rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2A. Baseline balance tests of individual characteristics**

||**Pan**|**el 1: Demographi**|**cs**|||
|---|---|---|---|---|---|
||Control|Treatment|Difference|P Value|Observations|
|Average age|22.849|22.780|-0.069|0.623|1601|
|Age 16-19|0.131|0.163|0.032|0.086*|1601|
|Age 20-24|0.582|0.529|-0.053|0.038**|1601|
|Age 25-27|0.287|0.309|0.022|0.365|1601|
|Married|0.048|0.063|0.015|0.225|1601|
|Cohabiting|0.284|0.302|0.018|0.445|1601|
|Separated/Widowed/Divorced|0.010|0.009|-0.001|0.792|1601|
|Never married|0.658|0.627|-0.031|0.209|1601|
||**Panel 2: Educ**|**ation- Highest leve**|**l completed**|||
||Control|Treatment|Difference|P Value|Observations|
|None/Informal only|0.020|0.031|0.011|0.187|1601|
|Primary (grades 1-6)|0.196|0.189|-0.007|0.730|1601|
|Junior High (grades 7-9)|0.224|0.243|0.019|0.380|1601|
|High School (grades 10-12)|0.269|0.257|-0.012|0.597|1601|
|Completed High School or above|0.291|0.280|-0.011|0.624|1601|
||**Panel 3: Inco**|**me-Generating Act**|**ivities(IGA)**|||
||Control|Treatment|Difference|P Value|Observations|
|Any IGA|0.364|0.391|0.027|0.284|1601|
|Self-employment|0.316|0.333|0.017|0.476|1601|
|Wage-employment|0.048|0.058|0.010|0.410|1601|
|Monthly earnings in LD<br>(unconditional on IGA)|2,377.181|3038.042|660.861|0.084*|1597|
|Monthly earnings in LD (conditional<br>on IGA)|6,570.887|7875.539|1,304.652|0.145|602|
|Any savings|0.333|0.342|0.009|0.708|1601|
|Anyloan outstanding|0.041|0.03|-0.011|0.232|1601|
||**Panel 4: Ownershi**|**p of Assets(either**|**Jointly or Alone)**|||
||Control|Treatment|Difference|P Value|Observations|
|Owns cell phone|0.694|0.632|-0.062|0.012**|1601|
|Owns electronics|0.255|0.284|0.029|0.213|1599|
|Owns jewelry|0.403|0.394|-0.009|0.729|1600|
|Owns furniture|0.429|0.391|-0.038|0.139|1601|
|Owns poultry|0.186|0.19|0.004|0.850|1600|
|Owns vehicle|0.017|0.016|-0.001|0.853|1601|
|Owns dwelling|0.187|0.175|-0.012|0.541|1600|
|Owns land|0.152|0.147|-0.005|0.806|1600|
||**Panel 5: Empowe**|**rment and Psycho**|**social Measures**|||
||Control|Treatment|Difference|P Value|Observations|
|Any money of your own?<sup>28</sup>|0.798|0.790|-0.008|0.708|1601|
|Entrepreneurial Ability score (0-1)|0.509|0.501|-0.008|0.698|1070|
|Self-regulation score (0-1)|0.655|0.650|-0.005|0.582|1598|
|Satisfaction w/ life overall (0-1)|0.429|0.421|-0.008|0.766|1600|
|Satisfaction w/ job/ business, if||||||
|<br>applicable (0-1)|0.276|0.326|0.050|0.075**|1161|

28 The wording of this yes/no question was “Do you most of the time have money of your own for basic uses that you alone can decide how to use, without having to ask for permission?” 30
# **Policy Uncertainty, Trade, and Global Value Chains: Some Facts, Many Questions**<sup>1</sup>

Cristina Constantinescu, Aaditya Mattoo and Michele Ruta<sup>2</sup> World Bank _Keywords_ : Economic Policy Uncertainty; Trade Growth; Global Value Chains. _JEL Codes_ : F13; F14

> 1 We would like to thank Bernard Hoekman for detailed comments on an earlier draft of this paper. 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. 2 Cristina Constantinescu, World Bank, 1818 H street, Washington DC, USA. Email: ineagu@worldbank.org Aaditya Mattoo, World Bank, 1818 H street, Washington DC, USA. Email: <u>amattoo@worldbank.org</u> Michele Ruta, World Bank, 1818 H street, Washington DC, USA. Email: <u>mruta@worldbank.org</u>
the firm for participating, compliance may be lower. For example, in an experiment to evaluate the effect of financial literacy training on female entrepreneurs in Uganda, only half of the treatment group invited to participate in the training actually attended (McKenzie and Weber, 2009). Other recent business training experiments have found higher attendance rates when given to members of microfinance groups (e.g. Berge et al. (2010) report 83% of the women in their business training experiment in Tanzania attended often enough to get the completion certificate), but 100 percent compliance is unlikely.

Table 5 reports the power of an experiment with 300 treatment and 300 control firms to detect the types of treatment effects often targeted as results of projects like those in Tables 3 and 4.<sup>6</sup> It also reports the size of the treatment group one would need in order to achieve 90 percent power, assuming an equal-sized control group. The results in the first column show that if a oneoff follow-up survey is conducted of treatment and control firms, the sample sizes needed to detect program effects of interest are larger than the number of firms participating in typical SME programs, and indeed larger than the total number of manufacturing firms with 10 or more employees that exist in some African countries. For example, with treatment and control groups of 300 firms each, the power is only 12.9 percent for being able to detect a 20 percent increase in sales, 41.7 percent for detecting a 10 percent increase in employment, and 36.8 percent to detect a 5 percentage point increase in the proportion of firms exporting (from an assumed baseline rate of 12% of firms exporting).

The next three columns consider the improvement in power from also using baseline data, under a variety of assumptions about the autocorrelation in firm outcomes (ρ). Firm data exhibit considerable variability, much of it seemingly genuine (Fafchamps et al. 2010a), and available data suggest that the autocorrelation of profits or sales over periods of 6 months to 1 year is likely to be 0.5 or less (McKenzie, 2011). As a result, adding baseline data improves power slightly, but not dramatically – with ρ = 0.5, we would still need treatment and control groups each of 3547 to detect a 20 percent increase in sales with 90 percent power.

Carrying out multiple rounds of post-treatment follow-up surveys and pooling these multiple measures can be used to further increase power (McKenzie, 2011). For example,

> 6 Specifically, I assume mean sales of 1000 with a standard deviation of 3000, mean employment of 29 with a standard deviation of 21, and that 12% of firms in the control group export.

10
# **6. The role of socio-economic characteristics in driving (the reduction in) forest fire occurrences**

Having established that the FIP program was effective in reducing end-of-the-agricultural cycle forest fires, the next question is whether the program was equally effective in all forest areas, or whether there are marked differences in the response to the program by the communities surrounding the forests. We use responses from the 2014 Living Standards Measurement Study (LSMS), elicited between January and March of that year, to characterize villages surrounding the forests. Next, we assign each forest grid cell to the “sphere of influence” of the geographically most proximate village (see Appendix D for details about the matching process) and define the set of grid cells in the same sphere as a “subforest”. By doing so, we assume that economic activities in a specific forest location are undertaken by inhabitants of the nearest village. We then calculate fire occurrences at the subforest level and relate the observed fire occurrences to the characteristics of the associated village.

Table 4 presents the socio-economic characteristics of the villages surrounding all 77 protected forests in Burkina Faso, as well as those of the subsamples of project and non-project forests. As shown in Column (1), the average village consists of about 175 households, nearly all households engage in agriculture, about half of them use inorganic fertilizers and the same holds for the use of organic fertilizers. The level of education is fairly low, household assets amount to FCFA 270,000 (or about US$ 400), the average distance from the village to the nearest protected forest is about 1.5 kilometers, and the average distance from a village to the main road is about 3 kilometers. More importantly, comparing Columns (2) and (3) of Table 4 we see that differences between the FIP and non-FIP villages tend to be small.

We implement two types of village-level analyses. First, we explore whether the characteristics listed in Table 4 are correlated with the subforest-level November forest fire occurrences before 2014. We do so by regressing the average of November fire 26
9 There are various means by which players interfere in the reform's implementation process. It should be noted that some players may support or oppose a given reform in a passive way.

# **PLAYERS' TRANSACTION COST OF INFLUENCING REFORMS' LEVEL OF ACHIEVEMENT**

The cost of influencing a particular player is a function of his/her existing political power, and of the magnitude of change it wishes to incorporate into the proposed reform. Players can influence policy makers by demonstrations, by meetings and presentation of their political positions, or simply by monetary means of future support of a policy maker that affect his/her immediate decision.

# **ESTIMAT][NG THE LEVEL OF ACHIEVEMENT**

Based on the variety of actions and the cost (also measured by level of effort) associated with the players' attempt to affect the reform, a measure of the reforms' achievement level can be estimated Three levels of achievement are considered in our framework: a high/full level, a medium/partial level and a low/failure level. Achievement can be measured both in terms of fulfillment of the reform components, and the time frame needed for such achievement.

# **ESTIMATING THE LIKELIHOOD OF ACHIEVEMENT OF THE VARIOUS REFORMS' LEVELS** - **THE DELPHI PROCESS**

The information provided in the tier one procedure is then used in a Delphi process (Preble, 1983). We assumed that the reforms are independent of each other, so implementation of one reform does not affect the others.<sup>4</sup> To simplify, we assumed further that achievement levels are not continuous. We ranked achievement levels as "Low", "Medium", "High", and "Very High." We attached a four-stage value scale to the probability for achieving each level, namely: (1) Low 0-25 percent, (2) Medium 26-50 percent, (3) High 51-75 percent, and (4) Very High 75-100 percent. We repeated the Delphi process until convergence was achieved. We defined convergence as the attainment of an empirically determined level (CV) of the coefficient of variation (CV).

> 4This assumption is necessary for analytical purposes in order to apply the Delphi technique. In reality, the reforms can be expected to have complex and dynamic interactions that would require more sophisticated techniques to analyz-. or predict.
undergoing physical inspection. In Section 2 we argue that Albania’s tariff liberalization, which largely preceded the risk management reforms, would have reduced incentives to evade tariffs. Here we offer evidence from administrative data suggesting that compliance was indeed high in Albania. We rely on two indicators of possible non-compliance. The first indicator is the number of consignments that were allocated to yellow channel (documentary inspection) but then upgraded to red channels on the decision of the customs agent. An upgrade to physical inspection does not necessarily indicate non-compliance, but it does imply that there are grounds for more intrusive inspection. The second indicator is the existence and amount of a penalty, which is evidence of infractions (i.e., violations of customs regulations) severe enough to incur one.

Table 3 shows that even at the onset of our reform period the degree of measured compliance with customs regulations was very high. Panel A shows that the proportion of Albanian import declarations going from the yellow channel to the red channel is low in every year from 2007 to 2012. For example, in 2007, 50 out of 102,163 declarations allocated to the yellow channel (Panel A) and only 82 out of 76,476 declarations allocated to the red channel (Panel B) committed an infraction of customs regulations and hence paid a penalty. This high degree of measured compliance was maintained throughout the sample period. The high degree of compliance we observe suggests that _ex-post_ most of the physical inspections prove to be unnecessary. Conditions like these are amenable to rapid reductions in inspections once the risk management systems are in place and operating.

17
- Pan, Guangchen, Shengyao Qiu, Xin Liu, and Xiaoke Hu, “Estimating the economic damages from the Penglai 19-3 oil spill to the Yantai fisheries in the Bohai Sea of northeast China,” _Marine Policy_ , 2015, _62_ , 18 – 24.

- Petridou, E., D. Trichopoulos, N. Dessypris, V. Flytzani, S. Haidas, M. Kalmanti, D. Koliouskas, H. Kosmidis, F. Piperopoulou, and F. Tzortzatou, “Infant leukaemia after in utero exposure to radiation from Chernobyl,” _Nature_ , 1996.

- Prime Minister of Vietnam, “Decision 1880/Q-TTg on the compensation to the provinces of Ha Tinh, Quang Binh, Quang Tri, and Thue Thien-Hue following the marine environmental incident.,” September 2016.

- , “Decision 309/Q-TTg on the revision of Decision 1880/Q-TTg on September 29 2016 regarding the compensastion for the provinces of Ha Tinh, Quang Binh, Quang Tri, and Thue Thien-Hue following the marine environmental incident.,” March 2017.

- Strobl, Eric, “The economic growth impact of natural disasters in developing countries: Evidence from hurricane strikes in the Central American and Caribbean regions,” _Journal of Development Economics_ , 2012, _97_ (1), 130–141.

- Vnexpress, “Vietnam Health Ministry Declared Fishes in Central Provinces are Safe for Consumption,” https://vnexpress.net/tin-tuc/thoi-su/bo-y-te-cong-bo-hai-san-bon-tinh-mien-trung-bao-daman-toan-3750773.html 05 2018. Accessed July 29, 2018.

- VOA, “Formosa disaster destroyed toursim industry in central Vietnam,” https://www.voatiengviet.com/a/formosa-giet-chet-du-lich-mien-trung/3606970.html November 2016. Accessed July 29, 2018.

- , “Vietnam Bans Unsafe Seafood in Central Provinces,” https://www.voanews.com/a/vietnam-bansunsafe-seafood-in-central-provinces/3316289.html 05 2016. Accessed July 29, 2018.

- Wirtz, Kai W., Nick Baumberger, Susanne Adam, and Xin Liu, “Oil spill impact minimization under uncertainty: Evaluating contingency simulations of the Prestige accident,” _Ecological Economics_ , 2007, _61_ (2), 417 – 428.

25
and outcomes. Because we will be looking at burley club members, we only work with the 1,368 households that actually grew tobacco in the 2004/05 season. (The total sample size of the IHS is 11,280 so that over 10 percent of the households grow tobacco—see Appendix 1.) In consequence, our analysis can only illustrate differences among tobacco growers and cannot in principle be extended to non-tobacco farmers.<sup>13</sup> Sample statistics from the IHS of key variables by club membership are reported in Table 3.

The sample includes 337 growers that do belong to a burley club and 991 tobacco growers that do not belong to a club. The two groups are similar in terms of several characteristics of the household and of the household head: the gender composition and marital status are almost the same for members and non-members and the health condition of the household heads is quite similar as well. Club members show a larger proportion of heads who have completed primary education (18 versus 11.4 percent), while non-members show a larger proportion of heads with no education or incomplete primary education (77.6 versus 68.7 percent).

The main difference across household characteristics is found in the number of members. Club member households have on average almost one member more than non-members. This is meaningful since burley tobacco is very labor-intensive. Also, land holdings of club members are only slightly bigger: the median total land area is 3.5 acres for members and 3 for non-members. The bottom panel of Table 3 displays the expenditure pattern of the two groups. There are no major differences in per-capita expenditures in food, clothing, health, education and recreation. However, some differences show up in items such as housing, furnishing, transport and communications.

Tobacco farming does show sharp disparities between the two groups. These are reported in Table 4. The most striking differences arise in output per acre and sales per acre, which are 40 and 45 percent higher for tobacco club members respectively. While our empirical analysis explains this difference in performance in detail, it is worth noticing at this point the dissimilar utilization of inputs, especially of fertilizer. Club members tend to use more fertilizer of the types UREA and CAN, while non-members use intensively the

> 13See section 3 for a detailed discussion.

15
|Dependent variable: log hourly wage<br>Table 10: Returns to technical upgradin|(1)<br>g within exp|(2)<br>orting firms b|(3)<br>y firm size,|(4)<br>OLS estima|(5)<br>tes|(6)|
|---|---|---|---|---|---|---|
|A. Micro and small firms (employment < 100 workers)|||||||
|Received technical upgrading (=1 if yes)|0.070***|0.048***|0.054***|0.053***|0.054***|0.053***|
||(0.007)|(0.007)|(0.011)|(0.011)|(0.012)|(0.012)|
|Observations|146,079|146,079|146,079|146,079|146,079|146,079|
|B. Medium size firms (employment between 100 and 500 workers)|||||||
|Received technical upgrading (=1 if yes)|0.060***|0.050***|0.044***|0.044***|0.044***|0.043***|
||(0.005)|(0.004)|(0.006)|(0.007)|(0.007)|(0.007)|
|Observations|926,724|926,724|926,724|926,724|926,724|926,724|
|C. Large firms (employment > 500 workers)|||||||
|Received technical upgrading (=1 if yes)|0.053***|0.037***|0.029***|0.028***|0.027***|0.027***|
||(0.004)|(0.005)|(0.005)|(0.005)|(0.006)|(0.006)|
|Observations|12,936,331|12,936,331|12,936,331|12,936,331|12,936,331|12,936,331|
|Worker controls|No|Yes|No|Yes|No|Yes|
|Worker effects|No|No|Yes|Yes|Yes|Yes|
|Firm controls|No|Yes|No|Yes|No|Yes|
|Firm effects|No|No|No|No|Yes|Yes|
|State effects<br>Notes: Robust standard errors clustered by firm in parentheses. ***<br>are: age, race, gender, schooling level, and skill level based on o<br>tenure; male to female workers ratio; share of workers with less th<br>share of workers with more than high school completed; average log<br>2-digit industry dummies. Sample restricted to firms that exported in|No<br> p<0.01, ** p<br>ccupation. Fir<br>an high scho<br>wage; share <br>all years dur|Yes<br><0.05, * p<0.1<br>ms controls a<br>ol completed; <br> of white work<br>ing 2009-2012|No<br>. All regressi<br>re: log emplo<br> share of work<br>ers; share of <br>.|Yes<br>ons include ye<br>yment; avera<br>ers with exac<br>workers in ea|No<br>ar effects. W<br>ge age of wor<br>tly high scho<br>ch of skill leve|Yes<br>orker controls<br>kers; average<br>ol completed;<br>ls 1 to 4; and|
# 1990. Institution de Prevoyance Retraite du Senegal, Dakar.

1992. Institution de Prevoyance Retraite du Senegal. 34 ans au service du 3eme age. Senegal.

ISSA (International Social Security Association). 1987. Committee on Provident Funds. Ninth Meeting of the Committee. Geneva.

-. 1988. Report on the Sixth Meeting of Heads of ISSA Member Organizations in the EnglishSpeaking Caribbean, Geneva.

-. 1990. Tenth African Regional conference, Yaounde. Geneva 1991.

-. 1990. Report of the ISSA Regional Meeting of Asia and the Pacific on the Methods of Financing Social Security With Special Reference to Long-term Benefits. Social Security Documentation: Asia and Pacific Series: 14.

-. 1992. Reforma del Sistema Nacional de Prevision Social en la Argentina. Estudios de la Seguridad Social No. 71: pp.11-124; ISSN: 0379-0266

Instituto Salvadoreflo de Seguro Social. 1990. _Memoria Estadistica. ISSS._ Instituto de Seguridad Social de Trabajadores del Estado. 1993. _Informe Estadistico._ ISSTE.

Instituto Venezolano de Seguros Sociales. 1992. _Memoria. IVSS._ Jamaican Government. 1993. _Economic and Social Statistics of Jamaica._ Jamaica: Jamaican Government.

James, E. and Robert Palacios. "Costs of Administering Public and Private Pension Schemes", Finance and Development, June 1995.

Japanese Organization for International Cooperation in Family Planning. 1989. "Population Aging in Asia." Tokyo.

Johnson, Paul. 1985. "The Economics of Old Age in Britain: A Long-Run View 1881-1981." CEPR Discussion Paper Series (February).

Ju, Chen and Gavin Jones. 1989. "Ageing in Asia: Its Socioeconomic Consequences." Singapore: Institute of South Asian Studies.

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Kendig, H. Akiko Hashimoto and Larry C. Coppard. 1992. _Family Support for the Elderly._ Oxford: Oxford University Press.

132
is given by _y1 a'-p-Ab (14)_

# Ill. STEADY STATE

Agents in the economy choose at any point in time whether to belong to the formal or the informal sector. We do not allow the simultaneous participation- in both sectorst In the steady state the ratio of. informal to formal production or capital is constant (constantfi) and the economy grows at a constant rate. We analyze first the case when there is free entry into either sector and afterwards the case when there are some entry costs into the formal sector.

No Entry Costs to the Formal Sector in steady state informal agents want to switch to the formal sector whenever _VY'(k5 Vc (kD 90) p_ (15) _3 (O)_ = _e,JF_ = _A'r_ [C-(1-c)J : ~~~~~P _=-Alog(1-b) >O_ . ~. _P._ ~ is related to the decrease in utility due ~ ~ ~ ~ ~ ~ ~ **to** expete capital expropriation. The function _r_ or, where, gOJ can be thoug,ht of as the difference in utilities before fines.

- -13-
By disclosing risks to cities, the results presented here can raise awareness, inform resource planning, inspire further research, particularly at local levels, and promote the shift towards managing risks rather than emergencies.

# **2 Background**

The assessment of risk is highlighted as a central activity in defining priorities and building resilience in the Hyogo Framework for Action 2005-2015 (UN-ISDR 2005), signed by 168 nations and international organizations at the 2005 World Conference on Disaster Reduction. Risk identification supports a wide range of decision-making processes for different actors on how risk should be managed from the public to the private sector (e.g., Hsu et al. 2012, Cutter and Finch 2008, Fuessel 2007). Quantifying risk and estimating future losses are not only the first steps in any disaster risk reduction program; the resulting scenarios of a risk assessment are increasingly incorporated into sustainable development approaches in different sectors in order to climate- and disaster-proof investments. Once the severity and geographical extent of risks have been assessed and the drivers of risk are better understood, appropriate and costeffective countermeasures can be systematically identified and implemented. Depending on the scale, risk assessments support multiple applications, for example, urban planning, investment prioritization, land use planning, building codes, and disaster risk financing solutions.

A range of perspectives on risk assessments and indices has emerged, ranging from quantitative calculations on losses to qualitative analysis capturing also intangible impacts. Interesting initiatives have developed mainly at national level but a few have also been completed at global as well as urban scale.

Global level: Two main risk assessment initiatives have been undertaken with the goal of identifying multi-hazard risk worldwide on the basis of grid cells with sub-national extent. First, the _Global Disaster Hotspots_ , developed by the World Bank and Columbia University (Dilley et al. 2005, Lerner-Lam 2007) produced detailed geospatial data on risks of mortality and economic losses for six major natural hazards.

5