rafmacalaba/datause-encoder-data huggingface dataset:
60 An alternative, highly tractable model is that of McConnell and Strazheim. If one can live with an idealized representation of space, this model has many attractive features. The most important, beyond its tractability, is that it is the only model to incorporate congestion and emissions, translate emissions into health damage, and permit the possibility of health damage to influence choices. Its serious drawback is that its only choice variable is residential location.

Space and Coligestion. A defining characteristic of these models is their representation of space and congestion. Such a representation is needed not only to estimate external costs associated with congestion, but to relate emissions to "modal activities" (e.g., acceleration, idle) affected by congestion. Space (and congestion) needs to be represented even if a pollutant of concern is regional in scope (such as ozone, in some situations). While, in this case, the location of the precursor emissions would not affect prediction of ozone concentrations, the degree of congestion under which driving occurs would influence such concentrations by affecting the amount of HC and NOx emissions produced per mile.

The options for addressing space (and congestion) include: (i) ignoring space entirely (Wheaton); (ii) taking space into account implicitly through equations that make speed a function of roadvay capacity and VMTs or modal choice (Deaton); (iii) idealizing space (McConnell and Strazheim, Kim); (iv) estimating trip origin-destination matrices from traffic counts (sketch planning models); (v) using traffic volume data as a baseline but permitting trip and modal choices to be affected by travel time, which is dependent on estimated traffic volume by corridor (Cameron); (vi) representing space as a network and solving for equilibrium traffic flow
**Figure 2: Distribution of China FDI stock by industry and country of origin, 1990-2004**

China FDI stock by industry, 1990-2004 <!-- Start of picture text --> 70%<br>63.14%<br>60%<br>50%<br>40%<br>30%<br>21.66%<br>20%<br>10%<br>3.33% 2.89% 2.32% 1.89% 0.66% 0.37% 0.29%<br>0%<br>Manufacturing Property and Wholesale, Construction Transport Agriculture, Health care Scientific Education<br>utilities retailing and forestry, animal research<br>catering husbandry and<br>fishery<br>Percentage (%)<br><!-- End of picture text --> China FDI stock by country of origin, 1990-2004 <!-- Start of picture text --> Germany<br>France Other<br>2%<br>1% 9%<br>Great Britian<br>2%<br>Singapore<br>5%<br>Hong Kong/Macau<br>South Korea<br>45%<br>5%<br>British Virgin<br>Islands<br>6% Taiwan Japan USA<br>7% 9% 9%<br>Hong Kong/Macau USA Japan Taiwan<br>British Virgin Islands South Korea Singapore Great Britian<br>Germany France Other<br><!-- End of picture text --> Source: National Bureau of Statistics of China, 2005.

30
hierarchy,<sup>7</sup> might lead low caste individuals to tolerate norm violations by high caste individuals. The prediction that low caste individuals refrain from punishing high caste individuals in an LHL treatment is what we call the _caste submission hypothesis_ .

The third hypothesis that our design enables us to examine is that, controlling for the caste status of the norm violator, high caste compared to low caste individuals punish more severely norm violations that hurt members of their community. Sen (2000) and Rao and Walton (2004) argue that _inequality of agency_ is a consequence of social exclusion. To adapt Sen’s language, the aspect of agency that we investigate is the capability to punish those who violate social norms. Repression and social exclusion might induce fatalism, undermining the selfconfidence and the motivation required to punish violators of cooperation norms. We call this the _caste culture_ hypothesis.

Our results unambiguously refute the caste conflict and the caste submission hypotheses. Instead we find, in line with the caste culture hypothesis, that low caste compared to high caste individuals punish norm violations less often and less severely. This result is robust to controls for wealth, education, and participation in village government. In fact, the effect of individual differences in wealth is very small, always insignificant, and sometimes not in the expected direction.

Further, we show that the low castes’ lower propensity to punish has nothing to do with differences in the underlying social norm. We measure the underlying social norm in our experiment by player B’s expectations about punishment. Regardless of caste status, the vast majority of subjects in the role of player B expected that they would _not_ be punished for cooperation in the social dilemma game but _would_ receive high punishment for defection – a clear indication that cooperation was considered the normatively right thing to do in this game. Thus, low and high castes have the same cooperation norm in the social dilemma game.

> 7 Even in contemporary rural India events like the following are still reported: “When a Dalit ... refused to sell _bidis_ [hand-rolled cigarettes] on credit to the nephew of an upper caste village chief, the upper caste family retaliated by forcibly piercing his nostril, drawing a string through his nose, parading him around the village, and tying him to a cattle post” (cited in NYU, 2007, p. 60, from _Indian Express_ (Bombay), April 28, 1998).

5
# **Gender-Differentiated Impacts of Tenure Insecurity on Agricultural Performance in Malawi’s Customary Tenure Systems**

Klaus Deininger<sup>a</sup> Fang Xia<sup>b</sup> Stein Holden<sup>c</sup> a World Bank, Washington DC b Research Institute for Global Value Chains, University of International Business & Economics, Beijing c Norwegian University of Life Sciences, Aas JEL Codes: J20, J21, J22, J23, J24, Q10, Q11, Q12 We thank Daniel Ali, Blessings Botha, Davie Chilonga, Thabbie Chilongo, Mercy Chimpokosero-Mseu, Alejandro de la Fuente, Time Fatch, Thea Hilhorst, Henry Kankwamba, Holger Kray, Talip Kilik, Ben Linkow, Francis Mkhupa, Valens Mumvaneza, Inmnocent Pangapanga, Richard Record, Hardwick Tchale, and Daan van Setten for comments that helped to improve the quality of the paper. The views presented are those of the authors and do not necessarily represent those of the World Bank, its Executive Directors or the member countries they represent.
wr' (AY) =a" +b’ At a” bb >0 {Az} {PIS}, Hi, XT} r € {i,w}.

We To ~~pte~~ rqyr r rygr ~~bY Ar”~~ rer Yr r\cr rT CS = fp D(p)dp = [dp*** (1+ ah = a PH" — PM) (1 +) P
41 a number of other issues discussed above can significantly raise the value' of benefit incidence studies to policy makers.

Of course, even if researchers adhere to these guidelines, benefit incidence analysis remains an exercise in the allocation of government outlays rather than rigorous research into welfare-based benefit measures. Thus, while we believe this literature is very infornative, we also believe that it is desirable, whenever possible, to complement benefit incidence studies with other forms of research. One direction for additional research is to estimate the full consumer surpluses associated with policy reforms. Inportant work in this area includes the on-going research efforts of the World Bank's Living Standards Measurement Survey. Another important line of inquiry includes more "institutional" research into the details of service delivery that thwart the utilization of programs targeted by lower-income households. Inportant examples of this sort of research include Whittington et al., Meerman, Meesook, and Chemichovsky and Meesook.

In addition to formal research efforts, project teams wil typically perform benefit incidence analyses under severe time and financial constraints. How can more fonnal research inform field work and mnissions that need reliable benefit incidence answers under more severe time pressures? In general terms, the research findings suggest several focal points for short-term missions. First, compared to other countries at similar levels of development, does the expenditure pattern for the country in question suggest a misallocation due to an unusual share of GDP spent on general goods as opposed to social services? Second, with any social service, do expenditures seem skewed towards the primary urban area relative to population living in the secondary urban or rural areas, and are expenditures concentrated on services that studies reveal benefit households in higher income deciles? Such concentrations of benefits suggest reforms, such as reallocation of services to rural areas, and increasing user fees for the social services consumed at higher income deciles. Costs recovered could then be allocated to services known to benefit the poor. Third, studies that have examined the reasons for low participation of poorer households in public services of interest to the mission can give the mission a head start in identifying potential institutional barriers prevrwnting the poor from consuming services. Armed
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Table 12: Regional log Wage Premia - All Workers - 2000, 2010

|Change in log wage premium:|(1)|(2)<br>OL|(3)<br>S|(4)|IV<br>1980 earn<br>(5)|
|---|---|---|---|---|---|
|Panel A: Census-1991-2000||||||
|Regional tariff change|0.413|0.432***|0.174|0.487***|0.843***|
||(0.342)|(0.141)|(0.137)|(0.148)|(0.326)|
|Earnings pre-trend (80-91)|||-0.274***<br>(0.056)||0.437*<br>(0.260)|
|Earnings pre-trend (70-80)||||-0.088<br>(0.063)||
|State fixed effects (26)||✓|✓|✓|✓|
|R-squared|0.031|0.648|0.691|0.652||
|First stage partial F-Statistic|||||21.17|
|Panel B: Census-1991-2010||||||
|Regional tariff change|0.583|0.165|-0.258|0.251|0.829|
||(0.510)|(0.292)|(0.233)|(0.294)|(0.531)|
|Earnings pre-trend (80-91)|||-0.438***<br>(0.077)||0.688**<br>(0.302)|
|Earnings pre-trend (70-80)||||-0.151<br>(0.095)||
|State fixed effects (26)||✓|✓|✓|✓|
|R-squared|0.032|0.601|0.666|0.608||
|First stage partial F-Statistic|||||22.42|

Decennial Census data. Positive coefficient estimates for the regional tariff change imply larger declines wages in regions facing larger tariff declines. 411 microregion observations. Standard errors (in parentheses) adjusted for 112 mesoregion clusters. Weighted by the inverse of the squared standard error of the estimated change in the relevant regional wage premium share. Due to a lack of hours information in 1980, pre-trends for _earnings_ computed for 19801991 and 1970-1980 periods. To address potential endogeneity of 1980-1991 pre-trend due to overlap with dependent variable in 1991, 1980-1991 pre-trends are instrumented using the 1980 earnings premium level in column (5). The 1970-1980 trend is weakly related to the 1980-1991 trend, so this potential instrument is not utilized. First-stage partial F-statistics reported for instrumental variables specification. *** Significant at the 1 percent, ** 5 percent, * 10 percent level.

74
Policy Research Working Paper 8201

# **Abstract**

Rural access is among the most important infrastructure elements to stimulate economic growth in rural and remote areas. The sustainability of feeder road maintenance is a challenge in many developing countries. Many feeder roads are unpaved and need to be maintained frequently, but they are often neglected under budget pressure. Output- and performance-based road contracts are an instrument to ensure the sustainability of road maintenance. Contractors are required not only to improve roads, but also to maintain them. Using micro data from household surveys in Zambia, the paper examines the impacts of output- and performance-based road contracts on agricultural production. It shows that the contracts have a significant impact on crop production, especially maize and groundnuts, two major crops grown in the study area. The paper also finds that the measured impacts are associated with actual road maintenance works, regardless of contractual methods. Any road work can improve people’s connectivity, even if it is not an output- and performance-based road contracts. The impact of the contracts is catalytic: more road works were implemented on contract roads than non-contract roads, holding everything else constant. This is an important contribution to the sustainability of road maintenance. Finally, road improvement works are found to facilitate farmers’ market participation, but the impact seems weak. There may be other constraints. Transport service costs are found to have a negative impact on farmers’ market sales. Thus, although roads are improved, transport services may be not available or too expensive, which still hamper farmers’ market participation.

This paper is a product of the Transport and ICT Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at aiimi@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
growth bypassed the sectors and states where it would have had the most impact on poverty based on a model calibrated to largely pre-reform data (Datt and Ravallion, 2002). By this view, the composition of the higher growth would mean that it by-passed many of India’s poor.

Against this view, it can be conjectured that India’s growth process has changed— implying a new set of parameters in the relationship between growth and poverty reduction. Ravallion and Datt (1996) studied a period in which the development strategy emphasized rapid development of the capital goods sector in a largely closed economy.<sup>6</sup> The strategy assumed that the capital stock and industrial structure could be manipulated exogenously through central planning, even in a largely market-based economy. The strategy was also founded on “trade pessimism”—the beliefs, grounded in the experiences of colonialism, that India could never compete in global markets until its domestic capital stock had been greatly expanded, coupled with a distrust of foreign (Western) countries as a source of essential goods. These assumptions were questioned in both academic and policy circles at the time, and with greater veracity as the years passed, in the light of the evidently poor economic performance.<sup>7</sup> The success of China’s pro-market reforms starting in 1978 also fueled doubts in the 1980s about India’s economic strategy. The policy debate raged for many years, but it was not until a balance of payments crisis that reforms started in earnest, in the early 1990s. (As is evident in Figure 1, the macroeconomic difficulties appear to have also stalled progress against poverty.) Trade liberalization was combined with efforts to support higher productivity in the private sector.<sup>8</sup> Supporters argued that these reforms would allow India to better exploit its comparative advantage in labor-intensive goods and services, and that this would directly benefit the poor; by this view, the reforms would “..favour the poor by beginning to remove the pervasive bias that exists against the employment of unskilled labour” (Joshi and Little, 1996, p.221). The hope was that the post-reform urban economy would be more effective in reducing both urban and rural poverty.

> 6 On the history of thought on development strategies and their implications for poverty, with specific reference to India, see Lipton and Ravallion (1995).

> 7 Some observers in India at the time had questioned these assumptions, raising concerns about labor absorption (given high population growth) and (hence) poverty reduction; in particular see Vakil and Brahmanand (1956). Chakravarty (1987) provides an insightful account of the history of thought on India’s (pre-reform) development strategy. For a broader analysis of industrial policy in developing countries, emphasizing the endogeneity of industrial structure to factor endowments, see Lin (2009).

3
# **5 Summary and Conclusions**

With a long history of political instability and conflict, as well as weak infrastructure and mountainous terrain, Afghanistan is particularly vulnerable to economic and natural shocks. The 2007-2008 global food crisis was no exception. Afghanistan experienced rapid increases in the prices of staple foods and other commodities due to both international and domestic factors. Our work makes several contributions in helping to understand how the people of Afghanistan were affected by, and how they coped with, these price shocks. While there has been an abundance of research on the causes of the food crisis, less has been done on household responses to the crisis. The household-level research that exists often uses pre-crisis survey data and simulation methods. We use the nationally-representative NRVA household survey data to assess the impact of rising food prices on household welfare. The data give us the opportunity to capture actual household behavior during the peak of the global crisis, providing rare insight into short-term coping mechanisms in a poor, conflict country.

Over the course of the fieldwork (August 2007 – September 2008), the food price index increased by 40%, with some of the largest price increases (100%) seen in wheat – the Afghan staple. Our analysis examines the food-based coping mechanisms (i.e., reductions in calorie and nutrient intake and dietary diversity) that households employ in the face of such price shocks. During this time, food insecurity (households with per capita daily calorie intake less than 2,100 calories) increased by 10 percentage points to a striking 34% in the fourth quarter; that is, in the summer of 2008 over one-third of Afghan households were unable to meet the minimum daily energy requirements.

We find that increases in the price of wheat flour lead to large reductions in real monthly per capita food consumption, but smaller declines in per capita daily calorie intake. Households are able to buffer the shock to energy intake by changing the composition of their diets. In particular, households move away from nutrient-rich foods like meat, fruits and vegetables toward staples like wheat, seen through changes in the composition of household expenditures on various food groups in response to the price increases. The large declines in food security experienced by Afghan households potentially can have serious implications. In particular, low levels of dietary diversity have been statistically linked to poor 22
Appendix Table 4a: RDD estimates of gaps in student language test scores associated with teachers hired pre- and post-FPE (multiple <u>bandwidths).</u>

||Bandwidth:|1|2|3|4|5|6|7|8|9|10|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Pooled|Coef|0.025|-0.115|-0.149|-0.133|-0.212|-0.233|-0.240|-0.207|-0.188|-0.148|
||SE|(0.205)|(0.152)|(0.125)|(0.110)|(0.100)|(0.092)|(0.086)|(0.079)|(0.075)|(0.072)|
||Pval|0.902|0.448|0.232|0.224|0.035|0.012|0.005|0.009|0.012|0.040|
|Kenya|Coef|-0.733|0.183|-0.202|-0.230|-0.376|-0.404|-0.533|-0.435|-0.260|-0.255|
||SE|(0.428)|(0.361)|(0.306)|(0.252)|(0.245)|(0.248)|(0.225)|(0.208)|(0.197)|(0.197)|
||Pval|0.105|0.615|0.512|0.365|0.129|0.106|0.020|0.038|0.187|0.196|
|Madagascar|Coef|-0.082|-0.313|-0.172|-0.238|-0.286|-0.328|-0.333|-0.379|-0.314|-0.307|
||SE|(0.476)|(0.347)|(0.286)|(0.244)|(0.214)|(0.202)|(0.191)|(0.174)|(0.159)|(0.153)|
||Pval|0.863|0.370|0.548|0.332|0.184|0.105|0.082|0.030|0.049|0.046|
|Mozambique|Coef|-0.367|-0.526|-0.539|-0.446|-0.364|-0.169|-0.019|-0.004|-0.099|-0.108|
||SE|(0.705)|(0.471)|(0.384)|(0.316)|(0.290)|(0.267)|(0.238)|(0.218)|(0.206)|(0.194)|
||Pval|0.611|0.275|0.168|0.164|0.212|0.529|0.935|0.984|0.631|0.577|
|Tanzania|Coef|-0.030|-0.220|0.140|0.102|-0.299|-0.355|-0.272|-0.191|-0.192|-0.064|
||SE|(0.366)|(0.323)|(0.291)|(0.248)|(0.245)|(0.210)|(0.201)|(0.172)|(0.172)|(0.159)|
||Pval|0.935|0.498|0.630|0.682|0.224|0.092|0.178|0.268|0.266|0.689|
|Togo|Coef|0.259|-0.019|-0.215|-0.317|-0.268|-0.303|-0.278|-0.168|-0.122|-0.189|
||SE|(0.429)|(0.309)|(0.276)|(0.243)|(0.230)|(0.227)|(0.224)|(0.225)|(0.215)|(0.191)|
||Pval|0.551|0.952|0.438|0.196|0.248|0.186|0.216|0.458|0.571|0.324|
|Uganda|Coef|0.328|0.067|-0.149|0.174|0.151|0.090|0.023|0.095|0.013|-0.015|
||SE|(0.509)|(0.406)|(0.315)|(0.259)|(0.247)|(0.229)|(0.212)|(0.196)|(0.184)|(0.180)|
||Pval|0.523|0.870|0.637|0.503|0.541|0.696|0.915|0.629|0.945|0.935|

Notes: Table reports coefficient estimates (Coef), Standard Errors (SE), and p-values derived from estimating Equation (1). Each estimate and associated standard error and p-value are the result of a separate regression model. Pooled model includes country fixed effects.

A-7
**Table 4. Impacts on Consumption Components**

|**Measure of Shock**|_Food_<br>(1)|_Health_<br>(2)|_Education_<br>(3)|_Durables_<br>(4)|
|---|---|---|---|---|
||**Panel A**<br>|**: Total**<br>|||
|t * (rainfall z-score> 2)|-16.054<br>[10.956]|0.828<br>[2.683]|-5.821**<br>[2.559]|-16.554*<br>[9.231]|
|Baseline Median|283.5|3.314|4.103|7.869|
||**Panel B**|**: Urban**|||
|t * (rainfall z-score> 2)|-40.622**|-5.967|-8.830*|-51.447***|
||[16.306]|[4.775]|[5.094]|[16.087]|
|Baseline Median|336.1|4.571|11.82|15.91|
||**Panel C**|**: Rural**<br>|||
|t * (rainfall z-score> 2)|7.954|5.190*|-2.860|-3.366|
||[14.200]|[2.986]|[2.292]|[8.135]|
|Baseline Median|250.4|2.481|1.961|4.378|

Observations: 26,587 Total; 11,225 Urban; 15,362 Rural. Notes: Results from diff-diff regression controlling for age, gender, years of education, marital status and race of the household head as well as location (urban or rural). Robust standard errors in brackets clustered at the municipality level. Consumption on food, health services, education and durable goods are monthly p.c. terms in Quetzales of 2006. The Z-score indicates the number of standard deviations above the rainfall mean (since 1980). t is the before-after dummy. *** p<0.01, ** p<0.05, * p<0.1 calculations by the authors based on data from Encovi (INE) and Insivumeh.

27
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