rafmacalaba/datause-encoder-data huggingface dataset:
short-term real interest rate and real exchange rate raises the explanatory power of the regressions above 50 percent. Finally, adding the other risk measures further raises the _R_<sup>2</sup> to 66 percent, although several of them carry insignificant coefficients due to their high degree of collinearity.

Table 6: Covariates of the common factor

|Variable||All|countries||
|---|---|---|---|---|
||I|II|III|IV|
|∆VIX|-0.021|-0.019|-0.017|-0.010|
||(-4.230)|(-5.300)|(-5.740)|(-3.090)|
|∆Real Fed Rate||0.219|0.217|0.133|
|||( 3.000)|(3.110)|(2.34)|
|∆Real Exchange rate|||-0.030|-0.020|
||||(-2.910)|(-2.180)|
|∆Risk appetite||||0.012<br>(-0.160)|
|HYS||||0.001|
|||||(0.200)|
|BOAS||||-0.010|
|||||(-1.340)|
|MCOY||||-0.335|
|||||(-3.380)|
|_R_<sup>2</sup>|0.407|0.492|0.539|0.666|

Notes: The dependent variable is the common factor from the full sample estimates in the first column of Table 3. ∆VIX is the first difference of the log VIX, ∆Real Fed Funds rate is the first difference of the real funds rate, ∆Real exchange rate is the first difference of the log real effective exchange rate of the U.S. dollar, HYS is the Bank of America Merrill Lynch High Yield Spread, BOAS is the Bank of America Below Options-Adjusted Spread, MCOY is Moody’s Corporate Bond Yield Spread, ∆ Risk appetite is the first difference of the risk appetite index. T-statistics in brackets computed with heteroscedasticity-consistent standard errors. The regressions include a constant.

# **4.4 The exposure to global financial shocks**

Overall, the results in Table 6 support the view that the common factor driving equity returns across the world can be interpreted as a summary representation of global financial conditions. This raises the question of what determines countries’ exposure to – them or, in other words, the sensitivity of their asset prices to global shocks. This is a first-order policy question that has attracted increased attention following the global crisis.

In our model, the sensitivity of each country’s equity returns to the common shocks is given by its respective factor loading. The estimated loadings (shown in Table A4 in the Appendix) are very similar across the three specifications in Table 3: in the full country sample, their pairwise correlations exceed .98, while for the advanced-country sample the correlation exceeds .86. However, the loadings display considerable variation across countries. In the full sample they are all positive, except for three emerging countries (Colombia, Indonesia and Slovakia) that exhibit very small negative values. On 22
between access to water and sanitation and incidence to diarrhea. In rural areas, however, two variables are found to be statistically significant both for children and infants, namely possession of a toilet collected to drainage and purchase of bottled water. Interesting, self-treatment of piped water supply does not show a significant correlation with the incidence of diarrhea. The presence of piped water in the community (though not in the dwelling) is also correlated with the incidence of diarrhea in the case of children but not of infants. However this is expected in that infants tend to be breastfed and are hence less exposed to impurities in water.

**Table 5.12: Cross-tabulation of incidence of diarrhea and access to water and sanitation**

<u>(Proportion of children who had diarrhea)</u>

|||Urban<br>|<br>||Rural<br>||
|---|---|---|---|---|---|---|
||Serv<br>|ice<br>|Pearson<br>|Serv<br>|ice<br>|Pearson<br>|
||Yes|No|Test|Yes|No|Test|
|**Infants: 0-12 months old**|||||||
|Piped water in dwelling or yard|.26|.50||.33|.30||
|Community covered by piped water|.50|.50||.27|.31||
|Dwelling connected to sewerage|.25|.40||.33|.32||
|Toilet connected to drainage|.24|.40||.17|.31|*|
|Latrine|.32|.40||.33|.31||
|Excusado lavable|.43|.40||.31|.31||
|Only treats water|.33|.32||.33|.31||
|Only buys bottled water|.23|.32||.05|.31|**|
|Treats and buys bottled water|.29|.32||.33|.31||
|**Children: 13-59 months old**|||||||
|Piped water in dwelling or yard|.27|.29||.37|.20||
|Community covered by piped water|.35|.21||.32|.44|**|
|Dwelling connected to sewerage|.25|.31||.30|.39||
|Toilet connected to drainage|.24|.33||.16|.35|**|
|Latrine|.32|.33||.40|.35||
|Excusado lavable|.29|.33||.40|.35||
|Only treats water|.27|.26||.39|.37||
|Only buys bottled water|.22|.26||.14|.37|***|
|Treats and buys bottled water|.36|.26||.43|.37||

_Notes_ : Refers to illness during the month previous to the survey. Null hypothesis (homogeneity of users and non-users) is rejected at: ***99%,** 95%,*90%. _Source:_ World Bank calculations using the ENCOVI 2000, Instituto Nacional de Estadística – Guatemala The same exercise is performed for the incidence of respiratory illnesses, cross-tabulated against use of fuel wood for cooking (Table 5.13). Those households that do use fuel wood are further sub-divided according to whether or not they have some kind of chimney for ventilation of the kitchen. It is important to note that the definition of ‘respiratory illnesses’ used in the ENCOVI questionnaire is rather vague, identifying whether or not children had suffered from a very broad range of   complaints including cold, cough, bronchitis, chokes or respiratory infections during the previous month. The results show that (in most cases) the use of fuel wood in the home is not significantly correlated with the incidence of respiratory disease. However, what does seem to matter quite significantly is whether households burning fuel wood have a smoke escape in the kitchen.

46
5 are likely to make for larger informality effects. First, there are minimal copayments and no joining fee in Thailand’s UC scheme – everyone is covered automatically with a 100 percent subsidy. By contrast, in _Seguro Popular_ households with the means to pay are supposed to contribute, so opting out of the formal sector scheme does not eliminate contributions.<sup>3</sup> Second, the relative generosity of the two schemes differs. Both are less generous than the social security scheme, but the gap is wider in Mexico: in 2000, spending per enrollee in _Seguro Popular_ was just 66 percent that of the social security program; by contrast, on a per-beneficiary basis Thailand’s UC scheme spends 85 percent of the amount spent by the social security scheme.<sup>4,5</sup> Third, the two countries’ social security schemes treat dependents differently: Mexico’s covers dependents while Thailand’s does not. Thus in Mexico once one household member is in the formal sector, there is no incentive – from a health coverage perspective – for additional members to seek a formal-sector job. In Thailand, by contrast, at least prior to the UC reform, the health coverage rules gave each household member an incentive to seek a formal-sector job. Health coverage rules prior to the reform thus created an incentive for Mexican households to limit the number of formalsector workers to one, but for Thai households to keep adding formal-sector workers. The informalizing effect of the Thai reform ought therefore to have been greater – a larger fraction of workers will have been encouraged to formalize prior to the reform, but discouraged from formalizing after the reform.

> 3 It needs to be acknowledged that in practice contributions are less than would be expected from application of the means-testing rule to household survey data, and are a good deal lower for most workers than the social security contribution.

> 4 The Thai scheme excludes a few high-cost interventions (e.g. renal transplants) but for the most part the schemes are fairly comparable in terms of their generosity.

> 5 One feature of the Mexican SP program that may limit the disincentive to formalize is that it provides access to a different network of health facilities – sometimes these may be more accessible for a household than the facilities operated by the formal-sector health scheme.
# **1 Introduction**

Value-addition to raw commodity exports is an important (aspirational) industrial policy strategy for commodity exporters in the Global South. This is because, aside from the increase in export revenues, adding value to commodities prior to export, as opposed to exporting them in their raw form, can stimulate job creation and growth of the local economy (Wang, 2013; Rodrik, 2022). The local economic effects of industrial (trade) policy measures have thus gained significant attention in the literature (Busso et al., 2013; Wang, 2013; Goldberg and Pavcnik, 2016; Grogan, 2023). However, many of these studies focus on the "first-order" impacts of industrial (trade) policies, such as employment (Glick and Roubaud, 2006), wages (Cling et al., 2005; Glick and Roubaud, 2006) and income (Picarelli, 2016). Little is known about the "second-order" effects on outcomes such as health and education (Atkin, 2016). Meanwhile, these "secondorder" effects play a key role in determining the long-run impact of these policies.

In this paper, we explore how agro-industrialization policies affect human capital accumulation in low-income environments using administrative data from Rwanda. Theoretically, the effect of agro-industries or any local economic shock on human capital accumulation is ambiguous. On the one hand, local economic shocks boost household income, which enables them to invest in the schooling and healthcare of their children. On the other hand, standard economic models suggest that an increase in wages and employment opportunities for lowskilled workers, resulting from a boom in the local economy, increases the opportunity cost of schooling and reduces the marginal returns to higher education, thereby reducing educational attainment, particularly in environments where regulations on compulsory schooling for children are loosely enforced (Atkin, 2016; Mensah et al., 2019; Carrillo, 2020). Thus, the net effect of these competing factors determines the extent to which local economic shocks influence human capital accumulation in host communities.

We leverage the rapid policy-induced expansion of coffee mills in Rwanda in the 2000s to estimate the effects of early life exposure to local economic shocks on students’ performance in the medium term. In an attempt to increase the value of Rwanda’s coffee exports, the government implemented a National Coffee Strategy, which sought, among other things, to liberalize the coffee industry to allow competition and also increase the adoption of modern technology in coffee processing through the rapid establishment of coffee mills across the country (Macchiavello and Morjaria, 2021). Between 2000 and 2013, the number of coffee mills in the country increased from 3 to 226 (Figure 1). Prior to this, Rwandan coffee was primarily processed by farmers using traditional dry methods whereby farmers pulp the cherries at home using rocks before drying them on mats, resulting in low-quality beans (Macchiavello and Morjaria, 2021). However, the coffee mills, otherwise referred to as coffee washing stations, use the so-called wet 1
|Year 2008|0.095*|0.056|0.056|-24.71**|-17.43**|-12.25**|
|---|---|---|---|---|---|---|
||(1.73)|(1.39)|(1.18)|(-2.30)|(-2.17)|(2.03)|
|Machinist×Year 2002|0.024|0.023|0.010|-29.16***|-20.80***|-17.04**|
||(1.23)|(1.23)|(0.55)|(-3.72)|(-3.70)|(-3.97)|
|Machinist ×Year 2004|0.031|-0.018|-0.007|-36.50***|-27.87***|-22.06***|
||(1.57)|(-0.55)|(-0.27)|(-3.32)|(-4.00)|(-4.27)|
|Machinist ×Year 2008|0.003|0.018|-0.021|-48.71***|-36.75***|-26.48***|
||(1.57)|(0.34)|(-0.39)|(-2.41)|(-2.59)|(-2.69)|
|Intercept|0.105|-0.327|-0.314|16.67|7.739|6.430|
||(0.38)|(-0.86)|(-0.95)|(0.35)|(0.32)|(0.31)|
|**_Panel B: FE-IV model_**|||||||
|Fabricator ×_Di_×_Tt _|0.222**|0.593***|0.482***|25.34*|18.05**|15.05**|
|_Instrumented_|(2.01)|(4.49)|(4.68)|(1.76)|(2.16)|(1.99)|
|Machinist ×_Di_×_Tt_|0.268***|0.182**|0.290***|-7.054|6.42|1.974|
|_Instrumented_|(2.93)|(2.43)|(3.21)|(-0.25)|(0.30)|(0.10)|
|Hausman test chi 2|`†`|5.69|1.12|`†`|0.42|0.35|
|p-value||0.682|0.997||1.000|1.000|

_Notes_ .

The number of observations is 429. Numbers in parentheses are _t_ -statistics based on robust standard errors. *, **, and *** indicate statistical significance at the 10 percent, 5 percent, and 1 percent levels, respectively. `†` indicates that model fitted on the data fails to meet the asymptotic assumptions of the Hausman specification test.

35
analyze panel data from a wide range of both industrial and emerging markets. One study differentiates 31 different provinces within China and another, the 5 former Yugoslav states.

Most of the studies (Table 1) find causality from insurance development to economic development. Richterkova and Korab (2013) apply a meta-analysis assuming random effects that applies weights according to the size of the database. They find an ‘effect size’ that supports the case that insurance development has a positive effect on economic growth. However cases of neutral or reverse causality are also found, sometimes depending on the country, the time period or even on the specific insurance type involved.

Many of the studies add conditioning variables in order to isolate the impact of the insurance variables chosen (usually insurance penetration – the ratio of insurance premiums to GDP, or insurance density – premium per capita), and the impacts of these provide some insight into the role country specific factors play in helping or hindering the transmission process. These may help to account for different results obtained as to the importance of the insurance sector according to the stage of development of the country or region.

Table 1: Causality studies

|Author|Year|Countries|Years|Dependant<br>Variable|Explanatory<br>variable|Methodology|Conditioners|Findings|
|---|---|---|---|---|---|---|---|---|
|Ward and<br>Zurbruegg|2000|14 OECD<br>and 5<br>emerging<br>markets|1961-<br>1996|Real GDP|Total real<br>premiums|Granger<br>causality with<br>co-integration<br>analysis||Causality can<br>run either way<br>according to<br>the country<br>circumstances.|
|Webb, Grace,<br>Skipper|2002|55<br>countries|1980-<br>1996|GDP/<br>capita|Life<br>premium,<br>P&C<br>premium,<br>bank credit|3sls<br>regression|Education,<br>exports,<br>gov’t cons.,<br>investment|Insurance and<br>banking<br>jointly<br>causative|
|Kugler and<br>Ofoghi|2005|UK|1966/71<br>-2003|GDP,<br>GDP<br>growth|Premiums<br>disaggregated<br>by type of<br>insurance|VAR,<br>Granger||4 products<br>supply<br>leading, 1<br>product<br>demand<br>following,<br>others<br>ambiguous|
|Boon|2005|Singapore|1991-<br>2002|GDP<br>growth|Growth in<br>bank loans,<br>market cap<br>and insurance<br>premiums -<br>disaggregated|VAR,<br>Granger||Insurance and<br>capital<br>markets<br>promote<br>growth over<br>long term,<br>bankinga|

treated with due caution in consequence. Measures of financial development employing M2 must also be questionable given the assumption of constant velocity of money.

8
# **Table 2: Regression Results for Per Capita Income**

Dependent variable is the logarithm of per capita GDP at PPP

||2S<br>a|LS<br>s.e.|P-Value for<br>F-statistic<br>Test of Overidentifying from first-stage<br>Restrictions<br>Rearession|Number of<br>Observations|
|---|---|---|---|---|
|Full Sample|||||
|Voice and Accountability|0.888|0.109|0.167<br>23.025|173|
|Political Instability and Violence|1.394|0.177|0.485<br>6.301|155|
|<br>Govemment Effectiveness|1.102|0.128|0.127<br>12.080|156|
|Regulatory Burden|1.133|0.143|0.874<br>18.413|166|
|Rule of Law|1.335|0.179|0.122<br>7.983|166|
|Graft|0.990|0.112|0.018<br>12.069|155|
|Sample of Countries Appearing in|All**Six Aggr**|**egates**|||
|Voice and Accountability|0.907|0.123|0.175<br>17.559|154|
|<br>Political Instability and Violence|1.390|0.177|0.491<br>5.979|154|
|<br>Govemment Effectiveness|1.095|0.130|0.106<br>11.177|154|
|Regulatory Burden|1.085|0.155|0.792<br>16.998|154|
|Rule of Law|1.237|0.171|0.071<br>6.952|154|
|Graft|0.977|0.111|0.017<br>11.567|154|
|Non-OECD Sample|||||
|Voice and Accountability|0.685|0.143|0.093<br>14.259|149|
|Political Instability and Violence|1.811|0.532|0.712<br>1.190|131|
|<br>Govemment Effect(veness|1.561|0.452|0.486<br>2.552|132|
|<br>Regulatory Burden|0.880|0.169|0.990<br>11.250|142|
|Rule of Law|2.079|0.670|0.365<br>1.227|142|
|Graft|1.192|0.317|0.096<br>2.227|131|

26
index of output of generation source _l_ ( _QltEGEN_ in Appendix A) is not identical to the kWh measure ( _QltkWh_ ); the total generation output index ( _QtEG_ ) is not a simple sum (  _l_<sup>_Q_</sup> _ltkWh_ ) but a CES index of the components that take into account the different prices.

The output from each generation source is also expressed as a nest of CES functions; for nuclear, hydro, wind and solar there is a “Resource” input that represents non-produced inputs such as suitable rivers, and land with wind and sun. In any given period, these resources are given by an upward sloping supply curve to represent the short-run costs of developing such regions. This is similar to the representation in Vennemo et al. (2013) and Sue Wing et al. (2014) as discussed in the Appendix A.

The electricity generation and distribution system in China is dominated by a few large enterprises and tightly regulated by the NDRC. The prices are set by the NDRC after negotiation among the generators, distribution monopolies and major users. The dispatch order (which generation units are used at any moment) is determined for the most part by a “fairness” principle and a loose aim to promote renewables; it is not set according to least-cost dispatch, and it does not reflect bid prices for electricity in a competitive wholesale generation market. For a carbon price to work in the electric power sector, there must thus be reforms that would allow price signals to matter – that the net cost of fossil fuels reflects the carbon price, and that dispatch is sensitive to the prices asked by the generators. Our policy simulation here is predicated on the assumption that such reforms will take place.

There are 33 markets for the commodities; that is, there are 33 endogenously determined prices that equate supply with demand for the domestic commodities identified in the model. The total supply consists of domestically produced goods and imported varieties; these are assumed to be imperfect substitutes. There are three markets for the factors of production – land, capital and labor – and three prices to clear them.

The base case simulation is determined by the projections of the exogenous variables and the initial stocks of debt, capital and labor force.<sup>6</sup> Given the initial stocks, we solve for the three factor prices and the 33 commodity prices that clear the markets in the first period. This gives us all the quantities for the first period, including investment that augments the next period stock.

> 6 The projections of the exogenous projections are described in detail in Appendix A. The population projections come from UN Population Division’s _World Population Prospects: 2012 Revision_ , downloaded from their web site, <u>http://esa.un.org/unpd/wpp/unpp/panel_population.htm</u> .

‐6‐
**Appendix 4:** _Calculation of Foreign Prices ($)_

|_Country_|_Knee_<br>_Arthroplasty_|_Shoulder_<br>_Arthroplasty_|_TURP_|_Tubal_<br>_ligation_|_Hernia_<br>_repair_|_Excision of_<br>_skin lesions_|_Adult_<br>_Tonsillectomy_|_Travel_<br>_cost_|
|---|---|---|---|---|---|---|---|---|
|BARBADOS|||||||1,478|401|
|BELGIUM|1,927|2,637|3,424|2,008|2,282|736|845|380|
|BRASIL|5,088|5,627|5,638||2,763|1,931|2,717|961|
|CHILE|3,733|4,966|4,825|2,755|3,071|1,607|1,965|857|
|COSTA RICA|||3,819|||192|1,145|342|
|DOMINICAN<br>REPUBLIC|1,240|||||||265|
|EGYPT|2,738|1,734|1,743||1,062|573|364|715|
|FRANCE|1,645|2,172|4,148|1,251|2,200|763|663|336|
|GERMANY|3,144|3,619|4,505|982|2,787|929|994|337|
|HUNGARY|637||||1,317|||415|
|INDIA|662||1,263|113|701|512|175|1,008|
|JAMAICA|||||||||
|JORDAN|4,564||||1,387|874|419|810|
|MEXICO|4,706|7,773|4,224||3,686|3,086|3,288|410|
|PERU|2,390||4,719||2,719|184|315|638|
|PHILIPPINES|2,312|2,402|1,768||1,667|748|1,122|1,204|
|POLAND|3,672|1,213|||2,400||898|441|
|SINGAPORE|5,381||||3,913|4,515|3,967|808|
|THAILAND|2,860|3,874|2,551||1,715|750|1,194|793|
|TRINIDAD<br>andTOBAGO|2,249|2,249|1,928|803|884|578|884|500|
|U.K.|4,955|6,199|7,190||3,158|2,334|3,090|307|

_Sources:_ Vanbreda International, and Expedia.

32
The incidence of moderate poverty is also found to be positively correlated with the amount of funds allocated to projects in the provinces. Poorer provinces (with a high poverty incidence) are likely to obtain larger amounts, suggesting that Zambia’s Investment in roads is driven more by poverty than by economic criteria (Graph 6).

**Graph 6: Correlation between road allocation and poverty**

<!-- Start of picture text --> Western<br>Northern<br>Eastern<br>SouthernNorth Western<br>Lusaka<br>Central Luapula<br>Copperbelt<br>30 40 50 60 70 80<br>moderate poverty<br>roads Fitted values<br>30<br>20<br>10<br>0<br><!-- End of picture text --> _Notes:_ Y axis is the province road commitments over total road commitments (measured in percentage). X is the moderate poverty province share (measured in percentage).

Although selection according to poverty and low population may be perceived as legitimate from a poverty and spatial inequality perspective, it may not be sustainable in a medium-long term perspective. The economic epicenter of the country, the Copperbelt region and the mining industry, has for instance only received a marginal share of road allocation over the observed period, despite the fact that these are the areas where one would expect the highest rate of return. Road allocation to sparsely populated and poor regions brings much lower economic returns than in industrial and densely populated regions. In this regard the _World Development Report 2009 (World Bank 2009)_ gives evidence of the threshold effect of investments in areas with low economic density. By offering people and industry incentives to stay, governments are only isolating these lagging areas more. Lagging regions are then highly subsidized from the richest regions. The preference for an allocation according to poverty criteria may become problematic, since it does not necessarily encourage industry to flourish.

It is worth noting that over the period 2008 to 2011, there is some negative correlation between provincial-level support for the major opposition party (PF) at the time and the share of allocations the province receives for roads (Graph 7). This negative relationship (albeit relatively weak statistically) adds weight to Baldwin’s (2008) findings that project selection often depends on political party support. Road allocation may have to some extent been being used as a reward for political loyalty. However, the Northern and Western provinces are outliers and as the graph shows, this correlation remains weaker statistically than the correlation with poverty.

13