rafmacalaba/datause-encoder-data huggingface dataset:
than half their cattle, sheep and goats and in Kidal they lost half their cattle.

# **Figure 1: Number of livestock before the crisis and at time of survey**

<!-- Start of picture text --> Before the Crisis At time of Survey<br>30<br>26<br>20<br>18 17 18 18<br>14 15 14 15 16<br>13<br>11 11<br>8<br>7 7 7<br>5<br>2 1 0 0<br>Cattle Sheep Goats Camels<br>Gao Kidal Timbuktu Gao Kidal Timbuktu Gao Kidal Timbuktu Gao Kidal Timbuktu<br><!-- End of picture text --> (Source: _Évaluation De La Situation Socio-Economique Des Populations Du Nord Mali Et Leurs Priorités,_ World Bank 2016) Following the signing of the Peace Accord, households in northern Mali consider that the government is not managing reconciliation, decentralization (a cornerstone of the Accord) or infrastructure rehabilitation very well, nor is it creating a climate of trust between the different communities and ethnic groups or addressing the stigmatization of Tuareg and Arab groups. In Kidal, practically all households state that the government is poorly managing all of its problems with the exception of decentralization. In Gao, over half the households believe that the government is inadequately managing all of these problems except reconciliation and the fight against stigmatization of which 47% think management is poor. In Timbuktu, 48% of households believe that reconciliation is being poorly handled and 54% to 73% state that all other issues are being poorly managed.

Perceived security is low in northern Mali and citizens feel insecure during the day and at night. In this climate of uncertainty the overall level of confidence is also low. Only 74% of households trust people of other ethnic groups and only 70% trust strangers. The level of overall confidence is lower in Gao than in Kidal and Timbuktu. In addition, over 40% of households do not trust the judicial system and political parties. Nevertheless, in the three regions, over 90% of households trust neighbors, people of the same ethnic group, teachers and health workers, as well as traditional and religious leaders.

6
A Road to Trust 2004). For example, Glanville and Paxton (2007) argue that: “encounters with persons who do not share one’s sociodemographic characteristics could be particularly important in gauging how much to trust people in general.” This is consistent with the argument that the spread of television ( _i.e._ , an increase in the opportunity cost of time) erodes trust levels (Putnam 2000 and Olken 2006). Further, this might explain why suburbanization was responsible for part of the decline in social capital (trust being one of its components) in America (Putnam 2000), as it increases the cost of participating in social activities. Recent evidence from trust games suggests that trade frequency positively influences trust (Henrich et al. 2001; Huck et al. 2006). Following this line of argument, a potential avenue for building generalized trust is to increase both social and market faceto-face interactions with people outside one’s immediate circle. This could be achieved by reducing costs associated with those activities.

In this paper we analyze the relationship between a reduction in transaction costs and generalized trust. We use road construction as a proxy for reduction in transaction costs. We improve upon previous research on the determinants of trust by using panel data collected from 2,100 households in 135 rural communities of the Philippines in 2003 and 2006. As a result, we provide more reliable estimates by controlling for household and community fixed-effects in all regressions. It is important to note that our sample covers some of the poorest municipalities in some of the poorest provinces of the Philippines. About 72 percent of the sampled households were engaged in farming in 2003. Thus, our results should be interpreted as the role of reduced transaction cost on trust at the initial stages of development. Further, the Philippines is a very low trust environment. In 2001, 3
|Tabl|e 5.10: Incidence<br>Hait|of Educatio<br>i(percent),|n Level in Metr<br>2001|opolitan|
|---|---|---|---|---|
|Quintile|No education|Primary|Secondary|Tertiary|
|1 (poorest)|33.3|30.7|35.5|0.5|
|2|24.7|32.2|40.8|2.3|
|3|20.8|40.1|36.6|2.5|
|4|18.8|34.6|42.7|4.3|
|5(richest)|10.0|19.5|52.4|18.2|

_Source:_ Calculations based on HLCS 2001.

# **6. Poverty Profile**

After counting the extreme poor we need to know who they are, the character of their poverty, where they live, and what they do. Comparing average levels of poverty for different categories is useful for learning about which population groups are falling behind or catching up in terms of poverty. This is useful for the design of policies: we would like to know not only whether, for example, more- or less-educated people are more likely to be poor in Haiti, but how the relative odds of being poor compares among rural and urban areas and among the nine regions. This section addresses poverty based on P0 for various population groups in 2001. The poverty profile constructed is based on data from the Haitian household surveys (HLCS). The main questions addressed are: (1) who are the poor, (2) what are the characteristics of poor households, (3) where do they live, and (4) where do they work.

30
- 24 -

rather than discrete time was used, and a non-entry price rather than a non-entry output. This entry constraint implies that the output of rival firms will increase if the dominant firm reduces its own output Yt below the non-entry output Yt * Production function (2) and borrowing constraint (4) are the same as above. The derivation of the minimum variable cost function (11) proceeds exactly as in section 3.2 above, so there is no need to repeat it here.

We incorporate minimum variable cost function (11) into objective function (1'), augment it by borrowing constraint (4) and entry constraint (18), thereby obtaining the following Lagrangean:

- co

- (19) _L e,,_ **_D9._** _c (Pyt)_ **_(YC)_** - **_G:()_** _- (iU,_ C.) (At) - _(u,,)_ (&) _t=s_

- _(Ppd) (IXt)_ - _(Pkt) (IkC)_ +

- _- 2 [DAt,1_ - _(ptv,) (ICpt,) + (P,pe) (KR)_ - **(Pkcl) (Kui)** _(Pkt)_ (Kt) **_k4_** _[ DRtl. -_ **_ct_** _(Y4 - Yt) I_ } ,

where the superscript "o" denotes optimality in the situation without any CIT. Differentiating (19) with respect to uutput, setting the derivative equal to zero and denoting the optimal output by Yo , we obtain the following first-

# order condition:

In (20) Pyt is not a constant, since we do not assume perfect competition in this section. Equation (20) in effect boils down to the condition that in equilibrium MR - MC + k4ct. We are interested in examining the sign of the term k4c1. The reaction coefficient ct is a non-negative constant by assumption. So we need to determine only the sign of k4. The Lagrangoan multiplier X4
43 <!-- Start of picture text -->
1.024<br>1.022<br>1.02<br>1.018<br>1.016<br>1.014 W(R=0.965)/W(R=0.985<br>1.012 )<br>1.01<br>1.008<br>1.006<br>0.85 0.9 0.95 1<br>Rm<br><!-- End of picture text -->

> B. Average ratios of welfare when _R_  0.965 to welfare when _R_  0.985,

> assuming  0.001 and a proportional cost of 1.5%

Figure 12: Average ratios of welfare under repression to welfare with no repression.

# 5.2 Changing the distribution of potential income

In the above calculations we used the US income distribution in 2011. But the income distribution changes over time and across countries. How will changes in the income distribution affect our measures of the welfare cost and the benefits from regulations? In an attempt to get a partial answer to this question we try two additional distributions. The first, denoted by _teta1_ , is obtained from the income distribution in Table 2 ( _teta_ ) in the following way. We assume that the potential income ( <sup>2</sup> ) of the most productive agent (the agent indexed 1) is 0.046 instead of 0.146. The remaining 10% of aggregate potential output are distributed evenly over the rest of the population. In Figure 13, _teta1_ is in red and is very close to _teta_ (in blue) except that the agent index 1 has now  0.21 rather than  0.38 that he had before the change. The second income distribution, denoted by _teta2_ , is constructed as follows. We take the index of the agent _i_ and compute<sup></sup><sup>_i_</sup><sup>_i_/5.77. This is similar to Figure 6 that uses</sup><sup>_i_</sup><sup>_i_. Here we divide by 5.77 to insure</sup>
24

- PRIO (2005). The Armed Conflicts Database, version 3. <u>http://new.prio.no/CSCW Datasets/Data-on-Armed-Conflict/UppsalaPRIO-Armed-Conflicts-Dataset/ArmedConflicts-Old-Versions/</u>

Razin, O., and S. M. Collins (1997). “Real Exchange Rate Misaligments and Growth,” _National Bureau of Economic Research Working Paper_ #6174. (September): Cambridge, MA.

Sachs, J. D. and A. M. Warner (1999), Sources of Slow Growth in African Economies, Journal of African Economies, 6(3): 335-76 Sachs, J. D. and A. M. Warner (1995) “Natural Resource Abundance and Economic Growth,” NBER Working Paper No. 5398, Cambridge, MA.

Sambanis (2004), “List of Civil Wars: 1945-1992”, available at <u>http://pantheon.yale.edu/~ns237/index/research.html#Data.</u> Schwartz, J., P. Halkyard, S. Smith (2006) “Post-conflict Infrastructure – trends in aid and investment flows.” The World Bank. Private Sector Development VP. News Letter (note no. 305).

Schwartz, Jordan, P. Halkyard, S. Smith (2006) “Post-conflict Infrastructure – trends in aid and investment flows.” The World Bank. Private Sector Development VP. News Letter (note no. 305).

Spatafora, N. and A. Warner. 1999. “Macroeconomic and Sectoral Effects of Terms of Trade Shocks: The Experience of the Oil-Exporting Developing Countries.” _IMF Working Paper_ WP134.

Wijnbergen, van S. (1984) “The Dutch Disease: A Disease after all?” _Economic Journal_ , 94: 41-55.

Wijnbergen, van S. (1986). “Aid, Export Promotion and the Real Exchange Rate: an African Dilemma.” Mimeo, The World Bank.

Weiss, M. (2004). “World Bank Post-conflict Aid: Oversight Issues for Congress.” CRS Report for Congress. Congressional Rsearch Service, the Library of Congress. Order Code RS21819.

Williamson J. (1994). “Estimates of FEERs,” _Estimating Equilibrium Exchange Rates_ . Williamson (ed.) Washington DC: Institute for International Economics.

World Bank (1998). _Assessing Aid_ . New York: Oxford University Press.

World Bank Database, World Development Indicator (WDI 2006).
movement trends over time by geography, and across different high-level categories of places such as workplaces, retail and recreation, groceries and pharmacies, parks, transit stations, and residential. These measures are explicitly considered proxies for social distancing and we focus on the first, workplace-related mobility, as the most relevant to economic activity and most prominent in the policy debate. The reports consist of per country downloads (with 131 countries covered initially), further broken down into regions/counties in some cases. Because location accuracy and the understanding of categorized places varies from region to region, Google does not recommend using these data to compare changes across countries or regions with different characteristics. To address this, our empirics rely only on within-area variation across time and reporting or categorization differences are absorbed in included fixed effects.

This measure is limited by the degree to which coverage of smart phones offers a representative sample of the population. As Annex 1 shows, few developing countries show coverage of smart phones above 50% and Ethiopia, Nigeria, Sudan, Bangladesh, and Pakistan hold up the bottom of the top 50 countries with rates under 20% of coverage. This said, several developing countries also have reasonable coverage when we adjust for the share of adults in the population: the United Kingdom: 100%, Sweden: 96%, the United States: 95%, Italy 67%, Japan 63%, Brazil 52%, and South Africa 50%. While clearly not representative, the differences between Italy and Japan on the one hand and Brazil and South Africa on the other are not so large as to justify throwing out the possible information on how developing countries may differ. Further, while we may miss the mobility of for instance, micro firm owners without smartphones, many of their customers will have them and the shutting down of the firm will be partially registered.

Data on restaurant reservations in the United States are taken from OpenTable.<sup>9</sup> Movie release and theater revenue data for Sweden are from International Movie Database.<sup>10</sup>

**COVID-19 Cases:** Though there may be several mechanisms through which cases translate into lower mobility, we interpret this as a signal to individuals about the likelihood of a serious negative health outcome. National cases can inform about the overall evolution of the disease, while local numbers finetune the proximate threat.  We standardize by the corresponding population in the figures.  In some regressions, we can expand the sample by using log (cases) and the population scaling is absorbed in the corresponding fixed effect. Global data are drawn from the Johns Hopkins Coronavirus Resource Center. Country-specific regional data come from national sources:  the United States:  Johns Hopkins Coronavirus Resource Center; Brazil, Italy, Japan, South Africa, Sweden, and the United Kingdom: from national sources (see Annex II).

> 9 <u>www.opentable.com</u>

> 10 <u>https://www.boxofficemojo.com/</u>

4
Based on those estimates, migrant households eligible for PEP were 3.9 pp less likely to have children less than one year old, 7 pp less likely to have one-year-olds, and 1.8 pp less likely to have two-year-olds. As expected, PEP eligibility had no significant impact on the likelihood of having three-year-olds given the program’s implementation timing. In addition, the results are robust to the exclusion of control variables.<sup>10</sup> When we restrict our sample to data collected at baseline and in 2020 (panel A), we only observe a policy impact on the probability of having children one year old or less, which aligns with the program’s rollout. For that reason, in panel A, we observe policy impacts that are not statistically different from zero for the likelihood of having children two and three old. As we add the years 2021 data in panel B, we observe a policy impact on the probability of having children less than one year old, one year old, and two years old.

The results in panels A and B suggest that PEP’s fertility impacts were not only immediate but also grew larger one year after the program’s rollout, reflecting the usual delay in benefiting from regularization. For example, access to social services requires having PEP plus a SISBEN vulnerability score, which can take time to obtain from public authorities. Likewise, it can be time consuming to find a formal job, which explains the program’s larger impact one year after implementation.

In sum, our main findings align with the timing of the program’s rollout and robustly support our hypothesis that PEP reduced household fertility.

# **VI. A Robustness Tests**

We conduct a series of sensitivity checks to gauge the extent of attrition in our sample and assess the robustness of our findings to various sample changes.

# _Attrition Concerns_

Since we exploit the panel nature of the survey data for our analysis, a natural concern is the extent to which attrition may bias our findings. We conduct several robustness checks

> 10Results are available upon request.

12
Table 1 – Vulnerability data

|**Indicator**|**Disaggregation**|**Granularity**|**Source**|**Type**|
|---|---|---|---|---|
|Climate Policy Relevant Sectors (CPRS)|Per sector|NACE1-2|University of Vienna|Transition risk|
|Carbon intensity|Per country/sector|NACE2|Eurostat|Transition risk|
|GHG intensity|Per country/sector|NACE2|Eurostat|Transition risk|
|Wildfire|Per region|NUTS2|Think Hazard|Physical risk|
|Coastal flood|Per region|NUTS2|Think Hazard|Physical risk|
|River flood|Per region|NUTS2|Think Hazard|Physical risk|
|Extreme heat|Per region|NUTS2|Think Hazard|Physical risk|
|Drought|Per region|NUTS2|Think Hazard|Physical risk|
|Landslide|Per region|NUTS2|Think Hazard|Physical risk|
|Sectors vulnerable to physical risk|Per sector|NACE1-2|Marco research|Physical risk|

Source: Authors.

**To assess the vulnerability of CGSs to transition risks, we map two-digit sectoral exposure data to emission data per industry obtained from Eurostat and transition sensitive sectors from the academic literature.** We base our classification on that of Battiston et al. (2017) who identify a set of Climate Policy Relevant Sectors (CPRS). Within the CPRS classification, most sectors are identified at either the first or second level of sectoral disaggregation. However, some sectors are identified at the third or fourth level. Since our samples are at the first and second levels of disaggregation only, we weight sectors according to the number of subsectors that are identified as climate policy relevant.<sup>6</sup> On top of the CPRS sectors we add the companies that provide water supply, sewerage, and waste management services (NACE E) since we find that the aggregate GHG emissions in this sector are relatively high but it falls outside the CPRS classification into six high-level sectors of the economy (i.e., fossil fuels, utilities and electricity, energy-intensive, buildings, transportation, and agriculture). The detailed mapping can be found in Annex 2. Since the CPRS is a binary classification (i.e., either relevant or not) this does not allow us to rank sectors in the amount of transition risk present. We therefore also obtain Eurostat data on CO2 and GHG emissions. We first use this data to estimate the amount of exposure of CGSs to the sectors with the highest GHG emissions. We then proceed by identifying those sectors that could potentially cause the highest absolute losses to CGSs by defining a risk metric that combines exposure with GHG emissions. Using equation (1) we identify sectors _i_ that are highly relevant for CGSs from a

> 6 To map our 2-digit level exposure data to the 4-digit CPRS we weigh 2-digit sectors using a factor 1.0 if all the subsectors are in CPRS, a factor 0.5 if two or more subsectors are in CPRS, and 0 if one or no subsectors are in CPRS.

8
physicians reported that “misreferral of patients to private practice” and “payments for scheduling of medical procedures” occurred “a lot” or “always”.

# **FIGURE 6. PERCENTAGE OF USERS REPORTING INFORMAL PAYMENTS IN SELECTED COUNTRIES, 2000-2006**

<!-- Start of picture text --> Russia (2000)<br>Paraguay (2006) Hospital<br>General health services<br>Kosovo (2000)<br>Guatemala (2005)<br>Zambia (2003)<br>Madagascar (2006)<br>Mozambique (2004)<br>Albania (2005)<br>Tajikistan (2003)<br>Guinea (2005)<br>Ghana (2000)<br>Sierra Leone (2002)<br>0 5 10 15 20 25 30 35 40<br><!-- End of picture text --> Sources: World Bank Country Diagnostic Surveys (various years) and LSMS (various years).

Often informal payments are not uniform across payers. An Albanian study found that income had no effect on the probability of having to pay informally (Hotchkiss et al. 2005). By contrast, evidence from Uganda and Peru suggests that the richer pay more, and receive more health care in return (Hunt 2007). Similar evidence emerges from the Kyrgyz Republic (forthcoming), which also found that patients making informal payments and those making official payments are not the same raising the possibility that patients pay informally to avoid paying official fees. Studies in China of “red packages” paid to providers report that payments average between US$16-36 per hospital visit, with referral hospitals averaging US$44, roughly 90 percent of average half-monthly income (Bloom, Han, and Li 2001). Data from a Living Standard Survey in Tajikistan showed that informal payments deterred poorer households from seeking care, and also affected the appropriateness of care received (Falkingham 2004). In some cases, informal payments drive households into poverty by forcing them to sell assets or borrow at unfavorable rates to pay for health care (Lewis 2000; Falkingham 2002). These same problems can confront households even when they rely on the private sector. However, what is insidious in the former case is that health care financed and 52
are less likely to participate in the online job portals than males.<sup>14</sup> On the demand side, the employers who use the online job portal are smaller and younger and advertise higher salary jobs than the average employer in Pakistan, and many of them are in ICT.

# 3. Labor supply and demand

- 3.1 Labor market tightness and potential mismatches by basic qualifications

In this section, we use a Question and Answer format to navigate key research questions and findings. By exploiting the data feature that the information of both demand and supply sides is available, we measure and look into labor market tightness, which is the number of job vacancies per jobseeker. It shows the relative size of labor demand to labor supply. If the tightness is high, labor demand is strong relative to labor supply, and jobseekers may easily find jobs while employers may have difficulty in finding workers. That is, the market is a seller’s market. If the tightness is low, finding jobs is hard for jobseekers, but employers find workers easily. As illustrated in Figure 7, the tightness in Rozee.pk has been increasing, although it is below 0.2 in 2019Q1.<sup>15</sup>

**Figure 7. Tightness of the online labor market across 2012–2015**

_Source_ : Authors’ calculation using the data from Rozee.pk. _Note_ : Shown is the ratio of the number of new vacancies and the number of new jobseekers registered in a given quarter.

## **_Q. What level of education is the most demanded in Rozee.pk, and how easy is it for jobseekers to find jobs at their educational level?_**

## **_A. Undergraduate and postgraduate education is most demanded in Rozee.pk. However, due to a large number of jobseekers with undergraduate and graduate degrees, the job matching still_**

> 14 The females who do participate are more educated than the males in the portal. The proportion of those who have postsecondary degrees is 93% and 84% among female and male jobseekers, respectively. 15 The analysis here does not consider job postings and job seekers outside Rozee.pk; therefore, it does not portray the market tightness of the entire labor market.

8
<mark>WPS3560</mark>

# **Setting the Trade Policy Agenda: What Roles for Economists?**

## **Kym Anderson**

World Bank, University of Adelaide and CEPR Phone (+1 202) 473 3387 Fax (+1 202) 522 1159 kanderson@worldbank.org World Bank Policy Research Working Paper 3560, April 2005 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ ____________ Prepared for the IATRC Theme Day conference on _The Use of Economics in Trade Negotiations and Disputes_ , St. Petersburg, Florida, 5 December 2004.