rafmacalaba/datause-encoder-data huggingface dataset:
# **Tables: Mechanisms**

Table 7: Exposure to Coffee Mills and Household Wealth

||Wealth|Score|Poor (|0/1)|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|CatchmentArea X Post|0.0547**|0.0252|-0.0435***|-0.0293*|
||(0.0256)|(0.0269)|(0.0163)|(0.0166)|
|CatchmentArea|-0.0211|-0.0073|0.0184|0.0116|
||(0.0280)|(0.0286)|(0.0186)|(0.0189)|
|Controls|No|Yes|No|Yes|
|Municipality FE|Yes|Yes|Yes|Yes|
|Province X Year FE|Yes|Yes|Yes|Yes|
|R-squared|0.5625|0.5640|0.2083|0.2095|
|Observations|23513|23513|23513|23513|

Notes: CatchmentArea X Post is an indicator variable equal to 1 for a child living in a community within the vicinity of an operational coffee mill and 0 if otherwise. CatchmentArea is an indicator variable equal to 1 for a child living in a community within the vicinity of a coffee mill. Controls include rural-urban status, education level, and time trends interacted with coffee suitability and road density." _∗_ Significant at 10 percent level _∗∗_ Significant at 5 percent level _∗∗∗_ Significant at 1 percent level 37
neighbors increases migration (in Column 1) by almost twice ( _e_<sup>1</sup><sup>_._097</sup> _−_ 1). The impact of being in the same state is higher for men than women. Being in different states but neighbors also has a large positive effect. In column 1, we see that total migration is around 4.5 times ( _e_<sup>1</sup><sup>_._730</sup> _−_ 1) larger in this case and this effect is stronger for women.

The most important observation is that the coefficient for same-state-neighbor dummy variable is larger than the different-state-neighbor coefficient in every column. This difference is statistically significant. For example, in the first column, being neighbors and in the same state increases total migration by almost eight times ( _e_<sup>2</sup><sup>_._177</sup> _−_ 1), indicating that the state borders have a large negative effect on internal migration in India. To put it differently, migration between neighboring districts in the _same_ state is around at least 50 percent larger than migration between neighboring districts in _different_ states ( _e_<sup>2</sup><sup>_._177</sup><sup>_−_1</sup><sup>_._730</sup> _−_ 1). The state border effect is almost identical for men and women when we compare the differences between the relevant coefficients in columns 4 and 7.

As noted earlier, we treat the recently created states of Chattisgarh, Uttaranchal and Jharkhand as “different states” in most of our analysis. To confirm that our analysis is robust to this event, in the third column of each set, we create a separate state border category called “split states” to indicate two districts that were in the same state before 2000, but now belong to different states post 2000 due to the state split. For example, Godda and Banka used to be in the Bihar before 2000. After Bihar was split, Godda went to Jharkhand while Banka remained in Bihar. Thus Godda and Banka are coded as districts from “split states”. We see that the coefficient for the “split states and neighbors” dummy is never statistically different from the coefficient for the “same state and neighbors” dummy (columns 3, 6 and 9). This is consistent with the fact that the migration observed in the 2001 census largely predates the creation of the new states. If the state borders represented natural mobility barriers, the coefficient of the “split states and neighbors” dummy would have been closer to the “different states and neighbors” dummy rather than the “same state and neighbor” dummy. More convincing evidence will come from the 2011 census, when we will 17
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Table 1a: Growth in India's manufacturing sector

||1989|1994|2000|2005|2010|Share<br>1989|Share<br>2010|Growth<br>1989→2010|Growth<br>2000→2010|
|---|---|---|---|---|---|---|---|---|---|
|A. Employment levels||||||||||
|Total|31,976,544|34,823,686<br>9%|40,934,814<br>18%|45,135,758<br>10%|42,450,952<br>-6%|||33%|4%|
|Informal tradables|6,184,085|8,300,794<br>34%|12,833,159<br>55%|13,710,880<br>7%|16,423,692<br>20%|19%|39%|166%|28%|
|Informal non-tradables|19,699,080|19,758,028<br>0%|21,642,168<br>10%|22,685,924<br>5%|16,222,000<br>-28%|62%|38%|-18%|-25%|
|Formal tradables|1,844,234|2,201,461<br>19%|2,842,195<br>29%|3,413,113<br>20%|4,256,341<br>25%|6%|10%|131%|50%|
|Formal non-tradables|4,249,145|4,563,403<br>7%|3,617,292<br>-21%|5,325,841<br>47%|5,548,919<br>4%|13%|13%|31%|53%|
|B. Establishment counts||||||||||
|Total|13,630,773|12,445,471<br>-9%|17,044,168<br>37%|17,172,154<br>1%|17,890,526<br>4%|||31%|5%|
|Informal tradables|3,335,930|3,573,835|6,642,636|6,879,311|9,780,790|24%|55%|193%|47%|
|||7%|86%|4%|42%|||||
|Informal non-tradables|10,208,306|8,772,108<br>-14%|10,291,135<br>17%|10,171,737<br>-1%|7,973,908<br>-22%|75%|45%|-22%|-23%|
|Formal tradables|32,958|38,512|45,624|44,773|53,891|0.2%|0.3%|64%|18%|
|||17%|18%|-2%|20%|||||
|Formal non-tradables|53,579|61,016|64,773|76,333|81,937|0.4%|0.5%|53%|26%|
|||14%|6%|18%|7%|||||

Notes: Indian descriptive statistics taken from Annual Survey of Industries and National Sample Statistics. Tradables definition comes from Kothari (2014) based upon abovemedian values of exports plus imports to gross production at the three-digit industry level.
# **Human Capital, Tangible Wealth, and the Intangible Capital Residual**

_Kirk Hamilton and Gang Liu_<sup>_1_</sup> Keywords: Comprehensive Wealth, Intangible Capital, Human Capital JEL classifications: E21, E24 Acknowledgements: The authors are grateful to Marco Mira d’Ercole for his detailed comments and suggestions on the paper, and to Michael Toman, Martine Durand, Paul Schreyer, Anita Woelfl, Conal Smith and Romina Boarini for their comments on earlier drafts of the paper. Any remaining errors and omissions are the responsibility of the authors.

1 . Kirk Hamilton is with the Development Research Group, World Bank. Gang Liu led the human capital project at the OECD Statistics Directorate and is now working at Statistics Norway.
Temporary spatial closures are a type of “dynamic ocean management” in that they vary over space and time and are updated by the regulator in response to real-time data (Lewison et al., 2015; Maxwell et al., 2015). Dynamic ocean management is different from more traditional management approaches, like marine protected areas, which are time-invariant, and seasonal closures of an entire fishery, which are space-invariant. Dynamic ocean management is designed to reduce fishing in the areas and times where fishing is likely to cause ecologically undesirable outcomes. But it does not need to cover as much space-time as more traditional management approaches because it only targets relevant areas and times. For these reasons, simulations of dynamic ocean management find it can achieve the same ecological objectives as more traditional management approaches at a lower economic cost (Dunn et al., 2016; Hazen et al., 2018; Pons et al., 2022).

The same win-win idea motivates temporary spatial closures. By allowing fishing to continue in most places, temporary spatial closures could reduce juvenile catch while minimizing the cost of the policy to fishers. If temporary spatial closures cause fishers to search for new fishing grounds, they could reduce juvenile catch because there is substantial variation in relative juvenile abundance across space. This variation occurs because schools of fish tend to be age-segregated (i.e., each school of anchoveta contains mostly juveniles or mostly adults). The regulator believes the closures policy reduces total juvenile catch. They calculated that the 174 closures during the first and second season of 2017 and the first season of 2018 protected 1,049,411 tons of juvenile anchoveta (PRODUCE, 2017 _a_ , 2018 _c_ , _b_ ).<sup>13</sup>

# **II Data**

The recent emergence of vessel-level GPS data has enabled researchers to predict when and where vessels are fishing at a global scale (Kroodsma et al., 2018). These new data, made publicly available by the organization Global Fishing Watch, have expanded the set of

> 13The regulator does not describe how they calculate this number, nor do they define the meaning of “protected” in this context.

12
the world’s population. While the poverty dimension is available for 168 economies, other dimensions have lower levels of coverage, representing between 46 and 84 percent of the world’s population (see Table 5) when the +-5 year filter is included. The significant drop in the number of countries when adding dimensions beyond poverty is sometimes due to limited data access. Consequently, assessing these dimensions at the household level for certain countries is not feasible. For instance, China provides highly aggregated welfare data, consisting of only 20-bin urban/rural distributions rather than microdata files, while for other high-income countries, 400-bin data are extracted from the Luxembourg Income Study (LIS).<sup>24</sup> When we drop the +- 5 year filter and use information on full coverage from World Development Indicators the number of countries increases only slightly but the share of the population covered increases for many indicators as data for India and China can be included. More than three quarters of the World’s population is included across indicators with the exception of water where data is missing for India. In the tables below we present results including India, acknowledging that this is an underestimate of the number of vulnerable in India as we do not have information on which households are lacking water.

**Table 4: Number of countries with survey data and dimensions of vulnerability in 2019**

||All|Income|Education|Social<br>protection|Financial<br>inclusion|Water|Electricity|
|---|---|---|---|---|---|---|---|
|_Region_||||||||
|East Asia and the Pacific|26|21|15|13|9|14|13|
|Europe and Central Asia|31|30|25|18|26|24|24|
|Latin America and the Caribbean|31|25|17|18|17|16|15|
|Middle East and North Africa|14|12|6|5|9|6|6|
|Other High-Income economies|60|28|19|0|18|19|19|
|South Asia|8|7|6|6|5|5|6|
|Sub-Saharan Africa|48|45|37|32|34|36|37|
|_World_|218|168|123|91|118|120|119|
|_World (incl. data older than 5 years_<br>_and adding in datafrom WDI)_|218|168|129|92|118|126|125|

Source: Authors’ compilation from the GMD, ASPIRE, and Findex databases. Notes: Income refers to having less than 1.5*$2.15 per household member, education refers to no adult having primary schooling, social protection refers to not receiving any social protection, and financial inclusion refers to not having a bank or mobile money account. Water refers to access to limited standard water, and access to electricity is as written. The coverage year for education, water and electricity is based on the filters of +- 5 years from 2019, and for social protection is based on the filter of +- 5years from 2017.

> 24 For China, we used the Household survey CHIP 2013 for dimensions of education and social protection.

22
We postulate that the government has a budget, _S_ , available for distribution and wishes to transfer this budget in such a way as to reduce poverty. We specify a baseline case in which the government is assumed to have no knowledge of who the poor are or where they are located. It is therefore unable to distribute its budget in any manner other than a lump-sum transfer to the entire population of size _N_ . We thus calculate the impact of transferring _S/N_ to the entire population.

Kanbur (1987) shows that to minimize poverty summarized by the Foster-GreerThorbecke (FGT) class of poverty measures with parameter value _α>1,_ the group with the higher FGT( _α-1_ ) should be targeted on the margin.<sup>7</sup> Hence, to minimize the squared poverty gap (equal to a poverty measure from the FGT class with _α_ =2), target populations should be ranked by the poverty gap (FGT with _α_ =1) and lump-sum transfers made until the poverty gap of the poorest locality becomes equal to that in the next poorest one, and so on, until the budget is exhausted.

# **_7.2. Budget and Poverty Lines_**

We assume that the budget available for distribution has been exogenously set. As is intuitively clear, the potential benefits from targeting will vary with the overall size of budget. In the limit, as the budget goes to infinity, there is no need for targeting, as even a uniform transfer will eliminate poverty. As a benchmark, we identify the per capita consumption value of the 25<sup>th</sup> percentile of the consumption distribution.<sup>8</sup> We scale this consumption value by the total population. Our benchmark budget is set to equal 5% of this total value.

7 Following Foster, Greer and Thorbecke (1984) the FGT class of poverty measures take the following form: where xi is per capita expenditure for those individuals with weight wi who are below the poverty line and zero for those above, z is the poverty line and ∑<sup>_w_</sup> _i_<sup>is total population size.αtakes a value of 0 for the</sup> Headcount Index, 1 for the Poverty Gap and 2 for the Squared Poverty Gap. For further discussion, see Ravallion (1994).

8 The consumption distribution is constructed on the basis of the average, across _r_ replications, of householdlevel predicted per-capita consumption in the population census.

25
Table 3 — Treatment Effects on Learning Outcomes

|Panel A: 2012<br>|||||||
|---|---|---|---|---|---|---|
|Outcome:||Normalized|TestScores||Increased|rompre- to|
||Pre-pro|<br>gramtest|<br>Post-pro|gram test|<br>post-pro|<br>gram test*|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Treatment|0.007|0.009|O.325***|O321**|QaaI7e|=O.250F**|
||(0.090)|(0.096)|(0.094)|(0.097)|(0.059)|<br>(0.061)|
|Girl||-O.117***||-0.084**||0.034**|
|||(0.035)||(0.035)||(0.014)|
|Treatment < Girl||-0.008||-0.012||-0.017|
|||(0.056)||(0.056)||(0.020)|
|Controls|NO|YES|NO|YES|NO|YES|
|Observations|17,460|17,460|17,460|17,460|16,347|16,347|
|R-squared|0.000|0.173|0.024|0.233|0.055|0.067|
|Meanofdepvarincontrol|0.000|0.000|0.000|0.000|0.512|0.512|
|P-value:<br>T+ T x Girl=0||0.991||0.002||0.000|
|Panel<br>|||||||
|Bi2013.||Normalized|TestScores||Increased|rompre-to|
|<br>Outcome:|Pre-pro<br>(1)|<br>gramtest<br>(4)|<br>Post-pro<br>(3)|gramtest<br>(4)|<br>post-pro<br>(5)|<br>gramtest<br>(6)|
|Treatment|0.021|0.028|0.156|0.153|0.077|0.078|
||(0.091)|(0.097)|(0.106)|(0.110)|(0.051)|(0.051)|
|Girl||-0.033||-0.033||0.009|
|||(0.039)||(0.034)||(0.018)|
|Treatment < Girl||-0.044||-0.022||-0.005|
|||(0.059)||(0.052)||(0.028)|
|Controls|NO|YES|NO|YES|NO|YES|
|Observations|15,984|15,984|15,984|15,984|14.314|14,314|
|R-squared|0.000|0.204|0.006|0.242|0.006|0.031|
|Meanofdep<sup>var in control</sup>|0.000|0.000|0.000|0.000|0.536|0.536|
|<br>P-value:<br>T+TxGirl=0||0.860||0.226||0.190|

Notes: Robust standard errors in parentheses, clustered by village. *** p=<0.01, ** p=0.05, * p<0.1. All analyses are restricted to test-takers, defined as students who were present for pre- and post-program tests in a given year (2012 or 2013). Observations are at the student-subject-level. Controls include 2011 (pre-program) enrollment by gender, cohort fixed effects, and subject (Math, English, and Hind:) fixed effects where indicated, m Columns 2, 4, and 6. Columns 5-6 restricted to students who scored less than the maximum possible score (A) on the pre-program test.
is no significant difference in the performance of Models 8 and 9 when comparing deflation using quarterly or monthly CPI to deflation using the aggregated IMF CPI.

# _Alternative base survey_

The main results in Section 4.1 relied on base survey data that preceded the target survey round by less than two years. Had we relied on older base survey data, would that have compromised the performance of the promising imputation models? Figure 2 highlights a limited test of varying the timespan between the base and target survey rounds, by nearly doubling it from 1–2 years (in the case of using the TZNPS 2020/21 as the base survey) to 2–3 years (using the TZNPS 2019/20 as the base surveys). The full poverty estimates are shown in Appendix A, Tables A.8 to A.10. The results indicate that both base surveys perform similarly in terms of producing estimates close to the true poverty rates (except Model 4) with earlier round performs even better for Models 8-9 in TA 2 and TA 3.<sup>15</sup> That is despite the fact that the sample size of the more distant round, NPS 2019/20, is only a quarter of the sample in NPS 2020/21.

# _Varying poverty lines_

Finally, poverty as measured under the latest consumption data collected under TA 1 can vary by as much as more than two percentage points depending on whether we use the poverty line in the 2019/2020 TZNPS or in the 2020/2021 TZNPS. To examine sensitivity of our results to the poverty line, we re-estimate the main results shown in Tables 2 to 6 but using the poverty line in the 2019/2020 TZNPS instead of the 2020/2021 TZNPS.<sup>16</sup> The results, shown in Appendix A,

> 15 Figure 2 shows estimates that are obtained using the normal linear regression models. The estimates obtained using the empirical distribution of the error terms are similar (Appendix A, Figure A.1).

> 16 The poverty lines for 2020/21 and 2019/20 are based on data from the NPS and may vary due to changes in the household consumption questionnaire. While the food components of both years' poverty lines remain consistent, the

22
to non-resource GDP over 2000 (or most recent historical average). Non-resource revenue is calculated as the difference between total revenue and resource revenue,

**Table 2: Baseline set-up of the LTGM-NR** :

Selected parameters, initial conditions & trajectories of exogenous variables <u>(Symbol + indicates the parameter is taken from the standard LTGM)</u>

||**Model**|**SOURCE**|**TIME SERIES**|
|---|---|---|---|
|A. Parameters<br><br>||||
|Depreciation rate (δ)+<br><br>|Both|PWT 10|2000–2019 average|
|Labor share, non-res. sector (β0)<br><br>|Both|PWT 10|2000–2019 average|
|Resource rents share (γi)<br><br>|Both|GTAP|2004–2014 average|
|Resource tax rate (τR)<br><br>|Both|IMF-WCE|2000–2016 average|
|Non-resource tax rate (τ0) <br><br>|EBM*|WEO/WCE|2000–2016 average|
|World real interest rate (fW)<br><br>|EBM|FRED|US long-run real interest rate≈2%|
|Debt-elasticity of spread (ψ)<br><br>|EBM|SGU (2003)|Ranges between 0.001 and 0.1|
|Private investment crowd-in (λ)<br>|EBM|Chinn et al.|Average of industrial and EMDEs|
|Marginal propensity to invest(θ)|Default|<br>IMF-FAD|BBR: 2000–2017 average of<br>investment to expenditure ratio;<br>SSR:θ = 0;HR:θ = 1|
|B. Initial conditions||||
|GDP per capita+|Both|WB-WDI|2020 (or most recent)|
|Exports of oil (% of GDP)**|Both|UN-CT|2002–2019 average|
|Capital to GDP ratio:+|Both|PWT 10|2019 or most recent|
|Non-R sector|Both|Endogenous|Equalize initial MRPK|
|R sector (oil)|Both|Endogenous|Equalize initial MRPK|
|Reserves of oil|Both|BP/USGS|Most recent|
|External public debt (% GDP)|EBM|WB-WDI|2020 (or most recent)|
|External private debt (% GDP)|EBM|WB-WDI|2020 (or most recent)|
|C. Trajectoryof exogenous variabl|es,2021–|2050||
|Price of oil (2010 US$/barrel)|Both|WB-CPD|Most recent or 2000–2019 average|
|Discoveries of oil (barrels/year)|Both|BP/USGS|2000–2019 average|
|TFP growth, non-R sector|Both|PWT 10|2000–2019 average|
|TFP growth, oil sector|Both|--|Country-specific|
|Human capital+|Both|PWT 10|2000–2019 average|
|Population+(total, working-age)|Both|ILO|Forecast for 2021–2050|
|Private savings (% GDI)|EBM|WB-CPD|2000–2019 average|
|FDI+(% GDI)|EBM|WB-WDI|2000–2019 average|
|Private investment (% GDI)|Default|<br>IMF-FAD|2000–2019 average|
|Exogenous public investment∔|Both|IMF-FAD|2000–2019 average|
|Target primary balance (% GDI)|EBM|IMF-WEO|Country-specific|
|Exogenousprivate external debt<br>|EBM<br>|--<br>|Country-specific<br>|
|* EBM denotes External-Balance Model<br>** For simplicity, we assume that the reso<br>three industries from the set of commoditi<br>∔Default model: exogenous public inves<br>of total expenditure).|urce sector<br>es: coal, co<br>tment (% of|has one industry: oil (<br>pper, diamond, gas, g<br>GDP); External-Bal|N= 1). In practice, the user can choose up to<br>old, iron, lead, nickel, oil, silver, tin, and zinc.<br>ance Model: exogenous public investment (%|

20
and experiences with migrants abroad. For example, Aker, Clemens, Ksoll (2011) find that randomizing training to use simple phones among adult students increased their likelihood of seasonal migration. The perception of success is another interesting determinant for migration interest. Indeed, individuals who believe that the most successful person in the village is an international migrant are more likely to be interested in migration.

We find that past migration experience is a strong determinant for interest in migration. Respondents who have worked abroad in the past five years are more likely to want to migrate. This can either suggest: (a) path dependency, where former migrants are more likely to do another spell of migration, or (b) potential dissatisfaction from the local labor market upon return.

Finally, the results indicate many statistically significant social network variables, suggesting that knowing migrants who are either family or friends increase interest in migration, echoing findings from other countries (eg. Richter and Taylor, 2008). Moreover, our results confirm the relevance of including labor smugglers in the scope of social network related to migration, following Krissman (2005). Indeed, respondents who have been contacted by a broker are significantly more likely to be interested in migration than their peers.

# 5. Robustness checks and discussion

## 5.1.  Model performance

Table 5 presents performance indicators of the logistic models implemented using the top predictors identified by Random Forest and LASSO Stability Selection. These models are estimated on the testing sample (i.e. 20 percent of observations not included in the training of the models and the determination of the top predictors). The table compares the accuracy and sensitivity of the logistic models which respectively indicate the share of correctly predicted cases and the share of correctly predicted cases taking the value of 1.

**Table 5. Performance indicators of the logistic regression models**

|**Model**|**Accuracy**|**Sensitivity**|
|---|---|---|
|Female logistic regression model with top 30 Random|81.64%|16.67%|
|Forest predictors|||
|Male logistic regression model with top 30 Random Forest<br>predictors|72.00%|26.33%|
|Female logistic regression model with top 30 LASSO|82.52%|20.97%|
|Stability Selection predictors|||
|Male logistic regression model with top 30 LASSO Stability|70.57%|15.46%|
|Selection predictors|||

Source: Authors’ calculations from DESMIGRATIF (2018).

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