rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1: Baseline results, Non-OECD sample**

||UOP|B|OP|
|---|---|---|---|
||SAR<br>(1)|SAW<br>(2)|SAR<br>(3)|
|Income (#)|-3.5e-03 [0.026]|0.288 [0.029]***|-0.101 [0.036]***|
|Age (#)|0.799 [0.370]**|-0.667 [0.397]|1.061 [0.314]***|
|Age (#), squared|-0.116 [0.055]**|0.085 [0.055]|-0.151 [0.046]***|
|Female|-1.8e-03 [0.038]|0.063 [0.023]***|-0.024 [0.032]|
|Single, never married|-0.014 [0.029]|-0.051 [0.021]**|1.09e-03 [0.027]|
|Employment status||||
|Employed part time want full time|-0.030 [0.055]|-0.031 [0.027]|-0.022 [0.058]|
|Employed part time do not want full time|-0.072 [0.022]***|0.073 [0.029]**|-0.099 [0.027]***|
|Employed full time for self-employment|-0.122 [0.040]***|0.010 [0.030]|-0.127 [0.039]***|
|Employed full time for an employer<br>|-0.088 [0.023]***|-0.012 [0.036]|-0.089 [0.024]***|
|Highest education level||||
|Secondary to 3 year of tertiary|-0.050 [0.021]**|0.222 [0.015]***|-0.126 [0.037]***|
|4 years of tertiary and beyond|-0.197 [0.062]***|0.337 [0.045]***|-0.306 [0.055]***|
|Instruments for SAW||||
|City economic conditions are good||0.384 [0.044]***||
|Children under 15 in this household (@)||-0.055 [0.017]***||
|Instruments for SAR||||
|Religion is important|-0.184 [0.080]**||-0.184 [0.074]**|
|Sacrificingone’s life for beliefs|1.363[0.087]***||1.317[0.111]***|
|Country fixed effects|Yes|Y|es|
|Locality fixed effects|Yes|Y|es|
|Surveywaves fixed effects|Yes|Y|es|
|c11|2.341 [0.682]***|-0.824|[0.802]|
|c12|2.790 [0.674]***|-0.294|[0.791]|
|c13|3.199 [0.680]***|0.300|[0.790]|
|c14|3.639 [0.696]***|0.951|[0.785]|
|c15||1.526|[0.787]*|
|c16||2.367 [0|.798]***|
|c17||2.937 [0|.803]***|
|c18||3.426 [0|.805]***|
|c19||3.884 [0|.799]***|
|c110||4.196 [0|.813]***|
|c21||1.022|[0.653]|
|c22||1.457 [|0.647]**|
|c23||<br>1.854 [0|<br>.651]***|
|c24||<br>2.284[0|<br>.669]***|
|Observations|32663|30|787|
|Pseudo R2|0.148|||
|Log Pseudolikelihood|-29115.229|-850|76.036|
|Wald test of indp. Eqns.(p-value)||0.|036|

Notes: SAW stands for self-assessed wellbeing and SAR is self-assessed radicalization. The reference categories for the employment status is “Out of workforce or unemployed”, for the highest education level is “Completed elementary education or less”, and for the locality fixed effects is “Rural area or on a farm”. X(#) and X(@)denote Ln(X) and Ln(X+1), respectively. We use Ln(X) for income to exclude individuals with zero income and Ln(X+1) for the number of children under 15 in the household to not exclude households with zero values on this variable. Standard errors are clustered by country/wave and robust to heteroscedasticity. Asterisks denote significance levels as follows: *** p<0.01, ** p<0.05, * p<0.1.

24
having more downward mobility than the national average in the first period to having less downward mobility in the second period. Other states (Punjab, Maharashtra, and Tamil Nadu) may not achieve this impressive turnaround but also manage to significantly reduce their mobility over time.

It can be useful to compare mobility profiles for some states with different performances. We pick two states at the two ends of the range of performance, Orissa and Delhi, which respectively have below-average performance in terms of upward mobility and achieve much improvement in the two periods under consideration (Figure 4). We provide estimation results for upward mobility and downward mobility for these two states in Figures 6 and 7. Both states mostly show qualitatively similar patterns to the national trends. For example, there is a similar pattern of attenuation for downward mobility (Figure 7) for the poorly educated, the rural, and the disadvantaged social groups. But more interestingly, certain nuanced differences exist between them. In particular, having some college education is more strongly correlated with more upward mobility (Figure 6) and less downward mobility (Figure 7) for Delhi than Orissa. Furthermore, this correlation also becomes much stronger over time for Delhi, which points to education as an important factor in raising upward mobility. This pattern is qualitatively similar when we compare a pair of two other states that are closer to the middle range of the performance, Bihar and Punjab (Figures A.1 and A.2 in the Appendix).

# **VI. Conclusion**

We analyze welfare dynamics in the past 25 years using five “thick” rounds of the NSS survey data between 1987/8 and 2011/12. In the absence of actual panel data, we construct synthetic panels that allow us to investigate dynamic patterns. We find increasing general mobility as well as upward mobility over time for the country. Yet, a majority of the poor who have escaped poverty remain vulnerable. Our estimation results suggest that higher education levels, and self-employment and wage-earning activities, particularly for urban residents, are 27
are low. The worker has to repay the received unjustified benefits, but there are no violation fines.

Also, the "shoe-leather" costs of keeping the status of insured unemployment imposed in the form of search requirements have been small because employment offices have been overloaded with work. Since the beginning of transition in 1989 till 1994, employment offices have not increased their staff, although the number of unemployed increased many times, and the scope of their work also increased.

Because "double-dipping" has been legally allowed and because costs of remaining in "formal" unemployment are low, the Slovenian system of unemployment insurance seemed to encourage the unemployed to stay in insured, "formal" unemployment until benefits lapsed. This prediction is examined below.

# 3. DATA SOURCES

The Employment Office of Slovenia's records on registered unemployed are the main source of data for this study. For each unemployment spell, information was provided on the following issues:

- a) starting and ending dates,

- b) destination of exit (job vs. exit from labor force),

- c) date and reason of termination of last employment, if applicable, and

- d) personal characteristics of the worker.

We have information on all unemployment spells that were in progress as of December 31, 1986 or began between December 31, 1986 and mid-April 1993, altogether about 325,000 records.

For a subsample of registrations between January 1990 and October 1992, we also have the receipt of unemployment insurance benefits, consisting of the type of benefit (unemployment compensation, unemployment assistance, or unemployment assistance after internship) and starting 10
demonstrate how changing the value of h alters commonly held views of larger households being poorer on average.

For the analysis in this paper, where we aim to understand how the allocation of resources affects the global profile of poverty, we only have data on household size and not the age composition of each household. The implication of this is that we can only consider changing the single parameter, h, which describes the elasticity of needs to household size. The per-capita allocation sets h=1 which would be defensible if households only consumed non-shareable goods, such as food. When the initial “dollar-a-day” IPL was set (World Bank, 1990), and the decision to use per capita consumption as the measure of wellbeing was made, food budget shares were very high in most poor countries. Yet, it has been the case for some time now that budget shares allocated to food have been declining across most all countries of the world (Traub, 1992, Table 2), and the assumption of setting h=1 has become less tenable. As related evidence, the share of food and nonalcoholic beverages in gross domestic product (GDP) has dropped from 49 to 37 percent between 2005 (Muhammad et al., 2011, Table 2) and 2017 (World Bank, 2020b, Figure 1.4). While there is variation in values used for h, Johnson and Torrey (2004) note that adjusting needs by the square root of household size (i.e., p=1, h=0.5) is becoming common in international poverty comparisons.<sup>6</sup> Buhmann et al. (1988), Ruggles (1990) and Vleminckx and Smeeding (2001) provide examples of this, and Dudel et al. (2021) suggest that the square-root adjustment performs well, particularly for larger households.

> 6 The square-root adjustment is often applied in research work (Johnson et al., 2005; Ravallion, 2016; Smeeding, 2016; Taylor et al., 2011), policy work (OECD, 2015; US Congressional Budget Office, 2018), and international comparison of poverty and inequality, as done with the Luxembourg Income Study (LIS) (Buhmann et al., 1988).

7
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37 Table 4: The influence of checks and balances, information and electoral competitiveness on the decision to forbear

|(Probit estimation, marginal effe<br>scores in parent|cts atmean values <br>heses, standard err|ofindependent variables are reported,p-<br>ors are White-adjusted)|
|---|---|---|
|Dependent variable:<br>Forbearance|()<br>Entiresample|(2)<br>Maximum electoral<br>competitiveness|
|checks|-0.17<br>(0.22)|-0.29<br>(0.21)|
|checks *M,/GDP|0.38<br>(014|0.72<br>(0.09)|
|M,/GDP|-0.17<br>(a7|0.26<br>(a7|
|No. ofyearsfromfirst<br>yearofcrisis toyearof<br>nextelection|0.17<br>(0.008)|0.34<br>(0.02)|
|GDP/capita|-0.016<br>(0.27|-0.009<br>(067)|
|RadioandTV<br>independence|-0.08<br>(0.26)|-34<br>(0.02)|
||030|055|
||36|2|

Note: Income per capita not available for Taiwan from Jnternational Financial Statistics, IMF. Sample in Regression 2 is those countries that receive a six on the Executive Index of Electoral Competitiveness (IEC) from the Database on Political Institutions,

# Robustness

The results presented in Tables 1 - 4 are surprisingly robust, in view of the small sample size, to numerous different specifications and samples. Still, one might be concerned about the role of omitted variables, the quality of the variables proxying for uninformed voters and the cost to voters of special interest policies, and about additional, plausible specifications that are not examined above. This section reviews these concerns.

: One set of omitted variables is the numerous economic circumstances that make banking crises more likely, such as bouts of inflation and business cycle variables. On the one hand, though, it is unlikely that these circumstances are spuriously correlated. with the
# **_<mark>Are Large Firms Born or Made? Evidence from Developing Countries</mark>_**

**Meghana Ayyagari**

**Asli Demirguc-Kunt**

**Vojislav Maksimovic**

**JEL codes** :   O1, L26, L25

## **Keywords:** Firm productivity and growth; entrepreneurship; institutions; economic

development.

## **__________**

*Ayyagari: School of Business, George Washington University, <u>ayyagari@gwu.edu, Ph: 202-994-1292; Demirgüç-</u> Kunt: World Bank, ademirguckunt@worldbank.org, Ph: 202-473-7479; Maksimovic: Robert H. Smith School of Business at the University of Maryland, <u>vmaksimovic@rhsmith.umd.edu, Ph: 301-405-2125. We would like to</u> thank Miriam Bruhn, Roberto Fattal Jaef, Hiau Looi Kee, Aart Kraay, David McKenzie, and Sergio Schmukler for their valuable comments and suggestions on an earlier version of this paper titled “Do firms in developing countries grow as they age?” We thank Xiaoyuan Hu for excellent research assistance. .
# **4. Robustness**

First, we repeat the regression exercise using the full (0-10) democracy scale. The relationship between democracy and reform holds (Table 5). As above, it is weaker in magnitude but still significant at the 1% level when we control for Latitude, region fixed effects and the legal origin of countries. Based on the estimated coefficients, moving from the lowest to the highest value on the democracy scale increases the probability of reform by 17.1-35.5 percentage points.

Second, we report regression results using the OLS estimation method for the total number of reforms as the dependent variable (Table 6). The main results hold. Without any other controls, a move from low to high democracy increases the (log) number of reforms by 0.212 (column 1, Table 6) or 38.5% of the mean value of the dependent variable. The corresponding increase as a percentage of the mean value of the dependent variable equals a high of 53.9% with the religion fixed effects (column 6, Table 6) and a low of 27.7% with the region fixed effects (column 5, Table 6).

Next, we use the third measure of democracy from Freedom House (Table 7). These results are similar to the ones discussed above. The democracy-reform relationship is economically large and statistically significant at the 5% level when we control for Latitude and region fixed effects and at the 1% level in other specifications. Estimates in the table imply that moving from the lowest to the highest value of _Democracy_ 3 increases the probability of reform by 27.2 percentage points with no additional controls (column 1), a high of 40.3 percentage points with the religion fixed effects (column 6) and a low of 16 percentage points when we control for region fixed effects (column 5).

12
# FIGURE 7 Characteristics of recessions

## A. Share of countries with recessions

## B. Actual growth during recessions: World

<!-- Start of picture text --> Percent Percent<br>100 Advanced economies 8<br>EMDEs 6<br>80<br>4<br>60 2<br>0<br>40<br>-2<br>-4<br>20<br>-6<br>0 -8<br>1981-891990-992000-072008-092010-19 2020 t-3 t-2 t-1 t=0 t+1 t+2 t+3 t+4 t+5<br>C. Actual growth during recessions: Advanced D. Actual growth during recessions: EMDEs<br>economies<br>Percent Percent<br>8 8<br>6 6<br>4 4<br>2 2<br>0 0<br>-2 -2<br>-4 -4<br>-6 -6<br>-8 -8<br>t-3 t-2 t-1 t=0 t+1 t+2 t+3 t+4 t+5 t-3 t-2 t-1 t=0 t+1 t+2 t+3 t+4 t+5<br><!-- End of picture text --> D. Actual growth during recessions: EMDEs Source: World Bank.

Note: Recessions are defined as the period from the peak preceding a business cycle trough to the trough, with a trough defined as a year in which output growth is both negative and at least one standard deviation below its long-term average. Sample includes 91 recession events in 33 advanced economies and 190 recession events in 77 EMDEs during 1981-2020.

B. Unweighted averages of actual growth during recessions as defined in annex 2E denotes the peak year preceding the recession.

27
members have gone to live in another country in the past five years, capturing migration for study or other non-work purposes in addition to work related migration. We define a child as living in a _migrant household_ if the household has a member aged 19 and over who has ever been to the U.S. to work, or who has moved to the U.S. in the last five years for any other reason. The migrant member or members may have returned to Mexico or still be in the U.S. at the time of the survey.

Table 1 provides summary statistics for the key variables used in this study. Twenty two percent of all households in our sample with a child aged 12 to 18 have an adult member who has ever migrated to the U.S. Several recent studies of migration and schooling in Mexico have used the 2000 Mexican Census (Hanson and Woodruff, 2003; Lopez-Cordoba, 2004). The Mexican Census only asks about migration within the last five years. The ENADID questions on migration within the last five years are identically worded to the Census, and so for comparison we also calculate the proportion of households with migrants according to the Census definition. Table 1 shows that relying on the Census questions to define migrant status understates the proportion of households with migrant experience by almost fifty percent.

Examining migration within the last five years is likely not to be unduly restrictive for certain types of analysis. However, there are number of reasons to prefer looking at whether household members have ever migrated in examining the impact of migration on education. Schooling is a cumulative process, with each year building on the year before. Any impact of migration on schooling during the years of primary education may therefore affect schooling six to ten years later. A portion of this effect may be at the extensive margin: 10 percent of 12 year-olds in our sample are not currently attending school, which makes it likely they will not be attending 7
It could also be hypothesized that what we record as recall bias is actually caused by interviewer error. Due to the geographic distribution of fieldwork and the construction of the time elapsed variable, if certain interviewers did not accurately collect harvest or input information, this could be correlated with recall bias. To test for interviewer effects, enumerators’ fixed effects were added to the Malawi regressions (results not presented).<sup>10</sup> The coefficient on the main variable of interest, time elapsed since harvest, does not change, and remains not statistically significant.<sup>11</sup> Finally, it is important to recognize that the harvest date is assigned to households with some noise, since we do not have household specific reports on month of harvest (with the exception of Rwanda sorghum) and there can be variation within regions as to the specific harvest month of households. By using the simple binary indicator for a longer or shorter recall duration, we have tried to avoid this potential noise. However, this may still be problematic for households interviewed near the cutoff to the completed season. Specifically, this is where we assign time elapsed as 0-1 months when it should be 11 months (or 11 months where it is actually 0-1 month). We do robustness checks for this by excluding observations where time elapsed is less than 2 months or more than 10 months.

# **5. Conclusion**

Agriculture is critical for development, particularly in Sub-Saharan Africa, where a large share of households depend on farms as their main source of livelihood. Despite this, data on agriculture are often perceived to be both lacking and, when available, of poor quality (UN Statistics Commission, 2010). Little research has been undertaken to date on the quality of agricultural statistics. Where data are available that link agriculture with non-farm activities, information on farming activities may be collected as part of a larger, multi-purpose survey based on a single interview. At best, households are interviewed over a short period of time just right after the

> 10 Interviewer identification variables are not available for Kenya or Rwanda.

> 11 As noted earlier, it is possible that recall bias does not vary linearly with time. For example, a farmer could accurately remember production details up to a certain point following the event, then begin to forget as time passes. Alternatively, the slope could increase and then decrease over time, producing no slope on average. That is, the speed of decay might change as time from harvest increases. Unfortunately, due to small samples and the variance in our outcome measures, with the revised specification (a simple binary variable for longer versus shorter recall period), we are not able to explore this issue.

16
discussion. In order to contribute to the discussion, PROST modeled some changes in order to get better information in relation to trends in income and expenditure flows.

In this context, it is possible to consider an increase in the legal age of retirement, removing requirement combinations of age and contribution years currently in force, in order to allow, on the one hand, a more active life of workers and, on the other, to support this increase of life expectancy over time.

According to ENEMDU-provided information, between 2003 and 2016, an economic participation increase has been observed in individuals from different age groups (Figure 19). In general, the percentage of the economically active population showed an increase by one percentage point, shifting from 52% to 53% between the selected years. However, the greatest growth took place among people between 27 and 65 years of age.

Figure 19. Percentage of Economically Active Population per Age. Years 2003 and 2016 <!-- Start of picture text --> 0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>2016 2003<br>0.1<br>0<br>Age<br>15 19 23 27 31 35 39 43 47 51 55 59 63 67 71 75 79 83 87<br><!-- End of picture text --> Source: author elaboration based on ENEMDU In this context, a public policy scenario is outlined in which retirement age is raised by one year every 10 years as of 2028, keeping the rest of the model parameters constant. Figure 20 presents estimated trends in a pure financial result as compared to the baseline scenario between years 2018 and 2080.

29
where u� �γ��p� �X�β�, and γ� � ��<sup>�</sup> is referred to as a “shrinkage factor”. Given that σ�� is ��� ��<sup>�</sup> unknown, the Best Linear Unbiased Predictor (BLUP) is replaced with its empirical counterpart EBLUP: P<sup>�</sup> � ����� �P�������σ����, which can be rewritten as:

**�** �<sup>�</sup> � where β<sup>�</sup> is the Feasible GLS estimator for β and γ��� .  Thus,  p� is a weighted average of the ��� **�** �<sup>�</sup> direct survey estimate p� and the synthetic (model-based) estimate X�β<sup>�</sup> , and the weights are given by γ��. For p� with smaller sampling variances φ� the shrinkage factor gives higher weight to the direct estimate, while for p� with higher sampling variances a higher weight is assigned to the synthetic estimate. In areas that are not part of the survey sample, the prediction is based on the synthetic estimate X�β<sup>�</sup> , where β<sup>�</sup> �β<sup>�</sup> �σ����. The prediction error associated with p̂� takes account of the sampling � variance associated with p�, as well as the uncertainty associated with the estimate of β and σ� (see Rao, 2003 for more details).

When calculating the term φ� needed for the Fay-Herriot approach, there are several potential methods for considering the stratified and clustered two stage sample designs of the surveys used in this application. Perhaps the simplest approach is to use the Horvitz-Thompson (HT) estimator of P�, which can be expressed as: The term s� denotes the set of sampled units in domain _d_ . However, the HT estimator is often not preferred in SAE applications (National Research Council, 2013; Eurostat, 2013). It is commonly unstable, especially for small domains with few survey observations (a common challenge in this context). To address this, there are various alternative direct estimators that may out-perform the HT estimator.

Common practice in the World Bank has been to obtain sampling variances associated with the district-level poverty rates by taking the variance estimate from the survey data source and dividing it by the sample size for each domain to obtain a set of “smoothed” sampling variance estimates. This ignores components of the clustered sample design; however, these smoothed sampling variances are commonly less volatile than simple weighted direct HT estimates.

A final approach is to compute variance and the associated root mean square error of the mean using the linearized variance estimator approach—based on a first-order Taylor series (Wolter 2007). In sensitivity analyses this was the most stable variance measure of the three described here, and the preferred approach for this application. Annex G reports comparisons and analysis of sensitivity to the choice of domain variance measure. Final results are quite similar when comparing the second (“smoothed”) and third (“linearized”) options described, though as expected, the HT approach yields results that are more distant from those of the remaining two options. For more detail on the tradeoff between approaches for domain variance estimation, see Heeringa et. al., (2017); Molina and Rao (2010); Wolter (2007); and Kolenikov (2010).

22