rafmacalaba/datause-encoder-data huggingface dataset:
km of secondary and tertiary roads. While national highways constitute about 1.7% of the road network, they carry more than 40% of the total traffic volume.<sup>4</sup> To meet its transportation needs, India launched its National Highways Development Project (NHDP) in 2001. This project, the largest highway project ever undertaken by India, aimed at improving the Golden Quadrilateral (GQ) network, the North-South and East-West (NS-EW) Corridors, Port Connectivity, and other projects in several phases. The total length of national highways planned to be upgraded (i.e., strengthened and expanded to four lanes) under the NHDP was 13,494 km; the NHDP also sought to build 1,500 km of new expressways with six or more lanes and 1,000 km of other new national highways, including road connectivity to the major ports in the country. Thus, in a majority of cases, the NHDP sought to upgrade a basic infrastructure that existed, rather than build infrastructure where none previously existed.<sup>5</sup> The NHDP has evolved to include seven different phases, and our paper focuses on the first two stages. NHDP Phase I was approved in December 2000 at an estimated cost of Rs 30,300 crore (1999 prices). Phase I planned to improve 5,846 km of the GQ network, 981 km of NS-EW, 356 km of Port Connectivity, and 315 km of other national highways, for a total improvement of 7,498 km. Phase II was approved in December 2003 at an estimated cost of Rs 34,339 crore (2002 prices). This phase planned to improve 6,161 km of NS-EW and 486 km of other national highways, for a total improvement of 6,647 km. About 442 km length of highway is common between GQ and NS-EW.

The GQ network, totaling a length of 5,846 km, connects the four major cities of Delhi, Mumbai, Chennai, and Kolkata. Figure 1 provides a map of the GQ network. Beyond the four major cities that the GQ network connects, the highway touches many smaller cities like Dhanbad in Bihar, Chittaurgarh in Rajasthan, and Guntur in Andhra Pradesh. The GQ upgrades began in 2001, with a target completion date

> 4 Source: National Highway Authority of India website: <u>http://www.nhai.org/. The Committee on Infrastructure</u> continues to project that the growth in demand for road transport in India will be 1.5-2 times faster than that for other modes. Available at: <u>http://www.infrastructure.gov.in. By comparison, highways constitute 5% of the road</u> network in Brazil, Japan, and the United States and 13% in Korea and the United Kingdom (World Road Statistics 2009).

> 5 The GQ program in particular sought to upgrade highways to international standards of four- or six-laned, dualcarriageway highways with grade separators and access roads. In 2002, this group was only 4% of India’s highways, and the GQ work raised this share to 12% by the end of 2006.

7
Up to this point, the disaggregated analysis of the poverty impacts has been based on sectoral affiliation and thus it has been possible to link it directly to the sectoral results generated by the CGE model. However, additional policy relevant criteria can be used to identify other groups of households and to evaluate their specific trade induced poverty effects. In particular, we conduct impact analyses for rural and urban areas, by regions, by land ownership, by educational level and by occupation. Obviously, all these criteria are somehow correlated to basic categories included in the CGE model, for example the educational level is linked to the skilled/unskilled factor types, or the region to the prevalence of agricultural employment, but strict correlation should not limit the micro analysis. Using the full household survey information provides maximum flexibility and allows extracting ex-post additional information. Instead of roughly inferring the poverty impact on certain groups from correlations between the rigidly CGE embedded categories and micro characteristics, the generated counterfactual income distributions contain all the information needed and hence directly provide a quantitative assessment of the poverty impact on specific groups.

_<u>Table 18: Poverty and inequality impact of trade, urban and rural</u>_

||2001 levels <sup>BaU</sup><br>15 c|<sup>2001-</sup><br>hanges<br>Urb|Doha % of<br>BaU<br>change<br>an|Full % of<br>BaU<br>change|2001 levels|<sup>BaU 2001-</sup><br>15 changes<br>Ru|Doha % of<br>BaU<br>change<br>ral|Full % of<br>BaU<br>change|
|---|---|---|---|---|---|---|---|---|
|P0|19.6|-4.0|103.8|112.2|44.4|-12.1|103.1|108.2|
|P1|7.6|-2.0|103.2|110.1|20.1|-7.1|102.5|108.2|
|P2|4.0|-1.2|103.0|109.5|11.7|-4.7|102.3|107.7|
|Population %|83.7|1.3|99.5|93.2|16.3|-1.3|99.5|93.2|
|Contr. to P0|69.4|3.9|96.6|35.5|30.6|-3.7|102.3|119.8|

Source: Authors’ calculations.

Table 18 shows the poverty impact of trade by urban or rural residence. Interestingly, the share of urban households in 2001 (83.7 percent) is even higher, although not much, than the share of non-agricultural households (81.8 percent). Quite some households live in urban areas, very likely in urban peripheries, and earn their living primarily from agricultural wage-employment. Actually, only 66 percent of the agricultural households live in rural areas, while 5 percent of the non-agricultural households live in rural areas. The micro-simulations that generate the results of Table 18 also take into account ruralurban migration by assuming that households migrate to urban areas if _all_ employed household members leave agriculture. In the BaU, this causes the rural population to decline by 1.3 percentage points. The urban population accounts for almost 70 percent of the Brazilian poor in 2001 and this share rises in the BaU by 3.9 percentage points. Urban poverty declines under both trade scenarios with the decline being stronger under the full 47
female infidelity as a driving factor. Potential alternative explanations fail to reduce significantly the magnitude of that fraction.

From both angles, it seems that self-reported sexual behaviors are not very reliable. The new wave of Demographic and Health Surveys includes HIV tests. Both policy and research on the HIV/AIDS epidemic should take full advantage of the existence of this new data source and be careful about evidence that relies exclusively on self-reported behaviors without the possibility to confront them with objective information about the individual’s HIV status.

# **VI Conclusions**

This paper takes a departure from the standard approach for analyzing the determinants of the HIV/AIDS epidemic. Most of the literature either uses aggregate measures at the country (see for an example, Oster 2005) or the local level, or more recently, individual level data. This study exploits the couple recode in the new wave of Demographic and Health Surveys including HIV tests, and investigates the determinants of HIV infection at the level of the couple. This approach offers new perspectives on the dynamics of HIV transmission. Two results challenge common perceptions of the HIV/AIDS epidemic. While it would be interesting to confirm them in the forthcoming Demographic and Health Surveys in other African countries, these findings suggest that some prevention efforts should be better targeted.

The first finding is that, in the five African countries under investigation, at least twothirds of HIV infected couples are discordant couples in which only one of the partners is infected. This implies that prevention efforts towards the partners of individuals who have been identified as HIV positive should be a priority. Encouraging joint voluntary counseling and testing might be an important option in that respect.

The second finding is that a substantial proportion of HIV infected couples are discordant female couples in which only the female is infected. This is very much in contradiction with self-reported levels of female infidelity and is at odds with the common perception 18
# **Spelling (score: 0-78)**<sup>**e**</sup>

|Unadjusted|1010|35.28 (12.91)|1060|33.97 (12.79)|-1.58 (-4.01,0.85)|0.202|6-31 (20.5)|
|---|---|---|---|---|---|---|---|
|Adjusted|996|35.33 (12.85)|1052|34.04 (12.75)|0.71 (-0.34,1.76)|0.183|6-29 (20.3)|
|**Arithmetic(score:0-30)**<sup>**f**</sup>||||||||
|Unadjusted|1016|21.20 (5.47)|1062|20.15 (5.68)|-1.07 (-2.15, -0.00)|0.050|6-31 (20.6)|
|Adjusted|1009|21.20 (5.48)|1045|20.18 (5.69)|-0.49 (-1.32, 0.34)|0.243|6-29 (20.3)|

Results presented (i) for all children with outcome data (unadjusted) and (ii) for those with baseline measurements of each outcome and accounting for age, sex and stratification effects as the primary pre-specified analysis.

N=number of children eligible for follow up (not withdrawn or deceased)

> **a** Mean difference (intervention-control)  for scores on spelling and arithmetic are obtained from GEE analysis accounting for school-level clustering b Mean score and sd at follow

> **c** The same class 1 spelling task was given at baseline, 9 and 24 months follow-ups, with different words used for the 24 month follow-up.

> **d** Same addition task conducted at 9 months follow-up and at baseline, hence baseline adjustment is for the same task.

> **e** The same class 5 spelling task was given at baseline, 9 and 24 months follow-ups, with different words used for the 24 month follow-up.

> **f** Same arithmetic task conducted at at baseline, 9 and 24 months follow-ups, with different sums used for the 24 month follow-up.

> **g** Addition task conducted at baseline and arithmetic task containing addition, subtraction, multiplication and division conducted at 24 months follow-up, hence baseline adjustment for different task.

**Unadjusted** : All children with outcome measures, not adjusted for any baseline or study design characteristics.

**Adjusted** : for baseline age, sex, school mean exam score and literacy group (to account for stratification) and baseline measure of the outcome, where available.

25
# **1 Introduction**

Although poor households can be especially vulnerable to risk, many are not well served by formal insurance markets (Morduch, 1994, 2006). Rainfall risk is of special concern since it is a covariate risk that cannot be well-insured informally. This paper theoretically motivates and experimentally evaluates an innovation for expanding protection against rainfall risk: linking rainfall index insurance with savings accounts.

Rainfall index insurance was developed to provide risk protection while also remaining affordable and accessible (Hess, 2004; Skees et al., 2001). A typical policy covers drought risk during the early and middle phases of the growing season and flood risk during the late and harvest phases, underwritten using data from government weather stations. Asymmetric information problems are avoided, and claims adjustment is unnecessary, because payouts depend only on measured rainfall. Despite high theoretical benefits (Cole et al., 2011, 2013), early trials have shown limited demand at market premiums (Giné and Yang, 2009; Karlan et al., 2010a; Giné et al., 2008; Cole et al., 2011, 2013).

By contrast, saving is ubiquitous among the poor. According to the World Bank’s Global Financial Inclusion Database (Demirgüç-Kunt and Klapper, 2012), in 2010 over half of adults worldwide had an account at a formal financial institution, including 41% in developing economies and 24% even in countries with per capita income of $1005 or less. Informal saving and durable goods investment bring total savings participation rates higher still.

More pointedly, savings accounts with stochastic returns have shown promise in a number of contexts. Guillén and Tschoegl (2002) report in detail on the supplyand demand-side success of lottery-linked deposit accounts offered in Argentina, and Kearney et al. (2011) argue that positive skew in prize-linked savings account returns may effectively mobilize saving by low-income households.<sup>1</sup> In this paper we explore whether bundling rainfall insurance with savings can overcome specific and general barriers to effective risk management (Cole et al.,

> 1See also Tufano et al. (2011) and Cole et al. (2014).

2
where we estimate the effect of deep RTAs on: (i) the number of firms in _i_ that keep exporting at destination _j_ at time _t_ (i.e. surviving exporters), and (ii) the average export _per_ firm.<sup>18</sup> Deep RTAs reduce the number of firms surviving in the export market (columns 1-2) but increase the average export (value) _per_ firm (columns 3-4). This further confirms the selection effect of DTAs in developing countries, and points to welfare effects associated to adjustment costs and improved productivity deriving from the re-allocation of resources from less- to more-productive firms.

# **5.3 The extensive and intensive margin channels**

The export sales effect of deep RTAs may come from the intensive and/or the extensive margin of trade. Indeed, the reduction in trade costs associated to deep trade agreements may lead incumbent firms to boost their exports in destinations with deep RTAs (i.e. _intensive_ margin channel), or may allow new firms to start exporting in such destination (i.e. _extensive_ margin channel). In order to disentangle the overall export sales effect into the extensive and intensive margin channel, we calculate the change in _aggregate_ country-pair specific exports from: (i) incumbent firms – i.e. firms always exporting toward a given destination (intensive margin channel); and (ii) entry-exit firms to/from a specific market (extensive margin channel). Then, we adopt the decomposition approach as in Berman et al. (2012). Namely, we regress the aggregate exports by origin-destination-year for respectively incumbent and entry-exit firms on RTAs’ depth, and country-year fixed effects. The coefficients on RTA depth are respectively ˆ ˆ _βincumbent_ and _βentry_ . We then calculate the share of total exports from respectively incumbent and entry-exit firms ( _Vincumbent/Vtot_ and _Ventry/Vtot_ ), and multiply such shares for the respective elasticity _β_<sup>ˆ</sup> _incumbent_ and _β_<sup>ˆ</sup> _entry_ .

The results of this calculation are reported in Table 7. As expected, the elasticity to deep RTAs for both incumbent and entry firms are positive and significant, with a larger effect for incumbent firms. But since the share of total exports is larger for entry-exit firms than for incumbent firms, we obtain a lower contribution from the intensive margin channel to the aggregate effect. Namely, the intensive (extensive) channel accounts for 40% (60%) of the total impact of deep RTAs. The not too different contribution of the intensive and extensive margin channel suggests that deep RTAs represent at the same time a reduction in both the fixed and the variable export cost component. This is consistent with the view that several deep provisions in RTAs, such as the ones on technical standards, sanitary measures or services, allow to reduce fixed entry costs associated to divergent regulations and policy uncertainty.

> 18PPML estimator is used in Table 6.

14
# INFRASTRUCTURE FOR GROWTH AND HUMAN DEVELOPMENT IN PAKISTAN: A SIMULATION ANALYSIS OF FISCAL POLICY OPTIONS

# Jouko Kinnunen and Hans Lofgren<sup>*</sup>

JEL classification: C68, E62, O21 Keywords: Computable General Equilibrium, MAMS, Development Strategy, Pakistan. Sector board: Economic Policy ∗ Jouko Kinnunen is affiliated with Statistics and Research Åland (ÅSUB), Åland, Finland; Hans Lofgren is in the Development Prospects Group of the World Bank. 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.
Table 2: Baseline Estimates with Alternative Aggregators

|||Log(|DPR)|||Log(land v|alue per acre|)|
|---|---|---|---|---|---|---|---|---|
||mean|80th pctile|90th pctile|98th pctile|mean|80th pctile|90th pctile|98th pctile|
|_βH_ : _H ×_log_ιH_(0)|0.137*|0.129**|0.127**|0.141**|0.133**|0.0985*|0.0640|0.108*|
||(0.0737)|(0.0570)|(0.0599)|(0.0639)|(0.0588)|(0.0538)|(0.0473)|(0.0598)|
|_βT_ : _H ×_log_ιT_(0)|-0.267***|-0.204***|-0.179***|-0.233***|-0.128**|-0.130**|-0.100**|-0.114**|
||(0.0614)|(0.0569)|(0.0489)|(0.0522)|(0.0604)|(0.0551)|(0.0503)|(0.0481)|
|_p−_values:<br>|||||||||
|_H_0 :_βH_ =_−βT_<br>27|0.116|0.310|0.468|0.224|0.914|0.527|0.493|0.884|
|_H_0 :_βH_ =_βT_ = 0|0.000|0.000|0.000|0.000|0.058|0.054|0.116|0.059|
|_H_0 :_λ_= 0|0.000|0.000|0.000|0.000|0.0159|0.0142|0.0124|0.0136|
|_R_<sup>2</sup>|0.642|0.641|0.641|0.643|0.779|0.779|0.779|0.780|
|Observations|3,510|3,510|3,510|3,510|7,844|7,844|7,844|7,844|

_Notes:_ Robust standard errors in parentheses (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1), clustered on channel-position (head/tail). DPR data are from 2011-2014. All specifications control for channel fixed effects and include the following channel characteristics interacted with the head dummy ( _H_ ): a constant term, the total number of outlets on channel, whether channel is on tail of its parent channel (versus at middle or head), whether channel is a distributary (versus minor or sub-minor), and a full set of 23 division dummies. In addition, specifications in col. 1-4 include year dummies and specifications in col. 5-8 include a position on watercourse dummy. Column headings refer to the form of the _G_ function used to construct log _ιj_ ( _γ_ ) (see equation 16) with _γ_ set to zero.
speci...c variables.<sup>21</sup> We emphasize that taking the simple mean is for the purposes of estimation and not comparative statics (see Baier & Bergstrand, 2010).

# 5.3 Firm-heterogeneity

The heterogeneous ...rms model also indicates potential violation of the IID assumption. Leaving out the control for the proportion of ...rms exporting would lead to omitted variables bias. Furthermore, the model suggests how country-selection into trade is a function of the variables of interest and a potential source of sample selection bias. To address these issues and implement the heterogeneous ...rms model, a two-step procedure is needed.

In the ...rst stage, we estimate a probit model for the probability that country j exports to i, denoted �ij. Letting Tij be unity when exports from j to i are observed and zero otherwise, we write Predicted values of �ij are used to generate normalized predicted values for zij, z^ij (cf. equation (23)). The predicted probability �^ij can be used to estimate the inverse Mills ratio �^ij, which controls for the country selection e¤ect. Furthermore, HMR show how z^ij and �^ij can be used to account for ...rm selection. De...ne the propensity to export xbij � z^ij + ^�ij, which is a positive function of �ij. This is an estimate of the latent variable zij as a function of both observable and (an estimate of) unobservable trade frictions. As above, attaching a Pareto distribution to ...rm productivities allows us to map xbij to a b consistent estimate of the number of ...rms pro...table enough to export. We include this wij = ln(e<sup>�xbij</sup> �1), together with �^ij, in the second stage of our regression, modifying (29) to yield We estimate this second stage using non-linear least squares and use bootstrapped standard errors to allow for the fact that we have generated regressors in the second stage.<sup>22</sup> The ...rst stage and homogeneous goods models use standard errors clustered by country-pair. For reliable identi...cation, we need to have one variable in the probit equation that is not in the second stage. Our theoretical framework suggests variables that a¤ect a ...rm’s ...xed costs (fij) of exporting but not its variable costs.

Our ...rst-stage probit only di¤erentiates between zeros and ones and ignores the missing observations. Baranga (2009) suggests this may induce further selection bias. Addressing these concerns, we also ran speci...cations in which we estimated a preliminary probit for missing variables. Including an analogous

> 21 We thank an anonymous referee for the reference. Melitz’s derivation of MR terms in the country-speci...c case is similar to ours (cf Melitz’s equation 7 and footnote 10) but not exactly the same. This is in part due to di¤erences in treatment of the country-speci...c variable when i = j.

> 22 We perform 50 replications. Furthermore, we reject draws that spawn theoretically illegitimate values for xbij and �^ij :

20
happened to fuel prices and traffic density over the same period.<sup>4</sup> The ICR’s ability to report satisfactorily on outcomes is to a large extent dependent on the quality of the monitoring and evaluation (M&E) activities built into the projects. The evaluation of learning in World Bank operations found that only 23% of a sample of recently closed projects had established good M&E systems.<sup>5</sup> This seeming lack of attention to M&E should not be taken to indicate a lack of commitment on the part of the staff of development agencies to pursue their stated goals. Most of them are deeply committed to improving lives in developing countries. Another explanation therefore needs to be found why we know so little about the goods sold and bought in the ‘development market place’. This paper suggests that the salient features of the development business itself make it susceptible to market imperfections and misaligned incentives.

• First, the actual interactions between development agencies and their counterparts in developing countries relate to negotiations around the inputs and outputs (e.g. schools built; teachers trained), not outcomes (e.g. an improved literacy rate; a decrease in the repetition rate). The parties involved in the exchange are most often not directly benefiting from the provision of the outcome good, which is intended to help 'the poor'. In other words, the 'outcome good' that is the ultimate purpose of the engagement is not part of the transaction. If it ensues, it can be treated as a positive externality, and transactions can and will go ahead entirely oblivious of this part of their ultimate outcomes, as economic theory tells us.

• Second, the interests, incentives and actions of the development agency and the recipient country, respectively, are not perfectly known and observable to the other party. Several additional layers exist in this principal-agent problem: the recipient country’s government may or may not act in the best interest of ‘the poor’, who have incomplete information about the extent to which their representatives do what they are supposed to. Likewise, principal-agent relationships exist in development agencies between their boards of directors, management, and staff. Principal-agent

> 4 If fuel prices go up some people may decide to use public transport, thereby leading to a decrease in congestion and travel time even without any road improvements.

> 5 World Bank, 2014.

4