rafmacalaba/datause-encoder-data huggingface dataset:
answered yet. This study aims to address this question in the case of wind power in the Brazilian electricity mix.

The study shows that expansion of wind power would reduce economic welfare no matter whether a subsidy or a mandate is used to drive the expansion. This is because the cost of wind power generation is still expensive compared to most traditional sources of electricity generation in Brazil. However, we found that in Brazil, a subsidy causes lower welfare loss compared to the mandate to achieve the same level of wind power penetration. This is because the subsidy did not raise electricity prices but financed wind expansion through very small increases in consumer prices throughout the economy, whereas the mandate did lead to a considerable increase in the price of electricity. Since the subsidy causes less welfare loss compared to the mandate and it also causes higher reduction of GHG emissions than a mandate does, the study concludes that subsidy policy is superior to mandate for Brazil’s efforts in promoting renewable energy. Although this finding is consistent with general principles in public finance and with findings in other studies of renewable energy policy (e.g., McCullough et al. 2011; Timilsina and Landis, 2014), it should not be generalized because the results from CGE models are country specific: particular parameters, such as those reflecting electricity supply mix, and elasticity of substitution, can be significantly different across countries.

The sectoral results are different not only between the two policy instruments but across the sectors for a given policy instrument. Sectoral value added of fossil fuel industries decreased under both policy instruments whereas the value added of industries providing goods and services to wind power industry increased. The decrease in value added of fossil fuel industries is higher under subsidies compared to those with the mandate due to tax imposed on fossil fuels to generate a fund to finance the subsidy.

The analysis is carried out based on present (2012) data on electricity supply costs of various technologies to generate electricity. The capital costs of renewable energy technologies, including wind power technologies, have declined over time in the past. However, it is difficult to predict how far the cost reduction will go and when wind power technologies would be economically competitive with traditional sources of electricity generation. Even if they are competitive in terms of the levelized costs of electricity generation, utility planners still discount wind and solar because these are intermittent 15
<!-- Start of picture text --> 1200<br>1000 in<br>800 a ——<br>-=- ae °<br>600 -- 747<br>400<br>200<br>0<br>1960 1965 1970 1975 1980 1985 1990 1995<br>> - > > -Resource-rich ——— Resource-Poor<br><!-- End of picture text -->
12 vehicle ownership with population density is much greater at the urban level (-p0.4) than at the national level (-0.1). 19 Unlike at the national level, the causal relation between population density and motorization at the urban level runs in both directions. The boundaries of countries rarely change, while most cities can expand by annexing outlying areas which have lower densities than the urban core. By increasing travel speeds, motorization can induce decentralization of both job and residential locations, which expands the urbanized area and reduces densities. The lower densities raise transit costs or reduce transit service levels, which further promotes car ownership and use. Hence motorization is both determined by and a determinant of urban density.

At the national level, the share of urban population is another proxy for the spatial distribution of economic activities, but its effect on vehicle ownership is ambiguous. On the one hand, urbanization may be negatively associated with motor vehicle ownership because motorized road transport is more attractive in rural than in urban areas.20 With everything else held constant, rural households are more likely than are urban households 21 to own motor vehicles. On the other hand, urbanization is positively associated with per capita income.22 Cross country analysis indicates that the net effect of urbanization on vehicle ownership is positive.<sup>23</sup>

# _4.4 Saturation levels for motor vehicle ownership_

It is often hypothesized that motor vehicle ownership in high-income countries will increase at a declining rate with per capita income growth and eventually stop increasing when a saturation level is reached. A recent study estimated ownership saturation levels for fifty countries (under a "business as usual" scenario) at 770 passenger cars and 1,180 total motor vehicles per thousand people, and for thirty-five cities at 750 passenger cars and 1,080 total motor vehicles.24 These estimates exceed the maximum observed ownership levels in 1990 of roughly 574 passenger cars and 755 motor vehicles per thousand people (for the U.S.A.).

19 Ingram and Liu, "Motorization and the Provision of Roads." A small, negative, but insignificant relation between vehicle ownership and population density at the national level was found in Silberston, "Automobile Use." 2 0 Wheaton, "The Long-Run Structure of Transportation and Gasoline Demand." He found a negative but statistically insignificant relation between the share of urban population and the level of auto ownership.

21 Angus Deaton, _The Demandfor Personal Travel in Developing Countries_ (Washington,D.C.: World Bank, 1987).

22 For a summary of empirical evidence, see Gregory K. Ingram, "Pattems of Metropolitan Development: What Have We Leamed?" _Urban Studies,_ vol. 35, No. 7 (June, 1998), pp. 1019-1035.

23Ingram and Liu, "Motorization and the Provision of Roads." Ingram and Liu, "Motorization and the Provision of Roads."
**Figure 4c: IFLS Expenditure Data – Sample Size 300**

<!-- Start of picture text -->
-.5 0 .5<br>Difference in average ln hh expenditure p cap (weighted by std dev)<br>1 Draw 8 Strata Matched Big Stick Minmax<br>10<br>8<br>Baseline 64<br>2<br>0<br>4<br>3<br>2<br>3 years later<br>1<br>0<br><!-- End of picture text -->

**Figure 5a: LEAPS Math Test Score Data – Sample Size 30**

<!-- Start of picture text -->
-1 -.5 0 .5 1<br>Difference in average math test score (weighted by std dev)<br>1 Draw 8 Strata Matched Big Stick Minmax<br>3<br>2<br>Baseline<br>1<br>0<br>2<br>1.5<br>1<br>1 year later<br>.5<br>0<br><!-- End of picture text -->
column 5 shows that the results for both sales and total costs remain consistent also when using the ACLED dataset to measure conflict exposure, with the effect on profits being negative and marginally significant. These findings are reassuring, given that ACLED covers a substantially different—and smaller—set of countries, as well as a distinct set of conflict-related events compared to ICEWS. While all our results are consistent across these alternative definitions of conflict exposure, they also suggest that firms respond differently depending on the type of event, with the negative effects of conflict being more pronounced when the events are more violent.

Finally, Table A18 shows that our results remain unchanged when accounting for spatial correlation in the error terms, using the arbitrary clustering correction for standard errors proposed by Colella et al. (2023) to re-estimate our baseline model.

# **4.2 Mechanisms**

Our results show that conflict exposure reduces a firm’s sales and total costs. These effects are likely driven by a combination of production-side and market-level channels. One mechanism relates to the availability of production inputs. Higher conflict exposure may constrain access to domestic and foreign inputs—such as raw materials, intermediate goods, labor, or electricity—forcing firms to adopt a sub-optimal input mix. This reduction in input quantity and quality lowers overall output and, in turn, depresses sales. At the same time, conflict may reduce sales by changing the level and type of competition faced by firms. In particular, it is possible that conflict increases competition from informal firms, a phenomenon that is well-known to have negative consequences for formal ones. Finally, conflict may lower sales by decreasing demand or increasing the severity of the obstacles to a firm’s activity. In what follows, we take these hypotheses to the data to examine the mechanisms driving the negative effect of conflict on firms’ sales and production input expenditures.

## **4.2.1 Raw materials and intermediate inputs**

Table 2, column 1, shows that conflict exposure leads to a decline in firms’ expenditures on raw materials and intermediate inputs. This reduction could reflect either lower input prices or a decrease in the quantity of inputs purchased. As the WBES does not report prices and quantities separately, we cannot test this directly. However, given that conflict is unlikely to lower the unit cost of inputs, we read these findings as evidence of a conflict-induced reduction in the quantity of raw materials and intermediate inputs used in production.<sup>21</sup> Consistent with this interpretation, conflict also appears to reduce electricity use, another key production input. Firms more exposed to conflict events report

> 21This is line with evidence from previous studies for West Bank, India, Libya, and Ukraine (Amodio and Di Maio, 2018; Couttenier et al., 2022; Del Prete et al., 2023; Korovkin et al., 2024).

15
Policy Research Working Paper 5491

# **Abstract**

As natural disasters hit with increasing frequency, especially in coastal areas, it is imperative to better understand how much natural disasters affect economies and their people. This requires disaggregated measures of natural disasters that can be reliably linked to households, the first challenge this paper tackles. In particular, a methodology is illustrated to create natural disaster and hazard maps from first hand, geo-referenced meteorological data. In a second step, the repeated crosssectional national living standard measurement surveys (2002, 2004, and 2006) from Vietnam are augmented with the natural disaster measures derived in the first phase, to estimate the welfare effects associated with natural disasters. The results indicate that short-run losses from natural disasters can be substantial, with riverine floods causing welfare losses of up to 23 percent and hurricanes reducing welfare by up to 52 percent inside cities with a population over 500,000. Households are better able to cope with the short-run effects of droughts, largely due to irrigation. There are also important longrun negative effects, in Vietnam mostly so for droughts, flash floods, and hurricanes. Geographical differentiation in the welfare effects across space and disaster appears partly linked to the functioning of the disaster relief system, which has so far largely eluded households in areas regularly affected by hurricane force winds.

This paper—a product of the Poverty and Inequality Team, Development Research Group—is part of a larger effort in the department to understand household vulnerability to weather-related shocks. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at pflewitt@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
We start with r=1 and then repeat the exercise for second, third, fourth, and fifth priority rankings (r=1, ...5), giving us a matrix of skill set-ranking cells. Given the small sample size (17),<sup>20</sup> we can only generate this statistic at an aggregate level rather than disaggregating by variables such as industry or export orientation. This methodology provides some insights, but is likely to return unsatisfactory conclusions since results are highly dependent on our sample, which is not globally representative. Further, a global average will mask policy-relevant country or sub-national variation.

It is useful to tease out the sample heterogeneity in order to determine if certain industries, jobs, export-orientation, firm modernity, or other industrial structures have different demands for skills. For example, some may argue that developed countries need higher-order cognitive skills while developing countries only need basic cognitive skills. Or that certain industries, such as manufacturing, most value technical skills while others, such as services, put greatest value on socio-emotional skills. The data permit two ways of testing these assumptions. First, some of our data compare employer preferences across dimensions. For example, some studies disaggregate preferences of employers in the service and manufacturing industries within a single country or disaggregate their skills preferences for managers versus workers or consider skills preferences of employers in traditional as compared to modern firms in a single country. This disaggregation gives us insights into whether certain economic structures, types of jobs, and so forth, have different employer demand profiles while holding constant country-specific variables. Second, the heterogeneity of economic structures of the countries in our sample (small economies with few industries v. large, diverse economies, exporting versus nonexporting countries) allows us to explore if employer skills preferences differ along these dimensions.

To capture sample heterogeneity, and given our data limitations of using study results rather than raw data, we would like to carry out a meta-analysis by pooling the sample and estimating a multinomial logit model to determine which skills set is the most important for different dimensions of the data. We would estimate: Pr(sn = s) = α+ β1(GDPcapn) + β2(GDPn) + β3(Xn) + β4(In) + β5(Mn) + β6(BCn) + β7(En) + ε Where for each country n, _sn_ is the skill set most demanded by country n, _GDPcap_ n is GDP per capita, _GDP_ n is gross domestic product, _X_ n are exports as a percentage of GDP, _I_ n is degree of innovation

> 20 Only 17 of the 28 studies in our sample can be used for this exercise since only 17 rank the top five skills, while others rank the top skills gaps and others provide first-ranked skills but not lower level rankings.

10
Trujillo, L. and Nombela, G. - _Privatization and regulation of the seaport industry_ Stopford, M. (1997): _Maritime Economics._ Ed. Routledge, London.

- Sutter, M. (1997): "Privatized Ports Gain Amid Growing Pains". _The Journal of Commerce._ March.

- Suykens, F. (1996): "Influence of Port Tariffs on Maritime Transport". VI International Congress of Maritime Traffic and Port Handling. Vigo (Spain).

- Thompson, L.S. and Budin, K. J. (1997): "Global Trend to Railway Concessions Delivering Positive Results". Public Policy for the Private Sector, Note no. 134, December, World Bank, Washington DC.

- Tortello, M. (1998): "La organizaci6n de la estiba en los paises de Mercosur y Chile: El modelo portuario de Chile". _I Congreso Internacional de Estiba._ Puertos del Estado. Las Palmas de Gran Canaria (Spain).

- Webster, M. y Cohen, P. (1997): "Ports: Continued Buoyancy for Private Ports". _Infraestructure Yearbook 1997. Privatisation International._ Ed. Wade, J. London.

61
that are closely linked to women’s activities, to encourage women to participate in all programs, and to assist women in obtaining better access to agricultural inputs (Buchy and Basaznew, 2005).

In terms of other modes of delivery of agricultural information, a recent IFPRI evaluation of farmer field schools in East Africa showed enormous effects on agricultural productivity and income for female-headed compared with male-headed households in a matched difference-in-difference evaluation of three countries (Davis et al., 2009). However, Quisumbing and Pandolfelli (2010) and Meinzen-Dick et al. (2010) find that extension visits are likely to remain the main mode of transmitting technology and information to farmers. Extension information and training material must be adapted to women, who may have lower levels of education. Extension officers must also consider that women may not be able to travel beyond the village and that time constraints are often binding. Modern information and communication technologies, such as cell phones (which have seen significantly expanded use in developing countries) and radios, may also help in overcoming some of these constraints.<sup>17</sup> When women find it difficult to join male-dominated networks, women’s groups can play an important role in providing women with access to important services, such as extension. Women’s groups and other forms of collective action can be an effective means of building social capital and addressing gender gaps in other areas as well by reducing transaction costs, pooling risks, developing skills, and building confidence. At the national level, Meinzen-Dick et al. (2010) find that the capacity to provide services to women and to 29
**Table1:** Herfindahl Index: 1991- 2000

||**1992**|**1994**|**1996**|**1998**|**2000**|**Average**|
|---|---|---|---|---|---|---|
|Wheat|0.326|0.301|0.336|0.294|0.329|0.317|
|Dairy|0.333|0.320|0.287|0.329|0.384|0.331|
|Sugar|0.127|0.164|0.134|0.140|0.121|0.137|
|Vege|0.099|0.084|0.076|0.084|0.065|0.082|
|Cotton|0.340|0.411|0.296|0.364|0.363|0.355|
|Ranch|0.143|0.154|0.144|0.152|0.152|0.149|
|Other|0.127|0.149|0.141|0.114|0.099|0.126|
|Equip|0.593|0.603|0.463|0.479|0.538|0.535|
|Ag Services|0.121|0.113|0.104|0.130|0.143|0.122|
|Crop Processing|0.986|1.000|0.976|0.893|0.901|0.951|
|Distribution|0.136|0.110|0.154|0.165|0.132|0.139|
|Forest/Nursery|0.077|0.059|0.060|0.075|0.073|0.069|
|Manuf|0.083|0.105|0.071|0.073|0.073|0.081|
||<br>4-PAC C<br>|<br>oncentrati<br>|<br>on Ratio1<br>|<br> 991 - 2000<br>|<br>|<br>|
||**1992**|**1994**|**1996**|**1998**|**2000**|**Average**|
|Wheat|0.956|0.942|0.892|0.950|0.939|0.936|
|Dairy|0.866|0.881|0.863|0.835|0.843|0.858|
|Sugar|0.646|0.676|0.655|0.652|0.620|0.650|
|Vege|0.527|0.512|0.469|0.495|0.401|0.481|
|Cotton|0.953|0.948|0.887|0.899|0.897|0.917|
|Farm|0.704|0.673|0.601|0.644|0.639|0.652|
|Other|0.611|0.597|0.676|0.569|0.503|0.591|
|Equipment|1.000|1.000|1.000|1.000|1.000|1.000|
|Ag Services|0.608|0.573|0.523|0.590|0.610|0.581|
|Crop Processing|1.000|1.000|1.000|1.000|1.000|1.000|
|Distribution|0.633|0.551|0.690|0.694|0.642|0.642|
|Forest/Nursery|0.464|0.371|0.377|0.437|0.445|0.419|
|Manuf|0.513|0.465|0.456|0.458|0.451|0.469|

How many PACs are active in the agriculture and related sectors? Our database indicates over 200 PACs that were politically active during this period. Table 1 indicates the “market structure” of PACs in the political market place. Among farm PACs, cotton, dairy and wheat had the highest degree of PAC concentration. The four PAC concentration ratio in the lower panel of Table 1 shows that wheat and cotton farms were represented politically almost entirely by four PACs. Even dairy, ranch and sugar had high levels of PAC 20
# **Section A. Background on NAIVS**

# _<u>Section A1. Subsidized input package</u>_

The input package consisted of three vouchers:

- 1) Nitrogenous fertilizer—one bag of urea.

- 2) Phosphorous fertilizer. Option 1: one bag of DAP; Option 2: two bags of Minjingu Rock Phosphate (MRP) with nitrogen supplement (also called Minjingu mazao), depending on the farmer’s choice.<sup>1</sup>

- 3) Seed (10 kilograms of a hybrid or open-pollinated maize variety or a rice variety)<sup>2</sup> sufficient for half of a hectare of maize (Table A.1).

**Table OA1: Maize input packages proposed for NAIVS (for 1 ha)**

|**Options**|**N source**|**P source**|**Seed**|
|---|---|---|---|
|Option 1|1 bag urea|1 bag DAP|10 kg OPV or hybrid seed|
|Option 2|1 bagurea|2 bags MRP+10N|10 kgOPV or hybrid seed|

## **Table OA2: Input prices in the Arusha villages in 2009/10 (TZS)**

||Median value of<br>subsidy voucher|Median top-up<br>paymentrequired|Median total<br>input cost|
|---|---|---|---|
|DAP (50 kg)|12,000|16,500|25,000|
|Urea (50 kg)|23,000|17,000|42,000|
|Hybrid orOPVmaize seed (10kg)|17,000|17,000|34,000|

<mark>Source: VVC member baseline survey</mark> <mark>* As a point of reference in December 2010 $1 USD = 1480 TZS</mark>

# _<u>Section A2. Institutional Arrangements</u>_

The National Agricultural Input Voucher Scheme (NAIVS) was overseen and implemented by the Ministry of Agriculture, Food Security and Cooperatives of Tanzania (MAFC) in collaboration with other public agencies and private sector participants.

# **NAVIS Oversight**

The NAIVS National Forum was the apex organization and endorsed the allocation of vouchers across targeted districts based on the adopted guidelines and selection criteria guidelines for NAIVS implementation. The forum endorsed the NAIVS annual work plan and budgets

> 1 DAP is the most commonly used basal fertilizer in Tanzania. MRP, manufactured in Northern Tanzania, is technically less efficient, because a certain amount of nitrogen is needed for plants to absorb phosphorus. MRP+N was a new product at the time of the study in 2011, technically equivalent to DAP, but produced in very limited quantities.

> 2 The seed provided in the package was sufficient to plant half of a hectare (100 percent) of maize and one-quarter of a hectare (50 percent) of direct-seeded rice. The lesser quantity of rice was proposed because rice is a self-pollinating crop, and good quality seed can easily be multiplied by farmers themselves, thus encouraging the spread of new rice varieties.

2