rafmacalaba/datause-encoder-data huggingface dataset:
19

- No weighting. Despite the fact that Russian regions vary substantially by size and economic potential, the analysis below does not attempt to eliminate these scale differences by any type of regional weights. Instead, the underlying assumption of the models was that all regional governments are similar independent decision-making units which determine their budget expenditure allocations according to the combined local impact of identical supply and demand factors.

V.1. Relationship between the level of received transfers and the amount of subsidy spending. In all years, a high degree of the variation in total national economy expenditures and in their major components can be explained by two variables on the supply side: pre-transfer regional revenue and received federal transfers. This means that fiscally wealthier regions and regions receiving more federal transfers spent more on national economy items. Table 9 gives B coefficients, t-stats, constants and adjusted P<sup>2</sup> values for the two-variable regressions in each year for the total national economy expenditures<sup>2 2</sup> .

_Table 9. National Economy Regression Results, Primary Supply Factors, 1992-95 (OLS estimates here and in allfollawing tables, t-stats are in brackets)_

||**1992***|**1993**|**1994**|**1995****|
|---|---|---|---|---|
||**(N=86)**|(N=87)|(N=87)|**(N=86)**|
|**Pre-transfer**<br>**regional**|.368|.389|.396|.440|
|**revenue**|(27.9)|(21.5)|(21.2)|(29.5)|
|**Federal transfers**|.1364|.2846|.362|.308|
||(7.7)|(14.9)|(20.2)|(12.0)|
|**Constant**|34.3|36.07|-43.4|-30.8|
||9??|(1.2)|(-1.5)|(-1.6)|
|**AdjustedR**<sup>**2**</sup>|.91|.87|.92|.93|

*Excluding Koryaksky AO.

"*Here and in the following tables, Bashkortostan not included due to non-receipt of transfers in 1995.

The results in Table 9 could be expected to some extent from both statistical and substantive considerations. On the statistical side, it did not come as a surprise that the correlation between the level of pre-transfer revenues and the level of expenditures on the national economy is very strong, because of some degree of collinearity in these indicators. Given that regional budgets are balanced on the basis of cash (i.e., total revenues are very close to total expenditures for all years and all regions), and both pre-transfer revenues and national economy expenditures constitute a substantial portion of total revenues and expenditures respectively, it should be expected that these two indicators are correlated. The real news here is that, when controlled for variation in pre-transfer revenues, there is a strong positive correlation between the levels of federal transfers and regional subsidization.

It is also not very surprising to see that the correlation between these variables is positive.

22 In this and other tables outliers have been defined as those regions whose inclusion causes the coefficient of variation to rise above 2.0, and they have been excluded in all regressions.
# **Introduction**

Governments the world over strive to incentivize parents to ensure that their children attend school regularly through subsidies, fines, and truancy laws (BBC, 2005, Maynard et al., 2017). In developing countries, arguably the most significant innovation in social policy in the past few decades has been the introduction of conditional cash transfers (CCT) made to parents in order to incentivize a number of prescribed behaviors such as regular school attendance, and which are now implemented in over 60 countries (Parker and Todd, 2017).

When information frictions in parent-child interactions are taken into account, however, simply providing information to the parent about child attendance at school may improve attendance independently of any costly transfer, and incentivizing children themselves may be more cost-effective than incentivizing parents. While the role of children in their own schooling decisions is generally ignored by researchers and policy makers, repeated instances of peer effects in attendance decisions (Lalive and Cattaneo, 2006; Cipollone and Rosolia, 2007; Bobonis and Finan, 2009) suggest that children take part in decisions over whether they attend school (Kremer and Holla, 2009). In fact, when asked, a majority of surveyed children who have dropped out of school by age 15 in India, Ethiopia, Peru and Vietnam say that they themselves played the most important role in deciding to do so.<sup>1</sup> In this paper, we provide experimental evidence from a poor, rural African country setting that children have agency in decisions regarding their schooling as early as sixth grade, and that neglecting this reality may lead to inefficiencies in education policies. We present experimental evidence of the effect of three alternative policies targeting Mozambican girls in the last two grades of primary 1 More precisely, 40% in India, 58% in Ethiopia, 65% in Vietnam, and 80% in Peru. Authors’ calculations based on Young Lives Round 3 data (Boyden, 2014).

2
<!-- Start of picture text --> P(Xi=1)<br>0.8<br>O.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.1 a<br>0.0<br>ti] 10 20 30 40 50 60 70 80 90 100 110 120 130<br>P_MEAN_YR<br>PLOT ———_ CATTLE_MEAT ———~ CATTLE_MILK ———_ GOAT<br>——_ SHEEP CHICKEN<br>Mean Precipitat ion:C_MNeat=58 ,C_Milk=63 ,Goat=68 , Sheep=59 ,Chicken=f6<br><!-- End of picture text -->
the other hand, periods of booms also imply higher _opportunity costs_ of mothers’ time, which would adversely affect child outcome if mothers spend less time on taking care of the children. These studies find that the _opportunity cost_ channel dominates the _consumption_ channel, i.e., child health measures are counter-cyclical in high-/middle-income countries. The current paper finds the exact opposite in a low-income country setting. I attribute this to the fact that most households in my data live on a subsistence income, so that shocks to income have a first-order effect on spending on child nutrition and health.<sup>3</sup> On the other hand, I find no significant effect of coffee price change on female labor supply, and hence time investment on children.

This paper is also tangentially related to the literature on resource curse in the context of developing countries (see, for instance, Bazzi and Blattman, 2014; Berman et al., 2017, and McGuirk and Burke, 2020). This studies explore how international commodity price shocks affect the onset and duration of conflict in developing countries. For instance, Berman et al. (2017) show that an increase in food crop prices increases the incidence and onset of conflicts, while similar change in prices of cashcrops has no significant effect on conflict. McGuirk and Burke (2020) find contrasting result in Sub-Saharan Africa using international shocks to mineral prices as a source of variation. This suggests that commodity price shocks affect household welfare in a number of different channels. In this paper, I focus on exploring the effect on household consumption and investment in child human capital. While there was no significant conflict in the study areas and period, the inclusion of zone-year fixed effects would absorb any effect of conflict due to commodity price movement.

The rest of the paper is organized as follows. Section 2 briefly describes the datasets and some key summary statistics. Section 3 gives an overview of coffee production and marketing in Ethiopia. Section 4 describes the empirical strategy, while Section 5 presents the results. Section 6 concludes the paper.

# **2 Data**

## **2.1 Data sources**

The main dataset used in this study is the Ethiopian Socioeconomic Survey (ESS) data collected by the Central Statistical Agency (CSA) in collaboration with the World Bank (see Central Statistical Agency and World Bank (2012) for details about this data). This is an exceptionally

> 3According to UNICEF (2015), almost half of under-5 mortality is attributable to under-nutrition in poor countries.

5
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23
**Table 8: Estimated area of degraded lands in ECA as of 2000**

|Country|Degraded<br>byagriculture|Degraded<br>total area|Land suitable for<br>agriculture|
|---|---|---|---|
|Albania|400|2,700|544|
|Armenia|300|300|241|
|Azerbaijan|4,700|4,900|2,434|
|Belarus|0|1,300|15,274|
|Bosnia & Herzegovina|0|5,100|1,922|
|Bulgaria|3,400|11,100|5,975|
|Croatia|300|5,600|2,934|
|Czech Republic|7,300|7,900|4,779|
|Estonia|0|200|1,997|
|Former Yugoslavia|2,000|12,000|4,605|
|Georgia|700|700|1,800|
|Hungary|2,700|6,000|6,929|
|Kazakhstan|3,500|47,300|3,107|
|Kyrgyzstan|400|400|414|
|Latvia|0|4,400|5,258|
|Lithuania|0|1,200|5,287|
|Macedonia, FYR|0|2,200|634|
|Moldova|0|3,400|2,219|
|Poland|4,600|26,000|22,296|
|Romania|11,000|23,700|12,238|
|Russian Federation|100,800|380,800|219,696|
|Slovenia|100|1,600|665|
|Tajikistan|900|1,000|1,219|
|<br>Turkey|3,000|77,000|14,577|
|Turkmenistan|6,400|6,600|312|
|Ukraine|3,100|45,900|42,886|
|Uzbekistan|6,000|6,000|2,027|
|Total|161,600|685,300|382,269|

Source FAO (2000). Note: Area is reported in thousand hectares 45
in the Sefwi belt, parallel to the Ashanti belt (Pigois _et al._ 2003). Alluvial mining takes places along the rivers that run through this mineral-rich area: the Pra river and its tributary, and the Birim river. We focus on formal mines, for which production data are available.

We obtain mine-level production data from annual country reports published by the U.S. Geological Survey (USGS) for the years 1991-2003, and from corporate annual reports for the years 2004-2010. We cross-validate USGS and company data for the years preceding 2004. We exclude gold mined by artisans and traded by the Precious Minerals Marketing Corporation, which cannot be traced geographically. Our measure of mining activity covers all formal enterprises, which are medium and large-scale mines. We code mining location using a variety of sources, including annual reports, other corporate publications, technical industry reports, and high-resolution satellite imagery.

Some 18 formal gold mines operated in Ghana at some point in time between 1991 and 2010. Of these, 16 were operating in the decade preceding the year 2000; 12 of them were still operating in 1999. In the 2000s, there were 12 operating mines, of which 8 were still active in the year 2010. Table 1 lists the average yearly production volume by decade for each mine. Some alluvial placer operations in the Eastern region are quite small, producing on average 26 kg of gold per year. Mid-size mines have an average yearly production of about 2,000-5,000 kg. The largest mines in our study produce between 8,000 and 20,000 kg of gold per year. In the 1990s, the mine at Obuasi produced on average as much gold as all the other mines together. The production volume of Obuasi subsequently halved, and overall mine production decreased until the opening of new mines. In more recent years, a high level of production comes out of a small number of mines, each of which is fairly large. The largest is the Tarkwa mine, with about a quarter of overall production in 2010. The latest mine is the Ahafo mine, which opened only in 2008 and immediately became the second-largest mine. Figure 1 shows details on the evolution of output for the 10 largest mines over our study period. Overall production increased over time, as a function of enlargement of existing and opening of new mines. However, there is substantial variation in the evolution of individual mines. This is the variation we exploit.

Gold mining provides for a very small share of employment in Ghana as a whole. The sector as a whole – including formal and informal, large and small mines– employed only 0.72 percent of the country’s labor force in 2000, and 0.93 percent in 2010. But in the immediate vicinity 9
,<sup>tt</sup> where is private consumption, is government consumption, is investment in productive capital, GG<sup>ttpptt</sup> = GG<sup>G</sup> + GG<sup>I</sup> + GG<sup>KK</sup> + GG<sup>pptth</sup> + II<sup>AA</sup> is investment in adaptation capital, ΔS S is the change in stock, is export, is import, D is CC<sup>PP</sup> CC<sup>GG</sup> II the statistical discrepancy, is total government spending, is government spending on goods II<sup>AA</sup> SS XX MM DDSS and services, is government spending on interest, is government capital expenditure and GG<sup>ttpptt</sup> GG<sup>G</sup> is other government expenditure.<sup>tt</sup> GG<sup>I</sup> GG<sup>KK</sup> GG<sup>pptth</sup>

# 3.2. Insurance purchased from an insurance company

An insurance contract is actuarially fair if the premia paid are equal to the expected value of the compensation received. Given the design of the hurricane shocks above, the actuarially fair premium is simply ���� . If the insurance company takes a mark-up on top to finance its operation and allows for partial insurance cover the insurance premium can be expressed as: GGDD μμ<sup>II</sup> ����. θθ∈[0,1] AA<sup>II</sup> The insurance payout is given by the insurance cover and the actual damage in that year, AA<sup>II</sup> = θθ(1 + μμ<sup>II</sup> )GGDD . BB<sup>II</sup> II Historically, hurricane damage is zero in most years so that the insurance payout will also be zero in tt tt BB = θθGGDD most cases. Typically, the insurance premium depends on the variance of the insured events. Equation 4.2 in (Gray & Pitts, 2012) specifies a commonly used functional form μμ<sup>II</sup> where ���� are expected damages, AA<sup>II</sup> = θθ∗�DD is the standard deviation of damages and<sup>�</sup> + ββ∗SSDD(GGDD)�, is a parameter. From this, the markup can be derived as GGDD SSDD(GGDD) ββ ���� . ���� and are drawn from the empirical distribution of hurricane events of (World Bank, 2018) μμ<sup>II</sup> = ββ<sup>SSDD(GGDD)</sup> GGDD and the mark-up is an increasing function of the variance parameter β . GGDD SSDD(GGDD)

# 3.3. A contingency fund

A contingency fund is a separate account administered by the national government for the purpose of having funds available for immediate use after a hurricane disaster. The fund is assumed to receive regular payments from the government. If a share of hurricane damage is to be covered by the contingency fund, the regular payment must be θθ ����. Since the government administers the fund itself, the administration cost AA<sup>CC</sup> = θθ(1 + μμ<sup>CC</sup> )GGDD should be lower than the markup paid to the insurer (although poor investment decisions or inappropriate use of the fund could μμ<sup>CC</sup> increase the effective markup). Assuming that the contingency fund earns an interest rate of , the fund volume can be calculated recursively as DD<sup>CC</sup> VV 7
65

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65
Here, a procedure is used that combines the decomposition method introduced by Oaxaca (1973) based on a double equation model with the multinomial logit model for sector self-selection suggested by Bourguignon, Fournier and Gurgand (2007):<sup>4</sup> The combined procedure makes it possible to account for possible self-selection into labor force participation and into public or private sector work in a straightforward way, to estimate a flexible double equation model and to conduct a counterfactual decomposition of the wage differential between public and private sector. It consists of three steps: In the first step, a multinomial logit model for simultaneous selection into labor force participation and into the public or private sector is estimated. Second, the correlates of log hourly wages are estimated separately for public and private sector workers. Finally, the difference in mean hourly wages of the two groups is decomposed into characteristics and coefficients effects.

The basic model for steps one and two is given by Here ���������, �������, ��������� indicates the sector (in this case public sector, private sector or inactivity), �� refers to the wage associated with work in a specific sector of employment, ��∗ is a discrete choice variable that indicates the choice of a sector of employment. �� and �� are vectors of demographic, institutional, regional or labor-related explanatory variables. �� and �� are error terms. Equation (1) is a wage equation in the tradition of Mincer (1974) and equation (2) an equation for simultaneous choice of labor force participation and sector.

In the most basic double equation model, wage equations for the public and the private sector are estimated separately and without consideration for sector self-selection. However, if individuals have unobserved characteristics that affect both their choice of employment and their wages, then the error terms �� and �� will be correlated and estimates of �� that do not account for self-selection biased and inconsistent. To correct for this potential inconsistency, applied research has traditionally employed the bias correction method embedded in Lee’s (1983) extension of the Heckman (1979)

> 4 A combination of the selection correction strategy by Bourguignon, Fournier and Gurgand (2007) and decomposition by Oaxaca (1973) has previously been used in similar form by a small number of studies, including by Chen, Démurger and Fournier (2007). The exposition of this section draws on Bhaumik, Gang and Yun (2006), Bourguignon, Fournier and Gurgand (2007), Dimova and Gang (2007) and Gang and Schmillen (2014).

- 10 -
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