rafmacalaba/datause-encoder-data huggingface dataset:
_Figure 6:_ UK value added of final and intermediate goods and services <!-- Start of picture text --> $600,000<br>$500,000<br>$400,000<br>$300,000<br>$200,000<br>$100,000<br>$0<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>Final goods and services exports to EU Final goods and services exports to non‐EU<br>Intermediate DVA to EU Intermediate DVA to non‐EU<br>Intermediate<br>Final<br><!-- End of picture text --> A deeper look at the decomposition of value added sheds more light on the structure of GVCs in which the UK is involved. While intermediate value added of UK exports to the EU is similar to that of exports to non‐EU countries, the re‐exported value added content of exports, a measure of how much intermediates value added are used as inputs to produce exports in other countries, is much higher. This suggests that UK exports to the EU are an important element of EU exports, whereas they are important for domestic consumption in non‐EU countries.

# 3. Depth of trade agreements and UK‐EU trade

This section empirically investigates the extent to which the depth of the EU and of the PTAs the EU has with other countries had an impact on UK trade. The empirical estimation of the trade effect of PTAs is based on a gravity model similar to Mattoo et al. (2016) and Osnago et al. (2016). Different from the large body of literature on trade agreements (see Head and Mayer, 2014; Limão, 2016), we explicitly account for the depth of PTAs, exploiting the novel information on the content of trade agreements presented in Section 2. Moreover, we include an interaction term to accommodate heterogeneous effects of deep PTAs for the UK. Specifically, we estimate the following gravity equation: where �������� is bilateral exports from country _i_ to country _j_ in year _t_ , �������� is the number of legally enforceable provisions in the PTA between _i_ and _j_ (normalized between 0 and 1), ���� is a dummy variable equal to 1 if the exporting or importing country is the UK, ��� and ��� are importer‐time and exporter‐time fixed effects that account for country‐time specific shocks and the multilateral resistance terms (Anderson 13
Table 3 Impact of Hurricanes on GDP per capita (SS ≥ 3)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Linear<br>Regression|Linear<br>Regression<br>correcting for<br>Serial & Spatial<br>Autocorrelation|Panel Data<br>with Controls<sup>1/</sup>|Panel Data<br>correcting for<br>Dynamic<br>Models|
|MSWS|‐0.0115***|‐0.0115|‐0.0113***|‐0.0123***|
|Standard Error|(0.00195)|(0.00855)|(0.00324)|(0.00330)|
|R‐squared|0.367|0.434|0.111|n.a.|
|IHD|‐0.0151***|‐0.0167**|‐0.0169***|‐0.0186***|
|Standard Error|(0.00251)|(0.00840)|(0.00314)|(0.00328)|
|R‐squared|0.405|0.476|0.191|n.a.|
|WEI|‐0.0127***|‐0.0135|‐0.0134***|‐0.0145***|
|Standard Error|(0.00211)|(0.00914)|(0.00319)|(0.00329)|
|R‐squared|0.379|0.449|0.139|n.a.|

*** p<0.01, ** p<0.05, * p<0.1 1/ Investment, population growth, openness, log of GDP per capita. All for t‐1.

We can conclude that the impact of hurricanes in Central America decreases per capita GDP growth in a consistent and statistically significant way. With reference to IHD, an increase in a standard deviation reduces per capita GDP growth by 1.60 percent points, which represents half the average yearly growth of the region. The economic disruption of a shock like this is immense.

# 4. WINDSTORM IMPACT AT THE HOUSEHOLD LEVEL

One shortcoming of only looking at the impact of hurricanes on GDP is that, as a measure of economic performance and social progress, GDP fails to capture much of what we want to know about human well‐ being. Although GDP is a good starting point to interpreting the impact of adverse natural events, it is also important to understand its limitations in our context. A disaster could actually boost GDP because of the reconstruction efforts and measures taken by governments. Since GDP is meant to measure market activities –and by definition, self‐sustaining agriculture is not in the market– a reading of GDP could undercount or underestimate the impact of a disaster on poor rural populations. It is therefore necessary to consider other welfare measures in order to have a more accurate picture of the impact of hurricanes in the region. In an effort to delve deeper into the effect of hurricanes on the well‐being of the population in Central America, this section evaluates hurricanes’ impact on income and poverty, as per the World Bank’s definition. Even though data are limited, especially for the countries hit most by storms (e.g. Nicaragua and Guatemala), we shed light on the potential impacts for Central America.

14
Ra, Sungsup, and Bipul Singh. 2005. “Measuring the Economic Costs of Conflict”. Asian Development Bank: Working Paper Series No. 2.

Seddon, David, Gurung, Ganesh, and Jagannath Adhikari. 1998. “Foreign Labor Migration and the Remittance Economy of Nepal”. _Himalayan Research Bulletin_ XVIII (2): 3 – 10.

Seddon, David, and Jagannath Adhikari. 2003. “Conflict and Food Security in Nepal”.

Kathmandu: Report to Rural Reconstruction in Nepal.

Sharma, Kishor. 2006. “The Political Economy of Civil War in Nepal,” _World Development_ 34 (7): 1237-53.

Sharma, Hari Bhakta, and Tika Ram Subedy (eds). 1994. _Nepal District Profile_ . Kathmandu: National Research Associates.

Verwimp, Philip. 2003. “Testing the Double-Genocide Thesis for Central and Southern Rwanda,” _Journal of Conflict Resolution_ 47 (4): 423-42.

- World Bank. 2004. “Social Change in Conflict-Affected Areas of Nepal,” _Social Development Notes: Conflict Prevention & Reconstruction_ 15: 1-4.

Yabiku, Scott T. 2005. “The Effect of Non-family Experiences on Age of Marriage in a Setting of Rapid Social Change,” _Population Studies_ 59 (3): 339-54.

31
Operationalizing the estimation of the parameters of the indirect utility function requires an <u>f</u> estimate of the electricity production function to compute estimates of = ∂∂ for for each household and billing cycle. We assume the following functional form oo(ss, AA, εε) ppii ∂∂ssii (ss, AA, εε)pp(EE) uu= 1,2,3,4 oo(ss, AA, εε) ∑4 exp (∑ i i i (8) MM where i are zero mean regression errors and the EEii = δδ + ii=1 ii=1 λλ are a vector of weather characteristics for the WWii)uu + εε household during billing cycle . This specification imposes the restriction that all marginal prices are ii positive, because εε i ) WW . The elements of are a constant, average daily temperature in the billing cycle, and average daily rainfall in the billing cycle. These TT MM ii ii=1 ii ii pp = nnxxpp(∑ λλ WW ooTTnn ss= 1,2,3,4 WW weather variables are included to account for the fact that different combinations of appliances within each of the four energy service categories are likely to be used in different combinations, depending on temperature and rainfall conditions within the billing cycle. For example, warmer weather with less rainfall, may cause the household to use different lights in the household with different intensities and each of these lights consume electricity at a different rate. Cooler and rainy weather may cause the household different combinations of general appliances that hotter and drier weather. For instance, the household may be less likely to use an iron and run their refrigerator more intensively during hot weather than during cooler and more rainy weather. Table 8 contains the nonlinear least equations estimates of equation (8) with the standard errors clustered at the household level. Figures 10-13 contains the distribution of billing cycle-level marginal prices for each of the four classes of energy services computing using the estimated parameters of equation (8), so that i ) where the i are the nonlinear least squares estimates of the i . MM ii ii=1 ii pp = nnxxpp(∑ λλ̂ WW ooTTnn ss= 1,2,3,4 λλ̂ λλ 13
|Figure<br>16:|Postneonatal Mortality Trends by Mother's<sup>. . . . . . . . .</sup><br>70<br>Education in Cote d'Ivoire, 1970-86 (LSMS 1985/86)|
|---|---|
|Figure 17:|Infant Mortality Trends Among Urban and . <sup>. . . . . . . . .</sup><br>71<br>Rural Poor in Cote d'Ivoire, 1970-86<br>(LSMS 1985/86)|

|Figure 18:|Trends in Infant Mortality Components among<sup>. . . . . . . .</sup><br>72|
|---|---|
||the Rural Poor in Rural Cote dllvoire, 1970-86|
|Figure 19:|Trends in Infant Mortality Components<sup>among . . . . . . . .</sup><br>73|
||the Urban Poor in Urban Cote d'Jvoire, 1970-86|
> associated with the average r measure of portfolio riskiness or weather sensitivity is thus 9.3.

The risk framework suggests that portfolio riskiness should vary with the first two moments of the weather distribution and with total wealth. Moreover, the effect of an increase in weather risk (a mean-preserving shift in the variability of weather) on the riskiness and profitability of the portfolio is likely to depend on the total level of household wealth. A problem with testing for wealth effects is that, at any given point in time, both accumulated wealth as well as the investment portfolio will reflect the farm household's subjective risk preferences, which may vary across the farmers in our sample.<sup>12</sup> The observed cross-household association between wealth and the risk characteristics of the asset portfolio, given heterogeneity in risk preferences, does not conform to the result that would be obtained by randomly assigning wealth levels across farmers.

The ICRISAT data enable us to use two procedures that may circumvent the bias due to risk-preference heterogeneity. First, to the extent that preferences are time-invariant, a fixed effects procedure will provide consistent estimates of wealth effects on portfolio allocations. However, because the moments of the weather distribution are also time-invariant (under stationarity), use of the fixed effects procedure does not allow the identification of the direct effects of the characteristics of the weather distribution on portfolio choice. An alternative procedure is to use inherited wealth, also available in the data, instead of current wealth. To the extent that the wealth inherited by a farmer is orthogonal to his preferences for risk, use of inherited rather than current wealth reduces biases associated with heterogeneity.

Based on the data from the six original villages where the longer time-series of weather are available, we report in Table 5 estimates of i) the 23
Policy Research Working Paper 5036

# **Abstract**

Based on extensive data collection in Uganda, this paper demonstrates that the rural access index, as defined today, should not be a government objective because the benefit of such investment is minimal, whereas achieving rural accessibility at less than 2 kilometers would require massive investments that are not sustainable. Taking into account the fact that plot size is limited on average to less than 1 hectare, a farmer’s transport requirement is usually minimal and does not necessarily involve massive investments in infrastructure. This is because most farmers cannot fully load a truck or pay for this service and, even if productivity were to increase significantly, the production threshold would not be reached by most individual farmers. Therefore, in terms of public policy, maintenance of the existing rural roads rather than opening new roads should be given priority; the district feeder road allocation maintenance formula should be revised to take into account economic potential and, finally, policy makers should devote their attention to innovative marketing models from other countries where smallholder loads are consolidated through private-based consolidators.

This paper—a product of the Transport Unit, Africa Region—is part of a larger effort in the unit to evaluate impact of rural roads on economic development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The author may be contacted at graballand@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
40 Some of the indicators we have used could be questioned. In some cases, alternatives could be tried: for example, it might be argued that DPT3 coverage is a more comparable indicator than full immunization. In other cases, such as the treatment of ARI and diarrhea in children, our indicators can be criticized (in these cases because the estimation of need is rather crude), but no better alternatives currently exist (cf. Boerma et al. 2014a). In yet other cases, it might be argued our indicators ought not to be included at all. For example, although the two cancer screening indicators appear among the examples of intervention coverage indicators in Boerma et al. (2014a), concerns have been expressed about the wisdom of breast cancer screening given the high rates of false positives (see e.g. Independent UK Panel on Breast Cancer Screening 2012); we include breast cancer screening because it speaks to the non-communicable disease agenda, and because it is still recommended by professional bodies, despite concerns about false positives.<sup>21</sup> Arguably, the biggest challenge in the future is extending the indicator set to get a more complete picture of service coverage. Boerma et al. (2014a) suggest several candidates for inclusion in a broader service coverage indicator set (cf. their Table 1), and propose 10 effective coverage indicators, five for each of the prevention and treatment domains (cf. their Table 2). We considered, in fact, several of these, but

> 21 There are issues, however, that we have been unable tom resolve satisfactorily on the data front. The 40-49 age range was chosen for the breast cancer screening indicator in part because of data availability. The WHS uses the 40-69 age range; however, the DHS uses the 15-49 or 40-49 age range. We have therefore worked with the lowest common denominator – the 40-49 age range. In the US, there is divergence of view over whether screening should start at 40 or 50, with the American Cancer Society and the Mayo Clinic continuing to recommend starting at age 40, and the U.S. Preventive Services Task Force in 2009 recommending starting only at age 50 instead of at age 40 as it had recommended in its 2002 recommendation. The 18-49 age range was chosen for the cervical cancer screening indicator also in part because of data availability. The WHS uses the 18-69 age range; however, the DHS uses the 15-49 age range. We have therefore worked with the lowest common denominator – the 18-49 age range. The WHS age range is more in line with national screening guidelines – currently screening is recommended (every three years) for the 21-65 age group in the US.

40
of 2001<sup>6</sup> , providing 58 quarters of data. Over this period the ENEU expanded coverage from 16 cities in 1987 to 34 cities by the end of 1992 and 44 cities by the second quarter of 2001. We include all 39 cities present by the end of 1994, although our results are robust to restricting the sample to just the 16 cities present in all years.

The ENEU only collects data on labor earnings for each household member in their principal occupation. We add this over household members and deflate by the Consumer Price Index for the relevant quarter from the Bank of Mexico to obtain real household labor earnings. To focus only on households for whom labor earnings are likely to be a main source of income, we restrict our sample to households with heads aged 25 to 49 years old. On average two percent of the observations have household labor income of zero. Using data from the ENIGH income and expenditure survey, which does include non-labor sources of income, we calculate that labor income represents 95 percent of total monetary income for urban households with heads in the 25-49 year old age range. In Section 5.2 we examine how mobility in labor earnings compares to estimated mobility in full income and in expenditure.

For our panel data analysis we then have 54 five-quarter panels, beginning with the panel of 3930 households which were sampled from the first quarter of 1987 through to the first quarter of 1988, and ending with the panel of 11,158 households that were sampled from the second quarter of 2000 through to the second quarter of 2001. We use unbalanced panels. Attrition is comparable to dwelling-based labor force surveys in other developing countries. Ten percent of households are observed for only one quarter, while approximately 65 percent of households can be followed for all five quarters.

We form pseudo-panels based on the household head’s year of birth and education level. Cohorts are defined by the interaction of five year birth intervals and three education levels (primary schooling or less, 7 to 12 years education, and more than 12 years education). For example, all household heads born

> 6 Since the second quarter of 2001, the ENEU was replaced by the ENE, which has now become the ENET - a national quarterly employment survey.

16
The only region that experienced monotonically increasing sibling correlation is Middle East and North Africa implying consistently declining educational mobility across cohorts. We take advantage of the recent approach of Bingley and Cappellari (2019) to estimate the share of sibling correlation due to intergenerational transmission of schooling from parents to children. We nd that relaxing the homogeneity and independence assumptions implicit in the standard methods of decomposition makes the estimated share much larger. In our sample, at least 60 percent of sibling correlation can be attributed to the intergenerational component, while there are some countries where the share is more than 80 percent (most in Sub-Saharan Africa). The average intergenerational share for the 53 countries is 74 percent. This suggests a dominant role for the parents in shaping educational opportunities of children, providing an argument in favor of causal analysis and economic policies focusing on the family. Evidence on the evolution of the intergenerational share, however, suggests a declining importance of the intergenerational transmission component in many countries, but the pattern is very diverse. In some cases, the trend in the intergenerational share is opposite to the trend in sibling correlation.

# References

- Ahsan, M. N., Shilpi, F., and Emran, S. (2023). Public Primary School Expansion, GenderBased Crowding Out, and intergenerational educational mobility. Working paper, DECRG, The World Bank.

- Ahsan, N., Emran, M. S., and Shilpi, F. (2021). Complementarities and Intergenerational Educational Mobility: Theory and Evidence from Indonesia. MPRA Paper 111125, University Library of Munich, Germany.

- Ahsan, N. M., Emran, M. S., Jiang, H., Murphy, O., and Shilpi, F. J. (2022). When Measures Conict: Towards a Better Understanding of Intergenerational Educational Mobility. Working paper, The World Bank.

- Alesina, A., Hohmann, S., Michalopoulos, S., and Papaioannou, E. (2021). Intergenerational Mobility in Africa. Econometrica, 89(1):135.

- Assaad, R. and Saleh, M. (2018). Does Improved Local Supply of Schooling Enhance Inter-

25
# **3. Perceptions and Choice Rules**<sup>**2**</sup>

We now explore in more detail why farmers, communities and governments are reluctant to protect themselves against weather-related events, such as a drought year or severe flooding. It is useful to begin by reviewing how protective decisions should ideally be made in a world where all decision makers follow the economic rationality of utility maximization. We can then examine how biases, constraints and simplified decision rules foster actions that depart from economic rationality.

Suppose that the decision maker is considering incurring an investment that has potential payoffs for the next _T_ years and that there is an annual probability _p_ of a drought in any given year _t_ . Should such an event occur, the annual benefit of investing in a measure that reduces the consequences of drought will be denoted as _B_ .<sup>3</sup> In this case, the decision to protect against this event could be made by observing whether the upfront cost (C) of the investment is less than the discounted stream of expected benefits ( _p_ B) i.e., if where _β_ is the decision-maker’s annual discount rate.<sup>4</sup> Implicit in (1) are three strong assumptions about how the decision maker estimates and values costs and benefits over time. Specifically:

- 1) all future benefits are discounted exponentially

- 2) individuals can estimate future probabilities of a drought

- 3) individuals can estimate the costs and benefits of the risk reduction measure

In practice, decision makers are likely to utilize simplified choice rules, focus on constraints as well as short-run benefits and costs rather than discounting the future exponentially and may not consider probabilities in their decision on whether or not to invest in the risk-reduction measure.

> 2 This section is based on Kunreuther, Meyer and Michel-Kerjan (in press).

> 3 For simplicity, we are assuming only a single drought event that occurs any year _t_ with a well-specified probability. The qualitative conclusions will not change if there are a number of possible events and probabilities vary over time. One could also make probabilities of drought in future years _t+n_ conditional on what happens in year _t_ to address issues of long-term drought and/or climate change.

> 4 We assume here that the decision is made solely choosing alternatives where the costs are less than the net expected discounted benefits.

6
|Covered well|2.75|2.44|2.79|
|---|---|---|---|
|Covered spring|5.42|2.80|5.72|
|River|28.87|8.81|31.19|
|Pond/lake/dam|4.15|0.00|4.63|
|Rainwater or other|0.06|0.11|0.05|
|**Type of toilet facility**||||
|Flush toilet|0.16|1.57|0.00|
|Improved(ventilated)latrine|0.29|2.03|0.08|
|Traditionalpit latrine|13.72|63.11|8.00|
|No facility,bush,field|85.83|33.28|91.92|

Of particular interest in this investigation are the possible external effects of other household’s investments and its impact on the common neighborhood environment. To capture these “neighborhood effects” variables with the average level of education and wealth, and the proportion of households with access to safe water and sanitation are created. These averages are obtained from the non-self means of the respective variables, which are calculated by summing the variable of interest over the sample cluster and then subtracting the observation from the household and dividing this difference by the number of households in the cluster minus one.<sup>9</sup> The proportion of households with access to safe water in a given location may be largely determined by geographical and climatic factors. Households living in the highlands, where peaks are as high as 4,550 meters above seal level, face very different constraints from those living in areas 110 meters below sea level, such as the Afar Depression. Altitude measurements were taken using a global positioning device for each of the survey clusters and are included in the analysis.

# **5. Econometric Specification**

This study examines the impact of the above variables on the probability a child is stunted and underweight. This choice for the dependent variable, rather than the actual Z scores, facilitates interpretation of the results, as most measures of malnutrition are reported as the proportion of children who fall in these categories. A child is stunted (y=1) if his or her

> 9 The clusters in the sample are relatively small and therefore it is likely the standard errors are large. This would bias parameters towards zero.

16