rafmacalaba/datause-encoder-data huggingface dataset:
||Total|1806<br>82.8%|354<br>16.2%|22<br>1.0%|2182<br>100%|100%|
|---|---|---|---|---|---|---|
|Extracted minerals’ valu|e<br>||||||
||Status|No mine|High value|Low value|Total||
|Mineral deposits|No|1806|0|0|1806|82.8%|
||Yes|0|83|293|376|17.2%|
||Total|1806<br>82.8%|83<br>3.8%|293<br>13.4%|2182<br>100%|100%|
|Types of mining activiti|es<br>||||||
||Status|No mine|Extraction|Transformation|Total||
|Mineral deposits|No|1806|0|0|1806|82.8%|
||Yes|0|271|105|376|17.2%|
||Total|1806|271|105|2182|100%|
|||82.8%|12.4%|4.8%|100%||

Source: authors’ calculation (based on USGS data).

Table 4: Summary statistics

||Obs.|Mean|Std dev.|Min.|Max.|
|---|---|---|---|---|---|
|Gini index|21433|0.256|0.200|0.0|0.940|
|Mean logarithmic deviation|21433|0.271|0.334|0.0|4.353|
|Theil index|21433|0.247|0.331|0.0|7.289|
|Sen index|21433|0.072|0.778|0.0|43.545|
|Std dev. rainfall (mm)|26014|4.538|4.617|0.0|88.428|
|Std dev. NDVI|26014|0.826|1.532|0.0|7.838|
|Log(Population)|26014|11.173|1.425|3.651|15.931|
|Log(Constant prices)|23745|1.051|3.239|0.0|17.424|
|Log(Average prices)|23745|1.078|3.301|0.0|17.767|

Notes: (1) The table shows descriptive statistics across a sample of 2,247 districts across 39 countries in Africa during 2001– 2012. (2) Inequality indicators are at the district level and measured using night-time light intensity. (3) The number of observations varies because some districts have observations with zero light intensity. (4) Constant (measured at constant 1998 US dollars) and average (measured as annual averages) prices come from USGS. (5) NDVI stands for Normalized Difference Vegetation Index, a proxy for agricultural productivity.

Source: authors’ calculations.

Table 5: The effects of mineral deposits’ and mining sites’ presence on lights Gini index Dependent variable: district lights Gini index

||No|prices|Constant|mineral prices|Average|mineral prices|
|---|---|---|---|---|---|---|
||<br>Model 1|<br>Model 2|<br>Model 3|<br>Model 4|<br>Model 5|<br>Model 6|
|Equation [2]:|||||||
|<br>Deposits [1=present]|0.087<sup>***</sup>|0.081<sup>***</sup>|0.190<sup>***</sup>|0.180<sup>***</sup>|0.176<sup>***</sup>|0.166<sup>***</sup>|
||[0.000]|[0.004]|[0.048]|[0.048]|[0.043]|[0.043]|
|Equation [3]:<br>Mining sites|0.011<sup>***</sup>|0.010<sup>***</sup>|0.095<sup>***</sup>|0.090<sup>***</sup>|0.088<sup>***</sup>|0.083<sup>***</sup>|
||[0.000]|[0.001]|[0.024]|[0.024]|[0.021]|[0.022]|
|Log(Prices)|||0.005|0.005|0.007|0.007|
||||[0.009]|[0.009]|[0.008]|[0.008]|
|Fixed effects|Yes|Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|

26
16 Table 4

# Estimates of the Return to Education

<!-- Start of picture text --> i) Rates of Return 13-27% Primary education at top of range.<br>Psacharopoulos (1985) Higher education at bottom.<br>World Development Report 1991 5-25% More recent surveys than those<br>quoted by Psacharopoulos.<br>Behrman and Birdsail (1983, 1985) 11% Quality-corrected survey data.<br>Glewwe (1991) <6% Abilty-corrected survey data.<br>McMahon (1987) 20% Cross-country regression<br><!-- End of picture text --> ii) Effects of Education Enrolment Rates on Growth Rate Baumol, Blackman and Wolff (1989) 0.038 (Primary), 0.066 (Secondary), 0.129 (Tertiary) regression coefficients converted to annual rates Barro (1991) 0.018 (Primary), 0.023 (Secondary). Mankiw, Romer and Weil (1992) 0.027 (secondary) regression coeffic-zjt converted to annual rate and divided by 0.38 to convert to a coefficient on the fraction of the school-age population attending secondary school. iii) Effects of Educational Attainment of Workforce on Growth Rate _World Development Report, 1991._ OA8 on ye 1 ars of education in 1960. 0.09 on increase in years of education in level 0 to 3. 0.04 on increase in years _of_ education level 3 to level 9. The figure of _0A8_ is expressed at an annual rate.

- _2.3 Education_ **_and Fefit_**

While it is not proven that rapid population growth is undesirable, there is a general air of concern over the expansion of the world population (Ehrlich, 1971). It is an empirical fact that educational improvements have been associated with declining fertility rates. This is partly a consequence of education lowering poverty, as the latter is associated with high fertility rates _(World Development Report, 1990)._ Psacharopoulos and Woodhall (1985) in their review of the literature suggest a number of ways by which education can influence fertility. It may change perceptions of the costs and benefits of having children, and it also influences the age of marriage and reduces the infant mortality rate. Education may also change attitudes to contraception. The evidence suggests that the effect of education depends on the average level of education of the population as well as on the level of education of the individuals concerned. In countries with
1.0 wo€ 0.8 2 fe) > w ne} © 5 > 0.6 oO = =] c oO 3 o 62<sup>0.4</sup> -fo} L o = vn 0.2

<!-- Start of picture text --> 0.0 £oO fo]@ Ss8 & 8 =&@ fo]8 uis§ oe§ 2 8B- &§& F Bte wo0D § F o€ ‘6€ Fwo<br>o Oo o a aD vo 2 w oO S Oo = = Q = = ° = 5<br>2 a Cc a 5 c a sS rad a = o 5 wu = $ iS a<br>c w2 © c wz c c a c & a a 2 c<br>rd(4 a] &3 ® a fadc S ac =o c© sa) & vn c c a < oO =5<br>o 2 Fd 2 & 2 G BW 2 2 a & £ & ke) 3 « rd<br>>a oDo a.o ()= 3s.hUA oOa ce)iEo ad& SSo£ €GS ao5 3== 4sSs FAif c©2 aa3<br>= a oO s 8 “ 3 n ><br>= Oo>c vnfo}3 ¢<br><!-- End of picture text --> Share of identified Vehicle Trade Volume that is Cross-Border Mim Share of identified Vehicle Trade Volume that is Domestic
provide lower bound estimates of the true impacts of school fees, this offers evidence that the negative effects of the economic crisis dominate the positive effects coming from a reduction in school fees. Furthermore, even though our IV is severely weakened for the crisis year (as discussed in section 4.2) and thus could only offer statistically significant estimates in two specifications (columns 1 and 4), the IV estimates have the expected negative sign and are two to three times larger than those of the non-IV estimates. These results concur with those for Table 2.<sup>43</sup>

# **6. Further Analysis of Related Outcomes**

# **6.1. Health Outcomes for Migrant Children**

Our theoretical model predicts that children brought along by migrant households may have better health outcomes (Corollary 1.1). This result is strongly supported by most, if not all, of the recent studies on China as discussed earlier. We re-examine this result with the RUMiC data, and investigate whether moving with parents impacts children's height, their body mass index (BMI) and the underweight/overweight status.<sup>44</sup> We regress these child health outcomes on a dummy variable indicating whether the child is living in the city with the migrant household, controlling for children’s and the household heads’ characteristics. The endogenous variable here is the child migration dummy variable and the IV is the public education spending shocks (the first-stage regression in this case is the reduced form regression). Unlike the previous regressions that were run at the household level, we run these regressions at the individual level for all the school-age children in the sample. Estimation results (Table 8)

> 43 Since we do not have data on migrants’ province of original residence for 2009, we do not control for these dummy variables. An alternative modeling option is to pool the 2008 and 2009 rounds of the RUMiC for analysis. While this option allows us to employ a richer econometric model by controlling for the year and city (fixed) effects, it does not offer more insights into the crisis year as discussed above. Estimation results on the pooled data (not shown) nevertheless confirm that school fees have a negative and statistically significant impacts on child migration.

> 44 The Body Mass Index (BMI), a measure of tissue mass (muscle, fat and bone) in an individual, is computed as the ratio of weight (in kilograms) to squared height (in meters). Using WHO’s guidelines, we consider that children with a BMI less than 18.5 and equal or greater than 25 are respectively underweight and overweight.

32
countries, Berg, Mattinen and Pattugalan’s (2013, p. 52) report “an overwhelming preference for cash over in‐kind assistance”.<sup>2</sup> But quantitative studies describe more varied responses. Ethiopian data from the late 1980s showed that across three rural localities, 19, 83 and 90 percent would have liked to have received food rather than cash payments (Webb and Kumar, 1995). Predilections for in‐kind transfers are also reported in surveys conducted in India (Khera, 2013), Niger (Hoddinott, Sandström and Upton, 2013) and, to a lesser extent, Afghanistan (UNHCR 2015).

Why, if given a choice, would many program beneficiaries rather receive an in‐kind transfer instead of cash? Southworth (1945), along with more modern restatements such as Currie and Gahvari (2008) and Cunha (2014) make clear that their models are predicated on an assumption that cash and in‐kind transfers are equal valued. But in practice, this may not hold as equivalent value can be affected by: the purchasing power of the cash transfer (e.g. given prices of purchased goods, how much food can be purchased with a cash transfer); the potential re‐sale value of in‐kind goods (if the in‐kind transfer could be sold, how much cash would be received); the transactions costs associated with receiving a cash or in‐kind payment; and the transactions costs associated with using a cash or in‐kind payment (on this, see Doocy and Tappis, 2017). Beneficiaries might also be concerned about price volatility. Suppose a household can chose between a food transfer and a cash transfer. Food prices might be either

> 2 In standard microeconomic theory, preferences are stable; along with prices and incomes, they determine choices that economic actors make. In papers such as Berg, Mattinen and Pattugalan (2013), the word preference is used in a more colloquial sense to denote the choice (food or cash) a beneficiary would make if they were allowed to choose their own payment modality. The latter approach is followed in this paper.

4
where _yf,b,m_ corresponds to the amount loaned to firm _f_ by bank _b_ in month _m_ for all types of loans as well as working capital and investment loans. The interest rate, collateral rate, and maturity of loans to firm _f_ by bank _b_ in month _m_ are additional credit outcomes that we analyze. Equation 2 includes firm-bank fixed effects, γb,f, and month fixed effects, γ _m_ , with robust standard errors double clustered at the bank and month levels. Furthermore, in some specifications, we include firm-month fixed effects, γf,m, to control for changes in the demand for credit.

A key identifying assumption for estimating the causal effects of the change in energy prices is that the trends in the outcomes of interest would have been the same across banks in the absence of the energy price drop. Although this assumption cannot be tested, we test for differences in bank outcomes and their trends before the energy price drop, using the regression outlined in equation 3, constraining the sample to the period before August 2014.

In equation 3, _yb,m_ corresponds to the outcomes of interest for bank _b_ at time _t_ , and the term _Trendm_ consists of a linear trend over time. As before, _ExposureEnergyb,Aug14_ captures bank _b_ ’s August 2014 exposure to the energy sector. Coefficient _β1_ measures whether the average outcomes of banks are statistically different as their exposure to the energy sector varies, whereas coefficient _β2_ measures differences in the trends of outcome _y_ across banks with varying exposures to the energy sector. Fixed effects at the month level, γ _m_ , are included in the regression. The results, summarized in Table A3 in the appendix, give credibility to the identification strategy, as they show that there are no statistically significant differences in the pre-shock averages and trends of the outcomes of interest. We conduct an additional pre-trends test using the loan-level data for energy sector borrowers prior to August 2014. The results, displayed in Table A4, also corroborate that there are no statistically significant differences in the loan terms and trends of banks with varying degrees of exposure to the energy sector in the months prior to the shock.

Finally, we test for the existence of nonlinear pre-trends across banks with different exposures to the energy sector prior to the shock. The specification, presented in equation 4, restricts the loan-level data to loans from firms in the energy sector.

12
<u>Table 2: Diference in Means of Control Variables</u>

||No mission|Mission|Diference|
|---|---|---|---|
|Log(Distance to coast)|5.76|4.01|1.76<sup>_∗∗∗_</sup>|
|Log(Distance to main river)|5.29|5.89|_−_0.60<sup>_∗∗∗_</sup>|
|Percentage of area within 10 km of water|0.07|0.10|_−_0.03<sup>_∗∗∗_</sup>|
|Malaria Ecology Index|11.64|6.82|4.83<sup>_∗∗∗_</sup>|
|Caloric Suitability Index / 1000|1.33|1.57|_−_0.24<sup>_∗∗∗_</sup>|
|Terrain Ruggedness Index|17.18|26.42|_−_9.25<sup>_∗∗∗_</sup>|
|Mean elevation|714.05|750.26|_−_36.22|
|Tropical dummy|0.86|0.56|0.31<sup>_∗∗∗_</sup>|
|Log(Distance to explorer route)|3.62|4.20|_−_0.58<sup>_∗∗∗_</sup>|
|Log(Distance to colonial railway)|5.28|3.59|1.68<sup>_∗∗∗_</sup>|
|18th-century population|11.46|23.48|_−_12.02<sup>_∗∗∗_</sup>|
|Precolonial city|0.01|0.04|_−_0.04<sup>_∗∗∗_</sup>|
|Log(Distance to Arab trade)|5.44|6.09|_−_0.64<sup>_∗∗∗_</sup>|
|Observations|6,512|380||

_Source_ : Authors’ calculations based on mission locations from Beach (1903) and control variables from multiple sources, detailed in appendix A. _Note_ :<sup>_∗_</sup> _p < ._ 1 _∗∗ p < ._ 05 _∗∗∗ p < ._ 01 15
# **Table 1:** Data transparency and country sovereign spreads: **Basic Statistics, 1995-2018**

_(Annual data)_

||**1**|**995-2008**|**Non-SSA**|**2**|**009-2018**|**Non-SSA**|**2**|**014-2018**|**Non-SSA**|
|---|---|---|---|---|---|---|---|---|---|
||**EMDEs**|**SSA**|**EMDEs**|**EMDEs**|**SSA**|**EMDEs**|**EMDEs**|**SSA**|**EMDEs**|
|Sovereign Spreads (in bps)|561|999|504|418|506|402|413|561|380|
|Sovereign Bond Return Index|295|327|294|443|282|466|416|266|439|
|Statistical Capacity Index (SCI)|73.7|62.6|76.3|75.3|63.1|78.1|75.4|63.0|78.3|
|- Methodology Indicator|64.1|47.5|68.0|67.2|50.2|71.2|68.2|50.8|72.3|
|- Source Data Indicator|84.9|81.6|85.6|85.3|82.1|86.1|84.7|80.9|85.6|
|- Periodicity and Timeliness Indicator|72.2|58.7|75.4|85.3|82.1|86.1|73.3|57.3|77.1|
|Data Transparency||||||||||
|- SDDS Subscription|0.4315|0.4286|0.4321|0.9110|0.9333|0.9066|0.9342|0.9500|0.9311|
|- SDDS Standard Compliance|0.1957|0.0476|0.2248|0.4849|0.2333|0.5344|0.5644|0.3833|0.6000|

_Notes. The sovereign bond spread and return comes from Bloomberg. WB data transparency is obtained from the World Bank, Statistical Capacity Index (SCI) while the IMF data transparency is obtained from the IMF, SDDS data._
Insuranc- Institute (NII), for each of the yea:. 1969-1976. The NIX was asked *o supply names and earning& data for each individual in the sample, on the basis of identity number. The *arnings records maintained by the Institute are based on employee earnings reported by employers for purposes of national insurance payments. The earnings data made available to us by the NII gave info_.mati **on** the total annual earnings for each individual, accumulated across employers, **as** well **as the** number of months in the **year** for which earnings **were** reported. Since the NII records **are** recorded only by the last **six** digits of identity numbers, multiple earnings records **were** provided for each identity number requested. The matching of the Individual trainee data with appropriate earnings data on the basis of **a** common name proved to be no easy task. Name changes, death, war casualty and migration were among the factors militating against any straightforward matching of the training data with the NII earnings records. In all, 1233 matchings were achieved - **some** 72 percent of the original male trainee sample (Table 1).

Table 1 Lenath of Course. Four Training Modes

|Length of<br>Course|Vocational<br>Secondary<br>School|Apprenticeship<br>|Industrial<br>School|Short<br>Teenage<br>Course|All<br>r Modes|
|---|---|---|---|---|---|
|1 year or less|<br>-|...-<br>5|----------------------|------------------<br>58|------------<br>63|
|13-35 months|-|79|-|1|80|
|3 years|118|572|31|-|721|
|4 years|347|-|22|-|369|
|Totalsample|465|656|53|59|1233|

---------------------------------------------------------------------

_______ In **a** series of regressions, each relating to one of the years 19691976, earnings (EARN) are regressed against type of training program (TP), holding constant the effects of training trade (TT), length of course (LEN), 8
from maize (MVAC 2003). Because of its higher population density, the south is the largest market for maize. Additionally, nearly all households—an estimated 97 percent—grow maize (IHS 2005). The production of maize is undertaken by households on subsistence grounds, and 81 percent of the population in rural areas is classified as subsistence farmers (IHS 2005). The availability of maize in the market is determined by the size of production and the degree of subsistence farming. Estimates of the share of marketed output are in the range of 5 to 25 percent of domestic production (IHS 2005; Jayne et al. 2008). Variation in rainfall and occasional droughts cause large fluctuations in the production of maize, and countrywide shortages of maize occurred in the 2001/2002, 2002/2003 and 2005/2006 marketing seasons. These shortages led to inflows of emergency food aid from international institutions and donors. In times of food shortages, domestic maize prices peak to import parity levels or even exceed these levels. Observed peaks in maize prices confirm this assessment. However, large maize price increases in 2008 were caused by other factors. Similar to the prices of most agricultural products, Malawi maize prices follow a distinct seasonal pattern, with highs at the end of the marketing season just before harvesting (January to March) and lows directly after harvesting (May to July). Increases from the minimum to the subsequent peak in market prices vary from 30 to 40 percent, which is very large in light of the importance of maize as a staple food for Malawi households.

The privatization of agricultural trade was accomplished in the 1980s and 1990s (Christiansen and Stackhouse 1989; Smith 1995). The Agricultural Development and Marketing Corporation (ADMARC), which previously employed a countrywide storage, marketing, and distribution network to buy and sell maize with the aim to guarantee food security, particularly in remote areas, has become redundant in the maize market in recent decades. Since 1994, ADMARC maize purchases have been significantly below 10 percent of total production and were negligible from 2000 to 2005. Furthermore, in terms of marketed output, which is much smaller than production but is more relevant in the case of subsistence farming, the role of 7
range. To guard against this possibility, we control for the square of the number of required documents ( _Documents_<sup>2</sup> ).

A number of studies have shown that small countries tend to trade more than the large countries and that the same holds for rich compared with poor countries. One might then speculate that the reason why income and population matter for the strength of the documentstime cost relationship is because income and population are picking up the effect of trade openness. To check for this possibility, we control for trade (exports plus imports) to GDP ratio (log values of the average of trade to GDP ratio taken over 2001-2005) interacted with the number of required documents ( _Documents*_ Trade to GDP ratio).<sup>4</sup> The argument in the previous paragraph can be extended to another variable: the quality of the overall business climate. For example, higher income countries are likely to be less regulated (less burdensome business climate). Hence, the differential effect of the number of required documents on the time cost across rich and poor countries could potentially be the differential effect across less and more regulated economies if regulation and required documents to trade are compliments for the time cost. To guard against this possibility, we control for _Documents*Business Climate_ , where _Business Climate_ equals the log of the average value over 2001-2005 of the overall score of economic freedom as measured by the Index of Economic Freedom, Heritage Foundation.

Another factor that is known to be correlated with income level and the business regulations is corruption. Typically, corruption falls with higher income levels and increases with various aspects of more stringent regulation (such as, the number of documents required to

> 4 We also experimented with using an overall (weighted) tariff rate, average values over 2001 to 2005 and taken from World Development Indicators, World Bank. However, our main results did not change much on adding the tariff measure interacted with _Documents_ to any of the specifications discussed in the paper. In fact, our results for the main interaction terms were strengthened by using the weighted tariff measure stated.

9
_Figure A2: Share of population affected by changes in market incomes by income groups and level of severity_ <!-- Start of picture text --> AT BE BG CY CZ<br>1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5<br>DE DK EE EL ES<br>1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5<br>FI FR HR HU IE<br>1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5<br>IT LT LU LV MT<br>1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5<br>NL PL PT RO SE<br>1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5<br>SI SK<br>1 2 3 4 5 1 2 3 4 5<br>Severely affected Less severely affected<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> Note: Severely refers to losses in market income of more than 11 percent (Scenario A vs. D). Income groups defined based on Scenario D.

Source: Own calculations based on EUROMOD I4.0+.

23