rafmacalaba/datause-encoder-data huggingface dataset:
Moreover, through these better yields, it may be possible to boost African agricultural TFP rates, which have already been rising. Empirical work at IFPRI on the sources of agricultural productivity growth among Sub-Saharan African countries shows that TFP growth shifted from a negative rate (-2.0 percent) between the 1960s and the 1980s to a positive rate of near magnitude (1.7 percent) from 1985 to 2003 (IFPRI, 2008). Moreover, between 2003 and 2005, 13 countries in Sub-Saharan Africa achieved annual agricultural growth rates greater than 5 percent. While agricultural performance has improved, there remains scope for further improvement. In countries which are better developed, such as South Africa, Nigeria, Benin, and Uganda, higher rural incomes can become a catalyst for TFP growth and non-farm expansion. However, in countries with low population densities like Sudan, Central African Republic, Chad, Niger, and Mali, it will be difficult to generate the scale and multiplier effects from agriculture to the nonfarm that helped lead to China’s boom. Hence, China’s rural experience is only applicable to a selective range of African economies where there exists potential for agricultural growth and nonfarm expansion. Finally, it is important to note that while niche industries like Kenyan horticulture exports can penetrate global markets, it is vital to develop adequate rural labor-intensive industry to reduce unemployment and accelerate poverty reduction.

# **IV. Exchange Rate Dynamics**

Fourth, one important ingredient of the Chinese economic success has been the maintenance of a competitive exchange rate and the avoidance of inflationary tendencies through prudent macro policies and high savings rates in private households. China is a manifest example of the value of keeping an undervalued exchange rate as a catalyst for exports and growth. According to IMF dataset, the Chinese currency lost about 70 percent of its value against the dollar from 1980 to 1995, as measured by the trade-weighted real effective exchange rate index.

24
**Table 14: CLASS: Student test scores regressed on classroom observation variables (level 1) and teacher test scores**

|||All tea|chers||Te|achers with|test score <|=0|Te|achers wit|h test score|>0|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Emotional Support|-0.0809|0.0186|||-0.3543|-0.105|||0.0109|-0.181|||
||(0.1742)|(0.126)|||(0.2658)|(0.196)|||(0.1869)|(0.143)|||
|Classroom Organisation|0.1189|0.122|||0.3015|0.142|||-0.0252|0.205*|||
||(0.1308)|(0.113)|||(0.2390)|(0.198)|||(0.1297)|(0.117)|||
|Instructional Support|0.2329|0.149|||0.2971|0.153|||0.2603|0.263*|||
||(0.2008)|(0.145)|||(0.3039)|(0.211)|||(0.2433)|(0.152)|||
|Student Engagement|-0.0376|-0.0239|||-0.0236|0.120|||0.0096|-0.131|||
||(0.1234)|(0.0844)|||(0.2049)|(0.176)|||(0.1472)|(0.118)|||
|CLASS PC1|||0.0736|0.0845***|||-0.0123|0.103*|||0.1352**|0.0336|
||||(0.0470)|(0.0294)|||(0.0735)|(0.0539)|||(0.0513)|(0.0499)|
|Teacher Test Score|-0.0567|-0.00814|-0.0444|-0.00213|0.2415***|0.143**|0.2218***|0.119*|-0.3509|-0.372|-0.3482|-0.359|
||(0.1178)|(0.0796)|(0.1075)|(0.0784)|(0.0681)|(0.0689)|(0.0687)|(0.0691)|(0.2811)|(0.315)|(0.2737)|(0.297)|
|Observations|4,619|4,586|4,619|4,586|2,333|2,300|2,333|2,300|2,286|2,286|2,286|2,286|
|R-squared|0.029|0.235|0.023|0.234|0.054|0.290|0.034|0.288|0.069|0.265|0.060|0.252|
|Controls|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|
|Teacher Test|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|F-Test: Observation vars.|0.441|0.065|0.121|0.005|0.507|0.266|0.868|0.063|0.089|0.058|0.011|0.504|
|F-Test: Student vars.||0.020||0.024||0.000||0.000||0.001||0.000|
|F-Test: Household vars.||0.000||0.000||0.000||0.000||0.000||0.000|
|F-Test: Teacher vars.||0.161||0.167||0.181||0.169||0.048||0.144|
|F-Test: School vars.||0.000||0.000||0.001||0.002||0.000||0.000|

Note: Standard errors clustered at the teacher level in parentheses. ***, **, and * indicate significance at the 1, 5, and 10 percent critical level.

54
framework, short-term impact corresponds to the first two periods. Medium-term impact is supposed to occur between periods 3 and 5. Long-term impact is felt beyond period 6.

# **_5. Concluding Remarks_**

This paper reviews some common approaches in modeling the poverty and distributional impact of macroeconomic shocks and policies, such as terms-of-trade shocks, fiscal adjustment, monetary policy, and trade liberalization. What is needed in these situations is a framework that adequately links a macroeconomic model to a model of the distribution of economic welfare at the individual or household level.

The approaches or specific models reviewed in this paper vary in the ways they specify the macroeconomy, the distribution of welfare, and the macro-micro linkages. Models that focus only on income distribution may be viewed as frameworks that keep the macro-model implicit. They can therefore be used in conjunction with assumptions about the response of the macroeconomy to shocks and policies. The second approach involves embedding distributional mechanisms within a general equilibrium framework, while the third class of models adopts a modular approach in linking poverty and distributional outcomes to macroeconomic shocks and policies. An emerging approach known as Top-Down/Bottom-Up or TD/BU (Savard 2005) is worth mentioning. It tries to account for the feedback effect from the micro-module to the macro and back until convergence is achieved. Which approach to adopt depends, of course, on the problem at hand, the data, and other constraints imposed by modeling resources, such as skills.

The reliability of a model designed to predict the poverty and distributional outcomes of macroeconomic shocks and policies depends on how well the model handles three _fundamentals_ . The first relates to the _heterogeneity_ of socioeconomic agents in terms of endowments (assets and personal characteristics) and behavior. The availability of reliable household data is a key constraint in accounting for such heterogeneity. The second fundamental concerns the modeling of transmission channels in terms of _institutional arrangements_ that control social interaction. A realistic representation of such arrangements allows one to estimate more accurately both the direct and the indirect effects of shocks and policies. Finally, the macroeconomic model should be designed to account for the interdependence among policy issues (stabilization, structural and 54
To the best of our knowledge, no empirical research has focused on female education as a potentially-critical determinant of vulnerability to extreme weather events. Assessing its importance and implications is a core feature of this paper. Drawing on an econometric analysis of panel data, we address two key questions: As climate change increases potential vulnerability to extreme weather events, can expanding female education neutralize this increased vulnerability? If so, how much would it cost?

The remainder of the paper is organized as follows. Section 2 provides a summary of losses from extreme weather events in developing countries during the period 1960-2006. In Section 3, we review recent projections of climate impacts, economic growth and demographic change. We focus particularly on projections by integrated assessment models that incorporate links between climate change and economic activity. Section 4 specifies a set of risk equations for weather-related disasters and estimates them by fixedeffects. In Section 5, we develop country-specific projections for female education. Section 6 uses our econometric results and education projections to forecast future risks under alternative assumptions about climate change. In Section 7, we use these projections to estimate the cost of reducing future weather-related risks through more intensive investment in female education. Section 8 summarizes and concludes the paper.

# **2.  Losses from Extreme Weather Since 1960**

The most comprehensive data on weather-related losses are maintained by the Centre for Research on the Epidemiology of Disasters (CRED) at the School of Public Health of the Université Catholique de Louvain, Brussels.<sup>1</sup> For developing countries<sup>2</sup> , the CRED data provide a sobering view of weather-related losses since 1960. Table 1 presents the

> 1  To be entered in CRED’s EM-DAT database, a natural disaster must involve at least 10 people reported killed; 100 people reported affected; the declaration of a state of emergency; or a call for international assistance.  Recorded deaths include persons confirmed as dead and persons missing and presumed dead. Total affected persons include people suffering from disaster-related physical injuries, trauma or illness requiring medical treatment; people needing immediate assistance for shelter; or people requiring other forms of immediate assistance, including displaced or evacuated people.

> 2  We define these as countries identified by the World Bank as Low Income or Lower Middle Income.

4
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54. Increased domestic financing of the fiscal deficit is risky, given its inflationary implications. Continued high levels of Central Bank advances to the Central Government will likely fuel domestic inflation and could start an inflation-devaluation spiral in a country which maintained fixed exchange rate for over 60 years. Central Bank advances to the Central Government rose during 1986-1987. Of the total, almost half was provided with no interest and the remainder at subsidized interest rates. Central Bank advances to the Central Government were facilitated by high reserve requirements on commercial banks. Furthermore, treasury bond sales to the public and to commercial banks also accelerated. This increase was made possible by maintaining high interest rates on treasury bills, with rates on these bills ranging between 9Z and 112 in 1987. As a result, the stock of domestic debt continued to expand (Table 9). If the rate of growth of debt is maintained at present levels, interest payments on the domestic debt would likely absorb a large share of Central Government revenues in the next few years.

Table 9: Debt Stock of tho Public Soctor. 1984-88 **(mlillione** of t)

||**19**|**84**|**19**|**85**|19|88|
|---|---|---|---|---|---|---|
||Domestic|External|Domestic|External|Domestic|Externnl|
|Central**Government**|1,699.4|2,278.2|1,837.9|**2,615.8**|2,088.6|2,878.5|
|Public Sector Institutions|840.9|647.1|929.7|810.2|988.9|902.2|
|Municipalities|223.8|12.2|230.2|10.4|248.1|9.2|
|Totel|**2,664.1**|2,932.6|2,997.8|**8,486.4**|8,270.6|8,769.9|
|Memorandum<br>Item|||||||
|Total**as**Xof**GDP**|42.8|**46.5**|44.6|51.1|43.6|60.6|
|Source: Central Bank of Hondura|s.||||||

55. As a result of liberal and excessive use of almost all possible sources of financing in the past. few years, the Government has virtually exhausted all prudent forms of financing the fiscal deficit for the next several years. Regarding external financing of the deficit, USAID indicated that it will scale down its transfers in the future. Owing to the completion of the El Cajon hydro-project and the weak pipeline of projects by the multilateral agencies coupled with projected increase in amortization obligations on external debt, net external financing for the Central Government will be at reduced levels in the next several years. On the domestic side, further large expansions in Central Bank advances to the public sector will likely fuel domestic inflationary pressures (Table 10 shows the financing of the public sector by the domestic monetary system).
Table 7: Parental Engagement with Primary School Staff

||(1)<br>Parents or<br>other<br>members<br>met with the<br>principal in<br>the past year|(2)<br>Number of<br>meetings<br>with the<br>principal in<br>the past year|(3)<br>Parents or<br>other<br>members<br>met with the<br>teacher of<br>the target<br>child|(4)<br>Number of<br>meetings<br>with the<br>teacher of<br>the target<br>child|(5)<br>Parents or<br>other<br>members are<br>part of the<br>school<br>committee|
|---|---|---|---|---|---|
|Treatment|0.067<sup>_∗∗∗_</sup>|0.222<sup>_∗∗∗_</sup>|0.009|0.138|0.014|
||(0.021)|(0.058)|(0.029)|(0.100)|(0.015)|
||[0.015]|[0.003]|[0.742]|[0.424]|[0.605]|
|Observations|3765|3760|2126|2122|3765|
|Control mean|0.212|0.398|0.321|0.624|0.138|

_Notes:_ *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.10. OLS estimates at the target child-level are presented using outcomes reported by the caregiver at endline. Standard errors clustered at the community level are shown in parentheses. All regressions include district fixed effects. Westfall and Young (1993) _p_ -values are shown in square brackets, where 10,000 replications were used. Estimates are weighted to give each community an equal weight in the analysis. Covariates are not included in the regressions.

its Family Care Indicator (Hamadani et al., 2010; Kariger et al., 2012). Our evidence indicates that parents in treated communities provide a more stimulating environment for their children. Parents were more actively involved in stimulating their children (scoring 17.1% of standard deviation higher than parents in the control group; _p <_ 0 _._ 01) and the children of the treated communities participated more in play at home (7.7% of a standard deviation higher than in control households; _p <_ 0 _._ 1).

Second, we find that parents of target children have greater engagement with the child’s primary school. It is important to note that the parental education component of the DICIPE program promoted communication between parents and their _preschool_ . What we find here is that parents’ greater involvement with educators spills over to the primary school environment. These results are shown in Table 7. Column (1) shows that parents and other household members in treated communities were 6.7 percentage points more likely to meet with the child’s primary school principal in the past year relative to control communities ( _p <_ 0 _._ 01). Compared to a control mean of 21.2%, this is a sizeable 32% increase. Column (2) shows that parents and other household members had 0.22 more meetings with the principal in the past year, on a control mean of 0.4 meetings (a 30
- 12 -

inundated and inaccessible because of the poor condition of roads. Thus, _ELEV_ and SLOP are expected to explain potential needs for road works to a certain extent. The data come from a global spatial data set, the Shuttle Radar Topography Mission (SRTM), which provides 90m resolution elevation data of the world. Under the ADSP, about 340 pipe culverts and 20 box culverts were installed. Still, five clusters of emergency works were carried out to repair washed away embankments in 2010, 2012 and 2013 (RDA 2014).

In addition, remoteness from a main trunk road, which is T4 in Eastern Province, is also considered important to predict project areas. On one hand, roads are generally supposed to form an integrated network. Since the primary road network has already been well maintained in Zambia, feeder roads close to it are more likely to be improved as well. On the other hand, the project aimed at providing transport connectivity to remote areas. If this is the case, more remote areas would receive more road works. To capture this effect, the straightline distance from each project site to T4 is calculated (denoted by _KMT4_ ). Note that as discussed above, agricultural potential is also an important instrument to determine the OPRC roads. Our model has already included _POT_ as an independent variable. The validity of the instruments will be ex post examined by conventional tests, such as exogeneity and overidentifying restriction tests.

An important policy question is why the OPRC intervention could increase agricultural production. It is obvious that nothing would happen unless significant road improvement works are actually carried out, even though a performance-based contract is made. When the follow-up survey was carried out, some rehabilitation works do not seem to have been completed.<sup>5</sup> Then, no impact would have materialized even though roads were under the OPRC.

> 5 Although the RDA status reports show that all the rehabilitation works were completed when the follow-up survey was carried out, the perception of road users was sometimes different. Some respondents along the treatment roads indicated that roads were still under rehabilitation, for example, D128 in Chipata District and U29 in Katete District (RDA, 2031).
**Table 1: School construction and maternal education: Levels, Differences and Difference-indifferences.**

||High|Low|Differe|nce in Dif|ferences|Robu|stness|
|---|---|---|---|---|---|---|---|
||INPRES|INPRES||||Ch|ecks|
||[1]|[2]|[3]|[4]|[5]|[6]|[7]|
|_Panel A: Years of Educati_|_on_|||||||
|1966-1970 Birth Cohort|5.776|9.411||||||
||(0.609)|(0.447)||||||
|1971-1975 Birth Cohort|6.773|9.155||||||
||(0.562)|(0.510)||||||
|Difference|0.997|-0.255|1.253|1.049|1.064|1.242|1.133|
||(0.192)|(0.177)|(0.258)|(0.261)|(0.306)|(0.481)|(0.500)|
|||||[0.016]|[0.008]|[0.024]|[0.052]|
|_Panel B: Completed Prim_|_ary Education (Yes=1)_|||||||
|1966-1970 Birth Cohort|0.631|0.939||||||
||(0.086)|(0.022)||||||
|1971-1975 Birth Cohort|0.766|0.935||||||
||(0.080)|(0.020)||||||
|Difference|0.135|-0.004|0.139|0.121|0.133|0.142|0.143|
||(0.030)|(0.021)|(0.036)|(0.039)|(0.041)|(0.074)|(0.074)|
|||||[0.040]|[0.012]|[0.152]|[0.096]|
|_Panel C: Literate (Yes=1)_||||||||
|1966-1970 Birth Cohort|0.633|0.934||||||
||(0.051)|(0.028)||||||
|1971-1975 Birth Cohort|0.79|0.937||||||
||(0.058)|(0.017)||||||
|Difference|0.157|0.003|0.154|0.13|0.132|0.154|0.142|
||(0.030)|(0.020)|(0.035)|(0.033)|(0.039)|(0.063)|(0.066)|
|||||[0.008]|[0.016]|[0.044]|[0.076]|
|_Controls (D-in-D’s)_||||||||
|District and year of birth|FE’s||NO|YES|YES|YES|YES|
|<br>Treated cohort interaction|<br>with school-aged child|ren in 1973 (ln scale)|NO|NO|YES|YES|YES|
|Treated cohort interaction|with enrollment_per ca_|_pita_in 1971 (ln scale)|NO|NO|NO|YES|YES|
|Treated cohort interaction|with 1970’s sanitation|program|NO|NO|NO|NO|YES|
|Sample|674|1,444|2,118|2,118|2,118|2,118|2,118|

Notes: Standard-errors in parentheses are clustered at the district of birth. Wild-bootstrap significance p-values in brackets.
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||||Preceding o|utcome was:||
|---|---|---|---|---|---|
||Initial|(_Stag_,_Stag_)|(_Stag_,_Hare_)|(_Hare_,_Stag_)|(_Hare_,_Hare_)|
|_HH_|-0.133|0.0360|-0.423<sup>_∗∗_</sup>|-0.159|-0.254|
||(0.0869)|(0.0362)|(0.119)|(0.126)|(0.201)|
|_H_ in _LH_|-0.0943|0.0152|-0.184|-0.146|-0.121|
||(0.102)|(0.0576)|(0.131)|(0.132)|(0.215)|
|_L_ in _LH_|0.0974|0.0152|0.0741|-0.130|0.0114|
||(0.0970)|(0.0386)|(0.122)|(0.133)|(0.173)|
|Type|0.354<sup>_∗∗∗_</sup>|0.109<sup>_∗∗_</sup>|-0.0516|0.174|-0.122|
||(0.0546)|(0.0439)|(0.0645)|(0.118)|(0.105)|
|Land|0.00584|-0.00153|0.00541|-0.00156|-0.00514|
||(0.00400)|(0.00242)|(0.00480)|(0.00682)|(0.00775)|
|High school|0.0622|-0.0777|0.183|-0.0840|-0.124|
||(0.0879)|(0.0593)|(0.0981)|(0.138)|(0.149)|
|Non-mud house|0.0519|0.0234|0.0678|0.0459|0.0358|
||(0.0629)|(0.0474)|(0.0842)|(0.104)|(0.112)|
|_N_|242|452|181|181|154|

_∗ ∗∗ ∗∗∗ p <_ 0 _._ 05, _p <_ 0 _._ 01, _p <_ 0 _._ 001 Table 3: Probit regression for _H_ versus _L_ players, by pair status. Marginal probabilities that a player chose _Stag_ are shown, with standard errors of the marginal effects in parentheses.

it would be natural for a person who adheres to the culture of honor to interpret the loser’s payoff as an insult to his honor and to retaliate. As shown in the anthropology and sociology literatures, the culture of honor is much stronger among high-caste men than among low-caste men. To test that this is indeed true of the communities from which we drew our samples, we implemented in 2013 a vignette-based survey of the norms for retaliation. The survey was conducted in 22 hamlets in the same area in which we had conducted the Stag Hunt experiment. The design follows a format developed by social psychologists for measuring moral attitudes (see, for example, Haidt, Koller and Dias, 1993). Subjects were presented with vignettes in which one individual, named Dinesh, behaves in a way that harms a second individual, Mahesh, who then responds violently. The subjects were asked whether or not they thought that Mahesh’s response was justified, and they were also asked the open ended16