rafmacalaba/datause-encoder-data huggingface dataset:
33 Table 2: Interacted GDP Growth Equations, Sub-Saharan Africa, 1965-82

||(1)|(2)|(3)|
|---|---|---|---|
|Oil|2.534|3.525|3.462|
||(2.08)|(2.18)|(2.21)|
|Coups|-0.827|-0.661|-0.662|
||(1.93)|(1.30)|(1.35)|
|Private Investment|0.260|0.519|0.382|
||(1.75)|(1.11)|(0.83)|
|Priv. Invest. 1974-82|<br>0.105|<br>0.086|0.063|
||(0.74)|(0.53)|(0.40)|
|Publin Investment|0.583|0.510|0.228|
||(2.16)|(1.57)|(0.65)|
|Public Invest. 1974-82|-0.617|-0.612|-0.360|
||(2.04)|(1.81)|(1.01)|
|Population|0.532|C.128|0.379|
||(0.94)|(0.06)|(0.20)|
|Terms of Trade|0.182|0.207|0.208|
||(2.13)|(2.20)|(2.28)|
|Gov Growth x Gov Share|0.718|0.668|0.390|
||(1.60)|(1.30)|(0.75)|
|Import Tax x Invest|-0.004|-0.020|-0.016|
||(0.59)|(0.95)|(0.81)|
|Export Tax x Invest|-0.003|0.010|0.009|
||(0.60)|(0.58)|(0.53)|
|Personal Tax x Invest|0.024|-0.006|0.005|
||(2.88)|(0.17)|(0.13)|
|Corporate Tax x Invest|-0.010|-0.031|0.023|
||(2.53)|(1.20)|(0.86)|
|Sales Tax x Invest|-0.000|-0.021|-0.024|
||(0.01)|(0.74)|(0.84)|
|Import Tax x Pop.||0.054|0.036|
|||(0.68|(0.46)|
|Export Tax x Pop.||-0.068|*-0.055|
|||(0.87)|(0.72)|
|Personal Tax x Pop.||-0.098|-0.128|
|||(0.55)|(0.74)|
|Corporate Tax x Pop.||0.088|0.041|
|||(0.76)|(0.35)|
|Sales Tax x Pop.||0.095|0.125|
|||(0.69)|(0.93)|
|Tax rate x Tax Growth|||0.965|
||||(1.79)|
|1974-82 Interaction|-0.244|0.100|-0.084|
||(0.13)|(0.05)|(0.04)|
|Constant|1.240|0.739|1.127|
||(0.61)|(0.32)|(0.50)|
|R2|0.505|0.465|0.469|

Note: The dependent variable is the annual logarithmic growth rate in GDP. Absolute values of t-statistics are in parentheses.
schools below that level. For example, India establishes a target of 30 for each class (Azim Premji, 2014).

In Malawi, as described above, the qualifying PTR level has historically been 60, but with more than half of schools having PTRs above this level, this targets teachers with inadequate specificity to bring down the maximum PTR effectively. Evidence from Malawi suggests that schools with PTRs above 90 typically achieve lower learning outcomes (Asim and Casley Gera, 2024). We therefore adopt a PTR of 90 as the threshold to consider schools highly understaffed and eligible to receive teachers under T2, for evaluation of the 2017 allocation of teachers.

However, as the allocation of teachers improves, we would expect the share of schools with PTR above 90 to reduce, making adherence to this rule more difficult. Indeed, the share of schools with PTR above 90 reduced from 32 percent in 2017 to just 16 percent in 2019. Applying T2 only to these schools would not identify enough beneficiary schools to utilize the full supply of teachers. Therefore, for 2018 and 2019, as the share of needy schools declines in response to optimal teacher allocations, we lower the threshold to 80, maintaining a similar share of schools identified as needy.

Over time, with continued need-based allocations, we would expect to be able to lower the threshold further and eventually to the government’s official target of 60.

## **Box 2.1** summarizes the modified rules.

## **Box 2.1. New rules for teacher allocation**

Target 1 (T1): deploys the least number of teachers needed such that schools would have at least one teacher per grade.

- Except schools with PTR of 60 or below or whose PTR would be reduced to 60 or below with addition of new teachers

- Except districts where more than 50 percent of schools meet T1 are excluded from receiving teachers based on T1, unless their overall district PTR is above 75:1.

Target 2 (T2): deploys the least number of teachers needed so that PTR reduces to below 90 (in 2017) or 80 (in 2018 and 2019).

# **Estimating need under the revised rules**

Employing these revised rules, we then estimate the need for teachers to fulfill these targets in each year from 2017 to 2019. **Table 2.1** shows the number of teachers required in each district in each year following the application of both rules, along with the number of schools needing teachers. In each year, the data on school-level PTRs is drawn from EMIS data, reflecting the previous year’s allocations of new teachers as well as other movements, deaths and retirements of teachers in the system.

7
biotechnology in the cotton market. The next section places biotechnology in the context of the global cotton market and reaches the following conclusions. First, some countries have reached full conversion to biotech cotton while others have not introduced the necessary legal and regulatory framework. Second, when cotton biotechnology is introduced, conversion takes place quickly. Third, the benefits appear to be relatively large, especially in developing countries where between 15 and 20 percent yield increases and 50 percent reduction in insecticide use have been observed. Section 2 discusses the reasons surrounding the biotech controversy and notes that opposition to biotechnology not only has blocked its adoption in Africa and other low-income countries, but also may have slowed down the development of second generation technologies. Section 3 explains how Africa missed the opportunity to embrace the technology. The last section concludes and discusses policy implications and lessons that go beyond cotton and Africa.

# **1. Biotechnology and the cotton market**

As in most aspects of life, the rules of allocating non-free resources are dictated by market forces or battles. Crops, including cotton, are no exception. Humans grow cotton for clothing while insects use it for food. Cotton growers win the battle by spraying the cotton plant with toxins. When the insects attempt to eat the plant (and, hence, the toxin), they die before inflicting irreversible damage. Here is where biotechnology comes in handy. Instead of the grower spraying the cotton plant, the toxin is inserted in the plant by genetically modifying the seed—a process similar to human vaccination.

Early stages of biotechnology made use of a family of soil organisms called _Bacillus thuringiensis_ (Bt) that produced certain toxins. Research by several public and private institutions in the early 1980s focused on inserting the Bt gene into tobacco plants. While initial results had limited success, a major breakthrough was achieved in 1988 by Monsanto—a biotechnology company—and by 1990 the first Bt cotton varieties were commercialized (see Tripp 2009 for an extensive discussion of the development of biotech cotton varieties).

Biotech cotton was introduced commercially in 1996 in Australia, Mexico, and the United States. China followed suit a year later and so did Argentina and South Africa in 1998. As of 2009, 10 countries have used the technology, accounting for more than half of world cotton area and 55 percent of production, a figure that may be higher if one accounts for illegal use of biotech varieties—illegal biotech is widespread in Pakistan as it was the case in India a few years ago.

At least three countries have adopted fully biotech cotton (Australia, South Africa, and the United States). Argentina, China, Colombia, India, and _— 3 —_
growth. In addition, we allowed for a quadratic effect, but again the dummy interactive performed best.

In line with some previous evidence, we expect that an increase in trade openness will promote growth. A better regulatory regime would increase growth through its effect on investment, productivity and new entry.<sup>6</sup> Many papers rely on indicators of rule of law that are based on survey data. A problem with such perception-based indices is that they are likely to be tainted by stage of development.<sup>7</sup> Still, as a robustness check we include the Kaufmann, Kraay and Zoido-Lobaton (2002) index in some of our regressions. Countries with a larger population and a larger market size are expected to grow faster because of scale economies. Countries that are land-locked and closer to the equator are expected to grow slower.

We first estimate Equation 1 with the log-level of real per capita income at PPP in 2000, in constant 1996 dollars (taken from the Summers-Heston dataset) as the dependent variable and openness measured using nominal trade/nominal GDP. Results are reported in Table 1. Column 1 reports the results including the regulation index but not the interactive term. All of the variables have the expected signs. We find that trade openness raises growth, but the coefficient is not significant. The results are similar to Rodrik et. al. (2002), who use rule of law in place of our index of regulations and also find no significant effect of openness on growth in crosssection regressions. The negative and significant coefficient on the regulation index implies that less regulated countries grow faster.<sup>8</sup> Population has no significant effect on growth.<sup>9</sup> Countries

> 6 Using detailed panel data on entry regulations in Europe and the United Status, Alesina et al. (2002) find that entry regulation greatly reduces investment and is more important than privatization in doing so. 7 See World Bank 2003 for an in depth discussion of the disadvantages of using perceptions based indicators. 8 The reader is reminded that higher values on that index reflect more regulation.

> 9 Alesina, Spolaore, and Wacziarg (2003) discuss the costs and benefits of size.  While there are several positive effects of size, heterogeneity has negative effects on growth and is likely to be larger in bigger countries.  Thus, it is not clear a larger size should be associated with higher income.

12
included to capture Chile’s default risk as perceived by foreign investors) and the cyclical component of the real exchange rate. We also included as exogenous variables the logarithm of the Chilean terms of trade in order to capture real external shocks and the Junk Bond spread (for reasons already outlined). Since we assume that Chile is a small open economy, it is intuitive to think that no biases arise by considering those two variables as exogenous. The Akaike, Schwartz and H-Q information criteria suggested a model with two lags and there is no evidence of serial correlation for this specification, according to the LM test.

Once again variable sources and definitions are presented in the Appendix B. The unit root tests performed (Appendix A) show that the null of non-stationarity is rejected at the 5% level for the inflation, interest rate and country risk series. Nonstationarity is also rejected for the terms of trade series at the 10% level and for the output gap and real exchange rate series at the 1% level.

The generalized impulse responses are presented in Figure 2. One should note that the domestic exchange-rate-indexed deposit rate does not present a statistically significant response to shocks to the EMBI spread, whereas the real exchange only presents a marginally significant response between the second and fourth months. Therefore, the impact of EMBI shocks on the real exchange rate seems to be relatively short-lived and small. In addition, the domestic deposit rate also seems to be resilient to shocks to the Chilean sovereign risk premium and so does the real exchange rate.

These conclusions are confirmed by the variance decomposition analysis (see Table 2). Shocks to the EMBI spread are only responsible for a small part of the forecast errors in the real exchange rate and the domestic deposit rate. In the first period, the EMBI accounts for 1.13% of forecast errors in the interest rate and 1.17% in the real exchange rate, whereas in period 10 the figures are 4.79% and 10.43% respectively. Hence, it seems that interest rates were insulated against external shocks in Chile, whereas the real exchange rate is slightly more vulnerable.

To sum up, the URR and other capital account policies seem to have been capable of reducing the pass-through of external shocks to the Chilean economy, especially as far as deposit interest rates are concerned. Nonetheless, the capital account policies did not completely insulate Chile, as the real exchange rate was more vulnerable to shocks.

In addition, we estimated a similar model for the Colombian economy for the period from January 1993 to June 2002. We included as endogenous variables the 8
well as on teacher attrition and the social context. _Absenteeism levels are also influenced both by teacher motivation_ and by the dispositions through which the teacher has been hired . . . .

UNESCO’s _Global Monitoring Report 2009_ (UNESCO 2009), although focused on governance issues, also reflects this view of intensive-margin effects: In Malawi, average teacher salaries are too low to meet basic needs. There, and in many other countries, _teachers often have to supplement their income with a second job, with damaging consequences for the quality of their teaching. . . ._ _Poor morale and weak motivation undermine teacher effectiveness._ Teacher retention and absenteeism and the quality of teaching are heavily influenced by whether teachers are motivated and their level of job satisfaction. _Evidence suggests many countries face a crisis in teacher morale that is mostly related to poor salaries,_ working conditions and limited opportunities for professional development (Bennell and Akyeampong, 2007; DFID and VSO, 2008). . . .

One consequence of low relative pay in Central Asia has been _an increase in the number of teachers seeking to supplement their income through a second job_ – a phenomenon that has been extensively documented in most Central Asian countries (Education Support Program, 2006). _This practice can have damaging consequences for the quality of education_ , with some teachers withholding curriculum to pressure students into private tutoring (Bray, 2003).

Similarly, the _Global Monitoring Report 2014_ (UNESCO 2014) notes that

[w]hen salaries are too low, teachers often need to take on additional work – sometimes including private tuition – _which can reduce their commitment to their regular teaching jobs and lead to absenteeism_ .

It is important to stress that these reports by international organizations all advocate multipronged approaches to improving teacher performance. None expresses the belief that raising salaries alone will be enough. Yet in each case, embedded somewhere in the argument is the view that salary increases and decreases have intensive-margin effects on the quality of teaching, as these quotes show.

# _C. Teachers in other countries_

We encounter this argument at the country level in countries other than Indonesia as well.

In the United States, advocates for raising teacher pay commonly cite these intensive-margin effects on teachers’ ability to better serve their students. A _San Francisco Chronicle_ opinion piece by the co-founder of Teacher Salary Project (TSP) argues that Teachers want to give their all, but _being financially stressed and moonlighting does not allow them to teach their best_ . (Calegari 2015) To bring this problem alive, the TSP has produced a short documentary about Laney, 4
# **1. Introduction**

Do higher food prices help or undermine efforts to reduce poverty in developing countries? This remains a critical issue in guiding policy, since most the world’s poor live in rural areas and work in agriculture. At the same time, expenditures on food account for the largest proportion of the budget of poor households. Global integration and international trade play a key role in determining food price developments that face poor farmers. The recent increases in food prices across the globe have led a number of governments to limit trade in agricultural products and intervene in agricultural markets, more often in the name of protecting the poor (Jacoby, 2013).

Most studies that have sought to assess the impact of higher food prices on the welfare of poor households have focused on whether the household is a net consumer or net producer of food. Since most poor households have been found to be net consumers of food, analyses using this approach typically conclude that higher food prices are likely to increase poverty in the short run (see, for example, Ivanic and Martin ((2008), (2014)).

However, there are some concerns with this approach. First, there is the issue of the reliability of the data on the net food position of households from household surveys. Substantial errors can arise from recall biases that lead to the underestimation of food production and from annualization of short recall responses that overstate consumption (see Headey (2016)). Second, there may be second-round effects beyond those on household income that can lead to households adjusting to higher food prices in ways that lead to lower poverty in the medium term. For example, food producing households could increase their use of fertilizers and/or invest in higher yielding seeds, that contribute to higher productivity and greater output. An increase in the demand for unskilled labor in rural areas, where poverty is concentrated, could result in higher wage income for the household. Finally, intra-household distributional decisions will influence the outcome of rising food prices in ways which are not captured in the net food position of the household.

This paper explores an important channel from food prices to poverty which has received less attention in the literature; the effects of rising food prices on child undernutrition. Child undernutrition is closely related to household welfare but can reflect the impact of household distributional decisions to rising food prices. This is important given the recent finding of 1
# **Explaining Gender Differences in Economic Outcomes in Burkina Faso**

Aletheia Donald, TM Tonmoy Islam and Anja Robakowski<sup>*</sup>

**Keywords:** gender, productivity, Burkina Faso **JEL:** J31, J7, O12

> *Donald: World Bank, <u>adonald@worldbank.org; Islam: Elon University, tislam@elon.edu; Robakowski: World Bank, arobakow@worldbank.org. This paper is a product of the Gender Innovation Lab (Office of the Chief Economist, Africa Region)</u> and of the Macroeconomics, Trade and Investment (MTI) Global Practice, and was prepared as a background paper for the 2023 Burkina Faso Country Economic Memorandum. We thank Kodzovi Senu Abalo, Markus Goldstein, Eugenie Maiga, Kofi Nouve, Daniel Pajank, Michael O’Sullivan and Christophe Rockmore for helpful comments. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Figure 4. Relative cancellation rates of MLS and non-MLS projects, based on propensity matched sample <!-- Start of picture text --> Cancellation rate<br>0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10%<br>With MLS Counterfactual Without MLS<br><!-- End of picture text --> Source: PPI Database, Feb 2016

# **_Conclusions and Next Steps_**

The analysis identifying counterfactual scenarios suggests a positive impact of multilateral support on PPP contract performance. The main result is that the observed cancellation rate for projects with MLS would have been 48% higher without support from multilateral development organizations. The dynamics that lead to this outcome are yet to be fully explored, but present important questions about the dynamics of project support associated with multilateral involvement in PPPs. Multilateral funding often involves policy advice, capacity building, oversight and risk mitigation, project preparation assistance, assistance in mediation or renegotiation, and other forms of support that could create ‘halo effects’ over and above direct funding. To what extent these kinds of support help mitigate problems that lead to project cancellation is an important area of further study, particularly considering that some kinds of multilateral interventions could potentially be replicated and extended to other projects. Moreover, some types of project support may be more positively impactful than others, and better understanding could help prioritize the oversight and support activities of both lenders and governments.

These findings may also be further expanded by (a) analyzing the effects of multilaterals by source of support (i.e., the specific institutions) and size of investment; (b) analyzing the effects of specific forms of multilateral financial support (e.g., guarantees, loans, etc.) on contract success; and (c) evaluating the impact of multilateral support on project performance outcomes (e.g., coverage expansion, improvements to service quality, efficiency and productivity gains, tariff reductions, etc.).

The latter two proposed analyses require gathering of additional project data. Once additional data is available to capture variations in project status over time, it will be possible to study the role of project and institutional factors on paths of contract performance (e.g., paths to distress and recovery from distress). An important question amenable to study with more historical data is what role multilaterals play in facilitating recovery from distress and reversion to operational status versus cancellation.

12
Table 6: Income effect of the trade reform on different household groups (CGE model)

|Income Category<sup>a</sup>|Base<br>(Million MD)|Change from Base<br>(%)|
|---|---|---|
|Total rural income|69,594|-11.78|
|Farm non-wage income|55,819|-12.78|
|Rural wage income|13,776|-7.73|
|Small farm income|18,313|-16.95|
|Medium farm income|20,651|-13.07|
|Large farm income|16,854|-7.91|
|Urban income|204,659|8.62|

Incomes are normalized by CPI.

25
_Gender Equality and Economic Development: The Role for ICTs_ 30 <!-- Start of picture text --> Figure 5<br><!-- End of picture text --> <!-- Start of picture text --> % Ratio of Girls to Boys in Primary & Secondary Schools<br>101 101 102<br>100 96 96 97 1991 / 92<br>92 1998 / 99 92<br>84 85<br>82 81<br>79 79<br>80<br>68<br>60<br>40<br>20<br>0<br>World High income High East Asia & Latin South Asia Sub-Saharan Middle East<br>income: Pacific America & Africa & North<br>OECD Caribbean Africa<br><!-- End of picture text --> <!-- Start of picture text --> Figure 6<br>(% of total labor force) Female Labor Force<br>50<br>44 44 45 46 46 46 1990 1996 2002<br>43<br>41 42 42 42 42<br>40 40 41<br>40<br>35<br>34 34<br>33 32 33<br>30 29<br>26<br>24<br>20<br>10<br>0<br>World High income East Asia & Europe & Latin South Asia Sub-Saharan Middle East<br>Pacific Central Asia America & Africa & North<br>Caribbean Africa<br><!-- End of picture text -->