rafmacalaba/datause-encoder-data huggingface dataset:
tion at Coffee Research Institute (CORI) and have been planted in mother gardens. 143 out of the 593 have been planted out in‐station in a CWD infected garden for field evaluation.” It is not clear, however, how many of these varieties will withstand the disease in infected areas. Furthermore, even under the assumption of complete success, producing plantlets at large quantities and finding effective ways to distribute them to coffee growers will be a monumental task, especially in view of the limitations of the ex‐ isting replanting program (see next section).

# **_6.2 The Coffee Replanting Program_**

The replanting program was introduced in 1992/93 with the objective to enhance the productivity of the sector by replacing old robusta coffee trees with newer, high yield‐ ing varieties (free of charge) at the rate of 5 percent per annum, and to expand the area under arabica. The program has been administered by UCDA, which contracted out the production and delivery of seedlings to about 900 private nurseries. During the last 12 years, a total of 135 million trees have been distributed—101 million robusta and 34 mil‐ lion arabica. The program peaked during the 2002/03 season when almost 30 million trees were distributed.

While there have been numerous reports from the public and private sectors as‐ sessing the progress of the program, there has been no thorough independent evalua‐ tion of the program, despite the resources that have been spent on it. These reports have questioned the effectiveness of the program, with the most important problem being the low survival rate of new plantlets, believed to have been in the order of 50 to 60 percent. The main reasons for such low rate include poor growing conditions at the nurseries (hence low quality of plantlets) and distribution during the wrong season. In turn, this has been attributed to delays in reimbursing nursery operators; on some occasions nurseries have not been paid at all and they have abandoned their operations. The most recent estimate is that UCDA owes nurseries some Ush 5‐7 billion (equivalent to US $2.7‐3.8 million). Further, it appears that the new trees are affected by the wilt disease at the same rate as the old trees, which may explain why despite the fact that 101 million of robusta trees have been distributed under the program, an estimated 136 million ro‐ busta trees have been destroyed by the wilt disease.

The 2005 UCDA/MAAIF Monitoring and Evaluation Report (p. 21) argued that poor seedling production at the nurseries may have contributed to the incidence of the wilt disease. It also argued that only coffee specific programs such as the former Farm‐ ing System Support Programme and Coffee Rehabilitation Programme should be en‐ couraged to solve the problems of the coffee industry since it is difficult for other cur‐ rent programs such as the Area Based Agricultural Modernization Programme and Na‐ tional Agricultural Advisory Services (NAADS) to address coffee problems due to their divergent objectives. Interestingly, most of NAADS activities appeared to be ineffective according to the earlier evaluation (UCDA/MAAIF 2004, p. 11), because they only con‐

**_— 14 —_**
Table 3) is 709, firms will be denoted as "small" ("large") if their average number of employees is below (above) this level. The result is that we have 818 small firms and 185 large firms. It is clear from the first column of Table 5 that the distribution of the number of employees is rather skewed (e.g. the difference between the mean and the median number of employees). This, of course, raises the question whether the mean should be taken as a cut-off value. We will return to this issue below.

Table 5 shows firms statistics for the whole sample and for the sub-samples of small and large firms.

**Table 5 Large and Small Firms (based on the mean of number of employees).**

||_TOTAL SAM_<br>_f rms)_|_PLE (1003_<br>|_Sub-sample._<br>_FIRMS (818 _|_SMALL_<br>_Sub_<br> _firms)_<br>_FIR_|_-sample: LA_<br>_MS (185firm_|_RGE_<br>_s)_|
|---|---|---|---|---|---|---|
||MEAN|MEDIAN|MEAN|MEDIAN|MEAN|MEDIAN|
|NEMPL|709|286|<br>297|252|2529|1308|
|K|14.51|2.41|<br>3.45|1.75|63.4|15.1|
|EB/K|0.25|0.19|<br>0.25|0.19|0.27|0.21|
|LTDEBT/K|0.10|0.00|<br>0.09|0.00|0.16|0.03|
|LOAN/K|0.58|0.30|<br>0.57|0.29|0.62|0.34|
|CREDIT/K|0.16|0.07|<br>0.16|0.06|0.17|0.11|
|A(SAL)/K|1.18|0.69|<br>1.22|0.69|1.00|0.63|
|IPAY/K|0.11|0.04|<br>0.11|0.03|0.11|0.065|
|CF/K|0.06|0.04|<br>0.07|0.04|0.02|0.03|

All variables are scaled by the capital stock K, except NEMPL (number of employees) and K (US$ millions) On average small firms have first of all a lower investment to capital ratio than the large firms. Secondly, small firms have less long-term debt. During the estimation period and in line with policies in the pre-transition period, Bulgarian banks mostly extended uncollateralized loans. After the transition began, many firms simply stopped servicing their debts. In such cases any extended long-term debt is equivalent to a subsidy. Hence, the term soft budget constraint is appropriate to describe such a situation which, see 3.1, applied to many (formerly) state-owned firms which are typically represented by the large firms in our sample.1<sup>5</sup> Third, small firms have a higher cash flow to capital ratio.

> 15 Since a majority of these companies were state-owned and there were no clear bankruptcy procedures put in place, these companies were surviving through the loans extended by the banks. Unfortunately, we do

16
the empirical studies in PSP examine behavioral responses from diverse perspectives with relevance to assessments of the costs of targeting.

For example, the results of Cox and Jimenez suggest that the introduction of public transfer programs, such as unemployment insurance, often causes the well-off to cut back on their private transfers. They found evidence of that for the Philippines, and elsewhere found similar effects for Peru (Cox and Jimenez 1992). Sahn and Alderman explore the much heralded issue of the effects of a targeted subsidy on labor supply incentives (also see Atkinson, PSP). They estimate the food stamp scheme in Sri Lanka to have produced on average a 2 to 3 days monthly reduction in labor market participation, which translates into roughly 33 percent of the value of the subsidy benefits. Such behavioral responses may alter the distribution of the costs and benefits expected by policymakers.<sup>7</sup> Public employment schemes are another example. Participants must provide labor in exchange for a cash or food transfer. In so doing, they must forgo other work and the incomes they would have earned had the scheme not been available. This is a cost to participants that should be netted out in order to measure actual gains from the scheme. As we have seen, Ravallion and Datt find that earnings in two villages in the absence of the EGS would have been equal to around one quarter of that earned on the public works projects. As a result of these costs, and the additional nonwage costs, the scheme entails a net transfer of about half of its budget to the poor.

As always, one should be careful in drawing lessons for policy from all such empirical results. They do not imply that public intervention is unnecessary or that it should be discouraged. In general, displacement-such as of private transfers by public ones, or of private employment by public employment-will be _partial_ (many recipients are still better off), and conclusions will also depend on differences in _coverage_ such as of private versus public transfers. There may also be reasons to prefer government programs. For example, private transfers may not be able to deal with widespread crises where income shocks are covariate (such as in a recession or drought). Public 33
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE competition to lower prices and spread availability so that access to high-speed Internet is not limited to companies and well-off individuals.

# Financing Angola’s infrastructure

To meet its most pressing infrastructure needs and to catch up with developing countries in other parts of the world, Angola needs to expand its infrastructure assets in key areas (table 16). The targets outlined in table 16 are purely illustrative, but they represent a level of aspiration that is not unreasonable. Developed in a standardized way across African countries, they allow for cross-country comparisons of the affordability of meeting targets that can be modified or delayed as needed to achieve financial balance.

**Table 16. Illustrative investment targets for infrastructure in Angola**

||Economic target|Social target|
|---|---|---|
|ICT|Install fiber-optic links to neighboring capitals and submarine cable.|Provide universal access to GSM signal and<br>public broadband facilities.|
|Power|Develop additional 2,028 hectares of large-scale and 305 hectares of<br>economically viable small-scale irrigation.|n.a.|
|Transport|<br>Develop 8 MW of new-generation capacity and 2,120 MW<br>interconnectors (no-trade scenario).|Increase electrification to 24.1 percent (42.4<br>percent urbanand 9.1percentrural).|
|WSS|Achieve regional (national) connectivity with good-quality 2-lane (1-<br>lane) paved road.|Provide rural road access to 42 percent of the<br>highest-value agricultural land, and urban road<br>access within 500 meters.|

_Source:_ Mayer and others 2009 ; Rosnes and Vennemo 2009; Carruthers, Krishnamani, and Murray 2009; You and others 2009. _Note:_ WSS = water supply and sanitation; ICT = information and communication technology; GSM = global system for mobile communications. n.a. = Not available.

Meeting these illustrative infrastructure targets for Angola would cost around $2 billion per year over a decade. About 70 percent of this total relates to capital expenditure, and the remaining 30 percent to operating expenditure, suggesting that Angola’s priority should be investment, though it must not neglect to maintain its assets. In the case of transport and irrigation, investment consists primarily of asset rehabilitation, whereas for other infrastructure sectors investment should be focused on expanding the asset base.

The largest share of spending needs relates to the power sector, followed by water and sanitation and transport. The power sector requires sustained spending of $785 million annually due to major capital investment needs. The second-highest needs are in the water and sanitation sector, with annual spending needs of $574 million needed to meet the MDGs (table 17).

35
rights and stable rule of law, and by the governance framework, such as adequate control of corruption. In an analogous fashion, policy that seeks to enhance investment financing should probably focus on improving the level of development of the financial sector, as opposed to narrowly-conceived investment credits and incentives. Such well-functioning financial systems are more likely to ensure a superior mobilization and corresponding allocation of saving toward the most productive investment opportunities, and raise the level of investment in the economy overall.

# **References**

- Acemo ̆glu, K. Daron & Simon Johnson (2005). “Unbundling Institutions”. _Journal of Political Economy_ 113(5) (October): 949–995

- Acemo ̆glu, K. Daron, Simon Johnson & James A. Robinson (2001). “The Colonial Origins of Comparative Development: An Empirical Investigation”. _American Economic Review_ 91(5) (December): 1369–1401

- Acemo ̆glu, K. Daron & James A. Robinson (2012). _Why Naitons Fail: The Origins of Power, Prosperity, and Poverty_ . New York, NY: Crown Business

- Agosin, Manuel R. & Roberto Machado (2005). “Foreign Investment in Developing Countries: Does it Crowd in Domestic Investment?” _Oxford Development Studies_ 33(2) (Spring): 149–162

- Arellano, Manuel & Olympia Bover (1995). “Another Look at the Instrumental Variable Estimation of ErrorComponents Models”. _Journal of Econometrics_ 68(1) (July): 29–51

- Bai, Yan & Jing Zhang (2010). “Solving the Feldstein-Horioka Puzzle With Financial Frictions”. _Econometrica_ 78(2) (March): 603–632

- Bartelsman, Eric J., John C. Haltiwanger & Stefano Scarpetta (2010). “Cross-Country and Within-Country Differences in the Business Climate”. _International Journal of Industrial Organization_ 28(4) (July): 368–371

- Beck, Thorsten, George R.G. Clarke, Alberto Groff, Philip E. Keefer & Patrick Walsh (2001). “New Tools in Comparative Political Economy: The Database of Political Institutions”. _World Bank Economic Review_ 15(1) (January): 165–176

- Beck, Thorsten, Aslı Demirg ̈u ̧c-Kunt & Ross Levine (2000). “A New Database on the Structure and Development of the Financial Sector”. _World Bank Economic Review_ 14(3) (September): 597–605

- Benhabib, Jess & Mark M. Spiegel (2000). “The Role of Financial Development in Growth and Investment”. _Journal of Economic Growth_ 5(4) (December): 341–360

- Besley, Timothy J. (1995). “Property Rights and Investment Incentives: Theory and Evidence from Ghana”. _Journal of Political Economy_ 103(5) (October): 903–937

- Blundell, Richard & Stephen R. Bond (1998). “Initial Conditions and Moment Restrictions in Dynamic Panel Data Models”. _Journal of Econometrics_ 87(1) (August): 115–143

- Bond, Stephen R., Anke Hoeffler & Jonathan R.W. Temple (2001). “GMM Estimation of Empirical Growth Models”. _Discussion Paper_ 3048, London, England: Centre for Economic Policy Research

19
In the present study we utilize these data for two purposes. First, we empirically analyze the main factors determining the location of Venezuelan migrants across Ecuador’s _cantons_ .<sup>17</sup> Second, we use the data to classify _cantons_ according to the estimated density of Venezuelan migrants in the cantons population. This allows us to compare the within-canton changes in labor market outcomes for _cantons_ with varying densities of Venezuelans in their population.

At the country level, we estimate that about 470,095 Venezuelans were living in Ecuador in the first quarter of 2019. This amounts to about 3% of the population. Table 1 presents the top 10 _cantons_ by the estimated number of Venezuelan migrants (top panel) and by the share in the working-age population in the canton (bottom panel). As shown in the top panel, there is high regional variation, ranging from _cantons_ hosting almost 90,000 Venezuelan migrants (Guayaquil and Quito) to others having virtually none. In fact, four _cantons_ concentrate 52% of the 470,095 Venezuelan migrants estimated in the _Telefonica_ data: Guayaquil (87,505), Quito (86,386), Manta (32,405), and Santo Domingo de los Tsachilas (26,721).<sup>18</sup>

# **5 Location Choices of Venezuelans in Ecuador**

The goal of this section is to employ regression analysis to quantify the role played by a variety of geographic and economic factors in determining Venezuelans’ choice of region (defined as a canton) in the vein of Beine et al. (2019).<sup>19</sup> The geographic factors we consider are the distances from each canton to the main gateways into Ecuador for Venezuelans. According to the Ecuador’s official entry records, the majority of Venezuelans entered Ecuador by land from Colombia, through the Rumichaca bridge in the Tulcan canton (82%). Other land points of entry were San Miguel (5%) and Huaquillas (7%). Almost all other Venezuelans arrived by air through the airports of Quito and Guayaquil (5%). In our analysis, we focus on 3 gateways: the Rumichaca bridge and the airports of Quito and Guayaquil.<sup>20</sup> The second set of factors we consider measure economic conditions at the canton level.

> 17Ecuador is divided into 24 provinces, in turn subdivided into 221 _cantons_ .

> 18The highest concentrations of Venezuelans, relative to the canton’s population, are found in a few sparsely populated cantons. The ENEMDU survey cannot be representative of those populations. Accordingly, we omit them from our regression analysis.

> 19This exercise is in the spirit of gravity models of trade and migration flows, but considering a single origin and multiple destinations.

> 20More specifically, we compute the Euclidian distance between each of the points of entry and each canton’s centroid.

11
_Table 2: sets of conditions (box) that characterize the scenarios in the optimistic group (defined as in Figure 2) for Vietnam. Density is the probability of a scenario in the box to be in the optimistic group. Coverage is the probability of a scenario in the optimistic group to match the box._

|**Optimistic box**|‐<br>High redistribution (tax for cash transfers >8% of total consumption)|
|---|---|
|78% density|‐<br>Relatively high pensions (tax >5% of total consumption)|
|40% coverage|‐<br>Low population growth, high education (SSP5)|
||‐<br>Productivity growth for unskilled agriculture workers >2%peryear|

_Table 3: sets of conditions (box) that characterize the scenarios in the pessimistic group (defined as in Figure 2) for Vietnam. Density is the probability of a scenario in the box to be in the pessimistic group. Coverage is the probability of a scenario in the pessimistic group to match the box._

|**Pessimistic box**|‐<br>Relatively low redistribution tax for cash transfers <15% of total|
|---|---|
|47% density|consumption)|
|59% coverage|‐<br>High population growth, low education (SSP4)|
||‐<br>Productivity growth for unskilled agriculture workers <5%peryear|

If we assume that demography is a given factor, the two main levers to eradicate extreme poverty as fast as possible are agriculture productivity growth and redistribution. Below we explore what is feasible in Vietnam.

# **Redistribution**

Along with agricultural productivity growth, relatively high redistribution is the main lever to reduce poverty in Vietnam in our model. In the model, it is represented by a flat tax on consumption, which is redistributed to the entire population (except children) as a basic income (i.e. the same amount for each individual). We also model redistribution to the elderly as a tax on income from workers only, redistributed as a cash transfer to people older than 60 years. We apply these transfers to households’ consumption after pre‐existing tax and transfers. They can be seen, therefore, as additional transfers in addition to current ones.

According to the most recent data in the ASPIRE database, 6.3% of Vietnam GDP is used for social transfers (cash transfers only) and people from the first quintile get about 10 times less transfers per capita (in absolute terms) than people from the last quintile. The way we model cash transfers is more favorable to the poor since each person receives the same transfer in absolute terms.

Our results suggest that, to be in the optimistic scenarios in terms of poverty reduction and shared prosperity, Vietnam needs to use 8% of GDP for universal social transfers, assuming that it is financed by a flat tax and people from all quintiles get the same amount. To deal with an aging population while reducing poverty, Vietnam will also need to use at least 5% of workers’ income to finance pensions.

# **Agriculture productivity growth**

If high redistribution is too costly, productivity growth for unskilled agricultural workers is another important driver of poverty reduction, even in scenarios in which the share of workers in agriculture is 7
Table 3. Determinants of civil war risk: 1946-2003 Natural resource endowments _mitigating_ the problem of weak states

|Context variable|Natural resources<br>Context variable|Fearon and Laitin<br>(2003) civil war|Prio \ Uppsala civil war|
|---|---|---|---|
||Interaction|||
|Risk of coupd’état|Quantitative measures of resources endowments|||
||Fearon and Laitin oil export dummy (observations 5135)|0.937***(0.329)|0.776***(0.231)|
|Model 26|Risk of coupd’état|0.210**(0.100)|0.244***(0.066)|
||**Interaction**|**-0.170(0.195)**|**-0.173(0.120)**|
||Humphreys oilproduction(observations 4917)|1.997*(1.210)|1.899***(0.696)|
|Model 27|Risk of coupd’état|0.203**(0.095)|0.209***(0.062)|
||**Interaction**|**-0.537(0.532)**|**-0.539**(0.233)**|
||Humphreys diamondproduction(observations 4917**) **|-0.116(0.237)|-0.396(0.458)|
|Model 28|Risk of coupd’état|0.207**(0.094)|0.217***(0.061)|
||**Interaction**|**-0.388***(0.116)**|**-0.113(0.157)**|
||Ross oil rentsper capita(observations 4988)|0.00015**(0.00007)|0.00014***(0.00005)|
|Model 29|Risk of coupd’état|0.200**(0.096)|0.202***(0.065)|
||**Interaction**|**-0.00008 (0.00007)**|**-0.00005 ****<br>**(0.00002)**|
|Coup d’état history in<br>past 5years|Quantitative measures of resources endowments|||
||Fearon and Laitin oil export dummy (observations 6767)|0.866***(0.277)|0.879***(0.206)|
|Model 30|Couphistory|0.528*(0.269)|0.636***(0.194)|
||**Interaction**|**-0.987*(0.612)**|**-0.964**(0.441)**|
||Humphreys oilproduction(observations 5061**) **|1.660**(0.818)|1.566***(0.507)|
|Model 31|Couphistory|0.131(0.283)|0.303(0.200)|
||**Interaction**|**-1.542(1.751)**|**-0.110(0.811)**|
||Humphreys diamondproduction(observations 5061**) **|-0.066(0.107)|-0.243(0.269)|
|Model 32|Couphistory|0.172(0.282)|0.316(0.201)|
||**Interaction**|**-3.541(3.972)**|**-0.204(0.831)**|
||Ross oil rentsper capita(observations 5527)|0.00012**(0.00005)|0.00013***(0.00004)|
|Model 33|Couphistory|0.276(0.280)|0.381*(0.196)|
||**Interaction**|**-0.00075 (0.00135)**|**-0.00006 (0.00007)**|
|Militaryregime|Quantitative measures of resources endowments|||
||Fearon and Laitin oil export dummy (observations 6850)|0.755***(0.280)|0.880***(0.203)|
|Model 34|Militaryregime|0.516*(0.291)|0.932***(0.199)|
||**Interaction**|**-0.330(0.571)**|**-0.948**(0.413)**|
||Humphreys oilproduction(observations 5123**) **|1.935*(0.999)|1.903***(0.472)|
|Model 35|Militaryregime|0.518*(0.282)|0.728***(0.196)|
||**Interaction**|**-1.494(1.365)**|**-1.264*(0.781)**|
||Humphreys diamondproduction(observations 5123**) **|-0.079 (0.111)|-0.354(0.410)|
|Model 36|Militaryregime|0.550*(0.287)|0.696***(0.196)|
||**Interaction**|**-3.545(4.225)**|**0.405(0.653)**|
||Ross oil rentsper capita(observations 5611)|0.00012**(0.00005)|0.00013***(0.00004)|
|Model 37|Militaryregime|0.589**(0.282)|0.728***(0.197)|
||**Interaction **|**-0.00120 (0.00148)**|**-0.00008 (0.00018)**|

Note: Results are from logit models where the dependent variable is 1 for years with war onset and zero otherwise. Table shows coefficients and standard errors in parentheses. Stars reflect levels of statistical significance: *** for 1%; ** for 5% and * for 10%.

33