rafmacalaba/datause-encoder-data huggingface dataset:
# **4.3 Extension to a food insecurity risk model**

Regression analyses in section 4.1 allow to advance our understanding of the heterogeneous impact of the RSQ on food consumption and we have largely exploited those results to calibrate the vulnerability component of our prediction model in section 4.2. Predictions for the period 2011-2015 were produced to show that the model is very much in line with historical values. In this section, we move from historical RSQ scenarios to a probabilistic hazard component in order to extend the prediction model to a food insecurity risk model. The objective is to illustrate how the proposed modeling framework can also be used to estimate a probability distribution of future food insecurity, which allows to accurately depict the volatility of national food insecurity caused by the variability in the quality of rainy seasons.

To that end, we produce a catalogue of 10,000 synthetic RSQ scenarios based on a statistical analysis of historical monthly precipitations in Mauritania.<sup>43</sup> The set of synthetic events is a probabilistic representation of all possible RSQ scenarios in the country. We calibrate a simplified food insecurity model using the 5-month SPI only in the hazard component in order to simplify the hazard simulation procedure<sup>44</sup> and we compute national-level food insecurity estimates for each individual synthetic scenario. We can then infer a probability distribution of future food insecurity from the resulting sample of 10,000 estimates. We show the result in Figure 10. This is an example of how our methodology can be applied to welfare risk assessment, which is critical to the design of adaptive social protection programs. Improved risk information can support governments’ preparedness for food welfare shocks through adequately scaled response systems and coherent risk financing strategies.

In line with previous studies on the risk of poverty such as that of Hill and Porter (2017) and Skoufias et al. (2021), we also adopt a different approach where we directly use our nationally representative sample of households and simulate food consumption responses under the 10,000 synthetic scenarios to infer households’ probability distribution of food consumption. This in turn allows to estimate the share of households exposed to the risk of food insecurity being defined as, for instance, those with a probability of being food insecure greater than 0.5. With this definition, we find that 31% of households are at risk of food insecurity in rural Mauritania.

> 43We fit a multivariate normal distribution to historical 5-month SPI values in the 34 departments of the study area over the period 1981-2022, and we generate 10,000 scenarios based on the estimated mean and covariance matrix.

> 44Comparison of this model’s estimates against historical data for the period 2011-2015 show a performance that is comparable to that obtained with the optimal model of section 4.2.

33
# **1. Introduction**

The first decade of the transition to a market economy has been very difficult for the countries of the Former Soviet Union (FSU). All the 15 Republics that constituted the Union experienced a deep recession between 1990 and 1995 with an average fall in GDP of about 40%. As they were starting to recover in 1996 and 1997, Russia defaulted on its debt and unleashed a financial crisis that reached all FSU republics with a subsequent new fall in GDP of several percentage points. Hidden and open unemployment and poverty increased consistently during the decade leading to a severe decline in living standards (WB 2005). Understandably, the populations of these countries have lived the 1990s as a painful experience that overshadowed the initial enthusiasm for reforms.

This is also the story of Moldova, a small FSU republic that suffered severe economic consequences following the desegregation of the Soviet Union, transitional reforms and the 1998 Russian financial crisis. By 1999, Moldova had lost almost 60% of its GDP<sup>2</sup> and had an estimated poverty rate (headcount index) of 71% (WB 2004). As for other countries in the region, the economic situation started to improve after the beginning of the new millennium. Between 2001 and 2004, GDP grew, on average, by 7% a year (GoM 2005) while it is estimated that poverty (headcount index) declined from 54.6% to 26.5% (GoM 2004). The beginning of the new growth period also coincided with the election of a new government in 2001 which increased public spending on social assistance very significantly during its first mandate (2001-2004).

Can poverty reduction be attributed to specific pro-poor government policies or is more generally the product of economic growth?

This paper addresses this question by focusing on one policy instrument which is specifically designed to support the poor and the vulnerable: social assistance benefits in cash (henceforth 'social benefits'). We carry out a welfare impact evaluation of social benefits using household panel data that cover the period 2001-2004. The purpose is to try to isolate from other effects the impact of social benefits (and their increase) on the welfare of Moldovan households and assess in this way the merits of social assistance policies.

The paper is organized as follows. Section 2 provides a brief review of the literature on poverty and social assistance in transition. Section 3 looks more in detail at the Moldovan case. Section 4 discusses some of the key issues we are confronted with in making a retrospective impact evaluation. Section 5 outlines the evaluation strategy we opted for. Section 6 illustrates data and variables used and section 7 discusses the results. Section 8 concludes.

# **2. Poverty and social assistance in transition**

The social protection system in the FSU countries went through important changes during the 1990s. The Soviet system did not really make a distinction between social insurance and social

> 2See http://www.cisstat.com/eng/mac-01.htm

2
...where T1 and T2 are dummies equal to one for groups that received training and books and books only (and zero otherwise), respectively. The coecients ( _δ_ 1 and _δ_ 2 ) are the corresponding impacts of each set of interventions. The impact of extra-training is estimated as the dierence between these coecients (i.e., _δ_ 1 _− δ_ 2 ), and test for statistical signicance of this dierence is conducted.

# 4 RESULTS

# 4.1 Descriptive results

In this section, we briey discuss the implementation of propensity score matching and discuss descriptive results. As described above, the control and the treatment group of students were matched using endline survey. Observations that happen to be o-common support were dropped, and the data is further trimmed by removing observations with probabilities in the top and bottom 1% for the corresponding group. The densities of propensity scores are resented in Figure A.2 (see annex). The matching results for the three groups of interventions are presented in Table 2 below, and Table A.1-A.2 (see annex). As Table 2, A.1 and A.2 show, there were statistically signicant mean dierences in some covariates before matching, and these systematic dierences have been addressed after matching (i.e. balancing is achieved). In other words, the factors that could attenuate or amplify the impact of the interventions, such as students' characteristics, their families' socioeconomic status, teachers' qualication and school resources, do not exhibit statistically signicant dierences between the treatment and control groups.

In addition, we present mean student outcomes after matching (both during baseline and endline surveys) in Table A.3 (see annex). The (matched) treatment and control groups, for all of the three interventions, do not exhibit systematic baseline dierences in outcome indicators. Below is a brief discussion for each intervention.

Training and Books: Before matching, half of the covariates that could potentially aect test scores had statistically signicant mean dierences between the control and the treatment group of students. The PSM has taken care of these dierences in these covariates. Teachers' qualications (formal training and years of experience) are similarthe majority of teachers have had formal education and about 14 years of 14
<!-- Start of picture text --> Overall '<br>2016 '<br>2094 Re<br>0.04 -0.02 0.00 0.02<br>Estimate<br><!-- End of picture text --> <!-- Start of picture text --> Overall :<br>2016 :<br>2014 |<br>-0.02 0.00 0.02 0.04 0.0€<br>Estimate<br><!-- End of picture text -->

<!-- Start of picture text --> Overall :<br>2016 '<br>2014 (-- '<br>-0.4 0.2 0.0 02 0.4<br>Estimate<br><!-- End of picture text --> <!-- Start of picture text --> Overall '<br>2016 '<br>2014 ————<br>-0.10 0.05 0.00 0.05<br>Estimate<br><!-- End of picture text -->
Table A.11: Logit analysis, 1985‐2015, Project Outcome (Dummy for Successful Outcome as independent variable) and ERR discrepancy between PAD and ICR as explanatory variables (Model 3)

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||Suc/Unsuc|Suc/Unsuc|Suc/Unsuc|Suc/Unsuc|Suc/Unsuc|Suc/Unsuc|Suc/Unsuc|Suc/Unsuc|Suc/Unsuc|
|GDP PC growth|0.0531***|0.0828***|0.0821***|0.0834***|0.0831**|0.0818***|0.0789***|0.0919**|0.0834***|
||(4.49)|(2.93)|(2.90)|(2.94)|(2.01)|(2.89)|(2.79)|(2.16)|(2.92)|
|CPIA rating|0.435***|0.169|0.164|0.162|0.156|0.152|0.149|0.153|0.166|
||(7.63)|(1.48)|(1.42)|(1.39)|(0.66)|(1.33)|(1.29)|(0.63)|(1.41)|
|CPIA scale dummy|0.133|‐0.461|‐0.460|‐0.466|‐0.423|‐0.533*|‐0.547*|‐0.607|‐0.609*|
||(1.31)|(‐1.46)|(‐1.46)|(‐1.48)|(‐1.03)|(‐1.68)|(‐1.72)|(‐1.45)|(‐1.89)|
|East Asia and Pacific|0.208*|0.160|0.166|0.166|0.149|0.166|0.178|0.104|0.146|
||(1.76)|(0.76)|(0.78)|(0.78)|(0.45)|(0.78)|(0.84)|(0.31)|(0.68)|
|Africa|‐0.314***|‐0.319|‐0.304|‐0.306|‐0.271|‐0.313|‐0.300|‐0.372|‐0.392*|
||(‐3.03)|(‐1.54)|(‐1.43)|(‐1.45)|(‐0.85)|(‐1.51)|(‐1.44)|(‐1.07)|(‐1.80)|
|East Europe and Central Asia|0.0709|‐0.0377|‐0.0259|‐0.0273|0.157|‐0.0129|0.0173|0.0751|‐0.0428|
||(0.56)|(‐0.14)|(‐0.10)|(‐0.10)|(0.45)|(‐0.05)|(0.06)|(0.21)|(‐0.16)|
|Latin America & Caribbean|0.0267|‐0.156|‐0.151|‐0.149|0.497|‐0.172|‐0.157|0.407|‐0.199|
||(0.24)|(‐0.71)|(‐0.69)|(‐0.68)|(1.43)|(‐0.78)|(‐0.72)|(1.15)|(‐0.90)|
|MiddleEast & North Africa|‐0.139|0.0287|0.0414|0.0436|0.221|0.0299|0.0426|0.167|‐0.0347|
||(‐1.03)|(0.11)|(0.15)|(0.16)|(0.53)|(0.11)|(0.16)|(0.38)|(‐0.13)|
|IEG evaluation method (PPAR=1)|‐0.0878|‐0.282*|‐0.285*|‐0.285*|0.115|‐0.311**|‐0.298*|0.0878|‐0.303*|
||(‐1.18)|(‐1.83)|(‐1.85)|(‐1.85)|(0.37)|(‐2.01)|(‐1.93)|(0.28)|(‐1.95)|
|Over estimation ofERR in PAD(1ifERR in PAD>ERR in ICR)||‐1.359***|‐1.356***|‐1.357***|‐1.298***|‐1.343***|‐1.341***|‐1.265***|‐1.351***|
|||(‐8.64)|(‐8.60)|(‐8.62)|(‐5.97)|(‐8.51)|(‐8.51)|(‐5.75)|(‐8.53)|
|ERR difference, log (of absolute difference [ratio])||‐0.522***|‐0.521***|‐0.523***|‐0.404***|‐0.518***|‐0.515***|‐0.390***|‐0.524***|
|||(‐7.49)|(‐7.45)|(‐7.49)|(‐4.02)|(‐7.42)|(‐7.41)|(‐3.85)|(‐7.47)|
|Log net commit. (m$)|||0.0217|||||‐0.260*|‐0.154*|
||||(0.33)|||||(‐1.87)|(‐1.77)|
|Project length||||‐0.0124||||‐0.0503|‐0.0106|
|||||(‐0.34)||||(‐0.88)|(‐0.29)|
|TTL performance|||||0.646**|||0.667**||
||||||(2.20)|||(2.24)||
|Preparation cost over commit, %||||||‐0.0607***||‐0.134**|‐0.0537|
|||||||(‐2.63)||(‐2.57)|(‐1.41)|
|Supervision cost over commit, %|||||||‐0.0560**|‐0.00242|‐0.0496|
||||||||(‐2.49)|(‐0.05)|(‐1.24)|
|Observations|5622|1820|1820|1820|945|1820|1820|945|1820|
|Pseudo R2|0.069|0.186|0.186|0.186|0.162|0.190|0.189|0.173|0.192|
|Sector dummies|Y|Y|Y|Y|Y|Y|Y|Y|Y|
|Sector x evaluation period dummies|Y|Y|Y|Y|Y|Y|Y|Y|Y|
|Sector x approval period dummies|Y|Y|Y|Y|Y|Y|Y|Y|Y|

Note: t statistics in parentheses, * p<0.10, ** p<0.05, *** p<0.01 37
# **3.1 Crisis protectionism leading to escalading protectionism?: Tit-for-tar retaliation, trade deflection, and cascading protectionism**

In this section we explore three channels through which current resort to trade remedy protectionism during the global crisis creates incentives for follow-on use by others of trade remedies and the possibly worrisome escalation of protectionism. To begin this exercise, we examine the use of trade remedies across countries over “common products” listed in Appendix table 1. The table presents 35 instances in which _more than one_ policy imposing country initiated a trade remedy investigation over the same 6-digit HS product(s) during the period covered by 1Q 2007 through 1Q2009. These 35 product examples cover more than 70 unique 6-digit HS codes. The table presents the name of the product, the 6-digit HS product(s) common to the investigating countries, the names of the countries undertaking the common investigations, as well as the name of targeted exporters associated with each investigation. The product list is organized numerically by the underlying HS codes.

The first channel is that firms may use a policy like antidumping to discipline foreign rivals in direct tit-for-tar retaliatory actions. While it is relatively rare for two economies to target one another with simultaneous investigations over essentially varieties of the same product, one possible example of direct AD retaliation within varieties of the same product involves the recent U.S.-EC interaction over sodium products (case 1 on table 5). In October 2007, the U.S. firm E.I. DuPont de Nemours & Co. was a domestic petitioner requesting initiation of a U.S. AD investigation over “Sodium metal” from France, accusing the French firm Métaux Spéciaux (MSSA SAS) of dumping into the U.S. market. In May 2008, the U.S. imposed a preliminary AD duty of 62.62 per cent. In July 2008, the EC initiated AD and CVD investigations over “Sodium metal” from the U.S. in the same 6-digit HS product category. In December 2008, in the U.S. investigation, the USITC ruled negative on injury determination, and thus no final measures were imposed. As the spring of 2009, the EC investigation was still ongoing.

Of course it is also not necessary for retaliation (i.e., the current trade remedy provoking use of future protectionism) to be limited to only instances in which countries import and export the same product. One country’s excessive reliance on the use of trade remedies can lead to its exporters becoming foreign targets in other products in the same industry, or in other industries altogether, thus expanding the potential escalation of protectionism.<sup>14</sup> This is of particular concern given the current economic crisis and the desire to avoid repeating the spiral of protectionism associated with the 1930s and the Great Depression era.

> 14 Studies of the retaliatory links and incentives created under antidumping include Blonigen and Bown (2003), Prusa and Skeath (2004), and Feinberg and Reynolds (2006).

16
# **INTEGRATING THE UNOFFICIAL ECONOMY INTO THE DYNAMICS OF POST-SOCIALIST ECONOMIES:**

# **A FRAMEWORK OF ANALYSIS AND EVIDENCE**

Daniel Kaufmann and Aleksander Kaliberda _*Daniel Kaufmann is currently Visiting Scholar at the Harvard Institute for International Development, on secondment from the World Bank.. Aleksander Kaliberda is at the World Bank, Kyiv Office, Ukraine. The views expressed in this paper are solely those of the authors (who take responsibility for all errors and omissions), and do not necessarily reflect official institutional views. Comments are welcomed. Internet: Dkaufinan@hiid.harvard.edu Akaliberda@worldbank.org An earlier version of this paper wasfirst presented as a draft at the Odessa conference in August 1995, entitled 'Economic Transition in the Newly Independent States '. The present version will be published in Economic Transition in Russia and the New States_ **_of_** _Eurasia. edited by B. Kaminski (Armonk, NY: M.E. Sharpe, Inc., 1996). It has benefitted from the work of 1. Dobozi and G. Pohl and from comments and discussions with them and with J. Balkind, S. Commander, E. Engel, A. Gelb, S. Johnson, B. Kaminsky, V. Kuznetsov, L. Lovei, F. Le Gall, B. Milanovich, M. de Melo, A. Revenga, A. Sundakov and U. Zachau. We are grateful to the country economists in the World Bank and to T. Helleniak of the FSU Statistic Unit for provision of country data._
capacity to pay. Specifically, governments can affect the value of bank deposits, interfering directly or indirectly in private contracts, through actions that are largely beyond the control of bank managers.<sup>5</sup> As a result, when dual agency problems arise, depositors respond not only to bank-specific risk (disciplining bank managers), but also to the probability that systemic factors erode the value of their deposits irrespective of the managers’ behavior.

In light of the above, the presence of systemic risk certainly has important implications for the literature on market discipline. In particular, the failure to find a link between market responses and traditional bank fundamentals does not imply the absence of market discipline. First, systemic risk might overshadow the informational content of observed (past) fundamentals as market participants (such as depositors) react to expected changes in future fundamentals. Second, as noted, traditional fundamentals do not explicitly take into account individual banks’ exposure to systemic factors. Therefore, variables omitted in typical specifications need to be taken into account. Third, in addition to bank exposure to systemic risk, the impending risk of government intervention may prompt depositors to pull out from all banks indistinctly. Overall, the failure to observe market discipline in the traditional sense may be indicating that market participants react to relatively more relevant systemic risk factors, a finding that can be interpreted as a signal of market discipline, albeit in a broader sense.

To document the importance of systemic risk on market discipline, we study the evidence from the recent systemic bank runs in Argentina and Uruguay during 2000-02, with a focus on the link between systemic factors and depositor behavior. We show that depositors indeed respond to the direct and indirect exposure to systemic risk as the latter increases. Moreover, we

> 5 For example, due to “too-big-to-fail” concerns, the government may be prompted to suspend the convertibility of all deposits to protect a few compromised banks. The placing of deposit rate caps and the ulterior suspension of deposit convertibility in Argentina in November-December 2001 may be explained, at least in part, using this argument.

5
meter and initial values, the model is solved for an initial steady state that satisfies the properties of the balanced growth equilibrium defined earlier. These equilibrium values are shown in Table 2, together with the gender wage gap for skilled workers (given that all unskilled workers earn a uniform minimum wage), <sup></sup> <sup></sup> , given in equation (22).

More specifically, the initial steady-state values are calculated as follows. The share of women in the active population is given by, from (31), <sup></sup> = 0263. The total share of unskilled workers, and the total share of skilled workers, both in proportion of the total active population are set equal to <sup></sup> = 0568 and <sup></sup> = 0432, respectively. The total unemployment rate in proportion of the total active population, <sup></sup> , is set at 0094, whereas the unskilled (skilled, respectively) unemployment rate, in proportion of the unskilled (skilled, respectively) active population is set at <sup></sup> = 0038 (<sup></sup> = 0168, respectively). The shares of unskilled female and male workers employed in the respective active populations, <sup></sup> and <sup></sup> , are set equal to 0608 and 0503; the corresponding shares for female and male skilled workers are given by <sup></sup> = 0114 and <sup></sup> = 031. The unemployment rates for female and male unskilled workers, <sup></sup> and <sup></sup> , both in proportion of the relevant active population, are set at 0029 and 0041, whereas the corresponding rates for female and male skilled workers, <sup></sup> and <sup></sup> , are set equal to 0249 and 0146. By implication, <sup></sup> = <sup></sup> = <sup></sup> + <sup></sup> = 0637 whereas <sup></sup> = <sup></sup> = <sup></sup> + <sup></sup> = 0544. Similar results hold for <sup></sup> = 0363 and <sup></sup> = 0456.<sup>21</sup> All these values match the actual data on the distribution of employment and unemployment available for 2016, published by Morocco’s Haut-Commissariat au Plan (HCP). Based also on HCP data, the share parameters <sup></sup> , <sup></sup> , and <sup></sup> are set at 0414, 0631, and 0184.

To estimate the subjective employment probability of labor type  suppose that hiring is random. Probability <sup></sup> can thus be approximated by the share of employment of type  in total population , that is, <sup></sup> = <sup></sup> <sup></sup> . Using the previous results give <sup></sup> = 0954, <sup></sup> = 0925, <sup></sup> = 0313, and <sup></sup> = 0681. Thus, the unskilled (regardless of gender) have a higher likelihood of being employed than the skilled, whereas among the skilled men are twice as likely to be employed as women.

> 21From (30) or (57),  = 05[1−()2], which is not equal to  +<sup></sup> , as given in the data, given that in the model skilled labor is defined in effective terms. To ensure that the value of <sup></sup> matches the actual share of skilled workers of gender , a multiplicative constant is introduced.

23
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