rafmacalaba/datause-encoder-data huggingface dataset:
might be correlated with the allocation of treatment (shifts in government tastes, for instance) or with outcomes (changes in enrolment due to changes state demographics), and further minimizes the potential for this bias.

# **_5.3. Presence of Other Educational Interventions in the School_**

An additional concern is whether part of the observed effects are driven by other policies also directed to improve schooling quality and accessibility that are simultaneously operating in the school. As noted, the inclusion of state specific time dummies captures any possible change in the allocation of resources to education within states over time. To absorb any remaining bias, we additionally include explicit controls for three other educational interventions.

First, we control for the share of _Oportunidades_ beneficiary students in the school as a measure of the intensity of the program in the school. _Oportunidades_ , formerly known as _Progresa_ , is a conditional cash transfer program that provides financial incentives for families to invest in the human capital of their children. Cash transfers are disbursed conditional on school aged children attending school and on household members engaging in a set of behaviors designed to improve health and nutrition (preventive checkups, prenatal care, and health education).

Second, we include the proportion of teachers under _Carrera Magisterial_ , a voluntary pay per performance scheme targeted to all educators. Principals and teachers are eligible for permanent wage increases if they perform well in an assessment process based on their education and experience and on their school performance (student and teacher test scores).<sup>16</sup>

> 16 See Skoufias (2005) for a review of impact evaluations on the _Oportunidades_ program. McEwan and Santibáñez (2005) provide an evaluation of the _Carrera Magisterial_ scheme.

19
initial Dutch disease effects on prices. Second, the peak actually occurs in later periods; because of the discount factor in the computation of utility, the welfare measure in the All-investing regime is lower than the three others since the benefits of future higher consumption are less important than near-term consumption for welfare. The highest welfare impact in the Allsavings regime can easily be understood in light of the explanation given earlier on the comparison of the impact of the shock across regimes.

However, exactly the opposite pattern occurs in the case of a temporary negative shock (Figure 4b). The all-savings regime (i.e. borrowing in a negative shock) now has the lowest welfare, followed by the All-consuming regime (i.e. reducing consumption), the Balanced case, and the All-Investing case (i.e. cutting infrastructure) in that order. The pattern reminds us that the Dutch disease effects are just one possible side of the movements of resource price, that price contraction and its opposite effects are just as likely.

With some simplification, the following are therefore the policy rules of thumb. When there is a temporary increase in the natural resource price, the best strategy suggested by the DSGE analysis in this paper is to bank the revenue windfall entirely in a sovereign fund and to draw on the interest income to smooth consumption through government transfers to households. When there is a temporary price decline however, the best strategy is to cut public investment in order to protect consumption as much as possible. This is due to two reasons: investment raises the prices of domestic goods, leaving less money for government to transfer to households; and public investment is nowhere near 100 percent effective in raising output (see discussion of the output elasticity of public capital). Thus, reducing investment is better than cutting transfers to households, which reduces consumption directly, or dissaving from the sovereign fund or borrowing, which incurs an interest premium when debt rises.<sup>9</sup> In the deterministic case (such as Go et al. 2013), a conservative Bird-in-Hand budget rule is recommended in order to avoid the long-term negative effects of Dutch disease when the resource export price shock is a positive and permanent and when the capacity in a poor country is low so that there is the possibility of boom-bust cycle from wasteful spending. The DSGE analysis in this paper extends prudent budgetary rules to the context when the price shocks are temporary and can be positive or negative.

# **3.2  Impact of price volatility**

> 9 Noting that several countries in West Africa have cut public investment in the wake of a negative shock,  Dessus and Varoudakis (2013) caution against this strategy, even if it is inter-temporally optimal.. They argue that the stop and go costs and the asymmetric response of investment to shocks (higher during bad times) could be a reason for the relatively low levels of infrastructure in the WAEMU countries. Consideration of of stop and go costs, beyond the standard adjustment costs of investment which is already in the model, beyond the scope of the paper.

27
# **References**

1. Abdi, H. (2007). The Kendall rank correlation coefficient. _Encyclopedia of Measurement and Statistics. Sage, Thousand Oaks, CA_ , pages 508–510.

2. Alatas, V., Banerjee, A., Hanna, R., Olken, B. A., and Tobias, J. (2012). Targeting the poor: evidence from an experiment in Indonesia. _American Economic Review_ , 102(4):1206–40.

3. Alix-Garcia, J.M., Sims, K.R.E., and Costica, L. (2019). Better to be indirect? Testing the accuracy and cost-savings of community leader surveys for poverty targeting. Working paper, University of California Davis.

4. Baird, Sarah, Craig McIntosh and Berk Ozler (2013) The Regressive Demands of Demand-Driven Development, Journal of Public Economics, Vol. 106, pp. 27-41.

5. Basurto, M. P., Dupas, P., & Robinson, J. (2019). Decentralization and efficiency of subsidy targeting: Evidence from chiefs in rural Malawi. _Journal of Public Economics_ , 4047.

6. Brown, C., Ravallion, M., and Van de Walle, D. (2016). A poor means test? Econometric targeting in Africa. Technical report, The World Bank, Washington, DC.

7. Cameron, L. and Shah, M. (2014). Mistargeting of cash transfers, social capital destruction, and crime in Indonesia. _Economic Development and Cultural Change_ , 62(2):381– 415.

8. Conning, J. and Kevane, M. (2002). Community-based targeting mechanisms for social safety nets: A critical review. _World development_ , 30(3):375–394.

9. Friedman, J., Hastie, T., and Tibshirani, R. (2001). _The elements of statistical learning_ , Volume 1. Springer series in statistics: New York.

10. Kilic, T., Whitney, E., Winters, P., 2013. Decentralized beneficiary targeting in largescale development programs: insights from the Malawi farm input subsidy program. Journal of African Economies, Volume 24, Issue 1, January 2015, Pages 26–56.

11. <mark>Kling, J. R., Liebman, J. B., & Katz, L. F. (2007). Experimental analysis of neighborhood effects.</mark> _<mark>Econometrica</mark>_ <mark>,</mark> _<mark>75</mark>_ <mark>(1), 83-119.</mark> 12. Pimhidzai, O., Fenton, N. C., Souksavath, P., and Sisoulath, V. (2014). Poverty Profile in Lao PDR. Technical report, The World Bank.

13. Roopnaraine, T. and Adato, M. (2004). A social analysis of the Red de proteccion social (RPS) in Nicaragua. _Washington, DC: IFPRI_ .

14. Subbarao, K., Del Ninno, C., Andrews, C., and Rodríguez, C. (2012). _Public works as a safety net: design, evidence, and implementation_ . The World Bank.

22
- Cohn, E. 1997. "The Rate of Return to Schooling in Canada." _Journal of Education Finance_ *23(2): 193-206.

- Cohn, E. and J.T. Addison. 1998. "The Economic Returns to Lifelong Learning." _Education Economics_ 6(3): 253-308.

- Dabalen, A. 1998. "Returns to Education in Kenya and South Africa: Instrumental Variable Estimates." University of California, Berkeley (mimeo).

- Duflo, E. 2001. "Schooling and Labor Market Consequences of School Construction<sup>in</sup> Indonesia: Evidence from an Unusual Policy Experirnent." _American Economic Review_ 91(4): 795-813.

- Edlnvest. 1999. "The Gambia: An Assessment of the Private Education Sector" (http://www.ifc.org/edinvestO).

- Fersterer, J. and R. Winter-Ebmer. 1999. "Human Capital and Earnings in Austria," in R. Asplund, and P.T. Pereira, eds., _Returns to Human Capital in Europe._ Helsinki: ETLA, The Research Institute of the Finnish Economy.

- Filer, R., S. Jurajda, J. Planovsky. 1999. "Education and wages in the Czech and Slovak Republics during transition." _Labour Economics_ 6: 581-593.

- Fiszbein, A. and G. Psacharopoulos. 1993. "A Cost Benefit Analysis of Educational<sup>Investment</sup> in Venezuela: 1989 Update." _Economics ofEducation Review_ 12(4): 293-98.

- Funkhouser, E. 1996. "The Urban Informal Sector in Central America." _World Development_ 24(11): 1737-51.

- Glewwe, P. 1996. "The relevance of standard estimates of rates of return to schooling for education policy: A critical assessment." _Journal ofDevelopment Economics_ 51: 267290.

- Griffin, P. and A. Cox Edwards. 1993. "Rates of Return to Education in Brazil: Do Labor Market Conditions Matter?" _Economics of Education Review_ 12(3): 245-56.

- Griliches, Z. 1970. "Notes on the Role of Education in Production Functions and Growth Accounting," in W.L. Hansen, ed., _Education, Income and Human Capital._ New York: National Bureau of Economic Research.

- Griliches, Z. 1969. "Capital-Skill Complementarity." _Review of Economics and Statistics_ 51(4): 465-8.

- Harmon, C. and I. Walker. 1995. "Estimates of the Economic Return to Schooling for the United Kingdom." _American Economic Review_ 85(5): 1278-86.

7
more severe north-south and rural-urban education gap. Gender gaps of a higher magnitude might well be observed for specific age categories at the state-level.<sup>27</sup> Third, analyzing the overlap between monetary and multi-dimensional deprivation-based poverty, there is interestingly is only limited overlap between the two. Somewhat surprisingly though, still about half of the children in the top 5 percent in terms of monetary wealth suffer from at least one severe deprivation. Hence, this suggests the importance to not target or analyze monetary and multi-dimensional deprivationbased poverty in isolation or as substitutes, but as all different dimensions of poverty.

Furthermore, this analysis informs about the persistence of child poverty, wherein the north-south and rural-urban discrepancy once again stands out. Northern and rural children not only face higher poverty rates, but also face much higher levels of chronic poverty, children that are poor throughout the first three waves. In addition, there are among the non-poor in the north and in rural areas a much larger share of children at risk of being poor in the next period, given a much higher share of transient poor, children that move in and out of poverty from one wave to another, whereas among the non-poor in the south and in urban areas, there is a larger share of children that were never poor throughout three waves.

Finally, an additional inter-generational analysis shows that northern and rural children are not only at this point facing on average the highest poverty and deprivation rates but also a lower chance of breaking the inter-generational cycle of poverty, as results suggest a stronger link between their parents’ education and their own poverty. This finding holds for children’s monetary poverty as well as for all seven deprivations.

Further research may want to consider how well the targeting of recent and planned development initiatives of the government and donor community in Nigeria overlap with these findings. It would be particularly interesting to know whether the current efforts show significant impact at a local level, even if not necessarily yet visible as a clear trend at state or regional level, as existing initiatives have been implemented at varying scale across the country. Given the in this study outlined poor condition of children and slow improvement of deprivation levels over the years at national, regional and state level, Nigeria’s efforts seem to be inadequate to reduce poverty and inequality between regions at a large scale. The World Bank’s 2018 Human Capital Index also indicates Nigeria’s poor status, the country is ahead of only five countries: Liberia, Mali, Niger, South Sudan and Chad. Though Nigeria slightly improved its

> 27 This analysis however refrains from a further breakdown to that level due to concerns about the representativeness of sample sizes.

58
# Appendix 5: Impacts on Individual Management Practices

Table A5.1 shows the breakdown of significant improvements in management practices within the Anexo K index:

**<mark>Table A5.1: Summary of Impacts at the Sub‐Index and Individual Practice Level</mark>**

||#|**Sub‐In**<br># sig. Ind.|**dices**<br># sig. Group|#|**Individual Prac**<br># sig. Ind.|**tices**<br># sig.<br>Group|
|---|---|---|---|---|---|---|
|Finances|8|6|5|29|17|15|
|HR|7|3|2|20|11|6|
|Logistics|7|5|2|31|8|9|
|Marketing|5|3|3|22|9|13|
|Production|8|6|8|39|22|30|
|TOTAL|35|23|20|141|67|73|

<mark>Note: lists number of practices that are statistically significant at the 5% level post‐intervention.</mark> Table A5.2 details the individual management practices that have treatment effects of 0.8 or more (on a 5-point scale).

|**Table A5.2: Practices that increase by 0.8 or more from at least one‐treatment**|||
|---|---|---|
||Individual|Group|
|_Finance Practices_|||
|System of monitoring and control of financial goals in place|**0.827*****|0.666***|
||**(0.175)**|(0.189)|
|Frequency at which financial objectives and goals achieved|**0.802*****|0.648***|
||**(0.205)**|(0.212)|
|Existence of a Master Budget|0.718***|**1.163*****|
||(0.263)|**(0.259)**|
|Control and Monitoring of Master Budget|0.765***|**1.016*****|
||(0.226)|**(0.241)**|
|How deviations from master budget analyzed|**0.909*****|**1.070*****|
||**(0.244)**|**(0.265)**|
|Structure of Control and Monitoring Indicators (KPIs)|**0.935*****|**0.956*****|
||**(0.247)**|**(0.237)**|
|Agenda of Financial Management Meetings|**1.055*****|**1.055*****|
||**(0.230)**|**(0.222)**|
|_HR Practices_|||
|Strategic objectives leverage people's and team's talent|**0.833*****|0.631***|
||**(0.206)**|(0.214)|
|Human talent development plans linked to corporate strategy|**0.809*****|**0.902*****|
||**(0.200)**|**(0.215)**|
|Strategic plan defined, that includes clear goals for human talent|**0.951*****|**0.910*****|

56
# **<u>Table 1: Housing ownership (continued)</u>**

|13 or more years of education||0.030|||0.052<sup>**</sup>|
|---|---|---|---|---|---|
|||(1.27)|||(2.03)|
|Wife's type of work (ref: not working)||||||
|Services||-0.055<sup>***</sup><br>|||-0.032<br>|
|||(-2.90)|||(-1.46)|
|Agriculture||<br>0.017|||<br>0.033|
|||(0.48)<br>|||(1.45)|
|Industry/manual||-0.054<sup>***</sup><br>|||-0.009<br>|
|||(-3.10)<br>|||(-0.35)<br>|
|DK/missing/other||-0.069<sup>***</sup>|||-0.076<sup>**</sup>|
|||(-2.74)|||(-2.04)|
|**Characteristics of the husband:**<br>||||||
|Husband's total number of sons ever born||0.001|||-0.003|
|||(0.54)|||(-1.24)|
|Husband's total number of daughters ever born||-0.002|||-0.003|
|||(-0.86)|||(-1.60)|
|Husband's age in years||0.010<sup>*</sup><br>|||0.004<br>|
|||(1.90)|||(0.70)|
|Husband's age in years, squared||-0.000|||-0.000|
|||(-1.40)|||(-0.54)|
|Husband's education (ref: none)||||||
|1 to 4 years of education||0.023<br>|||0.020<sup>*</sup><br>|
|||(1.19)<br><sup>*</sup>|||(1.70)<br>|
|5 to 8 years of education||0.035<br>|||0.010<br>|
|||(1.84)|||(0.84)|
|9 to 12 years of education||0.035<sup>*</sup>|||-0.001|
|||(1.73)<br>|||(-0.08)|
|13 or more years of education||0.074<sup>**</sup>|||-0.003|
|||(2.56)|||(-0.17)|
|Husband's type of work (ref: not working)||||||
|Services||-0.019|||-0.036|
|||(-0.93)|||(-1.56)|
|Agriculture||0.027<br>|||-0.025<br>|
|||(1.22)<br>|||(-0.88)<br>|
|Industry/manual||-0.037<sup>*</sup><br>|||-0.046<sup>*</sup><br>|
|||(-1.70)|||(-1.82)|
|DK/missing/other||-0.022<br>|||-0.038<br>|
|||(-0.80)|||(-1.11)|
|**Characteristics of the couple:**||||||
|Both spouses are working||0.032<sup>*</sup>|||0.010|
|||(1.94)<br>|||(0.37)|
|Husband-wife education gap||-0.003<sup>*</sup>|||0.000|
|||(-1.78)|||(0.08)|
|Household headship (ref: husband is head)||||||
|<br>Wife head of household||0.028|||0.067<sup>**</sup>|
|||(1.61)|||(2.46)|
|Other male household head||-0.035|||-0.084<sup>**</sup>|
|||(-122)|||(-266)|
|Other female household head||.<br>-0.072<sup>***</sup>|||.<br>-0.155<sup>***</sup>|
|||(-2.85)|||(-3.63)|
|Constant|2.195<sup>**</sup><br>0.499|0.137|3.566<sup>**</sup>|2.737|1.994|
||(2.38)<br>(0.39)|(0.12)|(2.07)|(1.63)|(1.25)|
|Observations<br>|71,313<br>71,313|71,122|142,585|142,585|142,324|
|_R_<sup>2</sup>|0.05<br>0.06|0.11|0.08|0.11|0.13|

_Note:_ Data for 41 countries from 2010-18. _t_ statistics in parentheses. Dependent variable is wife's housing ownership (sole and joint). Coefficients after OLS estimation. Standard errors clustered at the country level.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01. _Source:_ DHS.

20
# **<<A>>Facts and Figures on Malawi**

Prior to explaining and applying the proposed modifications to the PBM, we present a description of the Malawi economy, its maize market, and its domestic maize trade. The description provides background information that supports several assumptions underlying the empirical application.

_<<B>>The Malawi economy and the Malawi maize market_ Malawi is a landlocked country in sub-Saharan Africa that measures more than 800 km from north to south and 100 to 170 km from east to west. It is bordered to the north and northeast by Tanzania, to the northwest by Zambia, and to the south, the southeast, and the southwest by Mozambique. Lake Malawi, a large Great Rift Valley lake called Lake Nyasa outside of Malawi, forms part of the country’s eastern border and stretches from the northern tip to the lower third of the country. Malawi’s population increased from 10.5 million in 2000 to 13.9 million in 2010. The central and, particularly, the southern regions are relatively densely populated. Lilongwe, in the central region, and Blantyre, in the south, are the two largest cities. Each city had approximately 0.5 million people in 2000, increasing to slightly below 0.9 million in 2010. The majority of the population lives in rural areas (approximately 87 percent, from 2000 to 2010) and earns income from agriculture (77 percent of the population above 15 years of age, 84 percent in rural areas). Per capita GDP in 2009 (2008), expressed in purchasing power parity US$, is between 840 and 900 US$, making Malawi among the poorest countries in the world<sup>3</sup> . The poverty rate in Malawi is high; 52.4 percent of the population is poor, and 22 percent of the population is very poor (IHS 2005). There is considerable geographical spread in poverty. Reported poverty rates vary from 70 percent in remote rural districts to 25 percent in urban centers.

In Malawi, maize is dominant in both production and consumption. It is the primary staple food of households. Between and 52 and 65 percent of the total per capita calorie intake is 6
_Can we establish a clear market failure from the data?_ Empirically, these effects have proved difficult to document and quantify, let alone permit a ranking of goods by their potential for externalities or rents. Rodrik (2004) makes this point in _Industrial Policy for the Twenty-First Century:_ “I start from generic market failures, but then I take it as a given that the location and magnitude of these market failures is highly uncertain.” (p. 3). This almost agnostic view is supported by Pack and Saagi (2006) and Harrison and Rodriguez-Clare (2010) who review much of the industrial policy literature per se.

The problem is compounded by a worsening data challenge: As production becomes more fragmented, countries trade tasks, not goods, although official statistics only measure the latter. China does not export the high tech i-phone, rather it exports low to medium skill assembly tasks.<sup>5</sup> Hence, trying to identify externalities based on what is reported in the official export statistics is fundamentally misleading. A second challenge arises from recent literatures that stress that export success is less about sectoral growth than about a few “big hits,” which account for most of the export value, and where the “hit” includes a match between a very disaggregated product and a particular geographical market. This suggests that the relevant policy target is likely to be the entrepreneur rather than a sector per se.<sup>6</sup> Our overall empirical blindness on these points has led to the literature developing shortcuts to identifying potentially good sectors, three of which we discuss below.

> 5 First, in a world of increasingly fragmented production where some estimate that 50% of trade is somehow involved in GVCs with substantial trade in intermediate inputs, countries are increasingly trading tasks, not goods. Trade statistics show that China exports the iPhone and Vietnam silicon chips, but in fact it only produces assembly tasks worth 1-2% of the value added of the product. In fact, electronics is one of the _lowest_ value added sectors in China. Hence, while the focus probably should be on externalities pertaining to tasks, the data we have are on final goods.

> 6 See Easterly, Reshef and Schwenkenberg (2009). Similarly, Bernard et al. (2012) find that approximately 40 percent of exporting firms in the United States exported a single product to a single destination. Exporting is also rare in that a group of very large firms stands out from the rest.<sup>6</sup> Therefore, the difference between successful and unsuccessful exporters is found not in the degree of sector specialization, but in the scale of the “big hits.”

5
The impact <u>of efficient financial markets on the likelihood for brain gain</u>

|Migration Possibilities|Low<br>(S1f)|Medium<br>(S2f)|High<br>(S3f)|
|---|---|---|---|
|_Annual growth rates_||||
|Population|1.2%|1.1%|0.9%|
|Real Gross Domestic Product|3.4%|3.2%|3.1%|
|Per capita real disposable income|2.7%|2.8%|2.9%|
|Per capita domestic human capital|3.4%|3.2%|2.8%|
|Real physical investment|4.9%|4.8%|4.7%|
|Real educational investment|7.2%|7.6%|8.2%|
|Labor supply, unskilled|0.8%|0.7%|0.6%|
|Labor supply, skilled|4.6%|4.4%|4.0%|
|Wage, unskilled|2.6%|2.7%|2.8%|
|Wage, skilled|-0.9%|-0.4%|0.2%|
|Physical capital remuneration|-1.5%|-1.5%|-1.5%|
|Educational investment costs|-0.3%|0.0%|0.4%|
|Physical investment costs|0.1%|0.2%|0.3%|
|Consumer price index<br>_Others_|0.1%|0.2%|0.3%|
|Proportion of domestic skilled workers after 25 years|12.5%|11.8%|10.8%|
|Skilled workers migration rate after 25 years|2.5%|3.3%|4.5%|
|Elasticity of per capita domestic human capital accumulation t|o skilled mig|ration|-0.31|
|Source: authors’ calculations.||||

Naturally, given its first order nature, pushing further this mechanism ends up generating net brain gain when the elasticity of substitution between investment types exceeds 5 - that is, when a 10 percent increase in the relative remuneration of education over physical investment generates a 50 percent increase in education expenditures. This is indeed the amount required to compensate the departure of skilled migrants with new students, as discussed with baseline simulations<sup>26</sup> .

Is this realistic to envisage such a case? Probably not, given the inertia observed in the allocation of savings across investment types. On the one hand, young students are typically discriminated on developing countries’ financial markets (given their lack of collaterals), which casts doubts on the eagerness of banks to sharply increase tuition credits when the increase in skilled workers remuneration is associated with much higher migration (as it further lowers possibility for banks to recover their loans). On the other

> 26 The remuneration of skilled labor over its cost is 10 percent higher in S3 than in S1. And a 50 percent increase in education expenditure was estimated to be required to compensate for the drain of skilled workers.

22