rafmacalaba/datause-encoder-data huggingface dataset:
before 1990. This indicates that roughly 3 out of every 4 episodes in our sample took place in the 1980s, the years of 1986 and 1987 witnessing the largest number of episode starts. Table 1 presents the summary statistics for the 24 inflation episodes. Over the full sample, the average length of inflation episodes is roughly 15 quarters and 21 of them last for more than 8 quarters. Average rise in inflation, from trough to peak, is about 26 percent. In Table 1, we decompose our sample into three periods: 1984-1989, 1990-1995, and 1996-2001. The first period captures the period during which access to global capital markets was relatively limited. For the 12 episodes ending this period, the average length and the rise in inflation are about 13 quarters and around 44 percent, respectively.

<u>Table1: Summary Statistics for Inflation Episodes</u>

|||E<br>|pisodes Ending<br>||
|---|---|---|---|---|
||Full Sample|1984-1989|1990-1995|1996-2001|
|Number of episodes|24|12|8|4|
|Length (quarters)|14.83|13.33|18.13|12.75|
|Initial inflation rate|9.82|10.99|9.62|6.71|
|Ending inflation rate|35.85|54.61|19.87|11.56|
|Risein inflation|26.03|43.62|10.25|4.85|

Note: Figures are average values over the respective periods. Inflation figures come from our series obtained through nine-quarter centered moving average process.

There are 8 episodes between 1990 and 1995 with an average length of 18 quarters and an average rise in inflation of roughly 10 percent. It is interesting to note that during this period, which is characterized by increased access to capital markets, the average length of episodes are longer but the rise in inflation is substantially lower when compared to the 1984-1989 period. For the 1996-2001 period, we have only four episodes. Average length of episodes is around 13 quarters, similar to that of the 19841989 period. Average rise in inflation, on the other hand, is noticeably lower, around 5 percent, compared to other periods. In the next section, we will present the empirical framework and estimation results for 24 inflation starts in 15 EMEs between 1980 and 2001.

10
# **[Insert Table 1 about here]**

# **4.1** **_Demographic Information_**

Table 1 provides summary statistics of the pooled sample (2008 and 2009). The percentage of male respondents is 43.9%, consistent with national census averages (Russian National Census, 2002). The average age in the pooled sample is around 45. Most individuals (66%) live in households with three or more individuals, and 28.2% of individuals live in urban regions, defined as settlements with a population greater than 500,000.

With respect to employment, 52.5% are employees (both skilled and unskilled), while 25.5% are retirees. The education level of individuals in our sample is relatively high: only 8.4% of the sample has less than a secondary education, 29.9% completed secondary school, and 61.8% completed a special vocational/ technical school or initiated or completed their higher education, a characteristic that sets Russia apart from other emerging markets and makes it a particularly useful country to study.

The survey asks individuals to report their personal and household monthly income, but these values are missing for almost 30% of the sample, i.e., individuals who refused to answer.<sup>6</sup> For our main regressions in the next section we impute missing income observations and include dummies for brackets of family income rather than using income values.<sup>7</sup>

> 6 In our sample, mean personal monthly income for 2008 is US$ 762 (US$ 2,345 median income), while average family monthly income is US$ 1,494 (US$ 12,500 median). This compares closely with official statistics for 2008 of average per capita monthly income of US$ 1,404. Source: Russian Federation Federal State Statistics Service: http://www.gks.ru/bgd/regl/b10_06/IssWWW.exe/Stg/1/17-01.htm

> 7 The imputation methodology is based on regressions of family income on federal regions, gender, age, education categories, and self-assessed economic classification groups. The corresponding figures for each of the quartiles of the imputed income distribution are the following: Bottom quartile (1<sup>st</sup> ): monthly income

11
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more than 250 employees in Amadeus versus that in national statistics) and Columns 3 and 4 show the coverage of small firms (the ratio of firms and employment at firms with 10-50 employees in Amadeus versus that in national statistics). Column 5 shows the absolute value of the difference between the ratio of employment in small firms to the ratio of employment in large firms in Amadeus less the ratio of employment at small and large firms in national statistics. This ratio is used to test whether our Amadeus sample is biased towards larger firms.<sup>20</sup> We exclude a country from our dataset if two conditions are met: (1) if the ratio of employment in firms with more than 250 employees in Amadeus to that in national statistics (Column 2) is less than 50%, and (2) if the absolute difference between the ratios in Amadeus and national statistics of employment in firms with 10-50 employees to employment in firms with greater than 250 employees (Column 5) is more than 25%. Four countries do not meet the criteria: Iceland, Ireland, Luxembourg and Portugal. Since these cutoffs may be considered somewhat arbitrary, we also test if the qualitative results hold if we do not apply these criteria.

We believe that our inclusion criteria create the most comparable sample of firms across countries, but we should be cautious about deriving strong conclusions from direct cross-country comparisons. However, even if we have not eliminated all biases between countries, our basic test examines within-country differences across countries, and will not be affected unless there are systematic biases in reporting industries within a country. Our final sample includes 3,371,073 firms in 21 countries: Austria, Belgium, Bulgaria, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Italy, Latvia, Lithuania, the Netherlands, Norway, Poland, Romania, Spain, Sweden, and the United Kingdom.

# _1.3  Industry-level entry variables_

We measure entry in a variety of different ways, both for all new firms and by size groups. We define a new firm as a firm that has age 1 or 2 and an old firm as a firm that has age

> 20 The discrepancy between Amadeus and national figures may also be explained by: (1) that for a significant number of firms in Amadeus, we do not have data on employment and (2) that for the purpose of cross-country comparisons, our Amadeus dataset excludes only proprietorships and partnerships.

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# _Empirical Application of the Specialization and Diversity Indices_

The next section provides descriptive statistics of these metrics for Indian manufacturing and services, and we highlight first three important issues for our empirical application. First, it is important to note that the indices are related to each other but also not redundant. The two indices measure different things. The specialization index identifies the extreme value for a district across all industries, while the diversity index considers the district’s composition as a whole. It is quite feasible for a district to have specialization and diversity values that are both above average. In the 2000 Indian data, about 20% of observations have above-median values on both indices, while 20% of observations have below-median values on both indices. The metrics have on average a correlation of about -0.14 across the surveys. This correlation is statistically significant at a 10% level, such that higher values of the specialization index are associated with lower district-level diversity values. The correlation, however, is sufficiently low that we can model the two indices together.

Second, the metric designs (especially specialization) can yield extreme values. This is even more prominent in this study compared to our prior work because we have incorporated more industries, and the specialization metric takes the maximum of abnormal concentration. Likewise, the sampled nature of our Indian data may contribute to outliers among small districts. Thus, we winsorize our specialization and diversity metrics at their 2% and 98% values. This winsorization is done separately for each year. We use these winsorized values for descriptive statistics in the next section, except where noted otherwise, to focus on the more meaningful variations and trends in the data. As in our prior work, when we move to growth estimations, we primarily report results that use a ten-point scale for the decile in which a district’s specialization or diversity falls compared to the whole set of Indian districts. For example, a district receives a value of three if its specialization level is between the 30th and 39th percentile for India. This approach makes the scales and variances of our indices more comparable and aids in interpretation, although we derive quite similar results with other approaches.

Finally, it is important to note that the indices do not directly relate to or build upon other properties of districts (e.g., size, income per capita, etc.). Both measures are calculated over industry distributions within districts and thus do not build upon these features specifically. This 10
related, comprising term Life and simple savings products. To overcome restrictions on branch sales, banks are marketing insurance to noncustomers through their credit card divisions. Non-Life sales are focused on A&H insurance.

_Consumer protection:_ There is no specific Bancassurance consumer protection law. The insurance supervisor accepts complaints and can mediate. A Life insurance policyholder protection fund exists.

# **South Asia**

## **India**

_Regulation:_ No limitations are in place on the Bancassurance model that may be employed. Banks are classified as corporate agents if they sell insurance. However, if a bank wishes to set up an insurance subsidiary, it must have a net worth of at least Re 5 billion (approximately $110 million) and a three-year track record of positive profitability. Ineligible banks may invest up to 10 percent of their net worth or Re 500 million (whichever is the lesser) in an insurer. Bancassurers are required to have a senior manager responsible for insurance activities, and staff who sell insurance are required to meet the individual agent-training requirements specified by the supervisor. Banks may not operate as brokers.

Insurance agents may operate out of a bank branch, but banks may have only an agency with one Life insurer and one non-Life insurer.<sup>5</sup> This rule is under review, with the likely outcome being that a zonal system (essentially differentiating between rural<sup>6</sup> and urban customers; see box 2.1) will be established.

## **Box 2.1 Draft Rules Regarding Bank Insurer Agency Agreements in India**

<mark>―The IRDA's exposure guidelines on Bancassurance have said that banks cannot tie up with more than one Life, one non-Life and one stand-alone health insurance</mark> company in any of the States.

<mark>―Further, provided that in case the agreement of general insurer/s does not have any health product to distribute, the Bancassurance agent may tie up with one more</mark> general insurance company carrying on exclusive business of health insurance.

<mark>―The regulator has also said that insurers other than the specialised insurer cannot tie up with any Bancassurance agent in more than nine States or Union Territories in</mark> Zone A and six States/Union Territories in Zone B.‖ _<mark>Source: Business Line,</mark>_ <mark>November 23, 2011.</mark> 40
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group schools. This is a large increase, given the small school sizes common in Sindh, equivalent to additional enrollment of 21 percent. Equivalence testing confirms that the SMS effect on total boys’ enrollment is significantly larger than that of MEET. No significant impact was observed from either intervention on girls’ enrollment.

**<u>Table 8B</u>** presents enrollment impacts by grade, specifically for male students. No significant impact is observed from either MEET or SMS on _katchi_ or Grade 1 enrollment, suggesting a lack of effect of the interventions on the likelihood of new school-age boys being admitted to school. However, significant impacts are observed from SMS on boys’ enrollment in Grades 2, 3 and 4, suggesting improvements in retention of male students throughout the school cycle because of the intervention. SMS schools had an average 2.2 additional students per grade in Grades 2-4 than control schools, equivalent to an increase in enrollment in these grades of 29 percent. The SMS impacts were significantly larger than those of MEET in Grades 2 and 3.

**<u>Table 8C</u>** presents impacts on girls’ enrollment. No significant impacts are observed in any grade.

# **4.1.3 Availability of teachers**

Turning to registered teachers, comparison of means **(Table 5)** suggests significant impacts from MEET and SMS on the proportion of schools with only one teacher: one-third of MEET schools (32 percent) and SMS schools (35 percent) had only one teacher at endline, versus more than half (52 percent) of control schools. Transition probability analysis also suggests significant impacts from both MEET and SMS on the total number of teachers available at schools: MEET schools were almost twice as likely as control schools to gain at least one additional teacher; 29.7 percent 38
Muslims as different variants of Islam are practiced across Nigeria (predominantly Sunni with a Shia minority).

The 2008 and 2013 DHSs also fielded questions of particular relevance to this study. The surveys also asked ever-widowed women (i.e., current and remarried widows) about who received most of their deceased husband’s property. Possible responses include: the respondent, another wife, the dead man’s children, his family, other, and husband had no property. The widow is also asked whether she was dispossessed of property, defined as not having received any of her late husband’s assets or valuables.

The 2013 round additionally asks current widows whether they experienced ill-treatment by in-laws. As discussed in section 2, abusive rituals and other forms of mistreatment inflicted by the late husband’s family are thought to be a common human rights issue in Nigeria. The questionnaire asks about violence perpetrated by the husband’s relatives, including whether the respondent was: blamed for the death, compelled to undergo cultural practices to prove her innocence, physically or verbally abused, and/or maltreated. Whether her children were illtreated is also queried. Unfortunately, the question was not asked of remarried widows, which makes it impossible to assess whether remarriage is associated with such violence, and results in a small sample size, particularly for Muslim women.

We impose a few sample restrictions. As the focus is with Muslim and Christian women, those who subscribe to a traditional religion (1.1 percent of the sample) are dropped. Observations with missing information for key variables, including a woman’s BMI, marital status, religion, ethnicity, education, whether she is the head of household, and age and education of the head are also omitted (5.8 percent of the surveyed sample).<sup>18</sup> This results in a sample of 66,320 women, of which 31,590 are Muslim and 34,730 are Christian. Muslims make up 55 percent of the rural and 39 percent of the urban sample. A total of 1782 villages are included in the pooled dataset. There is religious variation in 711 villages (around 40 percent).

**_Summary statistics_** : Table 1 reports key summary statistics for our sample of women aged 15 to 49, by religion and urban and rural location.<sup>19</sup> A number of between-group differences in mean attributes stand out. Muslims complete considerably fewer years of education—about 6 years less in rural and 4 less in urban areas. Gaps of similar size are apparent

> 18 We consider women who are usual resident members of the household and drop those who report being both the head’s spouse and not being married (57 women).

> 19 All graphs and tables with summary statistics use survey weights provided by the DHS.

13
<mark>transform and manipulate data structure to maximize data display for vegetation cover detection (Tammy, McGee, and Campbell, 2019). Vegetation cover here refers to all green spaces, including forests and agricultural plots within the study area. The NDVI is widely used to monitor vegetation change dynamics from local, regional to global scales and has been shown to be a useful measure of vegetative land cover change (</mark> Hassan et al., 2018 <mark>). The NDVI is calculated as:</mark> <mark>where NIR is near-infrared light and Red is the visible red light of the satellite’s electromagnetic spectrum. Bearing in mind the sensitivity of vegetation health to the broader climatic and local weather conditions in Cox’s Bazar area, H</mark> assan et al. (2018) generated <mark>NDVI data using images captured only in the dry season months, including February. We also added the NDVI analysis to show vegetation health and green cover changes in the study area. The NDVI can detect vegetation cover change between mass pre-refugee arrivals in 2017 relative to post-arrival for areas within and without of current refugee camp boundaries. The higher the NDVI value, the healthier the vegetation cover.</mark><sup>9</sup> We have identified some image post-processing issues to be considered when interpreting our results below. First, we observed that areas classified as “bare ground” in locations farther away from settlements are “cleared farmlands” usually visible in a post-harvest period in the study area. Thus, cleared farmlands contribute to an increase in empty ground areas, as reported in Table 2. As cultivated areas increased in any given year, perhaps, due to late-harvest, bare ground sites decreased, and vice versa. Second, due to hydrological dynamics (i.e., surface water appearances and flows), we combined the water body areas with cultured fisheries sites into a single class for simplification in Table 2.

Given that this geospatial analysis is interested in a change in forest cover, we are less concerned about the potential classification errors among the other classes, which can be attributed to our data’s coarse spatial and spectral resolution issues. However, we are highly

> 9 <mark>Very low NDVI values (0.1 and below) represent bare ground areas of rock, sand, or snow. Moderate values (0.2 to 0.3) correspond to shrub and grassland areas, and high values (0.6 to 0.8) indicate temperate and tropical rainforest covers (Weier & Herring, 2020).</mark>

13
This means that the welfare loss (as a proportion of total adjustment costs) due to incomplete smoothing is given by <u>(change in input because of credit constraint)</u><sup>2</sup> Welfare loss = (change in input because of demand shock)<sup>2</sup> In case the reduction in smoothing because of a credit constrained is small, the welfare loss is consequently small as well. On the other hand, if a credit constrained firm is forced to reduce its inputs strongly, the welfare loss will be much higher (because of the squared term in the numerator). Whether the actual welfare loss is therefore high or low is an empirical issue and needs to be estimated.

We can estimate the welfare cost of incomplete input smoothing for labor, but not for the capital stock and raw materials because we have no information on the percentage change of the use of inputs (only an ordinal variable indicating the direction of change). We can use the estimates from columns (3) and (4) in Table 5, implying that at a 30.7% temporary reduction in sales (the observed mean change) would imply a decline of permanent workers of -0.9% for firms that are not credit constrained, and a change of -6.9% for firms that are credit constrained.<sup>22</sup> Hence, the welfare loss due to the credit constraint is 44.4 times the total adjustment costs of firms that are not credit constrained.<sup>23</sup> This estimate of the welfare cost of imperfect smoothing appears therefore relevant for policy makers, even though this estimate does not take into account other costs of imperfect input smoothing – such as unnecessary fluctuations in employment (which is costly for the workers and the state) and the higher risks of bankruptcy (as Table 2 showed that firms relying on informal finance were less likely to have gone bankrupt than firms relying on formal finance).

# **5.  Discussion and conclusions**

In this paper, we have analyzed risk coping behavior by firms in Cambodia following the global financial crisis using a panel survey that was implemented before the crisis (August 2007-March 2008) and during the crisis (July – November 2009). The survey shows that firms in Cambodia were hard hit by the economic crisis between 2008 and 2009, with an average fall in demand (sales) of 30% (Table 1). Firms

> 22 (30 x (0.13-0.10)) = 0.9 (column 3) and (30 x (0.13+0.10)) = 6.9 (column 4). 23 (6.9-0.9)2/(0.9)2=44.4.

28
|LB - Ba'albeck Water and Wastewater Project|Claire Kfouri|
|---|---|
|LB - Ba'albeck Water and Wastewater Project|Claire Kfouri|
|LB - Ba'albeck Water and Wastewater Project|Claire Kfouri|
|LB - Ba'albeck Water and Wastewater Project|Claire Kfouri|
|LB - Ba'albeck Water and Wastewater Project|Claire Kfouri|
|LB - Ba'albeck Water and Wastewater Project|Claire Kfouri|
|Health Transition Project|Rekha Menon|
|Health Transition Project|Rekha Menon|
|Health Transition Project|Rekha Menon|
|Health Transition Project|Rekha Menon|
|Health Transition Project|Rekha Menon|
|Health Transition Project|Rekha Menon|
|Health Transition Project|Rekha Menon|
|Health Transition Project|Rekha Menon|
|Health Transition Project|Rekha Menon|
|Health Transition Project|Rekha Menon|
|Health Transition Project|Rekha Menon|
|Health Transition Project|Rekha Menon|
|EmergencyEconomic RecoveryCredit|WolfgangFengler|
|EmergencyEconomic RecoveryCredit|WolfgangFengler|
|Growth and Competitiveness Project|Alvaro S. Gonzalez|
|Growth and Competitiveness Project<br>|Alvaro S. Gonzalez<br>|
|Growth and Competitiveness Project|Alvaro S. Gonzalez|
|Growth and Competitiveness Project|Alvaro S. Gonzalez|
|Growth and Competitiveness Project|Alvaro S. Gonzalez|
|Growth and Competitiveness Project|Alvaro S. Gonzalez|
|Growth and Competitiveness Project|Alvaro S. Gonzalez|
|Growth and Competitiveness Project|Alvaro S. Gonzalez|
|Growth and Competitiveness Project|Alvaro S. Gonzalez|
|Growth and Competitiveness Project|Alvaro S. Gonzalez|
|Growth and Competitiveness Project|Alvaro S. Gonzalez|
|Growth and Competitiveness Project|Alvaro S. Gonzalez|
|Growth and Competitiveness Project|Alvaro S. Gonzalez|
|HIV/AIDS Prevention & Control Project|Jean J. De St Antoine|
|HIV/AIDS Prevention & Control Project|Jean J. De St Antoine|
|HIV/AIDS Prevention & Control Project|Jean J. De St Antoine|
|HIV/AIDS Prevention & Control Project|Jean J. De St Antoine|
|HIV/AIDS Prevention & Control Project|Jean J. De St Antoine|
|HIV/AIDS Prevention & Control Project|Jean J. De St Antoine|
|HIV/AIDS Prevention & Control Project|Jean J. De St Antoine|
|HIV/AIDS Prevention & Control Project|Jean J. De St Antoine|
|HIV/AIDS Prevention & Control Project|Jean J. De St Antoine|
|HIV/AIDS Prevention & Control Project|Jean J. De St Antoine|
|HIV/AIDS Prevention & Control Project|Jean J. De St Antoine|
|HIV/AIDS Prevention & Control Project|Jean J. De St Antoine|
|HIV/AIDS Prevention & Control Project|Jean J. De St Antoine|
|HIV/AIDS Prevention & Control Project|Jean J. De St Antoine|