rafmacalaba/datause-encoder-data huggingface dataset:
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<mark>Nicita, Alessandro. 2011. “Measuring the Relative Strength of Preferential Market Access,” UNCTAD Blue Series Papers 47, United Nations Conference on Trade and Development.</mark> <mark>Lyn, Gary and Andrés Rodríguez-Clare. 2013. “External Economies and International Trade Redux: Comment,”</mark> _<mark>The Quarterly Journal of Economics</mark>_ <mark>, 128(4): 1895-1905.</mark> <mark>Oberfield, Ezra. 2018. "A Theory of Input-Output Architecture,"</mark> _<mark>Econometrica</mark>_ <mark>86(2): 559-</mark> <mark>589.</mark> <mark>Ornelas, Emanuel. 2016. “Special and Differential Treatment for Developing Countries,” Chapter 7 in Editor(s): Kyle Bagwell, Robert W. Staiger,</mark> _<mark>Handbook of Commercial Policy</mark>_ <mark>, NorthHolland, Volume 1, Part B, pp. 369-432.</mark> Ornelas, Emanuel and Marcos Ritel. 2020. “The Not-So-Generalized Effects of the Generalized System of Preferences,” _The World Economy_ 43(7), pp. 1809-1840.

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The first successful integration of a full household survey into a CGE model was by Cockburn (2001) when he integrated the 3,373 households in the household survey into his constant returns to scale CGE model of Nepal. Subsequently, Cororaton and Cockburn (2007) integrated the 24,979 households in the household survey into their CRTS model of the Philippines.<sup>41</sup> The solution techniques used by Cockburn and his co-authors, however, will not typically allow incorporation of larger household data sets; and increasing returns to scale models present greater challenges for robust solutions. In this section, however, we employ a new solution algorithm for an increasing returns to scale CGE model with essentially no significant bound on the number of household agents.

In our central model, we estimate that the mean welfare gains to Russia, averaged over all households, from WTO accession are a rather substantial 7.3 percent of Russian consumption (with a standard deviation of 2.2 percent of consumption) in the medium run. Decomposition analysis reveals that over 70 percent of the gains are due to the liberalization of barriers against foreign investors in services. We estimate that 99.9 percent of the households will gain from 2 percent to 25 percent of their household income. We find that poor households gain slightly more than rich households on average, since the return on capital does not increase as much as the wages of skilled and unskilled labor. We conduct both piecemeal sensitivity analysis and systematic sensitivity analysis (the latter by executing the model 30,000 times with random parameter selection), and find that our results are robust with respect to parameter specification. But without foreign direct investment, services liberalization and Dixit-Stiglitz externalities, the welfare gains are only one-sixth of the gains in our model and seven percent of the households are estimated to lose. Our results show that, while incorporating the diversity of households though a real household model is important, incorporating

> 41 See also Annabi et al (2005), who have incorporated 3278 households into a constant returns to scale computable general equilibrium model.

63
4 expected income is the risk-free gross return on his endowment, part of which is invested in the risky project and part of which is invested in the riskless technology.

In period I the govemment has an option to undertake an econoniic policy reform.<sup>2</sup> I assume that a change in govemnent policy will lower the expected period 2 cash flow from the existing project: _f(sfsdfa1(s)f(d(sd <_ 0 _(4)_ o 0 where _at (s)_ is the cash flow in state s following the policy change. An unexpected policy reform will result in a loss in the value of the debt issued by the representative entrepreneur:

* s _min[rd{(l -w,), a;(s)]f(s)ds <r. (I-_ W.) _.(S)_ a

In order for the representative nonentrepreneur to agree initially to invest in the first project he must be given a guarantee that he will be compensated for losses arising as a result of changes in economic policy. I will assume that the reprsntative entrepreneur and nonentrepreneur have agreed upon constitudonal safegards that require the govemment to make such compensation3 The government has the auftority to order the private recontracting of the existing debt contract in conjunction with the proposed policy reform If the reduction in the cash flow on the existing project is not too large, an upward recontracting of _rd_ will compensate the nonentepreneur for the lower cash flow and greater default probability of the project In this case, there is no additional role for public policy to supplement the initial economic reform. In what follows, I will assume that the expected value of the new cash flow is strictly less than the riskiess rate so that recontracting _wiU not_ solve the distributional problem associated with the policy reform: 21 will not attempt to model why the govenmat would not have undertaken a reform prior to the start of period 1. By now there is a large liteHaure on policy reform [see, e.g., Krueger (1990) for a survey] that stresses the difficulty of defining "the government" as a benevolent guardian. Even when a govement is attempting to maximize welfare. it may pursue policies due to 'govenmenL failure" that are not the best polices. Some governments do, however, enact policy reforms that have [he potential to improve welfare if the private sector can be persuaded to undertake new investments that follow the lead of the govemmenL

> 3With reference to footnote l, this meas that the agents can wite fincal contracts, including simple debt contracts, that arte contingent on the state of nature s, but that they cannot wrte contracts contingent on specific policy changes. The pareto criterion is a simple rule that solves a potential time consistency problem with respect to policy reforms.
A challenging step of the BCEF method involves the identification of prior series for the unobserved ex-ante variance and ex-ante skewness of the world GDP growth, . In this regard, YY we modeled the prior ex-ante variance and ex-ante skewness of world GDP growth as pointed out in the Appendix A3<sup>16</sup> using the sign restrictions presented in Table 1 and the available forward-looking variance and skewness series of the risk factors.

Furthermore, based on point forecasts of world GDP growth (World Bank 2016), Table 2 shows that there is generally an overprediction pattern in the elicited predictions. Thus, opposite to the overpredicted forecasts, the initial guess for the prior global ex-ante skewness is to assign more weight to the downside section of the world GDP growth predicted distribution; i.e., to assign negative skewness values.

TABLE 2 – HISTORICAL FORECAST ERRORS OF WORLD GDP GROWTH PREDICTIONS

|Horizons:|Concurrent Year<br>(T)|T+1|T+2|Combined Horizons|
|---|---|---|---|---|
|Mean|0.22|0.98|2.31|0.82|
|Median|0.16|0.73|1.49|0.51|
|Std. Dev.|1.20|1.85|1.78|1.66|
|Observations|13.00|11.00|4.00|28.00|

Source: Global Economic Prospects (World Bank 2016).

Note: Realizations of world GDP growth based on WDI. Historical forecast error defined as predicted minus realized value. A positive HFE value indicates overprediction.

In this specific world GDP growth forecast, the mechanics of optimistic baseline forecasts assessed with more significant downside than upside risks are a summary and a statistical translation of the well-known phrase “hope for the best and prepare for the worst.” Finally, once priors for the alpha coefficients and the future variance and skewness of real GDP are selected, the posterior distributions that solve the cross-entropy optimization model presented in Subsection 2.5 are ready to be estimated.

> 16 There are multiple methods to obtain priors (Golan 2017). The procedure presented in Appendix A3 to proxy the prior distributions of the study parameters is only an alternative approximation.

22
# Do Cash Transfers Foster Resilience? Evidence from Rural Niger

Patrick Premand<sup>1</sup> & Quentin Stoeffler<sup>2</sup> Keywords: cash transfers; resilience; investment; income smoothing; diversification; savings; poverty; climatic shocks; Niger JEL Codes: O1, I3, Q1, H43 1 World Bank, ppremand@worldbank.org 2 Istanbul Technical University, stoeffler@itu.edu.tr. Corresponding author.

Acknowledgements: This paper is based on a collaboration between the World Bank and the Government of Niger. The study was supported by the Strategic Impact Evaluation Fund (SIEF), the Sahel Adaptive Social Protection Program at the World Bank, as well as the Niger Adaptive Safety Nets Project, managed by the Cellule Filets Sociaux (CFS) in the Prime Minister’s office of the Government of Niger. Quentin Stoeffler acknowledges support from Istanbul Technical University’s Bilimsel Araştırma Projeleri (BAP) unit, grant number SGA-2017-40617. We are grateful to Ali Mory Maidoka, Bassirou Karimou, Kadi Aboubacar at CFS, as well as Carlo del Ninno and Mahamane Maliki Amadou at the World Bank for a fruitful collaboration. Marc Smitz coordinated the baseline and follow-up survey and provided excellent contributions to data analysis and study implementation. Horacio Vera Cossio provided excellent research assistance. The Nigerien Statistical office (INS) collected baseline data. Riseal and the Swiss Tropical and Public Health Institute (STPH) collected follow-up data (with SurveySolutions). We are grateful for inputs and comments from Christian Bodewig, Stéphanie Brunelin, Paul Christian, Carlo del Ninno, Chloë Fernandez, Brad Mills, an anonymous DIME reference as well as seminar participants at Ibn Haldun University, the American University of Beirut, and the 2018 AAEA meeting. The computational reproducibility of the results has been verified by DIME analytics. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations; those of the Executive Directors of the World Bank or the governments they represent.
Because we rely on cross-sectional data for the estimation, there are challenges to causal identification of the impact of shortages on firm-level revenues or value-added. First, there is likely unobserved firm-level heterogeneity, which can affect both firm performance and shortages experienced by the firm. For example, more productive firms may be attracted to less shortage-prone districts. More productive firms are also more likely to acquire self-generators. Second, shortage is likely to be endogenous to growth and business climate at the district level. For example, areas that provide more friendly business environment and experienced faster economic growth can have higher demand for electricity. This will in turn result in worse power shortages (Allcott, CollardWexler, and O’Connell 2016; Grainger and Zhang, 2017). Finally, there is likely measurement error in our measurement of shortages, because we only capture districtlevel average outages, but not actual outage experienced by each firm. In the following, we present our estimation results while recognizing the above caveats of the identification strategy.

# **5. Empirical Results**

Following the approach outlined in Section 4, we first estimate the impact of electricity shortages on firm-level product revenues, and then we assess the impact of shortages on firm-level value-added.

# **5.1 Product Revenues**

Table 3 presents the results of our product revenue regressions in which the dependent variable is the natural log of product revenues at the firm level. There are 3,105 observations in the data with observations for all of the variables included in the regressions. Columns (1) and (2) include the duration index (SAIDI) as the shortage variable, while (3) and (4) include the frequency measure (SAIFI). The fifth column includes both.

The control variables for firms’ labor costs, raw material costs, fixed assets and total electricity costs all have positive, significant impacts on product revenues, as expected. Furthermore, the regressions explain a significant amount of variation in product revenues with Adjusted R-squared values around 0.96 across specifications.

In the first two columns, we find that a 10 percent increase in the average total duration of outages decreases firm-level revenues by between 0.14 and 0.28 percent. Since 8
and similar lists by the other Australian authors on whose work we build as the “Australian categories.”) We had to take proxies in the case of the category “Activities reserved by law to the profession,” which has a weight of 0.05. This category in the Australian list is scored at one if four or more activities are reserved to the profession. If there are two categories reserved to the profession, the Australian score is 0.5; for one or zero categories reserved to the profession the score is 0.25 and zero, respectively. In this case, we took the closest relevant proxy in the World Bank database, which was: for how many of the following actions is an economic needs test required: intra-corporate transfer (ICT), Independent Professionals as Employee of a local firm (IPC), and Service-Supplying Employee (SSE) of a firm based abroad. If an economic needs test is required for three (two, one, zero), we score the Australian matrix at 1, 0.5, 0.25 and 0, respectively.

Another example of no clear mapping is the case of restrictions on advertising, marketing and solicitation in the Australian matrix for accounting. We want to use all the information available in the World Bank database, so from the World Bank database we use the information on “minimum wage is required for IPC, ICT or SSE,” as our proxy for this category in the Australian matrix.

# **2.4 Transforming Our Services Trade Restrictiveness Indices into Ad Valorem Equivalents**

We rely on the papers by Warren (2000) in telecommunications (for both fixed line and mobile), Kalirajan et al., (2000) in financial services (for both banking and insurance), Kang (2000) in transportation services (for all four transportation sectors), Nguyen-Hong (2000) in professional services (for both accounting and auditing, and legal services) and Kalirajan (2000) for retail distribution services. Except for Warren, in all studies the authors regressed a measure of the price or costs of services against their STRIs and other relevant variables in a cross-country regression at a point in time to determine the impact of the regulatory barriers on the price of services.<sup>19</sup> Through these regressions, the authors estimated the ad valorem equivalents of the regulatory barriers in the countries of their sample.

Our methodology, which is explained in detail in Jafari (2014), assumes that the average of the international regressions of the price or cost impact estimated by the Australian authors

> 19Warren estimated quantity impacts and then, using elasticity estimates, was able to obtain price impacts and ad valorem equivalents.

10
- 22 -

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<mark>understand cross-country differences in functionally equivalent organizations (Gingerich 2013).</mark> As a complement to existing measures of comparative bureaucracy, direct survey measures of the experiences and perceptions of bureaucrats themselves provide an analogous source of data to household and enterprise surveys, but for the public service. <mark>This paper introduces the Global Survey of Public Servants (GSPS), a global initiative to harmonize large-scale comparable micro survey data from public servants around the world (accessible at: https://w</mark> <u>ww.globalsurveyofpublicservants.org</u> <u><mark>/). The GSPS was founded by the authors of</mark></u> <mark>this paper – researchers at or affiliated with the World Bank Bureaucracy Lab, the Stanford University Governance Project, University College London, and Nottingham University.</mark> The intention of the GSPS is first simply to encourage further surveying of public officials around the world. However, publishing data at a more granular level than has been available to date allows for research on questions of the state that are complementary to what has been undertaken to date.

<mark>To contextualize the GSPS, this paper introduces a conceptual framework of the survey, which reflects the dual academic-practitioner aims of the initiative. Such a conceptual framework is required given how limited the conceptual consensus is in any literature on the drivers of state capacity and administrative quality. The GSPS’s objective is to improve scholarly understanding of how public administrations and states around the world work and to help governments manage public servants better. As such, we require a theoretical lens that allows us to make choices on what measures to include in our effort.</mark> <mark>The paper then details the methodological approach of the GSPS and current state of play of the GSPS dataset (</mark> accessible at: <u>https://www.globalsurveyofpublicservants.org/indicators/</u> <u><mark>). We provide illustrations of how</mark></u> <mark>scholars can use the GSPS data to study core topics in comparative public administration</mark> 3
|**Agro-climactic**|||
|---|---|---|
|Precipitation|CHIRPS*|Wilmott and Matsuura(2018)|
|Elevation and slope|ASTER sensor|Jarvis(2008)*|
|Global forest cover<br>slope|<br>Hansen (2013)||
|Climactic region||Kottek et al(2006)|
|Crop yield estimates||IFPRI Harvest-Choice (Wood-<br>Sichra et al,2016)|
|Normalized Difference<br>Vegetation Index|<br>Sentinel 2|Sentinel 2*|
|**Market access**||Belghith et al(2020)|
|**Natural Disaster Risk**||UNEP/DEWA/GRID-Europe|

Note: * indicates that at least one variable from this set was selected in the model of welfare

# D. Geographic structure of Sri Lanka and Tanzania

Before describing the methodology used in the study, we briefly review the geographic structure of the two countries. Table 3 shows the subareas, areas, and super-areas for each country. Subareas are the lowest level for which shapefiles were obtained, and are therefore the most disaggregated level for which geospatial data could be linked with the survey. Areas are the target domains for the small area estimates, which are at a higher level than the subareas, but below the level at which the survey is considered to be representative. Finally, super-areas are the lowest level for which the household budget survey is considered to be representative.

_Table 3: Geographic structure of Sri Lanka and Tanzania_

||Sri Lanka|Tanzania|
|---|---|---|
|Sub-areas|||
|Name|GN Division|Village|
|Number in country|13,978|16,438|
|Areas|||
|Name|DS Division|District|
|Number in country|331|159|
|Super-areas|||
|Name|District|Region|
|Number in country|25|25|

Source: Authors’ calculations from 2012 census data.

9
Figure 3: Shocks in Sudan: Specific Types of Shocks Reported, Effects, and Coping Strategies, 2009 and 2014/15 <!-- Start of picture text --> (a) Number of Shocks Reported (b) Specific Types of Shocks Reported<br>Death of Hh. Head 2%<br>1%<br>More than 5<br>1%<br>Livestock Loss 15%<br>26%<br>8%<br>5 Shocks 1% Crop Disease 19%<br>3%<br>Severe Illness/Accident 7% 18%<br>4 Shocks 3% Floods/Drought 15%15%<br>5%<br>Death of a hh. Member 6%<br>13%<br>11%<br>3 Shocks 16% Dwelling Damaged 6% 12%<br>Severe Water Shortage 3% 8%<br>24%<br>2 Shocks<br>27% Fire 6%8%<br>Robbery/Assult 5%6%<br>60%<br> 1 Shock<br>47% Other Shocks 2%<br>3%<br>0% 20% 40% 60% 80% 0% 10% 20% 30%<br>2014/15 2009 2014/15 2009<br>(c) Self-reported Effects of Shocks in SDGs (d) Coping Strategies Used<br>Received Help from<br>Death of Hh. Head 11,760 Family/Friends, NGOs or 24%<br>33%<br>Gov't<br>Severe Illness/Accident 2,315 11,014<br>Sell Assets/Use 23%<br>Other Shocks 10,800 Savings/Rent Farm 25%<br>2,746<br>Livestock Loss 2,653 10,268 Increase Labor Supply 7%<br>13%<br>Robbery/Assult 2,086 10,030<br>9%<br>Fire 2,296 9,197 Borrowed Money 10%<br>Death of a hh. Member 7,364<br>1,612 16%<br>Other<br>10%<br>Floods/Drought 2,213 6,554<br>Crop Disease 1,511 5,351 Reduced Expenses orConsumption 3% 9%<br>Dwelling Damaged 1,4513,783<br>Kids Migrate or removed 4%<br>Severe Water Shortage 970 3,346 from School 1%<br>0 5000 10000 15000 0% 10% 20% 30% 40%<br>2014/15 2009<br>2014/15 2009<br><!-- End of picture text --> _Source_ : Authors’ calculation using 2009 NBHS and 2014/15 NHBPS data.

13