rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

The 2009-2010 economic crisis hit households in multiple ways, as workers lost their jobs, wage earnings were reduced, and remittances fell. The extent and severity of the impact on individual and household welfare has varied, depending on the nature of the shocks experienced, the policy response and the coping mechanisms available to households. Because such effects go beyond changes in consumption or incomes, subjective data can potentially yield important insights on both the impact of the crisis and its consequences. For example, the trauma associated with jobloss can go beyond the associated drop in household income, there may be increased insecurity about the future, and so on. The subjective impact of the crisis may therefore depend on the coping strategies adopted, the intrinsic value of employment, or the extent to which households value leisure, uncertainty or different types of expenditures.

Although subjective measures can capture impacts that are not fully reflected in objective (i.e. consumption- or income-based) measures, they also have important shortcomings, notably with regards to the comparability of responses across individuals and countries. There could be, for example, systematic reporting biases if different groups have systematically different perceptions of and attitudes to objective shocks (Bertrand and Mullainathan, 2001). This paper thus has two main objectives: (i) characterize variations in subjective perceptions of crisis impact across countries and, within countries, across individuals; and (ii) examine the extent to which subjective measures of impact are comparable, and whether they can be explained by the objective shocks experienced or the coping strategies available. The paper also analyzes the relevance of subjective measures of crisis impact, in particular their relation to other measures of well-being and socio-political outcomes, such as relative income status and perceptions of social mobility, satisfaction with life and perceptions of government performance. We use the second Life in Transition Survey (LiTS II), which allows such an exploration of the effects of the crisis, based not only on outcomes but also subjective perceptions, beliefs and choices, for 29 transition countries and five comparator countries in western Europe (France, Germany, Italy, Sweden and the United Kingdom).

While the poor report being affected by the crisis more (subjectively), household budget surveys suggest that the crisis had a marginal impact on poverty incidence and affected mostly the 2
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(monetary poverty, education & sanitation deprivations) and by 18 percentage points (monetary poverty, information & sanitation deprivations). However, the improvement in urban areas was only 5 percentage points for both cases. The increase was about the same in both areas when health quality is combined with monetary poverty and sanitation

# **_5.2 People’s perceptions about well-being_**

What are the perceptions of the households about the changes in wellbeing? Households were asked to provide their opinion on the situation of their living standards now compared to the situation 12 months ago. Figure 8 presents trends in (unfavorable) perception about wellbeing in urban and rural areas from 2000-11. In general, the perceptions of households about improvement in wellbeing do not parallel the reductions in overlapping deprivations. The responses more or less remained the same over the 2000 -2005 period. However, in both rural and urban areas, the negative perception about well-being compared to a year before the survey increased substantially in 2011. In 2011, about half of the households say that situation is worse now (at the time of the survey in 2011) than it was a year ago.

**Figure 8. Trends in unfavorable perception about well-being**

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Peru, the Philippines, Poland, the Republic of Korea (or South Korea), the República Bolivariana de Venezuela, the Russian Federation, South Africa, Thailand, and Turkey.

We focus mainly on positive equity inflows, which represent more than 84% of all inflow observations. Our focus on positive inflows reflects our goal to analyze whether firms issue more equity after foreign capital arrives to their country, and how those equity proceeds are employed. Negative capital inflows, on the other hand, represent a departure of foreign capital. Although it is conceivable that firms might repurchase equity when foreign capital departs their country (a negative issuance), existing empirical evidence finds no connection between outflows and investment behavior by publicly traded firms, which suggests that negative issuance is not a common response to outflows.<sup>10</sup> Our issuance data (explained below) do not provide information on stock repurchases, so we focus on the positive issuance implications of positive capital inflows. However, for robustness, we analyze the effects of negative inflows on equity issuance. Our findings confirm the view that negative equity inflows have no significant effects on equity issuance.<sup>11</sup> The data on equity issuance activity come from the Thomson Reuters Security Data Corporation Platinum database (SDC Platinum). This database provides transaction-level information on new issuances of common equity by publicly traded firms. The transactions in the database include 20,306 SEOs. The database also includes transactions related to initial public offerings (IPOs), which we do not use except when explicitly indicated. Because the database covers the universe of issuance transactions, we assume zero issuance activity for the

> 10 Tong and Wei (2010) and Claessens et al. (2012) investigate stock price reactions and real investment changes associated with the large capital outflows produced by the global financial crisis. They find a significant negative effect on stock prices, but no effect on investment. That finding is consistent with firms not responding to capital outflows and lower stock prices with significant repurchases of their shares.

> 11 Specifically, we find that negative inflows tend to produce an asymmetric response in equity issuance. Because negative inflows reduce issuances only slightly, even large negative inflows are still associated with large positive issuances.

10
6

**Table 3. Non-Participation Rate by Gender, Living Area, and Age Group (in %)**

|||_Livin_<br>|_g Area_<br>||_Age G_<br>|_roup_<br>||
|---|---|---|---|---|---|---|---|
|||_Rural_|_Urban_|_15-24_|_25-34_|_35-54_|_55-64_|
|_All_|50.7|50.2|51.4|67.3|47.5|37|44|
|_Net of individuals attending school_|45.1|46.6|42.9|55.1|46.4|37|44|
|_By gender:_||||||||
|_Male_|26.6|23.1|31.2|55|14.4|5.5|21.4|
|_Male, net of those attending school_|16.9|16.1|18.1|35.5|11.8|5.5|21.4|
|_Female_|72|73.5|69.8|78.3|69.6|66.6|73.5|
|_Female, net of those attending school_|69.1|71.9|65|71.3|69.1|66.6|73.5|

Source: EPCV 2004. Figures are displayed in percentage.

_Non-participation is also high among younger groups and decreases gradually with age before going up again for people aged 55 or more._ Over two-thirds of the population aged 15 to 24 is out of the labor force compared to less than 40 percent of those aged 35 to 54. Part of the youth is in school. However, over half of the young people aged 15 to 24 and not attending school remains out of the labor force (55.1 percent). Strikingly, the 25 to 34 age group displays a substantially higher nonparticipation rate than the 35 to 54. This gap appears to be starker for men than women, which is unsurprising given that the latter’s overall participation in the labor force is low. These figures suggest a late integration in the labor market for males in particular.

**Table 4. Reasons for Not Participating in the Labor Force by Gender and Living Area (in %)**

||_All_|_M_<br>|_ale_<br>|_Fe_<br>|_male_<br>|
|---|---|---|---|---|---|
|||_Rural_|_Urban _|_Rural_|_Urban _|
|_No work_|15.5|26.1|27.7|10.1|14.4|
|_Slack season_|1|3.7|2.6|0.3|0.5|
|_Student_|20.6|36.5|51.5|8|20|
|_Family/household obligations_|50.3|11.4|2.8|70.2|56|
|_Too young/old_|9.9|16.2|11|9.3|7.7|
|_Disabled_|2.2|5.4|3.9|1.5|1.2|
|_Other_|0.5|0.8|0.6|0.5|0.3|
|_Total_|100|100|100|100|100|

Source: EPCV 2004. Figures are displayed in percentage. Total might not always exactly amount to 100 due to rounding.

_Rural and urban non-participation vary across gender groups_ . Although differences between rural and urban areas are small when considering the whole sample (Table 3), men living in urban centers are more likely to opt out of the labor force than those in rural regions, whereas the opposite is true for women. For men, this observed discrepancy seems mostly due to the high proportion of students among non-participants in the labor market in urban centers. The gap between rural and urban areas narrows when considering non-participation rates net of those attending school (respectively 16.1 and 18.1 percent, see Table 4). While roughly similar proportions of men residing in rural and urban areas
the standardized average quality. At all quantiles, 10<sup>_th_</sup> , 50<sup>_th_</sup> and 90<sup>_th_</sup> we broadly find a downward slope with average score, implying that the covariates “explain” progressively less as average managerial quality improves.<sup>16</sup> Also consistent with this is the fact that as we progress from the bottom decile to the top, explanatory power falls. That is, the best managed firms in any country show the same pattern of weaker explanatory power that we find at the national level. As examples, Australia, France, Portugal, Sweden and German, New Zealand, Great Britain, Republic of Ireland show decreasing explanatory power moving up firm quality from the 10<sup>_th_</sup> , to 90<sup>_th_</sup> quantiles of firm quality. Argentina, Mexico Chile, India, Italy, Greece, Northern Ireland all show their greatest explanatory power at the 10<sup>_th_</sup> .

Figure 6 also confirms that the ratio of explanatory power at the top to the bottom deciles decreases with average aggregate level of management quality.<sup>17</sup> The observed pattern suggests that not only are we less and less able to explain management quality as the mean level rises, but that explanatory power falls more rapidly for the best firms than for the worst.

# **7 Conclusion**

By collecting standardized management data from around the world, Bloom and Van Reenen (2007) have given the study of management quality an important push forward. They document key empirical regularities at the mean and argue that, in particular, competition and ownership structure are key to explaining the average differences across their four top countries.

This paper builds on their work in two ways as it explores the process to which countries converge to the frontier in managerial quality. First, it exploits the entire sample of firms and middle income and poor countries to study the larger process of convergence across the

> 16 This contradicts the results from Table 9 which suggested that, for _z <_ 0 countries, explanatory power increased as firm quality increased. But this appears to be an artifact of the sample division that disappears once the entire sample is considered.

> 17Poland and Mexico, the only countries beginning negative at the 10 _th_ , should be ignored since that makes the ratio uninformatively positive. A negative RIF explained implies that, given the characteristics of a particular country’s firms, their average score should actually be better. For example, Mexico, at every quantile has enough college graduates, exports and those who have studied abroad would predict a lower gap with the US than is actually seen.

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 Tariff rate quotas (selected bovine meats).

**Table 2-5: Development of total trade of the EU-27 with Chile (2000-2007, in million €)**

||Imports by the<br>EU|<sup>Exports to Chile</sup>|Total trade|Annual change|Balance|
|---|---|---|---|---|---|
|2000|5215|3498|8713||-1718|
|2001|5197|3731|8928|2.5%|-1466|
|2002|4911|3172|8083|-9.5%|-1739|
|2003|5001|2962|7964|-1.5%|-2039|
|2004|7354|3120|10474|31.5%|-4233|
|2005|8157|3919|12075|15.3%|-4238|
|2006|12489|4281|16770|38.9%|-8207|
|2007|12576|4684|17260|2.9%|-7891|

_Source: Comext Database_

**Table 2-6: Development of trade in food and live animals (SITC 1) of the EU-27 with Chile (2000-2007, in million €)**

||Imports by the<br>EU|<sup>Exports to Chile</sup>|Total trade|Annual change|Balance|
|---|---|---|---|---|---|
|2000|736|70|806||-666|
|2001|938|66|1004|24.6%|-872|
|2002|964|67|1031|2.6%|-897|
|2003|1038|66|1104|7.1%|-972|
|2004|1157|49|1206|9.2%|-1108|
|2005|1374|56|1430|18.6%|-1318|
|2006|1605|65|1670|16.7%|-1540|
|2007|1645|84|1729|3.5%|-1561|

_Source: Comext Database_ Since the FTA between the EU and Chile came into force in 2003, the total volume of trade between the partners has more than doubled (Table 2-5). EU imports from Chile have grown by a factor of 2.5 (compared with 2002), while EU exports to Chile increased by about one third. Hence, Chile seems to have gained more than the EU both in relative and absolute terms. The EU-27 is now Chile's most important trading partner, just ahead of the US and China.

Trade in agricultural products was subject to less liberalization than in any other category. As a result, growth in agricultural trade was substantially lower than for trade overall (Table 2-6). Nevertheless, total trade in food and live animals between Chile and the EU grew on average by 11.9% per annum between 2000 and 2007.

17
For the descriptive statistics, we also focus on the most common type of schools, the 10361 „ordinary‟ schools<sup>24</sup> , to avoid mixing the effect of size and school type.

We further restrict our sample to those ordinary schools for which we have a complete set of „credible‟ information, that is, they have scores on the Ukrainian exam, do have teachers and students, have a student-teacher ratio less than 50 and a class size less than 35<sup>25</sup> .

To illustrate the effect of school size and class size we divide the samples of rural and urban schools, into four intervals, each interval having approximately about a quarter of the observations. School size is measured by the total number of students at the school, class size is measured by the number of students in the final grade divided by the number of final grade classes (for a vast majority of schools this is the 11<sup>th</sup> grade, for some it is the 12<sup>th</sup> grade).

We use several indicators of school performance

- the mean score

- the median score

- the percentage of students scoring above 173

- the percentage of students scoring above 150

- the percentage of students score below 135.5

- we also compute a „student participation ratio‟, that is, for those schools which have students that take the exam on a specific subject, we compute the ratio of students who take the exam divided by the total number of students in the highest grade<sup>26</sup> .

We also provide summary statistics for the explanatory variables we will use. We have two more measures of school inputs.

- Student-teacher ratio – the total number of students in the school divided by the total number of teachers in the school (these numbers are based on headcounts, not full time equivalents).

> 24 We do exclude „ordinary‟ evening schools. For the regression analysis below, however, we do include all types of schools, controlling for differences through type-specific dummies. 25  We lose about 5 % of the schools by doing this, mainly rural schools.

> 26 We have this indicator for those schools that we were able to match to the MoE database.  For some schools, we clearly have unrealistic participation ratios. We keep them in the sample here but will exclude these schools in further analysis – see below for details.

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# **Table 4**

# **Testing for differences in firm financing features across bank ownership types**

This table reports values for small, medium-sized and large enterprises and t-tests for differences in firm financing features across foreign, private, and government-owned banks. SE stands for small enterprises. ME stands for medium-sized enterprises. LE refers to large enterprises. *,**,*** denote significance at 10, 5, and 1 percent, respectively.

|Variable|Firm Type|Mean for<br>Private Banks<br>(Domestic)|Mean for<br>Foreign Banks|<br>Mean for<br>Government<br>Banks<br>(Domestic)|t-test<br>Private vs.<br>Foreign|t-test<br>Foreign vs.<br>Government|t-test<br>Private vs.<br>Government|
|---|---|---|---|---|---|---|---|
|Share of total loans (%)|SEs|8.37|11.32|13.12|-0.79|-0.29|-0.98|
||MEs|8.06|14.48|17.18|-1.96*|-0.39|-1.34|
||LEs|28.42|31.53|41.23|-0.42|-1.34|-1.53|
|Share of applications approved (%)|SEs|73.45|77.36|82.50|-0.49|-0.66|-1.11|
||MEs|81.50|72.85|89.89|1.11|-2.25**|-1.44|
||LEs|78.96|85.75|60.45|-0.88|0.82|0.59|
|Share of loans for investment (%)|SEs|40.43|44.88|56.36|-0.57|-1.17|-1.56|
||MEs|46.15|49.52|45.07|-0.47|0.56|0.13|
||LEs|50.35|51.72|53.50|-0.16|-0.23|-0.39|
|Share of secured loans (%)|SEs|69.54|85.91|77.84|-2.23**|0.88|-0.90|
||MEs|69.60|85.07|87.96|-2.20**|-0.56|-2.52**|
||LEs|62.10|72.95|85.07|-1.21|-1.42|-2.51**|
|Loan fees (% of size of loan)|SEs|1.31|1.13|0.71|0.55|2.20**|1.76*|
||MEs|1.07|0.94|0.81|0.42|0.64|0.76|
||LEs|0.83|0.69|0.78|0.45|-0.32|0.13|
|Interest rates on best clients (%)|SEs|11.81|10.96|10.31|0.47|0.29|0.81|
||MEs|10.06|9.83|10.64|0.13|-0.61|-0.32|
||LEs|10.01|8.86|10.05|0.61|-0.73|-0.02|
|Interest rates on worst clients (%)|SEs|17.12|15.48|13.28|0.74|1.05|1.90*|
||MEs|15.15|13.64|13.38|0.71|0.06|0.90|
||LEs|15.22|11.82|13.19|1.53|-0.65|0.97|
|Share of non-performing loans|SEs|7.47|7.08|7.76|0.13|0.02|-0.08|
|(% of total loans)|MEs|5.99|6.16|4.39|-0.09|1.12|0.88|
||LEs|4.83|3.72|1.45|0.52|1.98*|1.65|

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plague the Afghan economy and there are measurement errors in this baseline calibration. To overcome this, we do a second calibration exercise in which we calibrate the model to the US economy which is likely to have much fewer distortions than most economies and serves as our proxy of the frictionless economy. We present the results of this alternative exercise in the Appendix (Section 2), and the quantitative findings are very similar to what we observe in the benchmark exercise in which we calibrate the model to the Afghan economy. In the two calibration exercises, we assume that the countries differ with respect to the level of credit market efficiency �, aggregate taxes �� and the distribution of managerial ability ��. �. All other parameters are considered _global_ and apply to both calibration strategies outlined above. For both the calibrations, we follow the standard procedures used in earlier research which we discuss now.

# **5.1.1 Calibrating the Model to the Afghan Economy**

We first describe the calibration of the _global_ parameters that apply to all economies and hence remain invariant across the two strategies. The model contains six _global_ parameters – rate of interest �, depreciation rate �, rental rate of capital �, discount rate � and production function parameters � and �.

## **Calibrating Global Parameters**

We begin by assuming that the work life of an agent comprises of 40 years with each period in our model mapping to one-half of an agent's working life, i.e., 20 years. We fix the yearly rate of interest at 4% which means that the model rate of interest ��1.04<sup>��</sup> �1. The annual rate of depreciation is set at 8% which requires calibrated � to equal 1 ��0.92�<sup>��</sup> . The no-profit condition for the financial intermediary requires that the rental rate of capital � equals the sum of � and �.

To pin down the parameters of the production function: � and �, we follow Atkenson _et al._ (1996), Atkenson & Kehoe (2001) and many others and assign ����0.85, and then set � to be one-third of 0.85 to capture the capital share of income observed in the US. The period discount rate � is set to match the ratio of excess savings over investment to investment as observed in the US. Amaral & Quintin (2010) report this ratio to be 40% which in our calibration requires a discount rate of 0.46.

## **Calibrating Country-Specific Parameters**

To calibrate the taxes applicable in Afghanistan, we use the findings from the Enterprise Surveys. As documented in earlier sections, corruption, and crime-related losses are among the biggest concerns that Afghan firms face. Our discussion in the previous sections showed how the more productive firms in Afghanistan are more likely to be affected by both crime and corruption. The heterogeneity of the model 21
has a good representation across regions, and that developing countries are adequately represented.<sup>8</sup> Regarding the variable used to capture a country’s outward-orientation, we use lagged tariffs i.e. _TARi_ , _t_ − 5<sup>,</sup> (computed as the ratio of tariff revenues to imports) as a measure of trade openness. This is a more direct measure of openness than those often used previously (i.e. a trade output ratio, a ‘trade adjusted ratio’ obtained as a residual from an estimated relation of openness, or the Sachs-Warner index). As a consequence, our sample does not include the 1960-80 period covered in some of the earlier studies. Since most trade liberalization in developing countries started in the early 1980s, this may not be too damaging (Table 2).

Table 2a gives regional averages for the two main variables of interest, the inequality measure and our measure of openness, tariffs computed from customs data (see the annex for data sources and data manipulations). Table 2a shows little variation in the average measure of inequality within regions and persistent differences across regions while the measure of protection indicates (on average) a downward trend in all regions except Africa. Since much of the trade reforms in the 1980s often consisted of replacing NTBs by tariffs, what appears as an increase in protection could in fact represent either a reduction, or no increase in protection. In selecting tariffs as our measure of openness, we take refuge in the often-made observation that, on average, the average tariff level is an adequate approximation of the restrictiveness of a country’s trade regime, and arguably less

> 8 Only countries with economy-wide inequality measures (‘high-quality’ indices according to D-S) are retained in the sample. As a reference for comparison among the studies that concentrate on openness and inequality, the often-cited study by Spilimbergo et al. had 17 developed and 17 developing countries in their sample.

9