rafmacalaba/datause-encoder-data huggingface dataset:
# **3.2 Road closure obstacles as a determinant of market access**

## _Road closure obstacles_

The Israeli army deployed physical obstacles along roads and borders to defend Israelis against violence originating in the West Bank. A 500 km wall or separation barrier was built along (and sometimes beyond) the Armistice Line of the West Bank to prevent the entry of threats from the Palestinian side into Israel. Henceforth, all cross-border traffic was forced to pass through any one of nine green-line checkpoint border crossings, guarded by the Israeli army (see B’Tselem (2007)). Throughout the uprising, however, nearly a quarter million Israeli civilian settlers were dwelling deep inside the West Bank, beyond the protection of the wall. To defend those settlements, the army deployed hundreds of manned checkpoints, roadblocks, boulders, earthmounds, and other physical obstacles along the West Bank’s internal road network with the intention of monitoring Palestinian traffic passing along roads in the vicinity of Israeli settlements. Most of these obstacles constitute a “full stop”, meaning that vehicles cannot pass though them, prompting a detour for traffic making their way from origin to destination. Checkpoints denote an exception. While these do not constitute a “full stop”, passing through a checkpoint consumes a certain amount of time due to the screening of passengers and vehicles by the Israeli army and potential traffic congestion.

The UNOCHA Map Center did an excellent job of recording the progress of the wall’s construction, and keeping track of road obstacles’ geolocations, frequently updating their maps throughout and after the uprising as obstacles were moved and removed. Over this period, UNOCHA published Closure Update reports, which include detailed maps (in PDF format) showing the locations of obstacles that were deployed at the given points in time. We obtained a copy of their database in ArcGIS shapefile format which contained: (a) the geolocation of the separation barrier in its completed form, and for each road closure obstacle, (b) start-date, (c) end-date (if no longer active), and (d) type of obstacle.

The left panel of Figure 3 displays a map constructed from our data depicting a snapshot of the different types of obstacles along the major arteries of the West Bank’s internal road network as of January 2006. For reference, the map also includes the Palestinian Authority’s 11 West Bank governorate capitals. The right panel zooms in on a northwestern section of the West Bank, depicting locations of Israeli army obstacles lying along roads connecting the governorate capitals of Tulkarm, Nablus, and Ramallah. Various types of obstacles are visible, including checkpoints, less vigorously enforced checkpoints dubbed partial checkpoints, roadgates, and unmanned obstacles such as earthmounds. The fact that obstacles were deployed on some roads and not 12
There is no closed-form solution for V0 in equalities (1) and (2). However, given household consumption in both periods, the poverty line Z1, and a pre-specified value for either the insecurity or vulnerability index, we can empirically solve for the vulnerability line V0. In particular, since _P_<sup>_1_</sup> ( _P_<sup>_2_</sup> ) is a decreasing function of V0, we can iterate from the poverty line upward until we reach a value for V0 that provides the specified insecurity (vulnerability) index. But a practical note is that if V0 is close to Z0, the sample size for households in between the poverty line and the vulnerability line (i.e., with Z0 < y0 < V0) that can be used to estimate the vulnerability index _P_<sup>_2_</sup> can be small; which similarly holds with the estimation sample for _P_<sup>_1_</sup> when V0 is set close to the maximal observed household consumption level. One solution is identifying an adequately large sample size to start with (i.e., similar to ensuring that a population group has a sufficient sample size for statistical inference); another is to keep iterating from the poverty line upward but using estimation results only when estimated values show steady iteration.

Another issue that should be considered is the incremental (step) value used in the iteration. There always exists a tradeoff between using either a large incremental value or a smaller incremental value. The former would perhaps require less computer time but would provide a less full (and less continuous) range of solutions than the latter.<sup>18</sup>

# **3. Empirical Illustrations**

The above framework can be amenable to estimation using either true panel data or synthetic panel data that are constructed from cross sections. We provide examples in the next sections

> 18 A Stata program to estimate the vulnerability lines is available from us upon request.

17
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3 Mozambique offers an interesting setting for exploring potential disinhibition behaviors. Mozambique ranks among the 10 countries most affected by the HIV/AIDS epidemic. The prevalence rate is estimated to be between 14 percent and 17 percent in 2009, and has remained that high since at least 2002 (UNGASS, 2010). With a growing population, a constant prevalence rate implies that the disease is still spreading. The national effort to confront the disease includes increased access to ART. The number of patients under ART drugs has increased from 3,314 in 2003 to 170,198 in 2009 (UNGASS, 2010). Treatment coverage, however, remains low. In 2009, it was estimated that 42 percent of adults and 19 percent of children in need of ART were receiving treatment (UNGASS, 2010)<sup>3</sup> . Mozambique illustrates the ongoing debate on how to optimally allocate limited resources between treatment and prevention in Sub-Saharan Africa (e.g. Marseille et al., 2002), if the ultimate goal is to contain the disease. In this context, as stressed by Schultz (2004), robust estimates of the magnitude of disinhibition behaviors are critical for any informed decision making, since such estimates would allow accounting for externalities (whether negative or positive) associated with increased access to ART.

This paper is related to a strand of literature that focuses on disinhibition (or risk compensation) behaviors. The main proposition of this literature is that people may alter their behavior in response to perceived changes in risk<sup>4</sup> . In the specific case of HIV/AIDS and increased access to ART, the concern is that decrease in the perceived risk and the costs of contracting HIV associated with increased access to ART may lead to an increase of risky sexual behaviors (e.g. Eaton and Kalichman, 2007). Such disinhibition behaviors, if large enough, may (at least partially) offset the benefits of scaling up access to ART. This conjec-

> 3See Arndt (2006) for a simulation analysis of the long run impact of HIV/AIDS on economic growth in Mozambique, and in particular on human capital accumulation.

> 4See Peltzman (1975) for an early study on the introduction of mandatory car seat belts in the US.
A number of caveats exist. Our results cover only the formal sector, that is, firms that are formally registered and operating as legal entities. We also do not have data on micro enterprises (less than 5 employees) in our sample. The _dual economy_ view in development economics (originally associated with Harris and Todaro (1970)) predicts that informal firms should look very different from formal firms in terms of size, productivity, wages paid and the industries/markets they operate in. Recent studies such as La Porta and Shleifer (2008) also show that while informal firms account for a large portion of economic activity in developing countries, growth and development come from the creation of highly productive formal firms. Second, our data are only on the continuing/surviving firms and hence we have no data on firm exit rates. On a related note, the surveys are stratified only by industry, firm size, and geographical location and so we may not have a completely representative sample of firm ages, though the firms within the strata are randomly sampled. Finally, the ES data are based on survey data rather than individual country census. However, our enquiries with the survey implementation team reveal that mean response rates across our sample of countries is 70%,<sup>10</sup> which suggests that we have a representative sample of firms across economies. Overall, our analysis provides an important first step in exploring cross-country differences in the lifecycle of firms.

# **II. Data**

We use the World Bank Enterprise Surveys (ES), which are an ongoing initiative of the World Bank to benchmark the investment climate in different countries across the world and to

> 10 The 70% mean response rate across our sample of countries is superior to most other survey based studies. For instance, Campello, Giambona, Graham, and Harvey (2011) report response rates of 3 to 7% in their survey of how companies use credit lines during a financial crisis. Other studies in corporate finance report response rates between 7%-9% including Graham and Harvey (2001), Brav, Graham, Harvey and Michaely (2005), and Lins, Servaes, and Tufano (2010).

8
an estimated increase in forest loss associated with such provisions and no effect on RTAs without provisions.

# **4.1 Exploring Potential Mechanisms**

We explore potential mechanisms through which trade agreement provisions protecting biodiversity and forest might effectively mitigate forest loss arising from trade liberalization. In particular, we consider production and trade in forest products and agricultural commodities.

The international timber trade, especially in illegally harvested timber, is a clear threat to standing forest. If trade agreements include provisions protecting standing forest stock and requiring legal harvest and proper certification, this may support sustainable timber trade and harvest. We measure aggregate forest product activity using data from the UN Food and Agricultural Organization (UNFAO) including total forest-product exports and the total production of raw or minimally-processed forest-derived commodities including e.g. round logs, sawn lumber, etc. The trade data in particular corresponds to aggregate exports across all trading partners, not exclusively signatories to a particular trade agreement. We believe this is the appropriate measure to consider as trade liberalization may lead to substitution effects in bilateral trade flows that extend beyond the limited signatories of a trade agreement. As with our forest loss data, we aggregate this measures across all signatories to a trade agreement by year to construct an agreement-level panel of forest product activity.

Agricultural land expansion is one of the leading drivers of forest loss across the globe (Gibbs _et al._ , 2010; DeFries _et al._ , 2010; Busch and Ferretti-Gallon, 2017; Curtis _et al._ , 2018). At the same time, the literature has found that agricultural trade, in particular, responds strongly to RTAs (Grant and Lambert, 2008; Sun and Reed, 2010; Jean and Bureau, 2016). Trade liberalization may lead to increases in agricultural production at the extensive margin, resulting in deforestation as forestland is converted into agriculture. But trade liberalization might also facilitate agricultural intensification through increases in agricultural capital or technology which, in turn, may reduce pressures on agricultural land expansion and forest loss. If trade agreements include provisions protecting standing forest stock and/or protecting biodiversity, this may limit conversion 17
35

**Table A1.2: Australia (Cont.)**

|**Political**<br>**Units**|**Date**|**Procedural Rules and Transparency**<br>**Requirements**|**Numerical Targets**|**Escape**<br>**Clauses**|<sup>**Sanctions**</sup>|
|---|---|---|---|---|---|
|**Northern**<br>**Territory**|1995<br>2001|**Financial Management Act 1995:**<br>• The Treasurer is to publish quarterly financial statements in the<br>Gazette and audited annual reports which include the original<br>estimates of budget, results in respect of the major Government<br>Finance Statistics statements as reported by the Australian<br>Bureau of Statistics, and explanation of significant deviations.<br>The audited annual reports should be tabled in the Legislative<br>Assembly.<br>**Fiscal Integrity and Transparency Act 2001:**<br>• The Treasurer must publicly release and table the first and each<br>subsequent fiscal strategy statements for a particular<br>Government at or before the specific time. Changes can be made<br>by public release of a new fiscal strategy statement. Such a<br>statement should:<br>(a) specify medium-term fiscal objectives;<br>(b) explain the broad strategic priorities on which the budget is<br>or will be based;<br>(c) specify the key fiscal indicators against which fiscal policy<br>will be set and assessed;<br>(d) specify, for the budget year and the following 3 financial<br>years: (i) the Government’s fiscal objectives and targets; and (ii)<br>the expected outcomes for the specified key fiscal indicators; and<br>(e) explain how the fiscal objectives and strategic priorities relate<br>to the principles of sound fiscal management.<br>• The Treasurer must publicly release and table a fiscal outlook<br>report at the time of each budget, mid-year outlook report and<br>fiscal results report. The contents of these reports are specified in<br>the Act.<br>• The Under Treasurer must publicly release a pre-election fiscal<br>outlook report within 10 days after the issue of the writ for an<br>election.|**Fiscal Integrity and Transparency Act 2001:**<br>No specific numerical rules and targets. The<br>principles of sound financial management are:<br>• To formulate and apply spending and taxing<br>policies with consideration of the effect on<br>employment, the economic prosperity and<br>development of the Territory and giving rise to a<br>reasonable degree of stability and predictability;<br>• To ensure that funding for current services is to<br>be provided by the current generation;<br>• To manage financial risks faced by the Territory<br>prudently (having regard to economic<br>circumstances), and maintain Territory debt at<br>prudent levels.|N/A|•<br>Reputational|
-68 -

|Krueger, Anne O., Maurice Schiff, and Alberto Valdes (1988). "Agricultural Incentives in|
|---|
|Developing Countries: Measuring the Effect of Sectoral and Economywide Policies." Mm|
|<br>WoQd Bank Economic Revie, Vol. 2, Number 3.|

|Lecaillon, Jacques and Chdstian Morrisson (1986a). Economic Picies arnd Agicultural|
|---|
|Performance: The Case of Burldna Faso. Paris: Development Center of the Organizadon for|
|Economic Co-Operation and Development.|

|Lecaillon, Jacques and Christian Morrisson (1986b). Economic PQlicies and Agricull|
|---|
|Pejq=mance: The Case of Mali, 196041983. Paris: Development Center of the Organizadon|
|for Economic Co-Operation and Development.|

|Lele, Uma (1989). "Sources of Growth in East African Agriculture." The World Do&<br>Economic Review. Vol. 3, No. 1: 119-144.|
|---|
|Upton, Michael (1987). "Limits of Price Policy for Agriculture: Which Way for the World<br>Bank?" Development Policy Review. Vol. 5, 197-215.|
|Lofchie, Michael F. (1989). The Policy Factor: Aericultural Performance in Kenya and<br>Tanzgnia. Boulder: Lynne Rienner Publishers.|

|Mellor, John W. and Ahmed Raisuddin (1988). Agricltural Price Policy for Developing|
|---|
|Countries, Baltimore: The Johns Hopkdns University Press.|

|Mellor John W. and Bruce F. Johnston (1984). "The World Food Equation: Interrelations|
|---|
|Among Development, Employment, and Food Consumption." Journal of Economic Literature.|
|Vol. XXI (June 1984), pp. 531-574.|

|Poleman, Thomas T. (1981). "Quantifying the Nutrtion Situadon in Developing Countries."|
|---|
|Food Research InsItute Studies. Vol. XVIII, No. 1, 1981.|

|Rassas, Bechir, Charles Rabenarivo and Lawrence Meserve (1988). "Evaluation of the Food for<br>Progress Rice Program in Madagascar." Washington D.C.: International Science and<br>Technology Institute, Inc. (Processed).|
|---|
|Roberts, Coileen (1988). Trade. Aid. and Policy Reform. Proceedings of the Eighth Agriculture<br>Sector Symposium. Washington D.C.: World Bank.|
|Serageldin, I. (1989) Povert. Adiustment and Growth in Africa. Washington, D.C.: World<br>Bank.|
|Sharply, Jennifer (1986). Economic Policies and Agicultural Performance. The Case of Kenya.<br>Paris: De.velopment Center of the Organization for Economic Co-Operation and Development.|
|Scandizzo, Pasquale L. and Colh Bruce (1980). Methgdologies for MAeasuring<br>Agricultural<br>Price Intervention Effects, World Bank Staff Working Paper No. 394. Washington, D.C.:<br>World Bank.|
and consumption, and makes it very clear that poverty estimates differ between the two measures of wellbeing. Using the older 2005 PPP data and $1.25 per day poverty line, the poverty headcount based on income was 3.3 percent, while the estimate based on consumption is 1 percent. It is noteworthy that the consumption distribution dominates income over the entire range – at every candidate poverty line, poverty as measured by income is greater than it would be if measured by consumption. By no means is this finding uniform across countries that collect income and consumption: the relative rankings of the two measures vary substantially across countries.

Indeed, in part because there are few or no fixed, observable patterns in the relationship between income and consumption, no adjustments are made to income or consumption distributions in PovcalNet to make them more comparable. While the issue of zero incomes is not currently a very large percent of the global poverty count, it does have significant implications for poverty projections. Projections are based on increasing over time the measure of wellbeing (either consumption or income) by some estimated growth rate. If income is used as the measure of wellbeing, a growth rate applied to zero income does not change the income level. Projections by construction will never grow the zero incomes out of poverty. This is particularly relevant when examining poverty projections for Latin America, which predominantly uses income data. While poverty declines significantly over time in most regions, the projected poverty rate in Latin America is quite stagnant due in large part to this issue of zero values.

Issues of comparability are not limited to whether income or consumption is the welfare variable of choice. Research in data collection methods has established that factors such as the length of the recall period and the number of food items listed has a large effect on the resulting measure of estimated food consumption.<sup>19</sup> One prominent example of differences in recall periods having large effects on measured poverty is described by Deaton and Kozel (2005a). The India National Sample Survey Organization (NSSO)<sup>20</sup> , which had historically used a uniform 30day recall period (URP), changed recall periods during the 1990s, which resulted in significantly

> 19 For example, a question that asks how much rice a person ate in the last 7 days has a 7-day recall period.

> 20 The NSSO was started in the 1920s, representing the first major system of household surveys based on probability sampling in the world.

13
The results show that Honduras has the highest vulnerability to poverty rate (41%), and the Dominican Republic has the lowest (7%). The vulnerability incidence in Peru is 24%, indicating that almost a quarter of the households have a predicted probability over 0.50 of falling into poverty. In Mexico, the vulnerability rate is 20%, El Salvador 17%, Brazil 16%, and Ecuador 14%.

**Figure 4.** Vulnerability incidence (% of vulnerable households with a predicted probability greater than 0.50 including all hazards).

<!-- Start of picture text --> Honduras 41%<br>Peru 24%<br>Mexico 20%<br>El Salvador 17%<br>Brazil 16%<br>Ecuador 14%<br>Dom. Rep. 7%<br>0% 10% 20% 30% 40% 50%<br><!-- End of picture text --> Source: authors' calculations.

In Figure 5, the maps show the percentage of vulnerable households in each state in the case of the regressions with only one natural hazard at a time. In the case of Brazil, droughts are the hazards included in the model. The results show higher vulnerability in the north in line with the fact that poverty, exposure, and natural hazards concentrate more in that region and that households in this area have fewer coping mechanisms than those in the south. The Dominican Republic shows that some administrative units present a vulnerability rate for landslides notably higher than the national average. The vulnerability rate of the provinces Independencia, Baoruco, and Pedernales is 36%, 25%, and 24%, respectively. Also, the map shows that Barahona and Puerto del Plata have districts with a higher vulnerability rate than the rest. In Ecuador, the vulnerable households are concentrated in the east, the region with more effective poverty rates. But where the TH index of landslides suggests more potential damage is in the central region, where some cantons also show a high vulnerability. El Salvador's vulnerability rate for droughts, except for San Salvador, is very similar among provinces. But some differences are found between districts. In particular, some districts of Morazan and Ahuachapan show a high rate in comparison to districts of other provinces. Honduras shows that the administrative unit at level 1 with the highest percentage of vulnerable households when earthquakes are considered is Lempira (78%). The map shows that many other districts present a sizeable rate. But, in Lempira's case, all their districts present a significant rate. Lempira is located in the west, corresponding to a region with significant potential earthquake damage.

15
Table 6: Determinants of Price Differences in Food Markets in South Sudan

|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|(13)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|_Price Differential of_|||||||||||||
|<br>_Diesel_<br>0.145|0.144*|0.146*|0.112*|0.118*|-0.37|-0.47|-0.18|0.54|||||
|(0.07)|(0.07)|(0.07)|(0.05)|(0.05)|(0.36)|(0.34)|(0.43)|(0.36)|||||
|_Distance to Juba*Price Differential o_|_f Diesel_||||0.00|0.00|0.00|0.00|||||
||||||(0.00)|(0.00)|(0.00)|(0.00)|||||
|_Distance to Juba_|||||0.00|-1e-3**|-1e-3***|-1e-3***|-9e-4**|-9e-4**|0.00|0.00|
||||||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|_Road Grade (to Juba)_||||||-3.22|-2.6*|-6.61***|-3.83**|-3.82**|-3.87*|-4.11**|
|||||||(1.77)|(1.09)|(1.29)|(1.42)|(1.4)|(1.64)|(1.43)|
|_Road Length_|||||||0.00|-2e-4**|0.00|0.00|0.00|0.00|
||||||||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|_Differential of Consumption Per_|||||||||||||
|<br>_Capita_||||||||0.00|0.00|0.00|0.00|0.00|
|||||||||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|_Cereal Surplus_||0.00|0.00|||||-4e-6**|0.00|0.00|0.00|0.00|
|||(0.00)|(0.00)|||||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|_Average Rainfall on the road (to_|||||||||||||
|_Juba)_|0.00|0.00|||||||||||
||(0.00)|(0.00)|||||||||||
|_Northern State Dummy_|||||||||||-0.03|-0.04|
||||||||||||(0.15)|(017)|
|_Border Closure* Northern State_|||||||||||||
|_Dummy_|||||||||||0.04|0.07|
||||||||||||(0.04)|(0.03)|
|_Border Closure_|||||||||-0.04|-0.05|-0.07|0.00|
||||||||||(0.04)|(0.05)|(0.06)|(0.05)|
|_Border Closure*Beans_||||||||||0.08|||
|||||||||||(0.05)|||
|_Border Closure*Groundnuts_||||||||||-0.02|||
|||||||||||(0.07)|||
|_Border Closure*Maize_||||||||||0.01|||
|||||||||||(0.08)|||
|_Border Closure*Millet_||||||||||-0.1|||
|||||||||||(0.1)|||
|_Border Closure*Sesame_||||||||||-0.05|||
|||||||||||(0.05)|||

22