rafmacalaba/datause-encoder-data huggingface dataset:
gains. The livestock sector provides some balance to the crop sector which depends on more rain. If there is a dry period which is hard on crops, it will lead to more income for livestock. Livestock can therefore offer farmers some protection against swings in precipitation.

Figures 7 through 12 map the percentage change of livestock income caused by different climate scenarios in 2100. Figures 7 through 9 map the impacts to small farms for each of the three climate scenarios. Figures 10 through 12 map the impacts for large farms.

For the small farms, CCC and CCSR predict an increase in livestock net revenue across Africa in 2100. CCC predicts especially large benefits in the Sahel, Central Africa and East Africa. CCSR predicts similar increases in the Sahel, Central Africa and East Africa, but also in West Africa. By contrast to the other climate models, PCM predicts almost no change in livestock net revenue across Africa in 2100.

Figure 10 demonstrates that the CCC climate scenario leads to reductions in livestock net revenue across Africa in 2100 for large farms. The reductions are especially large in the Sahel, Central Africa and East Africa. The PCM climate scenario (Figure 11) leads to very similar results to those of the CCC, with slightly more severe damages in West Africa. In the CCSR scenario (Figure 12), there is an overall increase of livestock net income for large farms in West, Central and East Africa but a decrease in South Africa and North Africa.

# **7. Conclusion and policy implications**

This paper examines the climate sensitivity of livestock management in Africa. The study was based on a wide-scale survey of African farmers that measures livestock net revenues. The data were combined with climate and other variables. Net revenues per farm were then regressed on climate and other variables to test whether net revenue is sensitive to climate. The study found that livestock net revenues in Africa are highly sensitive to climate. The climate sensitivity, however, varies according to farm size. Large farms are hurt by higher temperature but small farms are not. Small farms can apparently switch from temperate animals to heat tolerant animals and will switch from crops to livestock. The result is that livestock income rises for small farms 19
6 educational capital stock series is just for convenience and nothing critical hangs on this association.

The dependent variable of interest will be the growth of GDP per worker. GDP per worker (as opposed to GDP per work-aged population or total population) is the appropriate variable for growth accounting<sup>9</sup> . Although the estimates of labor force participation are, like all aggregate data, subject to criticism, they are better than using population or workforce aged population figures for labor which implicitly assumes what is known to be false--that labor force participation is equal across countries. Growth rates of GDP are calculated from PWT5 data unless otherwise specified (but World Bank growth rates give nearly identical results).

B) Cwrnwth **accnmting r ngre s,inn** with hinman capital Mankiw, Romer and Weil (1992) suggest that the Solow aggregate production function framework extended to include human capital is a useful way to approach long run growth:

# _1) Y,sA(t)*K, k *Ht ''*L, '_

Assuming constant returns to scale and normalizing by the labor force suggests estimating (where lower case represents per worker quantities):

# _2) y=a(t)*kat*h_ a

Now if this specification is valid, this equation could be estimated either in levels or in rates of growth.

Since estimation in levels raises numerous problems (to which I return below), I will estimate the following 9 This output variable does raise one problem. My estimates of human capital are based on estimates of the educaional capital of the labor force aged population, while my output is output per estimated labor force (altbough not corrected for unemploymem) sothat systematic differences in the evolution of the labor force versus the labor force aged population (say through differental female labor force participation) could affect the results.
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related problems; benefits that students in primary grades are entitled to such as cash stipend, textbooks, mid day meal, school uniforms.

The film and posters contained key information while the calendar contained all of the information in detail. The learning assessment booklet outlined the grade wise minimum levels of language and math skills that children were expected to have, based on minimum level of learning (MLL) framework recognized by the government of India. Parents could use the assessment booklet to test their child to find out whether the child knows the minimum that is expected for his grade. The information in the communication tools was obtained from the basic education departments of respective state governments. Before the campaign was launched, all tools were verified and approved by the state governments for their content.

On average, about 770 residents of an intervention GP in UP and about 675 residents of an intervention GP in MP attended the three rounds. No information campaign was held in the control villages. The campaign team was blind to the baseline and follow up surveys. They were also unaware of the control GPs.

# **Follow-up survey**

Baseline survey participants were re-interviewed 12 months later by research assistants who had no knowledge of the intervention. To maintain this blinding, intervention group parents, students or committee members were not asked if they attended an informational meeting.

One year after the intervention, we conducted focus group discussions in 10 randomly selected intervention GPs in each of the states MP and UP. We conducted 2 focus group meetings in each village, one among low caste residents (SC and ST) and the other among residents belonging to general and backward caste categories (non-SC/ST). Each meeting had on average 12 participants who were asked if they remembered the campaign, whether they had discussed the information with anyone else in the village afterwards, whether they had taken up any school related issues with the teachers or VEC/PTA, why bigger changes were not seen and what could be done to improve the campaign. In these GPs, school committee members (i.e., VEC in UP and PTA in MP) were also asked the same questions as in the focus group discussions. In addition, they were also asked about any changes in the functioning of committee meetings and whether they ever tried to assess children’s learning in the school. These were individual interviews conducted in private, unlike the focus group discussions. 40 members participated in each state.

# **Analysis**

Teacher attendance is defined at the teacher level as the average of whether the teacher was present across the four visits. It takes values between 0 and 1. Teacher activity is defined at the teacher level as the average of whether the teacher was engaged in teaching across the four visits. It also takes values between 0 and 1.

10
Index Score below the threshold) and were formally employed (receiving the CR) opted out of making mandatory contributions to the CR and became informal, either by remaining employed and stopping their contributions to the CR or by switching to a non-contributory job.

# **II Related Literature**

From 1990–2005, the Colombian labor market underwent several legislative changes and economic reforms. Several studies analyze the impact of a change in labor taxes (Kugler 1999; Kugler and Kugler 2009), but these studies do not specifically address the effect of the health insurance provision on informal employment. Mondrag ́on et al. (2010) find that an increase in non-wage costs and in the minimum wage has a significant and positive effect on informal employment and a negative effect on informal wages. Gaviria and Henao (2001) report that a strategy used by households faced with a loss of employment is to participate in informal sector activities. Using data from the National Statistical Agency (DANE), these authors report an upward trend in informal employment<sup>11</sup> between 1996 and 2000 for both men and women (p.28). Unlike the studies above, we do not look at changes in labor taxes or the minimum wage, but we analyze the effects of the SR on informal employment. This analysis is important given that the cost of the SR is estimated to be 1.2% of GDP and because new reforms to the system are currently being considered in Colombia.<sup>12</sup> Other researchers have recently examined similar questions in other Latin American countries. For instance, in Argentina, Gasparini et al. (2009) find evidence of increased informality generated by a large conditional cash transfer program.

In Mexico, using the roll-out of Seguro Popular in different cities over time and controlling

> 11The definition of informality they use is as follows: workers in firms with fewer than 10 employees, nonprofessional independent workers, domestic workers, and unpaid family workers who work at least 15 hours

> 12One of the reforms that was implemented recently makes the POS and POS-S packages equal.

7
||MEKONG<br>BWI|MEKONG<br>PRECIPITATION|ZAMBEZI<br>BWI|ZAMBEZI<br>PRECIPITATION|
|---|---|---|---|---|
|Sample area (km<sup>2</sup>)<br>|189,000|189,000|334,000|334,000|
|Basin area (km<sup>2</sup>)|787,836|787,836|1,390,000|1,390,000|
|Sample area of<br>basin(%)|24.0|24.0|24.0|24.0|

**Table 4: Geographic results of BWI and Precipitation models for Mekong and Zambezi**

|Variable|Unit|Mean|Std. Dev.|Min|Max|Obs.|
|---|---|---|---|---|---|---|
|MEKONG(1988-<br>Precipitation|1990,1992-199<br>mm per<br>month|3)at ChiangS<br>70.229|aen<br>50.028|800.871|6158.064|50|
|Wetness value|Index|1.725|0.580|0.774|3.031|50|
|Lagged runoff|m3/s per<br>month|2266.331|1487.285|800.871|6158.064|50|
|ZAMBEZI(1988-<br>Precipitation|1990,1992-200<br>mm per<br>month|6)at Katima<br>86.503|Mulilo<br>72.245|0.098|268.066|201|
|Wetness value|Index|1.427|0.656|0.410|2.771|201|
|Lagged runoff|m3/s per<br>month|995.275|1090.253|146.526|5256.368|201|

**Table 5 : Descriptive statistics of variables used in regression**

33
29

**19** For more on this point with respect to rural Pakistan, see Adams (1995).

20It should be noted, however, that these Ginis of per capita household income for Bolivia and Brazil are based on the distribution of overall (that is, urban and rural) incomes, while the income Ginis used in this study are based on rural household income. In theory, one would expect that the distribution of rural household income to be more egalitarian than that of overall household income. See World Bank, World Development Indicators (Washington, DC: World Bank, 1998, Table 2.8).

21There is an active rental market for land in rural Egypt. Thus, while 75.7 percent of the survey households own no land, in terms of land access (that is, land owned plus land rented in) a slightly smaller percentage (61.6 percent) of the survey households have no land access.

22 For example, during the three-year period 1996-98, while wheat yields in Jordan averaged 1,170 kilograms per hectare per year, those in Egypt averaged 5,742 kilograms.

23In analyzing whether an income source is inequality-increasing or -decreasing, it is assumed that additional increments of that income source are distributed in the same fashion as the original units.

24Bent Hansen, The Egyptian Labor Market: An Overview, Development Research Department Discussion Paper N. DRD160 (Washington, DC: World Bank, 1985), p. 9.

25 In Egypt and Jordan government and public sector employment function as a kind of "work-welfare" program by providing work (and income) for large numbers of skilled and unskilled rural inhabitants. However, it should be noted that government/public sector employment in both countries favors the educated. In Egypt, for example, the government employment scheme of the 1960s -- which promised government employment to all high school and university graduates -- has in theory been abandoned. Yet in practice, most high school and university graduates still seek and find jobs with the government.

The continuing link between education and government employment in Egypt and Jordan can be demonstrated by estimating a probit selection model, where one is the decision to work for the government/public sector, and zero is otherwise. The results of this model, which is estimated on all males over 15 years of age, appear in Appendix Table 1. For rural Egypt, there is a steady increase in the size of the positive, "completed education" coefficients moving from elementary to high school and finally to university. For rural Jordan, although the coefficients for high school are not very large, there is the same rate of increase in the size of the education coefficients between elementary and university levels. As shown in the table, most of these education coefficients are highly significant. These results suggest
policy perspective, as this is the group that is likely to be induced to enroll by other interventions such as tuition subsidies.

The results of the instrumental variable approach implemented in this section suggest that at least some of the effect of college education is causal in reducing smoking prevalence. A similar result is obtained by Currie and Moretti (2003) who investigate smoking during pregnancy using the dates of college openings as an instrument for maternal education. Both instruments are likely to be effective at the college margin, which is the relevant margin for smoking decisions as documented in figure 2.

# **5. Conclusions**

This paper starts by analyzing smoking histories reconstructed from National Health Interview Surveys in the United States and retracing the evolution of smoking patterns across education groups. The main conclusion is that, after the gradual arrival of the information about the dangers of tobacco, the smoking prevalence among more educated people declined earlier and more dramatically.

The time pattern of the decline in smoking prevalence for individuals with college degrees suggests that at least some of the differential by education can be ascribed to a causal role of education in giving access to and processing the information. Figure 2 shows that the smoking prevalence among college graduates started to decline earlier than for the three other categories. The inflection point is in 1954, 10 years before the first Surgeon General’s Report, four years after the consensus in the medical profession, and one year after the first article in the general press on the dangers of smoking. Further, the results of multivariate regressions show that the effect of college education on smoking prevalence is robust even after controlling for income, indicating that the effect of education goes over and above the ability to generate a higher income.

This paper also argues that, because the decision to quit smoking is usually taken after education is completed, this margin of the smoking decision is cleaner to analyze than smoking initiation that often occurs before schooling levels are definitive. A detailed study of the incidence of smoking cessation using a proportional hazard model shows a very strong effect of education on the probability, for smokers, to quit smoking at any point in time. Very interestingly, for all birth cohorts that were of smoking age during the 19501970 period, it is during this interval that the effect of education is the strongest, independent of age, suggesting that the impact of education was at its peak when access to information about the risks associated with tobacco was not uniform.

Finally, in order to isolate an exogenous variation in college education, this paper exploits the fact that college attendance was used as a draft avoidance strategy during the Vietnam War period. Using the risk of induction in Vietnam as an instrument for college education, and controlling for actual Vietnam veteran status, instrumental variable 17
Table A6: Does the effect of ACASI differ between those who pass screening and those who don’t? (Individual IPV Questions)

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||=1 if responded y|es to _individual_ q|uestion in the fo|llowing category:|All|
||Controlling<br>Behavior|Emotional<br>IPV|Physical<br>IPV|Sexual<br>IPV|questions<br>pooled|
|**Panel A. Malawi**||||||
|SI _×_ Screen Pass (_β_)|0.08***|0.05***|0.01*|0.03***|0.05***|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|SI _×_ Non-Pass (_γ_)|0.10***|0.06***|0.01|0.03*|0.05***|
||(0.02)|(0.02)|(0.01)|(0.01)|(0.01)|
|Screen Pass|-0.00|-0.00|0.00|-0.01|-0.00|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|FTFI _×_ Non-Pass mean|0.12|0.08|0.03|0.04|0.07|
|_p_-value (_β_ =_γ_)|0.502|0.765|0.683|0.779|0.791|
|Number of individuals|1,715|1,711|1,712|1,709|1,716|
|Observations|11,887|6,802|10,181|5,095|33,965|
|_Efect of SI for the average_|_respondent_|||||
|Pooled SI efects|0.09|0.05|0.01|0.03|0.05|
|_p_-value|0.000|0.000|0.070|0.001|0.000|
|**Panel B. Liberia**||||||
|SI _×_ Screen Pass (_β_)|0.05***|0.02|0.02|0.02**|0.03**|
||(0.02)|(0.02)|(0.01)|(0.01)|(0.01)|
|SI _×_ Non-Pass (_γ_)|0.06**|0.00|-0.01|0.04**|0.02|
||(0.03)|(0.03)|(0.02)|(0.02)|(0.02)|
|Screen Pass|-0.02|-0.01|-0.01|-0.01|-0.01|
||(0.02)|(0.03)|(0.02)|(0.02)|(0.02)|
|FTFI _×_ Non-Pass mean|0.23|0.21|0.11|0.06|0.16|
|_p_-value (_β_ =_γ_)|0.840|0.711|0.358|0.419|0.860|
|Number of individuals|1,259|1,259|1,259|1,259|1,259|
|Observations|8,752|5,006|7,508|3,758|25,024|
|_Efect of SI for the average_|_respondent_|||||
|Pooled SI efects|0.05|0.01|0.01|0.03|0.03|
|_p_-value|0.000|0.479|0.434|0.005|0.013|

Note: Regressions are at the respondent- _question_ level (violence is not aggregated into indexes). Regressions include question-level fixed effects and individual controls (including all variables in Table 1). “Screen Pass” is defined by selecting “yes” to all questions in Table 2. Standard errors clustered at individual level in parentheses. ***, **, and * represent significance at 1%, 5%, and 10%, respectively.

29
## **Prioritization**

The prioritization is a combination of the criticality identified before and the risk to the infrastructure. A prioritization matrix is built to combine criticality and risk, and displayed below in Table 1, giving a 1 to a very low risk road and a 5 to a very high risk road. For example, a district identified as low priority will have a low risk, and will be low priority for investment, no matter the level of risk. On the other side of the spectrum a road identified as highly critical will range from medium to very high priority and therefore will be a priority for investment, as any disruption will cause a huge burden for users and the local economy.

_Table 1: Risk Matrix_

|||**Risk to the**<br> <br>|**infra**<br>|**struct**<br>|**ure**<br>|
|---|---|---|---|---|---|
|||**1**<br>**2**|**3**|**4**|**5**|
||**1**|1<br>1|1|1|2|
|**lity**|**2**|1<br>2|2|2|3|
|**tica**|**3**|2<br>2|3|3|4|
|**Cri**|**4**|3<br>3|4|4|5|
||**5**|3<br>4|4|5|5|

# **Results: Zambezia and Nampula**

## **Baseline Network Model**

We first selected the nodes and roads of Mozambique’s network that were useful for the analysis. Even if we focused on Nampula and Zambezia, we modeled the national network since trips from and to those provinces have origins and destinations in the entire country. The road national administration (ANE) provided the classified road network, and we completed it with an assessment of the conditions of unclassified roads in the provinces of Nampula and Zambezia using an innovative smartphone-based technique (Wang and Guo 2016, Espinet et al. 2017). This last set of roads was merged into the classified roads to obtain a complete transport network. The clean new network is composed of 798 links containing paved and unpaved roads, and primary, secondary, tertiary and unclassified roads (Figure 2).

We identified, in consultation with local authorities, 138 major Origin/Destination nodes including major population clusters, seaports, airports, border crossings and, of special interest for this study, agriculture production clusters in the provinces of Nampula and Zambezia (see Figure 16 in the annex). The agriculture clusters where identified based on the results from an agriculture production potential model, SPAM, developed by the International Food Policy Research Institute (IFPRI). The underlying data can be found in the annex.

The road network contains information on the traffic volume – provided by ANE and collected in the field for unclassified roads – and is shown in Figure 17 using annual average daily traffic (AADT). Using the measured traffic volumes and a simplified travel demand model, we estimated the traffic between each OD pair (see detailed methodology in the annex). A few roads – 275 km - where left out of the analysis as they did not give access to any of the identified nodes (a map highlighting these roads can be found in the annex).

7
-38-

# CONCLUSIONS

31. The research reported hers has found that the interest rate plays an important role in both short-run and long-run determinatioa of non-fuel primary commodity prices--the World Bank's 33 commodities price index and its sub-indices, agriculture (food and non-food) and minerals and metals.<sup>Co-</sup> integration and error correction techniques were applied. The central conclusion of these estimations is that the hypothesis that there is<sup>a</sup> stationary long-run relationship between the levels of commodity prices and interest rates cannot be rejected. These results are in line with the theory of commodities as financial assets and contrast with Powell's (1989) findings that interest rates play "little role in either the short run or the long run I2. After establishing the existence of co-integration between commodity prices and interest rates an error correction model was developed for<sup>each</sup> commodity price index and the interest rate. This estimated relationship confirms the results of the co-integration tests and evidences a remarkable forecasting ability. What is also interesting is the fact that the introduction of the price of oil as a macro-economic variable in the<sup>error</sup> correction models has a significant impact in explaining the variation<sup>in</sup> commodity prices.

33. Though a stationary long-run relationship cannot be established between the commodity price indices and money supply movements, causality