in which a</sup> reduction in the minimum wage<sup>generates labor mobility</sup> towards the formal sector. By<sup>assuming full-employment, the</sup> model will disregard the possibility<sup>of a reduction in the</sup> 2See Bourguignon (1988] for a survey<sup>of the evidence on</sup> this matter.

20
outcome in other studies. Second, since we have three treatments, we include an F-test that tests the null hypothesis that all three treatments are jointly zero. Finally, in addition to providing singleestimate p-values, we calculate sharpened q-values that control the false discovery rate when testing multiple treatments against multiple outcomes within a domain.

# **5. Impacts**

## **5.1 Short-term impacts on migration knowledge, beliefs, and intentions**

Table 2 uses the randomized placement of questions in the baseline survey, and the action-oriented measure of whether survey respondents request more information about wages in Europe by SMS, to provide the short-term effects of the interventions.

We begin by examining impacts on migration knowledge. Column 1 shows that all three interventions did succeed in significantly improving knowledge about backway migration relative to the control group. The estimated impact is 0.10 to 0.13 more questions right out of 5, which is a relatively modest 4 percent increase in the control mean, or approximately 0.1 standard deviation. The questions asked did not exactly correspond to the content of the informational video, and we see the largest improvements on two questions that did explicitly cover video content: knowing how expensive the backway is, and knowing that some backway migrants are forced to work for others without pay (Appendix Table C1). Column 2 shows that knowledge about migrating to Senegal improved from the Senegal alternative intervention by a similar magnitude of 0.12 questions and, surprisingly, also from the vocational training intervention. This improvement shows up most strongly in terms of individuals knowing they do not need a passport to travel to Senegal, and that they can legally work in most jobs in Senegal without having to apply for a work permit (Appendix Table C2).

In contrast, columns 3 and 4 show that we cannot reject the hypothesis that none of the treatments had significant impacts on beliefs about the risks of backway migration. Columns 5 to 8 then examine impacts on their anticipated likelihood of migrating the backway or to Senegal. The coefficients differ a bit between our across-subject and within-subject designs, and we do not see a clear effect on backway migration or migration to Senegal intentions. As noted, individuals had difficulty expressing their beliefs and intentions using the probabilistic scale, even with visual aids, 27
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**Figure 28: Herfindahl-Hirschman Index for extra-regional air travel (2013)**

<!-- Start of picture text --> Haiti 3,235<br>Belize 2,768<br>Suriname 3,390<br>Guyana 4,474<br>Jamaica 824<br>Dominican Republic 859<br>Bahamas 1,140<br>Barbados 1,748<br>Trinidad and Tobago 2,913<br>Antigua and Barbuda 1,703<br>St. Lucia 1,786<br>Dominica 2,236<br>Grenada 3,197<br>St. Kitts and Nevis 3,416<br> - 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 5,000<br>Herfindahl Index<br>Country<br><!-- End of picture text --> _Source: Authors’ calculation based on data by Diio (2014)._ 59
Figure 3 Flood model comparison using low- and high-resolution digital terrain data (STRM and Photogrammetry) Uncertainty has two main sources. (1) The quality of the DTM, which in this case is considered good; and (2) the rainfall estimate, which itself is dependent on the overall rainfall for a given return period and the losses to apply relating to canopy capture and drainage system storage, and infiltration or other losses. The flood extents and flow paths are therefore considered reasonably representative, however, with no reliable calibration data, the exact return period attributed to the flooding is likely to be less certain. Production of the flood maps for three return <u>periods took approximately 8 days’ work, including pre and post model data processing and model development.</u>

# 2.1.1. Community mapping

A third type of flood map is not based on a model, but instead uses local knowledge to identify historical areas of flooding. Such an empirical flood map is available for Dar es Salaam. Here, the community mapping initiative Ramani Huria<sup>3</sup> provides crowd-sourced information on the areas that had been flooded in recent flood events. By relying on actual observations, community mapping is better able to reflect nonmodelled factors such as the quality of local drainage systems, thus increasing accuracy. However, the Ramani Huria map only provides flood extents and does not offer information on return periods or flood depths. Thus, it does not distinguish different levels of flood risk.

> 3 <u>http://ramanihuria.org/</u>

6
# **Why Do Some Oil-Rich Countries Perform Better Than Others?  ***

**_Farrukh Iqbal and Youssouf Kiendrebeogo_**

## **_The World Bank_**

_Keywords: Child mortality, School enrollment, Public spending_ _JEL codes: O47, O15, I14, I25_

* We are grateful to Shanta Devarajan, David Evans and seminar participants at the London School of Economics for helpful comments.
Furthermore, this association is found to be much more appealing in lessdeveloped western province of Sichuan and central region of Henan province than that in more developed coastal region of Zhejiang. In Henan, there were 28.8 percent of inmigrants who completed high school or professional technical school whereas the corresponding figure for town local population was 19.6 percent. In Sichuan, the contrast is more revealing, with corresponding figure of 37.1 percent for in-migrants and 19.9 percent for local population.

Table 10 Educational attainment of town in-migrants (6 years old or above) by province: 2000

|By province|1|2|3|4|5|6|7|
|---|---|---|---|---|---|---|---|
|||(<br>(Zhe|%)<br>jiang)|||||
|intra-province migration|4.89|26.42|36.64|17.55|7.33|5.64|1.53|
|inter-province migration|3.09|32.45<br>(H|54.41<br>enan)|5.84|1.62|1.48|1.11|
|intra-province migration|3.55|17.33|37.09|15.16|14.06|9.22|3.59|
|inter-province migration|3.94|20.49<br>(Sic|40.61<br>huan)|15.47|9.05|5.90|4.54|
|intra-province migration|3.82|20.02|29.51|24.88|12.79|7.05|1.92|
|inter-province migration|3.58|20.94|33.35|16.37|12.75|8.78|4.23|

Sources: calculated from Tabulation on 2000 Population Census of the P. R. China, Table L7-6-1b and Table L7-6-2b.

note: 1, literate; 2, primary school; 3, secondary school; 4, high school; 5, vocational school; 6, college; 7, university or above.

We also further breakdown the level of educational attainment of town inmigrants by original place as we are interested in a question of whether inter-provincial in-migrants possess higher level of educational attainment than those in-migrants who are from the same province. Data (Table 10) show that in Zhejiang, among inter-provincial in-migrants, only 10 percent of them had high school education attainment or higher, whereas among intra-provincial in-migrants, as high as 32 percent of them had high school education attainment or higher. Thus the fact that a higher percentage of educated migrants coming from same province than other provinces seems to give some supports to what Zhao (1999) has found in her study that better educated people in rural areas probably were not necessary to leave countryside to migrate to other provinces and were likely to shift their jobs from agriculture to the non-agricultural sector in towns nearby instead.

However, when we further look at the data on educational attainment of inmigrants by original place in Henan and Sichuan, we do not find this kind of sharp contrast between those who came from same province and other provinces. For example, in Sichuan, about 44 percent of intra-province in-migrants had high school education or 11
# **Table 9.A. Model comparison: Bias of tin price forecasts**

(U.S. dollars per metric tonne)

|||||**Bivariate**|||**Machine**|
|---|---|---|---|---|---|---|---|
|**Approach**|**Horizon**|**Consensus**|**Futures**|**correlations**|**BVAR**|**OEM**|**learning**|
|Consensus|3|-910.2|...|...|...|...|...|
|Futures|3||-478.9|...|...|...|...|
|Bivariate correlations|3|||197.4|...|...|...|
|BVAR|3||||-1091.5|...|...|
|OEM|3|||||21.4|...|
|Machine learning|3||||||-37.7|
|Consensus|6|-1168.2|...|...|...|...|...|
|Futures|6||-675.4|...|...|...|...|
|Bivariate correlations|6|||691|...|...|...|
|BVAR|6||||-1636.4|...|...|
|OEM|6||||*|-244.5|...|
|Machine learning|6||||*||150.8|
|Consensus|9|-1357.1|...|...|...|...|...|
|Futures|9||-814.1|...|...|...|...|
|Bivariate correlations|9|||1541.5|...|...|...|
|BVAR|9||||-2613.5|...|...|
|OEM|9||||*|-497.3|...|
|Machine learning|9||||*||290|
|Consensus|12|-1402.1|...|...|...|...|...|
|Futures|12||-939.4|...|...|...|...|
|Bivariate correlations|12||*|2621.6|...|...|...|
|BVAR|12||*||-3148.8|...|...|
|OEM|12|||*|*|-661.4|...|
|Machine learning|12|||*|*||435.5|
|Consensus|15|-1422.2|...|...|...|...|...|
|Futures|15|...|...|...|...|...|...|
|Bivariate correlations|15|...|...|...|...|...|...|
|BVAR|15||...|...|-4091.6||...|
|OEM|15||...|...|*|-831.2|...|
|Machine learning|15||...|...|*||682.9|
|Consensus|18|-1430.2|...|...|...|...|...|
|Futures|18|...|...|...|...|...|...|
|Bivariate correlations|18|...|...|...|...|...|...|
|<br>BVAR|18||...|...|-4286.7|...|...|
|OEM|18||...|...|*|-838.7|...|
|Machine learning|18||...|...|||890.3|
|Consensus|21|-1380.7|...|...|...|...|...|
|Futures|21|...|...|...|...|...|...|
|Bivariate correlations|21|...|...|...|...|...|...|
|BVAR|21||...|...|-4027.8|...|...|
|OEM|21|*|...|...|*|-17103|...|
|Machine learning|21||...|...||*|1102.4|
|Consensus|24|-1379.6|...|...|...|...|...|
|Futures|24|...|...|...|...|...|...|
|Bivariate correlations|24|...|...|...|...|...|...|
|BVAR|24||...|...|-4202|...|...|
|OEM|24||...|...||-901.2|...|
|Machine learning|24||...|...|||1346.6|

_Source_ : World Bank.

_Note_ : Bias is defined as the difference between the actual price and the predicted price. The forecast period is 2015Q12022Q1. Forecasts for OEM are only available at the semi-annual frequency; forecasts for all other approaches are available at the quarterly frequency. Diagonal entries indicate estimates of average bias for the respective approach. * indicates significant difference between the forecasts of any pair of approaches, at the 5 percent significance level according to a t-test (bias) or F-test (RMSE).

48
Table 37

|Dependent variable: F|iscal adjust|ment when|needed ba|sed on deb|t/revenue||||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid expenditure|-0.026|-0.042|-0.053|-0.066|-0.059|-0.030|-0.200**|-0.125*|
|(% of GDP)|(0.030)|(0.033)|(0.038)|(0.041)|(0.059)|(0.091)|(0.095)|(0.067)|
|**Economic factors**|||||||||
|GDP growth (t-1)||-3.374||-1.430|-1.380|-1.383|-1.189|-1.478|
|||(2.492)||(3.026)|(3.041)|(3.027)|(3.032)|(3.033)|
|Inflation (t-1)||-0.095||0.277|0.267|0.278|0.144|0.220|
|||(0.849)||(1.592)|(1.596)|(1.594)|(1.613)|(1.601)|
|U.S. Interest rate||-0.130**||-0.159**|-0.162**|-0.166**|-0.197**|-0.158**|
|||(0.060)||(0.074)|(0.075)|(0.075)|(0.078)|(0.074)|
|**Political factors**|||||||||
|Gini|||0.024|0.024|0.024|0.042|0.027|0.027|
||||(0.027)|(0.027)|(0.027)|(0.049)|(0.027)|(0.027)|
|Rule of law|||-0.273|-0.092|-0.095|-0.101|-0.911|-0.087|
||||(0.246)|(0.270)|(0.271)|(0.272)|(0.591)|(0.271)|
|Margin of Majority|||1.786*|1.995*|1.999*|1.977*|2.157**|-0.049|
||||(1.036)|(1.073)|(1.074)|(1.076)|(1.090)|(2.107)|
|Election's year|||-0.234|-0.270|-0.270|-0.271|-0.264|-0.268|
||||(0.209)|(0.211)|(0.211)|(0.211)|(0.211)|(0.212)|
|**Interactions**|||||||||
|EMEs * Rigidity|||||-0.015<br>(0.081)||||
|Gini * Rigidity||||||-0.001<br>(0.002)|||
|Rule of law * Rigidity|||||||0.029<br>(0.019)||
|Majority * Rigidity||||||||0.099<br>(0.089)|
|Observations|798|790|583|579|579|579|579|579|
|Number of ifscode|102|102|75|75|75|75|75|75|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|r2_p<br>Standard errors in par<br>*** p<0.01, ** p<0.05|0.00118<br>entheses<br>, * p<0.1|0.0127|0.0178|0.0297|0.0297|0.0301|0.0348|0.0322|

- 66 -
Moreover, it might also lower producer price inflation by increasing firms’ abilities to substitute domestic for foreign inputs.

Table 5 studies this link using gross trade data in column (1) and value added trade data in column (2). Results show that, controlling for each country’s average level of inflation as well as global changes in inflation for each time window, an increase in trade in both final goods and intermediate goods re-exported further (forward linkages) is indeed associated with a downward pressure on the level of the GDP deflator inflation. This is in line with global inflation trend observed in figure 1. Column (2) confirms these results when using trade in value-added terms. Turning to columns (3) and (4), we use more “standard” GVC participation indicators to analyze the link between GVC and inflation level. The negative coefficients of these indices suggest that greater GVC integration contributed to the decrease in inflation levels. Again, those results also hold when using CPI inflation as the dependent variable, as shown in table 8 in appendix.

# **Graphical representation of some results**

In an effort to make our results more accessible for a wide audience, we present our main insights in figures 4 and 5.

First, focusing on backward GVC participation, figure 4 shows the strong association between an increase in bilateral inflation correlation using the GDP delfator and bilateral backward participation. In this graph, each dot is a pair of World Bank Region, with the vertical axis representing the change in inflation correlation between domestic CPI inflation and World inflation between 2001 and 2011, while the horizontal axis represents the change in backward GVC participation for the same period. This representation, in changes, allows to control for country fixed effects in an intuitive way since the only variation that is used is the _within region pair_ change in both inflation correlation and GVC participation.

Second, figure 5 shows that stronger backward GVC participation (in the horizontal axis) is associated with an increase in CPI inflation correlation (in vertical axis) between each country and the World. Inflation correlation, here, means the correlation between domestic inflation and world inflation, and the positive slope implies that an increase in 18
crop, fishery, forest, and livestock production values) that are priors in the cross-entropy model, and the outcome of the cross-entropy model.

# **3.2.1 Regional accounts**

We collect regional accounts by sector from various sources into a global database. The data are not balanced over time nor at the geographic level. The variation in the reference year of the regional accounts data influences the temporal balance of the database. This mismatch can influence the regional distribution of the agricultural GDP that may be different than the target reference year of 2010. Given climate<sup>17</sup> and specifically rainfall is important input to crop and livestock production and may contribute to variation across years (Stanimirova et al., 2019; Y. Zhang et al., 2020), we attempt to reduce this source of error by averaging over multiple years when data are available similar to You, Wood, et al. (2014). However, this does not eliminate this mismatch. The availability of data varies when grouped by World Bank income (low or lower middle, upper middle and high income). The average absolute temporal difference (ATD) defined as the mean difference in years between the reference regional accounts and the target year (2010) is higher in the low and lower middle income group. Likewise, the mean deviation of the share of AgGDP by country over the year(s) is larger in low or lower middle compared to high income.

The global regional account database includes national and subnational units at various administrative levels.<sup>18</sup> Following Robinson et al. (2014) in their assessment of Gridded Livestock Of the World (GLW) 2.0, we summarize the average spatial resolution (ASR) of the input regional data, which is the square root of the land area divided by the number of administrative units. We find that on average the ASR decreases from high to low income groups.

# **3.2.2 Components**

Another source of uncertainty is indirect temporal inaccuracy propagated from the input datasets of the components, which are modeled. We discuss all five components of agricultural GDP: crop, livestock, forest, fish and hunting. The SPAM model (You, Wood, et al., 2014) is a result of several gridded modeled datasets including rural population density from Global Rural-Urban Mapping Project (GRUMP) Alpha version (Balk et al., 2006). Likewise, the Gridded Livestock of the World v2.0 includes rural population density in 2006 (GRUMP) along with other predictors such as precipitation (Hijmans et al., 2005) and a

> 17For a discussion on climate yield factors see Block et al. (2008).

> 18This also includes cases where administrative units at the same level are merged to match the geography of the regional accounts data.

24