rafmacalaba/datause-encoder-data huggingface dataset:
||Total<br>spending|Spending<br>at input<br>fair|Other<br>spending<br>on inputs|Fertilizer<br>per acre|Fertilizer<br>per acre|Land<br>cultivated|Yield|
|---|---|---|---|---|---|---|---|
|Agricultural|0.04|0.45|0.40|0.03|-0.18|0.20|0.08|
|savings|[0.30]|[0.30]|[0.43]|[0.19]|[0.17]|[0.94]|[0.06]|
|Low Commitment|0.02|-0.08|-0.28|-0.59|-0.11|-0.98|-0.02|
|Savings|[0.50]|[0.25]|[0.44]|[0.26]**|[0.21]|[0.65]|[0.06]|
|Low Commit*|0.49|0.91|0.30|0.24|0.58|-0.13|-0.05|
|Burkina Faso|[0.59]|[0.54]*|[0.63]|[0.33]|[0.27]**|[1.05]|[0.07]|
|Burkina Faso|-1.54|3.46|-2.58|0.57|0.30|-0.31|-0.07|
||[0.50]***|[0.79]***|[0.60]***|[0.31]*|[0.29]|[0.97]|[0.08]|
|Male|0.86|-0.25|1.52|0.51|0.03|1.81|0.14|
||[0.37]**|[0.84]|[0.56]***|[0.28]*|[0.24]|[0.89]**|[0.09]|
|Observations|603|603|603|571|570|581|603|
|R-squared|0.13|0.24|0.14|0.19|0.28|0.11|0.09|

Notes: Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1. The sample is T2, T3 and T4

**Table 9: Impact of Experimental Savings (LATE) on Agricultural Investment**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|||After one<br>|month<br>Other<br>||Ove<br>|r whole seaso<br>|n|
||Total<br>spending|Spending at<br>fair|spending<br>on inputs|Fertilizer<br>per acre|Fertilizer<br>per acre|Land<br>cultivated|Yield|
|Amount saved|0.0001|-0.0015|-0.0013|0.0010|0.0007|0.0001|0.0009|
||[0.0009]|[0.0010]|[0.0010]|[0.0010]|[0.0008]|[0.0002]|[0.0020]|
|Burkina Faso|-1.2669|5.8988|-0.0112|-0.8385|-0.5310|-0.2348|-2.3897|
||[1.6896]|[1.7474]***|[1.7069]|[1.7916]|[1.3335]|[0.3741]|[3.4828]|
|Male|1.0254|-0.3811|1.5394|0.9624|0.4970|0.1427|1.8254|
|||||[0.3910]*|||[1.0597]|
||[0.4217]**|[0.8474]|[0.6462]**|*|[0.4191]|[0.1083]|*|
|Constant|9.4292|5.1962|12.1826|-1.5551|1.2575|-0.5763|3.7750|
||[2.7792]***|[3.2800]|[3.2274]***|[3.1309]|[2.4265]|[0.7056]|[6.2733]|
|Observations|603|603|603|588|587|603|591|
|R-squared|0.0454|-0.1376|-0.0875|-0.5038|-0.2291|-0.1482|0.0019|

Notes: Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1. The sample is T2, T3 and T4 Other control variables included: ag savings, low commitment savings, low commitment X Burkina Faso The results in Table 9 underscore that it was the type of savings instrument rather than the amount saved that had an impact on agricultural investment. In and of itself the amount saved did not have an impact on spending, although it is worth noting that the amount saved varied significantly across the types of 22
The headcount rate is defined as the proportion of people below the poverty line. One concern applying the P0 measure is that each individual below the poverty line is weighted equally and, therefore, the principle of transfers is violated. Hence, it is possible to increase social welfare by transferring money from the very poor to lift some richer poor out of poverty. P0 takes no account of the degree of poverty and it is unaltered by policies that lead to the poor becoming even poorer.

One measure of poverty that takes this latter point into account (at least in weak form) is the poverty gap measures. P1 is the product of incidence and the average distance between the incomes of the poor and the poverty line. It can be interpreted as a per-capita measure of the total economic shortfall relative to the population. P1 distinguishes the poor from the not-so-poor and corresponds to the average distance to the poverty line of the poor. One problem with the poverty gap measure is that it will increase by transfers of money from poor to less poor (who become non-poor), and from poor to non-poor. Furthermore, transfers among the poor have no effect on the poverty gap measure.

The P2 measure of poverty is sensitive to the distribution among the poor as more weight is given to the poorest below the poverty line. P2 corresponds to the squared average distance of income of the poor to the poverty line. Hence, moving from P0 towards P2, more weight is given to the poorest in the population.

This paper sets its poverty bar very low. To define “extreme poverty” it uses the indigence, or “food only” poverty line—those with sufficient income to buy a basic food basket are above the line. The poverty line is based on the monetary value of food items only. This measure is based on the cost of a “minimum food-basket” equal to the FAO minimum caloric intake of 2,288 daily per household member (Rocha 1997). Households are classified as extremely poor if their total income is less than the cost of a basic food basket. In 2001, the poverty line was of R$80.92.

As most poverty studies in Brazil, no adjustment is made for the fact that young children do not need as many calories as adults do. Hence, the poverty rates presented in this paper may slightly overstate poverty in Bahia.

The analysis of poverty correlates is based on a multivariate analysis using the probit regression techniques simultaneously for Brazil and Bahia.

The analysis in this paper is based on available data: PNADs (1981-2001), Censuses (1991 and 2000), IQV (1990 and 2000), _Contas Regionais do Brasil_ , _Pesquisa Nacional de Saneamento Básico_ , and other data available online such as educational data from MEC/INEP, government spending data from STN/Ministry of Finance and various data from SEI ( _Superintendência de Estudos Econômicos e Sociais da Bahia_ , an autarchy of the Secretary of Planning in Bahia).

11
at the tariff line (Harmonized System 8 digits) are either zero or equal to the MFN tariffs.<sup>4</sup> We test the predictions of our model using data on preferential trade between the U.S. and Latin America. The empirical example investigated in this work is important since the U.S. is one of the wealthiest and largest economies in the world, and Latin America<sup>5</sup> constitutes a group of developing countries whose dependency on exports to the U.S. is signi fi cant.<sup>6</sup> Moreover, about half of Latin American exports enter the U.S. market through a myriad of preferential schemes.<sup>7</sup> The predictions of the model fi nd strong support in the data. Domestic importers and Latin American exporters’ lobbying activities signi fi cantly increase on average the U.S. tariff preferences granted to Latin American countries. Furthermore, the empirical exercise shows that U.S. importers (tend to) capture most of the rents generated by the tariff preferences granted to Latin American countries. These results offer a pessimistic view about using preferential trade schemes as a reliable source of gains for developing countries. These conclusions are robust to modi fi cations in the de fi nition of political organization, to the exclusion of agricultural goods, and to changes in the level of aggregation of the data.

The political economy of trade literature has been trying to evaluate the impact of foreign lobbying on U.S. trade policy. The relation between foreign lobbying and the determination of the U.S. MFN tariffs was investigated by Gawande, Krishna and Robbins (2004) using a Cournot-Nash competition model. They found that foreign lobbying decreases on average the MFN tariff applied by the U.S.. Kee, Olarreaga and Silva (2003) studied whether lobbying by Latin American exporters plays a signi fi cant role in the determination of the U.S. tariff preferences granted to that region. They concluded that Latin American exporters’ lobbying has a signi fi cant impact on U.S. tariff preferences, and the return to their lobbying activities

> 4See more details on tariff preferences in the empirical section.

> 5In this paper, the meaning of Latin America is extended to include Caribbean countries as well.

> 6On average 57 percent of Latin American exports were directed to the U.S. in the period of 1997-2000.

> 7Around 82 percent of preferential exports entered under the NAFTA regime (Mexico only) in the year 2000; CBTPA countries followed with 6 percent; GSP accounted for 4 percent; CBI for 3 percent and the Andean act regime for 2 percent of Latin America preferential exports to the United States. Other special import regimes, such as the Civil Aircraft, Pharmaceuticals and Dyes accounted for the rest of non-programclaimed imports of the US from Latin America.

4
19 morbidity.<sup>2 4</sup> This is surprising since most societies allocate resources in a manner than apparently places a higher priority on reducing adult deaths and adult morbidity than reducing infant mortality; in many developed countries, the bulk of health spending is concentrated on the very old. What lies behind what economists would call a revealed preference for better adult health over reductions in infant and child mortality?25 In the developed countries there 24 See World Bank ( 389a) "Brazil: Adult Health in Brazil: Adjusting to New Challenges," for assessm nt of the data on adult health in Brazil, a developing country with more information than most.

25 I am grateful to William McGreevey for stating this problem in terms of revealed preference. He notes; "Research needs to help reformulate our use of indicators in such manner as to assure that the indicators adequately reflect revealed preference and not just an imposed value judgment implicit, for example, in a narrowly-conceived program such as UNICEF GOBI strategy that only emphasizes the needs of one age segment of the population." Revealci preference in most countries suggests heavy preference weights for adult health, even taking into account that the unit costs of dealing with adult morbidity are higher than unit costs of dealing with infant mortality. In the U.S., an Institute of Medicine study attempted to elicit judgments from health professionals on the relative undesirability of death at different ages. The death of an adult (15-59) was judged by those interviewed as 2.5 to 10 times more undesirable than the death of a child. The death of a person 60 years or older was judged to be 0.2 to 0.1 times more undesirable than the death of a cnild.
Lucas, Robert. 1988. "On the Mechanics of Economic Development." _Journal of Monetary Economics_ 22(1): 3-42.

Maddison, Angus. 2006. "World Population, GDP and Per Capita GDP, 1-2003 AD." <u>http://www.ggdc.net/Maddison/Historical_Statistics/horizontal-file_2006.xls.</u> "Male Circumcision Thwarts HIV Infection" 2005. _Science._ August 5.

Mankiw, N. Gregory, David Romer, and David N. Weil. 1992. "A Contribution to the Empirics of Economic Growth." _Quarterly Journal of Economics_ 107(2): 407437.

"Manufacturers Urged to Donate Bilharzia Drugs" 2006. _Financial Times._ October 25. Marcus, Erin N. 2006. "The Silent Epidemic: The Health Effects of Illiteracy." _The New England Journal of Medicine_ 355(4): 339-341.

Mathers, Colin D. and Dejan Loncar. 2006. "Projections of Global Mortality and Burden of Disease From 2002 to 2030." _PLoS Medicine_ 11(3): 2011-2030.

McGee, T. G. 2001. "Urbanisation Takes on New Dimensions in Asia's Population Giants." processed. Washington, DC: Population Reference Bureau.

McGuire, James W. October 2001. "Social Policy and Mortality Decline in East Asia and Latin America." _World Development_ 29(10): 1673-1697.

- -----. March 2006. "Basic Health Care Provision and Under-5 Mortality: A Cross-

National Study of Developing Countries." _World Development_ 34(3): 405-425. McKeown, Thomas 1976. _The Modern Rise of Population_ . London: Edward Arnold.

----- 1979. _The Role of Medicine: Dream, Mirage, or Nemesis?_ Princeton, NJ: Princeton University Press.

Mead, Robert W. and Victor Brajer. 2005. "Protecting China's Children: Valuing the Health Impacts of Reduced Air Pollution in Chinese Cities." _Environment and Development Economics_ 10(6): 745-768.

-----. 2006. "Rise of the Automobiles: the Costs of Increased NO2 Pollution in China's Changing Urban Environment." _Journal of Contemporary China_ 15(47): 349-367. Medlin, Carol Ann, Mushtaque Chowdhury, Dean T. Jamison, and Anthony R. Measham. 2006. "Improving the Health of Populations: Lessons of Experience," in Jamison, Dean T., Joel G. Breman, Anthony R. Measham, George Alleyne, Mariam Claeson, David B. Evans, Prabhat Jha, Anne Mills, and Philip Musgrove (eds.),

51
The previous findings that poor countries invest less in R&D than rich countries and that the returns to R&D are higher in poor countries beg the question of why poor countries invest less in R&D. The following section addresses this question.

> IV. Determinants of R&D: Why Do Rich Countries Spend More than Poor Countries?

# _A. Related literature and data_

There are very few studies of the determinants of R&D across countries. Two such studies (Varsakelis 2001; Bebczuk 2002) suffer from small samples and, as result, inconsistent estimates due to inability to deal with country-specific effects and endogeneity of the explanatory variables. Here we again apply the GMM system estimator to our larger sample.

We begin by analogy to the investment literature (see Serven 2003, for example) assuming equilibrium where the marginal product equals the cost of borrowing, but then ask what factors may impede this equalization and hence explain why, given the high estimated rates of return, we do not observe more R&D investment.

We first include a proxy for the long term real cost of borrowing. As a first approximation, we employ the nominal 30-90 day lending rate deflated by the CPI. Though this has the most international coverage in the IMF _International Financial Statistics,_ it is still not complete. Further we eliminate countries showing negative real interest rates or values above 40% which we assume are unsustainable long run levels, and capturing stabilization problems, as was the case in Argentina and Brazil in the 1980s. Together, these imply a reduction in the sample to 30 countries. As a second alternative we include the actual gross fixed investment rate which we presume reflects the opportunity cost of investment as well as other factors pertaining to the investment climate. A disadvantage is that the impact of other variables determining R&D investment must be interpreted as effects beyond what they may have on physical 15
the national parks (or park systems) as control SEAs. This brought the total number of (GMA and non-GMA) strata to five: a) Prime  b) Secondary <sup></sup>  Game Management Areas (GMAs) c) Specialized  d) Understocked 

- e) Non-GMA

A sample of 139 SEAs was drawn from the frame described above using probability proportional to size (PPS), where the size is based on the 2000 census of population and housing. The second stage of sampling was implemented using a list of households generated through a listing exercise, and a systematic probability sampling scheme. Two thousand eight hundred (2,800) households were sampled in total of which about half were from GMAs and the rest from non-GMAs. Very few observations (about 32) were lost due to non-response problems. Data were collected using household and community questionnaires, following a comprehensive pre-test in one non-sample GMA (Luano GMA) and one non-sample nonGMA (East of Chongwe).

# _2.2 Impact evaluation methods_

Many factors affect household welfare, and living in GMAs and participation in the CRB/VAGs are but some of them. Other factors include socio-demographic characteristics of the household, such as being female-headed, household size, age/sex composition of the members, the highest level of education within the household, etc. Yet other factors have community-wide effect on household welfare, such as levels of physical capital in infrastructure, social capital in community based organizations within the community, and access to markets. Under such circumstances, it is important to separate out the effects of the various confounding factors.

Many of the same factors that affect household welfare also influence the probability that the household lives in a GMA, or, if in GMA, that the household participates in CRB/VAG decisions. Indeed, ‘selection bias’ arises because some households may choose to move into GMAs and social, economic, or other conditions may not allow some households to move out of GMAs. Once in the GMA, they self-select themselves to participate or not to participate in CRB/VAGs.

Historical factors for creations of CHAs, which were later converted to GMAs, as well as criteria used by ZAWA to create recent GMAs are not always available in quantitative form, usable in the analysis. Such unobserved factors may also result in selection bias. One of the important implications of the selection bias is that the simple differences in average welfare between households living in and outside GMAs, or that for participants and non-participants in CRB/VAGs are not an accurate measure of respective impacts. Fortunately, a number of empirical techniques have been developed over the years for cross-sectional data that help to 5
|First stage<br>(1)<br>Table 6: Export participation and share of workers trained, IV estimates for sw|(2)<br>itchers|
|---|---|
|Dependent variable: Export (=1 if yes)||
|Industry-specific trade-weighted log real exchange rate<br>0.004***<br>(0.001)|0.004***<br>(0.001)|
|F-statistic<br>11.312|11.171|
|Second stage||
|A: Dependent variable: share of workers receiving technical upgrading||
|Export (=1 if yes)<br>0.056*<br>(0.028)|0.058**<br>(0.029)|
|B: Dependent variable: share of workers receving training in other categories||
|Export (=1 if yes)<br>18.501<br>(19.465)|18.849<br>(19.603)|
|Firm effects<br>Yes|Yes|
|Firm controls<br>No|Yes|
|Observations<br>29,347<br>Notes: Robust standard errors clustered by firm in parentheses. *** p<0.01, ** p<0.05, *<br>regressions include year effects. Firms controls are: log employment; average age <br>average tenure; male to female workers ratio; share of workers with less than h<br>completed; share of workers with exactly high school completed; share of workers with <br>high school completed; average log wage; share of white workers; and share of workers<br>skill levels 1 to 4.|29,347<br> p<0.1. All<br>of workers;<br>igh school<br> more than<br> in each of|
the expansion of sectors intensive in land use. In the case of Iraq, the linear solution overstates the decline in wages and therefore understates the decline in labor intensive sectors. In the cases of Jordan, Lebanon, and Egypt, the deviations between the results from the linear and GE solutions are mostly small (below 2%).

# **5. Summary and conclusions**

The paper quantifies the economic effects of the Levant conflict using a global computable general equilibrium model with new details on the Levant economies. The analysis factors in both the direct economic effects of the Syrian war and the advance of ISIS and its opportunity costs due to foregone deep trade integration initiatives in the region. Unlike less formal approaches, the CGE framework ensures consistency, includes important sectoral detail, and captures second-order feedback effects, which are most significant for Syria and Iraq. In addition, the paper develops a modified version of the GTAP database with economic and trade detail pertinent to the Levant economies and accurately reflecting trade preferences on the eve of the Syria war.

The analysis suggests that Syria and Iraq bear the brunt of the direct war costs, losing 14% and 16% in per capita welfare, respectively. All other Levant economies lose in per capita terms, but not in aggregate terms because the inflows of refugees boost population numbers, and therefore consumption, investment, and labor supply. Lebanon’s per capita welfare losses reach close to 11%, while those of Turkey, Egypt, and Jordan do not surpass 1.5%. The difference between aggregate and per capita welfare effects have been most pronounced in Lebanon, where the 28
# **2. Rice production and consumption in Liberia: A brief review**

The Comprehensive Assessment of the Agriculture Sector prepared by Liberia’s Ministry of Agriculture (2007) suggests that Liberia’s agriculture can be characterized as comprising of three different production systems. First are large plantations which focus on export crops (rubber, palm oil, coffee and cocoa). Most of the production originates from plantations that are privately owned, but there are also a number of smaller state owned plantations operated by the Liberian Palm Products Corporation and the Liberian Cocoa and Coffee Corporation. A second component of Liberia’s agriculture sector consists of privately owned commercial farms of medium size which also focus on industrial crops for export and to a lesser extent on livestock for the local market. Finally, the bulk of the population engaged in agriculture belongs to small household farms which rely on traditional production techniques that generated low yields due among others to a lack of inputs, and thereby focus on subsistence production. These household farms are small, with most of them being of around one hectare in size or even less (Republic of Liberia, 2006).<sup>3</sup> In terms of consumption, rice is the main staple food, followed by cassava and other food crops. Production data are scarce, but some estimates are available from the FAO. According to these estimates (table 1), cassava production has better resisted to the conflict than rice production, which has fallen from about 180,000 tons at the start of the conflict to 110,000 tons today, while the population has increased substantially over the same period. By contrast, cassava production appears to have increased from 380,000 tons to 490,000 tons.

Two different systems of rice cultivation co-exist in Liberia. Upland rice cultivation is more prevalent, with 63 percent of producing households using this method of cultivation, as compared to 17 percent of households using and swamp rice cultivation methods (the rest, 21 percent of producers, combine both techniques). Upland cultivation is prevalent in River Cess, Grand Kru and Nimba, while swamp rice is found in Lofa County thanks in part to donor funding for agricultural development projects (Republic of Liberia, 2006). Even in swamp or lowland areas, productivity or yields per hectare are often low, and well below that of neighbouring countries, and in the country as a whole, locally produced rice is used mainly for auto-consumption and subsistence. Among the constraints tom productivity, households have identified the following: lack of seeds and tools (mentioned by 50% of households), lack of financial capital to purchase agricultural inputs (31 percent), lack of household labour

> 3 In fact, 53.6% of rice farms were between 0.2ha – 1.19ha with a further one-quarter of rice farms from 1.2ha – 1.69ha. For cassava, 70% of farms are of less than 0.69ha (Republic of Liberia, 2006).