rafmacalaba/datause-encoder-data huggingface dataset:
during 2002-2011, which is an average of 11 more surveys per year. Does this mean the total number of household surveys for every year needs to increase by 11 surveys on average?

Note that, going forward, the 109 additional surveys are a minimum requirement and are not necessarily sufficient to meet the two goals for the following reasons. First, this number assumes that all countries would repeat the same number of surveys carried out in the 2002-2011 period. If any country were to reduce surveys, other countries would need to fill the gap, which means an increase in the number of surveys needed to meet both the goals. Second, this number does not include any additional surveys for countries that already carried out more than three surveys in the 2002-2011 period.

We now interpret this analysis at an annual base assuming the number of household surveys in 2011 is a steady state.<sup>13</sup> In 2011, a total of 62 poverty data points were available and meeting the two targets appears to imply an average of about 73 data points per year, an increase of 11 data points per year or around 18 percent. However, just monitoring of the total number of household surveys per year is not enough; rather, it is necessary to see where the data are coming from. As mentioned above, surveys coming from countries that have already three or more surveys in the ten years would not count toward meeting the targets. Table 2 shows that 51 of the 62 data points include countries that already conduct three or more surveys per year; only 11 are from countries with less than three data points. Since the additional 11 data points would need to come from countries with less than three data points in the 2002-2011 period, the total number of surveys from those countries need to be doubled.<sup>14</sup>

**Table 2: Breakdown of 62 countries which had poverty data points in 2011(by the frequency of surveys carried out in past ten years)**

|**Number of Data**<br>**Points between**<br>**2002-2011**|**Number of**<br>**Countries**|**Cumulative**<br>**Percentage**|
|---|---|---|
|1|1|1.6|
|2|10|17.7|
|3|4|24.2|
|4|2|27.4|
|5|4|33.9|
|6|1|35.5|
|7|2|38.7|
|8|6|48.4|
|9|7|59.7|
|10|25|100.0|
|Total|62||

Furthermore, to meet target 1, 86 additional surveys need to be carried out over ten years in the countries with one or no survey, an average of 9 additional surveys per year. According to Table 2, only one out of

> 13 This assumption is not so strong because the number of surveys per year is quite stable around 60 since 2002 (see Figure 1.)

> 14  The numbers are similar when other ten year time intervals are considered (e.g., 2009, 2010, etc.).

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# **I. INTRODUCTION**

Due to increasing carbon dioxide concentrations it is predicted that global warming would have a variety of environmental and socio-economic impacts over the long term. In recent years particular attention has been paid to hydropower generation, because it can offer a supply of renewable energy. At the same time, however, hydropower is clearly among the most vulnerable areas to global warming because water resources are closely linked to climate changes.

To analyze the potential impacts of climate changes on the hydropower industry, the current paper aims to develop a hydrological model using a simple multivariate time series technique. The model is applied to three hydropower projects in India, Sri Lanka and Vietnam to check the methodological applicability. The results may be tentative in terms of both methodology and implications, but comparing those three cases, the paper will quantify the major impacts of climate changes on hydrology, whence hydropower projects as a whole. The paper finally attempts to draw some tentative policy implications for hydropower planning and operation.

The world economy is now faced with considerable risk and uncertainty caused by climate changes. Global average temperature would increase by 1.4 to 5.8oC during the period: 1990–2100. Annual precipitation may also increase or decrease—depending on regions—by 5 to 20 percent, compared with the past 30-year mean (IPCC, 2001). Although these figures may have to be considered a preliminary estimate and the debate in fact remains far from conclusive, it is worth considering potential impacts of global warming on any aspect of the economy and possible mitigation measures.

In particular, anticipated climate changes may bring about a dramatically different situation of energy. For instance, if the warming of Earth is accelerating and the decarbonizing energy strategy remains costly, the relative (shadow) price of renewable energy to fossil fuel could be soaring. Some countries with large water resources may become “water-rich” economies,
11 level of education. Most of the self-employed are farmers or livestock breeders (46 percent of the selfemployed) while 17.6 percent are sales-persons, and 15.5 percent are traders or shopkeepers.

_Wage earners – 30.4 percent of the employed – form the second major group of employed people_ (Figure 4). Figure 5 shows that, on average, wage earners exhibit higher levels of education than the overall population: only 35 percent of them have no schooling, while 33 percent attended secondary school, and 11 percent studied beyond high school. According to Figure 4, less than a fifth of wage earners report working for a private company. They are mostly employed by the public sector (40 percent of all wage workers) or smaller structures such as private individuals or households (35 percent).

**Figure 4. Types of Employment and Employer (in %)**

<!-- Start of picture text --> 52.1%<br>Self-employed<br>Unpaid family 9.1% Public sector<br>worker/apprentice<br>Private company<br>8.4% Individual/household<br>Day laborer<br>Other<br>30.4%<br>Wage earner<br>0% 10% 20% 30% 40% 50% 60%<br><!-- End of picture text --> Source: EPCV 2004.

_Unpaid family workers and apprentices represent nearly a tenth of the employed population_ (Figure 4) _._ They work mostly for private individuals or households. They exhibit low levels of education: 70 percent never went to school whereas 25 percent attended primary school (Figure 5). Nearly twothirds of unpaid family workers and apprentices are aged 15 to 24.

**Figure 5. Types of Employment and Education (in %)**

<!-- Start of picture text --> Self-employed<br>Unpaid family<br>None<br>worker/apprentice<br>Primary<br>Secondary<br>Day laborer<br>Higher<br>Wage earner<br>0% 20% 40% 60% 80% 100%<br><!-- End of picture text --> Source: EPCV 2004.
# **4.  Non-Structural Empirical Evidence on Determinants of NFA Positions**

In this section of the paper we consider how China's net foreign asset position compares with that of other countries using cross-country regression analysis.<sup>4</sup> While our approach here is very non-structural, our choice of explanatory variables is for the most part motivated by the theoretical discussion of the previous section. Recall from Equation (14) that net foreign assets as a share of wealth are one minus the ratio of the per capita capital stock to per capita wealth. While this is simply an identity, it is useful for thinking about empirical determinants of the net foreign asset position. In particular, controlling for per capita wealth, variables which make a country a more desirable location for investment will lead to a more negative net foreign asset position. From Equations (11)-(13), per capita capital stocks will be higher in countries with higher productivity and/or less risky returns. Conversely, holding fixed the determinants of investment, the higher is the per capita wealth of a country, the larger will be the net foreign asset position of the country. Of course, this mechanical separation between wealth and capital stocks per capita is an oversimplification. After all, the model of the previous section has shown how the presence of sovereign risk creates a strong home bias in investment patterns. This implies a positive relationship between capital stocks per capita and wealth per capita, and so the overall effect of per capita wealth on the net foreign asset position becomes ambiguous.

We begin by documenting some simple bivariate relationships as a way of data description. In each of these we include a dummy variable for China, as a convenient way of summarizing the extent to which China does, or does not, conform to the crosscountry regularities that we describe. Our sample of countries consists of 62 countries where we have at least 15 annual observations on net foreign assets after 1980.<sup>5</sup> The data on net foreign assets are taken from Lane and Milesi-Ferretti (2001a). We

> 4 In part because of only recent data availability, empirical papers on the determinants of net foreign asset positions are scarce.  Exceptions are Lane and Milesi-Ferretti (2001a) and Calderon, Loayza, and Serven (2003).  Lane (2000) and Lane and Milesi-Ferretti (2003) document determinants of gross foreign asset positions among industrial countries, and Lane and Milesi-Ferretti (2001b) study relate decadal changes in net foreign asset positions in industrial and developing countries to per capita incomes, public debt, and demographic variables. 5 We drop from this sample Cote d'Ivoire which has a very large negative net foreign asset position and is quite influential for the magnitudes of the slope coefficients in the regressions that follow.

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formal education the individual has attained the higher is his or her wage or farm income. Education is the variable that has the strongest effect on incomes and wages, therefore bringing the Haitians up the education ladder is key for poverty reduction. Moreover, workers in rural areas in the low end of the income distribution earn more than their urban peers. This indicates scarcity of this type of workers which may be caused by out migration. Many women in rural areas receive lower wages than men controlling for human capital and other characteristics. There exists a return to social capital in rural areas but not in urban areas.

Finally, for farmers, access to more land in order to increase farm sizes is key to increase income. Therefore it would be important to facilitate small farm intensification in Haiti. Increased access to titles and infrastructural services are also important to increase productivity. As more farmers gain titles to their land more farmers would have an asset that could be used as collateral to obtain micro credit for further productive investments in the farm, including tools, fertilizer, and pesticides among other things.

A direct way of improving farm productivity and revenues is via the communitybased approach to land reform. Under this approach, beneficiary groups negotiate directly with potential sellers of suitable properties, and then obtain financing for the purchase of the land and complementary sub-projects and receive technical assistance. Two successful pilot projects in Brazil—the Ceará Rural Poverty Alleviation Project and the Cédula da Terra—redistributed about 640,000 hectares to benefit about 23,700 households using this approach.

# **4. Conclusion and Recommendations**

This paper examined farm and nonfarm employment and income generation in Haiti. Findings suggest that the four key determinants of employment and productivity in nonfarm activities are education, gender, location, and migration status. This is emphasized when nonfarm activities are divided into low-return and high-return activities. There is evidence that the nonfarm sectors are more vibrant in more populated areas, which are connected to markets and enjoy certain minimum standards of infrastructure. Hence, it is key that governments assist in augmenting the access to infrastructure services and human capital of the rural population so they can take advantage of increased opportunities.

There seems to be evidence that education is the key determinant of income of wage workers and farmers. The wage and producer income analyses reveal that education is key to earning higher wages and incomes; the more educated workers earn higher wages than their less educated peers. Producer income increases with farm size and access to tools, electricity, road, irrigation, and other farm inputs. Moreover, farmers with title to their land have an income 20 percent higher than farmers without a title.

The analyses presented in this paper provide guidance on a social agenda and poverty alleviation strategy for Haiti. It seems fairly clear that the economy must remain 23
- Shiells, Clinton R., David W. Roland-Holst, and Kenneth A. Reinert. 1993. "Modeling a North American Free Trade Area: Estimation of Flexible Functional Forms." _Weltwirtschaftliches Archiv_ . 129:1, 55-77.

- Swan, Trevor. 1960. "Economic Control in a Dependent Economy." _Economic Record_ , Vol. 36, pp. 51-66.

- Uribe, Martin, and Stephanie Schmitt-Groh é . 2017. _Open Economy Macroeconomics._ Princeton University Press, Princeton, New Jersey.

- V é gh, Carlos A. 2013. _Open Economy Macroeconomics in Developing Countries._ The MIT Press, Cambridge, Massachusetts.

- Whalley, John. 1985. _Trade Liberalization among Major World Trading Areas._ MIT Press, Cambridge, MA.

- Zof í o, Jos é L., Jorge D í as-Lanchas, Damiaan Persyn, and Javier Barbero. 2020. "Estimating Foreign and National Trade Elasticities in the EU Internal Market Using Generalized Transport Costs." Joint Research Center Working Papers on Territorial Modelling and Analysis No. 05/2020, the European Commission.

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- If COMANAV (Compagnie Marocaine de Navigation) vessels of 568 ton equivalent unit (TEU) capacity are used, the cost of shipping a 20 foot container between Casablanca and Tunis is US$311. However, if smaller vessels are used, the cost per TEU on the Cadiz-Tunis route is US$480 compared with US$530 on the Casablanca-Tunis route, 1/m a differential of 10.4%.

- Cost per TEU for Rotterdam-Tunis and Casablanca-Tunis are roughly the same 1/m close to US$530. However, because the distance between Rotterdam and Tunis is greater than that between Casablanca and Tunis, the cost differential per nautical mile is 120%.

- When small CTN vessels (182 TEU) are used, it costs more to ship a container from Casablanca to Tunis than from Rotterdam to Tunis or Cadiz to Tunis (by Ahlerlines).

2.4 The average cost of maritime transport to Tunis is US$40 per ton, or 9% of the average weighted value (US$450) of merchandise delivered f.o.b. Although this figure is consistent with those calculated in Chapter 1, these estimated costs are merely indicative and may only serve as guides.

2.5 These figures suggest that there are distortions associated with transportation. The cost of maritime transport is high because Maghreb shipping companies are too small to effect economies of scale in their cabotage traffic. In addition, linerconference freight agreements (which protect signatory countries from competition) may also be a source of excess costs: the nonconference rates applied by outsiders are generally _25_ % lower than conference rates. The field survey shows that these distortions are not ascribable only to sea freight costs. It reveals that rail transport through Algeria would give Morocco a significant relative advantage, in view of applicable freight charges. However, trans-Maghreb barriers prevent this option from being acted on (see Annex 1). And sea transport handicapped by the weakness of trade between the Maghreb countries and by the protection they afford shippers operating under their flags, the age of their merchant fleets, and the organization of work aboard their vessels.

# **B. Excess Cost Calculation: Transport Chain Case Studies**

2.6 Cost calculation details vary from one product to another. In general, however, the excess cost for each product is first calculated by metric ton using survey data. This value is then expressed in ad valorem terms and applied to groups of similar products, taking into account the unit values of the products and the percentage of the potential exports or imports to which this excess cost would apply. Each of the following three cases covers a particular product, and calculations are presented in increasing order of detail.
||(0.000221)|(0.000232)|(0.000230)|(0.000221)|
|---|---|---|---|---|
|Father literacy|-0.0311|-0.135|-0.0418|-0.0655|
||(0.183)|(0.198)|(0.193)|(0.183)|
|Mother literacy|0.0419|0.0227|0.214|0.0281|
||(0.187)|(0.208)|(0.196)|(0.187)|
|Improved sanitation|0.450|0.657|0.519|0.526|
||(0.641)|(0.629)|(0.636)|(0.638)|
|Distance to main road|-0.125*|-0.194**|-0.113|-0.115|
||(0.0702)|(0.0778)|(0.0724)|(0.0699)|
|Distance to market|0.0626|-0.0617|0.0544|0.0654|
||(0.0734)|(0.129)|(0.0800)|(0.0731)|
|Distance to main road squared|0.00361***|0.00348**|0.00322**|0.00313**|
||(0.00133)|(0.00140)|(0.00156)|(0.00133)|
|Distance to market squared|-0.000120|0.000255|-8.74e-05|-0.000130|
||(0.000266)|(0.000422)|(0.000281)|(0.000264)|
|Teff pricet-1||0.231***<br>(0.0378)|||
|Wheat pricet-1|||0.119***<br>(0.0383)||
|Maize pricet-1||||0.126***<br>(0.0430)|
|Constant|-5.753*|1.148|-6.271*|-6.120*|
||(3.331)|(6.376)|(3.503)|(3.311)|
|R-squared|0.022|0.063|0.034|0.031|
|Number of individuals|1,021|962|1,019|1,021|
|Observations|1,923|1,730|1,838|1,913|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 The coefficients on each of the crop prices are positive and strongly statistically significant. This suggests that changes in crop prices are associated with improved scores for height for age. For individual characteristics, age is negative and statistically significant. However, the impact of age is non-linear. Proximity of the community to a main road is associated with positive outcomes for height for age. This suggests that measures that increase the connectivity of rural communities will contribute to improving health and nutritional outcomes for children. Consistent with the results of Cintron _et al_ (2016), many of the control variables are not significant since these time-varying dimensions are unlikely to change substantially in such a short panel. We now turn, in Table 7, to our other indicator of child undernutrition, weight for height (wasting).

**Table 7: Fixed effects regression of crop prices on weight for height**

||**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
||**Other Covariates**|**Teff Prices**|**Wheat**|**Maize**|
||**(Prob > F**|**(Prob > F**|**Prices(Prob**|**Prices(Prob > F**|
|**VARIABLES**|**0.0000)**|**0.0000)**|**> F 0.0000)**|**0.0000)**|

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find that, if the reallocation shock accompanies the temporary shutdown shock, the recession becomes significantly protracted. The reallocation shock generates not only unemployment but also misallocation of capital and labor, because some of those firms that become suddenly more productive are too credit-constrained to produce at the larger scale corresponding to their new productivity. The misallocation gets undone slowly over time, as the creditconstrained productive entrepreneurs save up and overcome the constraint. The one-time reallocation shock leaves a persistent negative effect on aggregate TFP and GDP.<sup>4</sup> Both the shutdown shock and the reallocation shock have heterogeneous effects across firms. For young non-essential firms, which are more likely to be financially constrained, the shutdown shock has an important negative balance sheet effect. Some of them exit and, even for those that remain, it takes several years for their employment to fully recover. The reallocation shock causes a burst of creation and destruction.

Our model builds on the model with financial and labor market frictions in Buera et al. (2015). The most important difference is that we allow for recall (or rest) unemployment. Workers who are temporarily laid off due to the shutdown can return to their previous employer without having to go through the hiring market subject to matching frictions (Alvarez and Shimer, 2011). This assumption is supported by the analysis of Hall and Kudlyak (2020), who show that unemployed workers with jobs (on furlough or temporary layoff) accounted for 93% of the increase in unemployment between the first and the second quarter of 2020. Unemployed workers with jobs returned to employment much faster than “jobless” unemployed workers who had no job to return to. Bick and Blandin (2020), Cajner et al. (2020) and Lee et al. (2021) provide further evidence on the importance of recall unemployment in 2020.

The second important assumption is the institutional setup in which firms can costlessly lay off workers during the shutdown, unemployed workers collect unemployment insurance benefits with a 100% replacement rate (Ganong et al., 2020), and the unemployment insurance is financed with long-term debt. This institutional setup captures the policy adopted by the US government through the Coronavirus Aid, Relief, and Economic Security (CARES) Act. Other countries, for example the UK, supported firms and workers in non-essential sectors during the pandemic through transfers to firms to cover wage bills during the shutdown.<sup>5</sup> In our model, supporting firms’ balance sheet by covering their labor costs and providing income insurance to workers are equivalent. They only differ in the label of the workers who are not working and whose wage is paid for by taxes.

> employment extracted from an online firm survey.

> 4Without these frictions, a pure reallocation shock would have no aggregate effect.

> 5The Paycheck Protection Program (PPP) in the US CARES Act did the same, but only for small businesses.

2
series often show clustering of volatility in addition (see Figure (1)). This stems from the fact that one crisis is often followed by another, or triggers major restructuring or resetting of macroeconomic policies which themselves induce swings in the economic performance of a given country.

Figure 1: Historical Changes in Selected Exchange Rates <!-- Start of picture text --> 270<br>250<br>230<br>210<br>190<br>170<br>150<br>130<br>110<br>90<br>70<br>50<br>30<br>10<br>-10<br>1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005<br>-30<br>-50<br>UYU/USD BRL/USD GBP/USD<br><!-- End of picture text --> Note: Quarter-to-quarter percentage changes of the UYU/USD, BRL/USD and GBP/USD exchange rates over the period 1975Q1-2006Q3; calculated using the end-of-period local currency to USD markets rates from IMF’s International Financial Statistics.

For determination of the currency benchmark for the foreign debt portfolio one requires an overall measure of riskiness of each currency, i.e. the overall measure of relevant exchange rate volatility. The estimation of volatility can be revised on a yearly basis and so can the currency benchmark for foreign-currency debt. However, one has to make a choice in regards to what period is to be covered in the estimation.

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