rafmacalaba/datause-encoder-data huggingface dataset:
# **2. Country Context: Sustained Economic Growth in the 2000s**

**The 2000s were a decade of sustained economic growth in Moldova, up to the global economic crisis** . After a tough decade in the 1990s in which economic downturns were frequent, GDP growth averaged around 6 percent per year from 2000 to 2008, until the global economic crisis hit. Growth collapsed by 6 percent in 2009 and remittances, which accounted for around 31 percent of GDP in 2008, fell dramatically in 2009 and remained at 22 percent of GDP by 2011.

**Moldova has recovered from the global economic crisis, but growth is still volatile (Figure 1).** Following the crisis, GDP growth rates averaged 6 percent in 2010 and 2011. In 2012, however, growth stopped, linked to the financial and sovereign debt crisis in the euro area and a severe drought that affected agricultural production. In 2013, the agricultural sector rebounded, increasing GDP by 9.4%.

**Figure 1. Apart from the crisis, Moldova had sustained economic growth during the 2000s** Real GDP growth (percent)

<!-- Start of picture text --> 10<br>8<br>6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text --> Source: WDI data.

**Despite significant progress and strong economic growth, Moldova remains one of the poorest countries in Eastern Europe and Central Asia** . As shown in Figure 2, GDP per capita in Moldova in 2011 was only higher than that of three countries in ECA: Tajikistan, Kyrgyz Republic and Uzbekistan. In fact, Moldova’s GDP level is similar to that of two of the poorest countries in Latin America: Honduras and Nicaragua.

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> **THE** RATIONALE **AND** PERFORMANCE OF

> **PERSONAL PENSION PLANS IN CHILE** Dimitri Vittas and Augusto Iglesias

Table of Contents <!-- Start of picture text --> I. Introduction I<br>II. Basic Operating Characteristics and their Rationale 3<br>III. The Performance of AFPs 19<br>IV. Policy Issues and Lessons of the Chilean Experience 33<br>References 38<br><!-- End of picture text -->
<!-- Start of picture text --> 12%<br>10%<br>8%<br>3<br>S 6%<br>s<br>= 4%<br>2%<br>0%<br>2008 2009 2010 2011 2012 2013 2014<br>mintertemporaly deflated no intertemporal defation<br><!-- End of picture text --> <!-- Start of picture text --> 0.36<br>0.35<br>0.34<br>= 0.33<br>S<br>0.32<br>0.31<br>0.30<br>0.29<br>2008 2009 2010 2011 2012 2013 2014<br>Binter-temporally deflated TM no inter-temporal deflation<br><!-- End of picture text -->
depending on the variety and location of the coca) and requires up to 300 man-days to harvest one hectare – about 2.5 acres – for one year (CIA, 2004).

Although there are over 250 different varieties of the coca plant, only a few are widely used today to produce cocaine for the illegal drug markets.<sup>5</sup> Cocaine production is a relatively simple process that can take place in small local workshops. The process of producing cocaine consists of three main steps: after being harvested and dried, the coca leaves are converted into coca paste, then into cocaine base, and then into the final product, cocaine (cocaine hydrochloride). The manufacturing process requires a few chemicals (precursors) such as sulfuric acid, potassium permanganate, ether, hydrochloric acid, acetone and ethyl ether, plus water, filters, and microwave ovens.

Depending on different factors such as coca variety, geography, bushes per hectare, etc., one hectare planted with coca bushes produces, on average, between 1,000 and 1,200 kg of fresh coca leaf per hectare per harvest. Between 1.1 and 1.4 grams of cocaine can be produced from 1 kg of coca leaf. Using an average of four harvests per year, and the yields described above, we can arrive at a ballpark production estimate between 5 and 6 kg of cocaine per hectare per year.<sup>6</sup> Cocaine hydrochloride, a white crystalline powder,<sup>7</sup> is a highly potent and addictive stimulant.<sup>8</sup> It is either snorted or dissolved in water and injected.

> 5 These are the Huanuco coca (in Bolivia and Peru), the Amazonian coca (in the Amazon River Basin), and Colombian coca (in Colombia, primarily) (https://www.cia.gov/saynotodrugs/cocaine_b.html). 6 These yields numbers were taken from different reports (CIA, 2004 and UNODC, 2005). The number used by UNODC to calculate potential production of cocaine in Colombia was 4.7 kg of cocaine per hectare per year until 2004, which, according to the source, is taken from a study undertaken by the US government under the name of “Operation Breakthrough”. However, recent field research carried out by UNODC in Colombia has found a large increase in this productivity estimate. In fact, for the 2006 report, UNODC uses a productivity estimate of 7.7 kg of cocaine per hectare per year. We will elaborate on this below.

> 7 Commonly used street terms for cocaine are: _blow_ , _coke_ , _snow_ , _nose candy_ , _flake_ , _big C_ , _lady_ , _snowbirds_ , and _wicky stick_ (see <u>www.dea.gov/concern/cocaine_factsheet.html, and www.streetdrugs.org).</u>

4
one (Ravallion 2003, Deaton 2005, Lakner et al. 2022, Prydz et al. 2022). To account for this, we multiply the per capita GDP growth in 2020 by 0.7 for surveys that use a consumption aggregate and 1 for surveys that use an income aggregate, based on the analysis by Mahler et al. (forthcoming).<sup>14</sup> For the roughly 3% of the global population without microdata in PIP, we use the regional average distribution as we did for 2019.

A concern with our counterfactual estimate is that it may be a poor approximation of what would have happened without COVID-19 for several reasons. For one, the growth forecasts may be inaccurate. While we are unable to test this, the growth forecasts are based on all the information available at the beginning of 2020. Second, even if the growth forecasts are accurate, the counterfactual distribution assumes that welfare in household surveys grows in accordance with the growth in real GDP per capita and that inequality remains constant. As shown by Mahler et al. (forthcoming), these two assumptions are on average relatively accurate compared to more sophisticated modeling approaches. Third, other unanticipated events happened in 2020. For example, in countries where environmental disasters hit in 2020, the difference between our 2020 estimates and the counterfactual captures the pandemic as well as the disaster. Given the size of the COVID-19 shock, it is reasonable to assume that most changes between the 2020 growth forecasts and realized growth rates are due to the pandemic.

Our preferred estimates of inequality and poverty are the net impact of the pandemic – that is the difference in 2020 between the counterfactual and the observed distribution (discussed in the next section). However, we also report the change in inequality and poverty from 2019 to 2020, which can be thought of as a gross effect.

# **3.3 Estimating the observed 2020 welfare distribution**

## **(i) Household surveys**

Microdata from household surveys are available for 20 countries in 2020. Twelve of these are in Latin America, a further six in Europe and Central Asia, and two in East Asia. These countries account for 14% of the global population and 78% of the population in Latin America (Table 1). While having a household survey is clearly superior to the other data sources used in 2020, it is important to note that changes in survey design imply that these surveys are subject to greater than usual uncertainty in 2020 (for detail see Castaneda et al. 2022).

> 14 An alternative approach would be to anchor the income or consumption from household surveys to national accounts making use of other sources of data (such as tax data from fiscal authorities). For a discussion of such methods, see Piketty et al. (2018) and Lustig (2020). Note also that Lakner et al. (2022) propose a global pass-through rate of 0.85 independent of the welfare measure used. However, they also find a consumption-specific pass-through rate of 0.72. This paper adopts pass-through rates differentiated by welfare aggregate from Mahler et al. (forthcoming) who have an updated sample compared to the previous study.

10
# **References**

<mark>Ai, C. and Norton, E.C., 2003. Interaction terms in logit and probit models.</mark> _<mark>Economics letters</mark>_ <mark>,</mark> _<mark>80</mark>_ <mark>(1), pp.123129.</mark> <mark>Aissa, S.B. and Goaied, M., 2016. Determinants of Tunisian hotel profitability: The role of managerial efficiency.</mark> _<mark>Tourism management</mark>_ <mark>,</mark> _<mark>52</mark>_ <mark>, pp.478-487.</mark> <mark>Alemayehu, F.K. and Tveteraas, S.L., 2020. Long-run labour flexibility in hospitality: A dynamic common correlated effects approach.</mark> _<mark>Tourism Economics</mark>_ <mark>,</mark> _<mark>26</mark>_ <mark>(4), pp.704-718.</mark> <mark>Arbelo, A., Pérez-Gómez, P. and Arbelo-Pérez, M., 2018. Estimating efficiency and its determinants in the hotel sector using a profit function.</mark> _<mark>Current Issues in Tourism</mark>_ <mark>,</mark> _<mark>21</mark>_ <mark>(8), pp.863876.</mark> <mark>Ashrafi, A., Seow, H.V., Lee, L.S. and Lee, C.G., 2013. The efficiency of the hotel industry in Singapore.</mark> _<mark>Tourism Management</mark>_ <mark>,</mark> _<mark>37</mark>_ <mark>, pp.31-34.</mark> <mark>Assaf, A.G. and Agbola, F.W., 2011. Modelling the performance of Australian hotels: a DEA double bootstrap approach.</mark> _<mark>Tourism economics</mark>_ <mark>,</mark> _<mark>17</mark>_ <mark>(1), pp.73-89.</mark> <mark>Assaf, A.G., Josiassen, A. and Agbola, F.W., 2015. Attracting international hotels: Locational factors that matter most.</mark> _<mark>Tourism Management</mark>_ <mark>,</mark> _<mark>47</mark>_ <mark>, pp.329-340.</mark> <mark>Assaf, A.G., Josiassen, A., Woo, L., Agbola, F.W. and Tsionas, M., 2017. Destination characteristics that drive hotel performance: A state-of-the-art global analysis.</mark> _<mark>Tourism Management</mark>_ <mark>,</mark> _<mark>60</mark>_ <mark>, pp.270-279.</mark> <mark>Baker, L.C., Phibbs, C.S., Guarino, C., Supina, D. and Reynolds, J.L., 2004. Within-year variation in hospital utilization and its implications for hospital costs.</mark> _<mark>Journal of health economics</mark>_ <mark>,</mark> _<mark>23</mark>_ <mark>(1), pp.191-211.</mark> <mark>Banker, R.D., Charnes, A. and Cooper, W.W., 1984. Some models for estimating technical and scale inefficiencies in data envelopment analysis.</mark> _<mark>Management science</mark>_ <mark>,</mark> _<mark>30</mark>_ <mark>(9), pp.1078-1092.</mark> <mark>Barros, C.P., 2005. Measuring efficiency in the hotel sector.</mark> _<mark>Annals of tourism research</mark>_ <mark>,</mark> _<mark>32</mark>_ <mark>(2), pp.456-477.</mark> <mark>Bryson, A., 2007. Temporary agency workers and workplace performance in the private sector. Discussion Paper, London Manpower Human Resources Lab, London School of Economics.</mark> <u><mark>https://doi.org/MHRLDiscussionPaperSeriesNo.3</mark></u> .

<mark>Butters, R.A., 2020. Demand volatility, adjustment costs, and productivity: An examination of capacity utilization in hotels and airlines.</mark> _<mark>American Economic Journal: Microeconomics</mark>_ <mark>,</mark> _<mark>12</mark>_ <mark>(4), pp.1-44.</mark> 29
Managing the quotas and licenses is already proving difficult and will continue to be so. The welfare of many groups involved in supplying bananas will become sensitive to decisions made by the management committee on bananas. This will make the setting of quotas vulnerable to political interference. Political objectives are likely to override commercial ones. Already the market has been made uncompetitive.

The volume of trade by fis which previously marketed Latin American bananas competitively in the European Union has been reduced by around 50 per cent since the introduction of the new policy. This is due to the restrictive 2.0 milion ton quota and to discrininatory nature of how quota and licensing are allocated. The reduced trade of these firmus has also reduced tleir economies of scale and increased their costs.

**Chart 3.3 The new EU policy is even more costly than lhe nHtiond polides I repl=aces**

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15
Finally, we provide some tentative evidence of a positive effect from quality of governance on the quality of infrastructure. In general, these results cast doubts on the real contribution of the government sector to GDP and on the last as a measure of value added. Section 2 presents some stylized facts concerning public investment and institutional quality. Section 3 summarizes the cross-country empirical literature. Section 4 describes our panel dataset, the estimation strategy and the empirical results. Section 5 assesses the policy implications of the findings and provides a brief conclusion.

# **2. Stylized Facts**

Table 1 reports the average levels of public investment over both GDP and total investment for each quartile of the distribution for both a quality of governance index and a measure of government’s checks and balances.<sup>3</sup> Public investment levels are somewhat stable for the first three quartiles of quality of governance, averaging 6.5 percent for public investment as a share of GDP and 33.9 percent for the ratio of public investment to total investment. The fourth quartile displays much lower values for both ratios, 4.3 percent and 18.7 percent respectively, suggesting that countries with the highest quality of governance scores spend less in public investment relative to the other countries.

For checks and balances, the pattern is similar, as the level of public investment over GDP (total investment) steadily decreases from an average of 8.2 (38.1) percent of GDP in the first quartile to 5.3 (26.6) percent of GDP (total investment) in the fourth quartile. The third quartile reports slightly lower values than the fourth one, but this may be also due to the dramatic reduction in the number of observations from the second to the last quartile.

The negative correlation between institutional quality and public investment levels can also be observed in the scatter plots in Figure 1, which also show the probability density functions for both the institutional quality variables. While quality of governance exhibits a Gaussian function, with a thicker right tail (including OECD countries), the figure for checks and balances displays a very different shape, suggesting a much higher number of countries with low scores.

[Table 1 about here]

[Figure 1 about here] Table 2 presents the average volatility of public investment per quartile of quality of governance and checks and balances. Both ratios of public investment show a decreasing trend as we move from the first to the fourth quartile, suggesting a negative relationship between the two variables. On average, countries belonging to the first quartile experience more than twice as much volatility in public investment than the countries belonging to the fourth quartile.

> 3 The observations for quality of governance and checks and balances used for the quartile analysis are the same ones employed in the regression analysis of public investments on quality of governance and checks and balances only, respectively.

3
is generally h2LQN the regression line, not above. They conclude that, once one controls for initial conditions, _Sri_ Lanka's performance is unexceptional. Single cross-country comparisons can sometime- be deceiving, but this particular criticism of the Sen and HPA methodology is hard to accept Ln my view. Under seemingly plausible assumptions about the underlying theoretical model determining social indicator performance, one can show that the Bhalla and Glewwe results (notably that Sri Lanka is below their regression line and that the line is downward sloping) are entirely consistent with the claims of Sen and HPA (Ravallion, 1986). Furthermore, Sri Lanka's excellent social indicators at the start date for the Bhalla and Glewwe study can be presumed to also reflect previous levels of public support (Sen, 1988, 1989b). It would clearly be hazardous to conclude that a low rate of improvement after that date indicated that support-led security had failed. An analysis of the time-series data on various social indicators (infant mortality, death rate, birth rate) for Sri Lanka over the period 1952 to 1981 suggests that public support, particularly through outlays on health, has been instrumental in the observed improvement over time (Anand and Kanbur, 1990).

HPA gives a lot of attention to the interesting comparison of China and India, in terms of their performance in public support over the past 40 years or so. (Also see Sen 1989b.) The relative success of China in enhancing longevity (with, the authors argue, similar rates of economic growth), is contrasted to the greater success of India in avoiding famine since the 1950s. While India has successfully avoided famine through effective public intervention over this period, China experiencpd what was arguably the worst famine in recorded history in 1958-61; the estimates of the excess mortality in that famine vary from 17 to 30 million personsl The freer press and more 26
(4) **_rk_** = yO _+_ **_Pr,_** _+_ ylXjr + eit Vi, Vt This SUR methodology avoids the errors-in-variables bias associated with the Fama and Macbeth (1973) methodology, but does not permit direct tests of the importance of 3 cross-sectionally, nor does it permit the cross-sectional parameters to vary over time. Importantly, the SUR methodology permits estimation using either portfolios of stocks, as in Fama and Macbeth (1973), or individual stock returns. Using individual stocks, this approach has one further advantage; based on the findings of Lo and MacKinlay (1990), grouping stocks into portfolios on the basis of observed characteristics can bias test results, a bias that is absent when individual stocks are used.

We introduce a third estimation methodology based on the panel data technique of "between estimators" described in Mairesse (1993). Using observations on i= 1,...,n stocks for each of t= 1,...T months, if the pooled model under investigation can be written as where yo is the overall intercept, Xi, denotes a vector of independent variables and the error term e satisfies the standard assumptions for residuals, then the between (stocks) estimator for the coefficients is obtained from the ordinary least squares estimator on the (cross-sectional) equation: where 5
financial opportunities. The most successful interventions will create multiple, repeated instances of situations where men and women, Roma and non-Roma, participate (more) equally and are acknowledged as equally competent at socially valued tasks.<sup>37</sup> Second, public and promotional campaigns can highlight successful women entrepreneurs and students, to help encourage girls and women to pursue education and business activities and to bring men on board regarding the benefits of women’s increased agency and employment opportunities. Roma women (particularly youth), unlike their non-Roma counterparts, believe that there are only limited role models in their community to aspire to, which makes it difficult for them to visualize career pathways. Showcasing a diverse range of successful Roma students and businesswomen at the neighborhood, village, or municipality level would go a long way in helping Roma girls (Robayo-Abril and Millan, 2019). The last experiment assigns females’ preference parameters values to males as a result of an application of the mentioned policies above.

**Figure 7:  observed and simulated labor market status proportions**

<!-- Start of picture text --> Males without children Males with children<br>100% 100%<br>90% 90%<br>80% 80%<br>70% 70%<br>60% 60%<br>50% FT 50% FT<br>40% PT 40% PT<br>30% UE 30% UE<br>20% NP 20% NP<br>10% 10%<br>0% 0%<br>Females without children Females with children<br>100% 100%<br>90% 90% 0<br>80% 80% 0<br>70% 70%<br>60% 60%<br>50% FT 50% FT<br>40% PT 40% PT<br>30% UE 30% UE<br>20% NP 20% NP<br>10% 10%<br>0% 0%<br><!-- End of picture text --> _Source:_ Author’s calculations based on 2017 UNDP-WB-EC Roma Regional Survey.

_Notes:_ Abbreviations: FT= employed full-time; PT= employed part-time; U= unemployed; NP= non-participant. Each bar represents the proportion of husbands and wives on each labor market state.

37Even though we are simulating the policy experiments as equating women’s parameters to men’s, policies aiming to promote cultural changes can also change men’s preferences and should target men. Hara and Rodriguez-Planas (2020) evaluated the effects of a Japanese reform in the 1990s that ended gender segregation in junior-high school. Their evaluation is on married men’s and women’ time spent in home production. They found that men and women of the cohorts that took classes under the reform share more equal home production: men augmented home production time while women diminished it.

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Benincasa et al. (2024) find that extreme weather events erode balance sheets as firms that experience monetary losses from shocks become more leveraged and are seen as less credit worthy. The study also notes that firms affected by weather shocks are more likely to invest in fixed assets as they replenish damaged capital (Benincasa et al., 2024). New investments can also lead to vintage effects where replenishment of capital means newer equipment with a lower environmental footprint. On the other hand, firms may become environmentally aware and therefore increase green investments or improve green management practices. A closely related study by Benincasa et al. (2024) covers European and MENA economies but relies on a self-reported measure of monetary damages from extreme weather events in contrast to the geocoded data on precipitation shocks used in this study.

The study also tests whether some of the channels highlighted in the global literature also apply to the MENA region. Overall, we see that negative precipitation shocks decrease labor productivity. The channel could be through a change in the composition of workers -- negative precipitation shocks are found to increase the share of temporary workers, potentially explaining the drop in labor productivity. Negative precipitation shocks also increase losses from power outages with some indication of increases in water outages although the finding is not as robust. Negative precipitation shocks limit access to finance by decreasing the likelihood that firms use banks to finance working capital and have access to overdraft facilities. A new channel uncovered in this study is governance. The intensity of negative precipitation shocks increases the amount of time senior management spends dealing with regulations and expectations of bribe solicitations. For the cross-section of 2019/2020 MENA surveys, there is a green module included in the survey instrument that captures green investments and green management practices. This study finds negative precipitation shocks increase the likelihood of adopting water management, more climate friendly energy on site, and machinery and equipment upgrades, suggesting scope for adaptation.

The study makes several policy recommendations. Firms in MENA economies are vulnerable to climate shocks. Building infrastructure will be crucial for the private sector in MENA given the increasing 4