rafmacalaba/datause-encoder-data huggingface dataset:
status group. To the extent that the latter group dominates the community (cf., Anderson, 2011), it may be able (and willing) to exclude the former group from accessing local public services. Thus, for low-caste households, sending a child, male or female, to a school dominated by higher castes is more costly than sending him or her to a caste-concordant school.

We examine these issues using a novel data-set that explicitly recognizes the geographic structure of villages and the social structure of their constituent settlements. Our evidence shows that, controlling for distance to school, the odds of ever enrolling are substantially lower for girls who would need to cross settlement boundaries to attend, an effect not present for boys and one that does not appear to be an artifact of school allocation decisions. Once we distinguish children by their caste, however, the crossing-boundary effect remains relevant only for highstatus girls; among low-castes, neither boys nor girls benefit from having a school located within their own settlement. This phenomenon can be attributed to the fact that many low-caste children reside in high-caste dominant settlements, where their families originally settled to provide services to the landowning class. In these settlements, low caste children are stigmatized and face high psychic costs of attending school. But, when the most convenient school happens to be in a low-caste dominant settlement, we find low-caste children are much more likely to enroll.

Experiments conducted in India by Hoff and Pandey (2006) suggest that castebased stigma plays a powerful and early role in human capital formation. Low-caste children perform poorly on cognitive tests when in the presence of their high-caste counterparts, but only if their caste status is made public; if caste is kept private, both groups do equally well. Munshi and Rosenzweig (2006), meanwhile, argue that low-caste households, who tend to have looser ties to ancestral kin networks, perceive a greater return to education (see also Luke and Munshi, 2007). Our findings imply that once the playing field is level—i.e., absent communal barriers and the associated stigma—low-caste children are actually no less likely to enroll in school than high-caste children. Indeed, low-caste girls, the most educationally disadvantaged group, would achieve substantially higher enrollment rates if given access to caste-concordant schools, whether these schools are placed inside or outside their own settlement. We will show that a policy of building village schools designed to serve low-caste children would increase overall enrollment by almost twice as much as a policy of placing a school in every currently unserved settlement, and would do so at one-sixth of the cost.

2
<!-- Start of picture text --> measures aimed at reducing the costs of domestic market entry are also promising, although our<br>results suggest that their impact will be less strong than for export costs or international transport<br>costs. The unifying factor behind both sets of measures is that they envisage diversification not as<br>a result of governments "picking winners" through industrial policy, but as a natural outcome of<br>winners picking themselves through an intensification of the Schumpeterian process at the heart of<br>the Melitz (2003) model.<br>It is interesting that the empirical evidence in favor of lower EU tariffs or higher home country<br>tariffs is considerably weaker than for the other trade cost factors we have identified. This suggests<br>that circumspection may be required in pursuing either more generous trade preferences or stronger<br>import protection in the hopes of promoting export diversification. It may be, as Collier and Ven-<br>ables (2007) suggest, that it is not necessarily more generous preferential rates that are required,<br>but complementary policies—such as rules of origin—that make preferences easier and cheaper to<br>access. However, this is a point that we leave for future research to address more fully in the light<br>of additional evidence comparing the extensive margin impacts of different preferential schemes.<br>Since Doing Business data on entry and trade costs are being updated annually, we are hopeful that<br>future research will also be able to exploit the availability of panel data both to assist with achieving<br>identification, and to provide greater clarity on the dynamics involved in the diversification process.<br>References<br>Acemoglu, Daron, Simon Johnson, and James A. Robinson, 2001, "The Colonial Origins of<br>Comparative Development: An Empirical Investigation", The American Economic Review, 91(5),<br>1369-1401.<br>Amin Gutiérrez de Pifieres, Sheila, and Michael Ferrantino, 1997, "Export Diversification and<br>Structural Dynamics in the Growth Process: The Case of Chile", Journal of Development Eco-<br>nomics, 52(2), 375-391.<br>Baldwin, Richard E., and James Harrigan, 2007, "Zeros, Quality, and Space: Trade Theory and<br>29<br><!-- End of picture text -->
<!-- Start of picture text --> Share of Non-Performing Loans<br>0.7<br>N<br>0.6 Stanbic<br>0.5<br>UCB<br>0.4<br>0.3 UCB+Stanbic<br>(Virtual)<br>0.2<br>Sector<br>0.1<br>0<br>1996 1998 2000 2002 2004<br><!-- End of picture text -->

**Figure 2**

Regressions in Table 3 confirm Figure 2. Models 1 and 2, which treat UCB and Stanbic as separate prior to privatization, indicate that UCB’s NPL share was significantly higher than that of the typical Ugandan bank while Stanbic’s was lower (in Model 1). Since privatization, Stanbic’s NPL share does not differ significantly from the rest of the banking system.<sup>19</sup> Models 3 and 4 indicate that the composite NPL share was not above the sector average prior to privatization (because Stanbic’s was relatively low).<sup>20</sup> Since the transaction, the NPL share has remained on par with other banks, once the asset allocation variables are included (Model 4).<sup>21</sup> Without the asset allocation variables (Model 3), the quarters since privatization variable is positive and significant, though 19 Because Stanbic is a foreign bank, we also need to take that coefficient into account when interpreting these regressions. In the models without asset allocation variables in Table 3 (i.e., the odd-numbered ones), the foreign ownership dummy is insignificant. Thus, those models suggest that Stanbic’s portfolio quality is now on par with foreign and domestic banks alike. However, when the asset allocation variables are included in the regression, the foreign coefficient is positive and significant. This suggests that domestic banks have higher NPL shares than they otherwise might because of the types of lending that they pursue. Phrased differently, if the foreign banks had portfolio allocations similar to domestic banks, the foreign banks’ NPL shares would be higher (on average) than those of domestic banks. Of course, banks choose their asset allocation strategies, and thus foreign banks’ reliance on lower risk activities could be taken as a sign of prudence.

> 20 There was however a reduction in the UCB’s NPLs due to the 1998 recapitalization described above. This can be seen in Figure 3.

> 21 Model 3 indicates that Stanbic’s portfolio quality was on par with all banks. Model 4 indicates that it was on par with foreign banks.

12
2

# **Abstract**

The claim by global trade modelers that the potential contribution to global economic welfare of removing agricultural subsidies is less than one-tenth of that from removing agricultural tariffs puzzles many observers. To help explain that result, this paper first compares the OECD and model-based estimates of the extent of the producer distortions (leaving aside consumer distortions), and shows that 75 percent of total support is provided by market access barriers when account is taken of all forms of support to farmers and to agricultural processors globally, and only 19 percent to domestic farm subsidies. We then provide a back-of-the-envelope (BOTE) calculation of the welfare cost of those distortions. Assuming unitary supply and demand elasticities, that BOTE analysis suggests 86 percent of the welfare cost is due to tariffs and only 6 percent to domestic farm subsidies. When the higher costs associated with the greater variability of trade measures relative to domestic support are accounted for, the BOTE estimate of the latter’s share falls to 4 percent. This is close to the 5 percent generated by the most commonly used global model (GTAP) and reported in the paper’s final section.

## **JEL codes: C68, D58, Q17, Q18**

**Key words:** Trade policy reform, computable general equilibrium modeling, agricultural protection, economic welfare

## **Contact author:**

Kym Anderson Development Research Group The World Bank Mailstop MC3-303 1818 H Street NW Washington DC 20433 USA Phone +1 202 473 3387 Fax +1 202 522 1159 kanderson@worldbank.org
estimator<sup>_10_</sup> . To ensure correct inference of statistical tests, we calculate Huber-White robust standard errors, which correct for heteroskedasticity.

Our reduced form approach to estimating the weighted CEF allows us to produce unbiased estimates of impacts of covariates on the population as a whole without explicitly describing the underlying mechanisms. However, this approach can only plausibly identify causal effects and requires the maintained assumption that E[εi|Xi] = 0. In some cases, this assumption is easily maintained, such as testing for the effect of living in one country versus another on COVID-19 related loss in income. That a particular household j lives in Ethiopia as opposed to Nigeria is unlikely to be correlated with any relevant (non-ignorable) omitted variables. Obviously, our reduced form approach allows us to say nothing about why households in Ethiopia have lost less income due to COVID-19 than households in Nigeria. The approach simply allows us to produce an unbiased estimate of the likelihood that a household in Ethiopia has lost income and compare it to the likelihood for households in Nigeria. This allows us to conclude that the simple fact of living in Ethiopia means that COVID-19 has had a different impact on household j’s income relative to the impact the disease would have had had the household lived in Nigeria.

However, in some cases, such as the gender of the household head or the location of a household in a rural region, the assumption of exogeneity may be more difficult to maintain. As an example, it is possible for the likelihood that household j is female-headed to be correlated with an omitted variable, such as disease susceptibility of male household members, and also correlated with concern for COVID-19 related health impacts. If male members of household j are more likely to die young due to disease, then that household is more likely to be female-headed and may also be more likely to be concerned about additional disease burdens. In this case, estimates of the effect of living in a female-headed household on COVID-19 related health concerns may be biased. Because of this, we encourage the reader to be cautious in interpreting some regression results as causal effects. This concern does not apply to estimates of population weighted means or totals, bias in which are fully corrected for by our calculation of sampling weights.

# **<mark>References</mark>**

<mark>29. A</mark> . Josephson and J.D. Michler. Socioeconomic impacts of COVID-19 in low income countries: Replication Code, Version 1.0, Zenodo (2020); <u>https://doi.org/10.5281/zenodo.4060416.</u> <mark>30. Food and Agriculture Organization of the United Nations: Voices of the Hungry. Bringing experience-based food insecurity measurement to the global level. h</mark> <u>ttp://www.fao.org/inaction/voices-of-the-hungry/background/en/</u> <mark>31. M. Nord. Introduction to Item Response Theory applied to Food Security Measurement: Basic Concepts, Parameters, and Statistics. Technical Paper, FAO, Rome.</mark> <u>http://www.fao.org/3/a-i3946e.pdf.</u> 21
HCI = Survival x School x Health 1 <u>— Under</u> 5 <u>Mortality Rate</u> o(sng-s ) — oP (Expected Years of School « ~~Harmonized Test Score~~ 14) y(@nG-z ) = e VYasrx (Adult Survival Rate—1)+YstuntingX(Not Stunted Rate—1))/2 Stunting — 0.35 $(3-14) — p0.08(-11) - 94 YasR(0.6-1) — 90.65(-0.4) — 9 77 Ystunting(0-6-1) — e0:35(-0.4) = 0.87
# **2 WHY SHOULD WE CARE ABOUT MACROECONOMIC VOLATILITY?**

Output growth volatility reduces people’s welfare through its effect on the labor market, earnings, and consumption. Households, in particular poor ones, have limited capacity to insure against shocks. Their earnings and consumption patterns follow therefore closely overall macroeconomic trends, increasing in good times, and decreasing in bad ones, leading to a strong macro-economic correlation between output growth volatility and consumption volatility (Figure 3). Volatile consumption patterns have a strong impact on households’ welfare, who would rather face a smooth path of consumption that an unpredictable one. Among others, Reis (2006), based on Lucas’ (1987) approach, shows that the welfare costs of output fluctuations in the United States amount to 5 percent of per capita consumption. And in developing countries, where fluctuations are of larger magnitude and shocks are more persistent, the welfare costs of output fluctuations are even higher. The World Bank (2000), for instance, using an approach based on Athanasoulis and van Wincoop (2000), reports welfare costs amounting to up to 10 percent of consumption for various Latin American countries.

**Figure 3: Volatility of Output Growth vs. Volatility of Consumption**

<!-- Start of picture text --> 16<br>14 TGO<br>12 ZMBTTO COG NIC NGA<br>MWI<br>10 BLZ CHL BDI<br>PAN SLE SYR<br>8642 NORGBRFRAAUTCHEBELUSASWENLDAUSCANCOLDNKITAPAKGTMZAFESPIRLFINNZLLKAEGYHNDPHLBENBFAINDLUXMEXCRIMYSECUKORBOLPRYISRTHAJPNPRTSENISLGRCSLVIDNCAFBRAHUNBGDMDGSGPMLTHTIGHAHKGMARKENPNGURYPERBWACIVVENDOMSDNCMRARGZAR LSO CHN<br>0<br>0 1 2 3 4 5 6 7 8 9<br>Output Volatility<br>Volatility of Consumption<br><!-- End of picture text --> Sources: author’s own calculations using WDI data. Volatility is measured as the standard deviation of GDP per capita growth between 1960 and 2005.

Output growth volatility also affects particularly poor households because of coping strategies that have long-term harmful consequences. Households’ preference for “smooth” consumption patterns is not only a matter of taste. The very limited ability of poor 6
(comprised of the poorest 10 percent of households) received a net cash benefit equivalent to an increase of 53.4 percent of their Market Income; these households in the poorest decile benefitted more from direct transfers and indirect subsidies than total direct and indirect taxes that they had to pay. In contrast, the rest of the deciles were net cash payers into the fiscal system, with the cash loss after taxes and cashable transfers (reduction in Market Income) ranging from –1.4 percent in decile 2 to –19.7 percent in decile 10 (the richest 10 percent of households). The fact that decile 2 (which is below the official poverty line) experienced a cash loss is consistent with the national poverty increase estimated after the combination of taxes and cashable transfers modeled. While there was a slight poverty gap increase at the national level, the effect was very small and likely because of the net cash gain in decile 1. Also, the fact that the cash loss was higher for richer deciles relative to poorer deciles is consistent with the estimated inequality reduction after the combination of taxes and transfers modeled.

**Figure 5-4 Households in decile 1 are net cash beneficiaries of fiscal policy in the West Bank and Gaza**

Net cash position of households after taxes and transfers, by deciles <!-- Start of picture text --> Net cash beneficiary<br>100<br>Contributions<br>Direct taxes<br>50<br>Indirect taxes<br>Education<br>Health<br>Indirect subsidies<br>Direct transfers<br>0<br>Net total benefit<br>Net cash benefit<br>-50<br>Poorest 2 3 4 5 6 7 8 9 Richest<br>Decile of Market income<br>Percent of Market income<br><!-- End of picture text --> Source: Authors' estimates based on PECS 2016/2017; LFS 2017. Notes:

[1] Net cash benefit refers to the system including all direct and indirect taxes, transfers and subsidies, and excluding in-kind health and education transfers. [2] Net total benefit refers to the system including all elements, including in-kind health and education transfers.

40
Almus and Eastern German firms which receive public R&D funds. Non-parametric matching to identified Counterfactual Czarnitzki with unbounded propensity score (distribution (2003) approximation). Random replacement samples drawn to check robustness. Non-experimental Brown et al. Test how ownership type affects the propensity to invest First step Tobit regressions to explore how ownership (2010) in R&D in Ukraine and firm origins are related to the intensity of different investment types. Second set of regressions calculates the productivity growth returns to different investment types (Neoclassical production function). Non-experimental.

Matsumoto et Public research Institutes R&D grants. Study economic Ad hoc design based on modeling and simulation. Case al. (2010) impacts for industry and sectors. studies for qualitative assessment. Non-experimental.

Narula and Promoting the incremental upgrading of existing Descriptive only, although paper mentions explicitly that Guimon subsidiaries towards demand-driven R&D. R&D activity is an IE. Non-experimental. (2010) of Multi National Enterprises subsidiaries. The interaction between national innovation systems and MNEs.

Thomson and Whether subsidies and tax incentives increase R&D Model consists of a vertically integrated firm which Jensen (2010) employment. Evaluate effectiveness of government produces both final goods and technology, via R&D. The grants firm’s objective function represents the discounted stream of profit. Hamiltonian profit maximization model. Estimation with GMM-Abond. Non-experimental. Pirtea et al. Explore the interactions of foreign direct investments Pooled panel GLS. Non-experimental. (2009) impact on the economy of host countries. Some countries separate such investments from R&D, some other have specific policies that target R&D. Krammer Study relationship between innovation’s output and Poisson negative binomial regression and FGLS estimator (2009) inputs and include various controls (year and regional dummies) to capture as much as possible of the unobserved heterogeneity.

46
Figure 14. Changes in inequality attributable to remittances <!-- Start of picture text --> 0.6<br>0.4<br>0.2<br>0.0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>-1.0<br>-1.2<br>2010-2012 2003-2010<br>Annual Gini points<br>Dominican Republic El Salvador Honduras Mexico<br><!-- End of picture text --> Source: Authors’ calculation from SEDLAC (CEDLAS and the World Bank). Note: The figure shows the Shapley Decomposition of inequality changes (Barros et al. 2006 and Azevedo et al. 2012).The figure shows changes in Gini points between 2003-2010 and 2010-2012, or the nearest years in case data for these years are not available.

# **Pensions**

Coverage of pensions (contributory and non-contributory) across Latin America has increased in recent years driven by the introduction and aggressive expansion of non-contributory social pensions and fewer institutional restrictions to accessing contributory pension programs (Rofman et al. 2008). In general, contributory pensions tend to be regressive and cover a relatively small proportion of the economically active population in many of the region’s countries (Perry et al. 2007 and Levy 2008). Low-skilled and low-income households have limited access to pensions as many are in the informal labor market. On the other hand, social non-contributory pension programs have grown considerably. Between 2000 and 2013, at least 18 countries in the region continued or implemented noncontributory pension programs, expanding access especially to those at the bottom of the income distribution (Rofman 2013). Some notable examples of programs that have increased pension coverage during the past decade include Argentina ( _Moratorium_ ), Brazil ( _Beneficio de Prestação Continuada),_ Mexico ( _70 y mas),_ Bolivia ( _Renta Dignidad_ ), Panama ( _100 a los 70_ ) and Chile ( _Pension Basica Solidaria and Social pension programs_ ).

The recent expansion of non-contributory programs has decreased the regressivity of pensions in the region. This means that changes in pensions have had an equalizing effect on income during this period. For instance, between 2010 and 2012, changes in pension income in six countries (Chile, Brazil, Panama, Argentina, El Salvador and Uruguay) were assoacited with a non-negligible decrease in inequality, over -0.1 annual Gini points (Figure 15). Not surprisingly, five of these countries designed policies targeted towards universal pension coverage. These programs are typically more generous than pension programs aimed only at poverty reduction among the elderly, labeled as “targeted” in 22
# **Figure 14(a): Mongolia: Return to skill by sector over time (Senior Secondary and above)**

<!-- Start of picture text -->
1.2<br>1<br>0.8 Agriculture/Mining<br>0.6 Manufacturing<br>Trade/Services<br>0.4<br>0.2<br>0<br>1998 2002 2007<br>Year<br>Return<br><!-- End of picture text -->

**Figure 14(b): Mongolia: Proportion of skilled labor by sector over time (Senior Secondary and above)**

<!-- Start of picture text --> 100<br>80<br>Agriculture/Mining<br>60<br>Manufacturing<br>40<br>Trade/Services<br>20<br>0<br>1998 2002 2007<br>Year<br>% skilled<br><!-- End of picture text --> 57
# **8 Appendix A**

## **_8.1 The Methodology used in the Analysis_**

We use a very simple methodology based on that used in Singh, Squire and Strauss (1986) and Deaton (1989; 1997, p185) for the analysis of household survey data. We represent the impact of price changes on an individual household, _i_ , using an expenditure function to characterize household consumption and factor supply behavior and a profit function to represent household production activities through unincorporated enterprises such as family farms. The expenditure function, _e_ ( _p,w,u_ ) is defined over a vector of prices of consumed goods, _p_ , a vector _w_ of the prices of supplied factors, and the utility level of the household. The profit function, _π_ ( _p,w,v_ ) is defined over the prices, _p_ , of goods produced or purchased as inputs, _w_ of prices of variable factors purchased; and _v_ of fixed factors such as land, management skills and any quasi-fixed inputs provided by the household to its own firm.

By the envelope theorem, the first derivatives of the expenditure function with respect to prices, _e_ p, indicate the quantities of goods demanded by the household, while its derivatives with respect to factor prices, _e_ w, indicate the negative of the quantities of factors supplied. Similarly, the derivatives of the profit function with respect to _p_ , represent the net output supply functions, its derivatives with respect to _w_ , _π_ w, indicate the negative of the quantities of variable factor inputs demanded; and its derivatives with respect to _v_ represent rates of return to fixed factors.

The vector of outputs of the family firm is given by _πp_ and the impact of a change in prices, _dp_ , of these goods on firm net revenue is given by _πpdp_ . Similarly, the impact of a change in prices of variable factor inputs on the net income of the firm is given by _π_ w _dw._ The impact of a change in prices on the cost of living of the household is given by _epdp_ and the impact of a change in factor prices on household returns is given by _ewdw_ . Under constant returns to scale, net profits are given by _πpp_ + _πww_ and are exactly exhausted by returns to