rafmacalaba/datause-encoder-data huggingface dataset:
survival than its physical productivity (TFPQ). Such a case could arise if some plants in the industry exercise a degree of market power that allows them to charge higher prices for their product than others. If this were the case, plants with higher TFPR could survive in the long run, even if they were less productive (in the sense of having lower TFPQ) than plants exiting the market. In such cases, empirical studies that measure establishment‐level productivity using TFPR might thus overestimate the “true” link between a firm’s productivity and the probability of its survival.

This kind of measurement error arises when factors other than inter‐firm gaps in factor productivity determine inter‐firm price differentials. Such factors include the many sources of product differentiation that firms use to lower product substitutability as well as idiosyncratic demand differences that can arise in markets. While such factors drive a wedge between marginal factor productivities and product prices in monopolistic markets, they are not easy to observe or measure. The complicates the empirical task of identifying and evaluating firm turnover, market size, and transport costs as observable and measurable elements of spatial competition.

To get around this problem, we control for the influence of product differentiation simply by focusing on Ethiopian industries that produce only relatively homogeneous goods. That is, industries where vertical product differentiation is not likely to be a significant source of product market power. Our goal is to ensure that the price differentials that we observe reflect horizontal product differentiation (consumer preferences over products driven by supplier locations) rather than vertical product differentiation (consumer preferences over products driven by quality differences). We focus on the following 4‐digit industries: non‐rice flour (ISIC=1531), white pan bread (ISIC=1541), and cinder blocks (ISIC=2695). To the best of our judgement, these products represent the most homogeneous of goods we can identify in the dataset. In total, our pooled sample covers more than 2,500 plant‐year observations.

Our analysis reveals that, in Ethiopia, low‐productivity plants (in terms of TFPQ) are more likely to exit the market than high‐productivity plants but only when we control for producers’ transport costs. It turns out that the same selection mechanism that gives local producers some degree of market power is weakened by high transport costs. Our finding is consistent with both 5
70. Finally, since the contents of the Unemployment Registry and the Labor Force Survey are somehow different, we use either source depending on its comparative advantage in addressing the question at hand. The table below summarizes the empirical strategy, together with the information on each dataset discussed in the previous section:

||Unemployment Registry|Labor ForceSurvey|
|---|---|---|
|Comments|ADVANTAGES<br>- Completedunempspells<br>- Verydetailedinformation<br>onunemploymentinsurance<br>benefits.<br>DISADVANTAGES<br>-Noinformationonsocial<br>assistance/support<br>- Information availableonly<br>at theindividuallevel<br>MAINUSE<br>- Analysisofunemployment<br>insurance|ADVANTAGES<br>- Informationonsocial<br>assistance/support<br>- Information availableatthe<br>individualandhousehold<br>levels<br>DISADVANTAGES<br>- Limitedinformationon<br>unemployment<br>insurance/social<br>assistance/support<br>-Individualsobservedfora<br>limitedperiodoftime<br>(censoredunempspells)<br>MAINUSE<br>- Analysisofsocial<br>assistance/support<br>-Analysisofhouseholdfactors|
|**Unemploymentduration**|Survivalmodel(unemp spell)|Survivalmodel(unempspell)|
|**Exit-to-employment**|Probabilitymodel(exit)|Probability model(exit)|
|**Job**<br>**search**<br>**and**<br>**match**<br>**quality**||Probabilitymodel(search)|

# Some methodological comments

71. Given the relative complexity of the safety net system, we expect tremendous variation to exist both between and within individual experiences. For this reason, it is necessary to take into account not only for benefit recipiency, but also differences in the type/quantity of the benefits received. For instance, once an individual becomes unemployed, she may or may not qualify for unemployment insurance benefits. If indeed she is entitled, the duration of such benefits would be a function, among other factors, of the her contributive history. Although, in a regression context, the use of an indicator variable for recipiency would get at the issue of entitlement, it would not be very useful at identifying behavioral differences between recipients with dissimilar contributive histories.

72. Similarly, the experience of any given recipient also varies over time due to the twotier nature of the unemployment insurance system, and to the limited duration of the benefits. For example, a worker who is entitled to five moths of unemployment insurance benefits and who remains unemployed for ten months, will receive high-level benefits (high replacement ratio) for three months, low-level benefits (low replacement ratio) for an additional two-

**19**
# _<u>Dependence on welfare</u>_

Prior analysis has shown that adolescent childbearing imposes additional costs on the state: teenage mothers are more likely to participate in social programs and receive higher social assistance income. The magnitude of these effects varies depending on the municipal characteristics.

Table 10 shows that the social assistance income of teenage mothers increases with the fraction of underemployed in their municipality. Indeed, a 1 percentage point increase in the fraction of people working less than 32 hours a week triggers a 7 percent increase in the social assistance income of teenage mothers. A higher fraction of underemployed is also associated with an increased likelihood of participation in some social program (for women who live with their parents only) and _ProCampo_ (for all teenage mothers: those who live independently and those who live with parents). Only the likelihood of participation in _Linconsa_ decreases with the fraction of underemployed. Overall, the data indicate that dependence on social welfare among teenage mothers is higher in the settings characterized by worse labor market and economic opportunities (captured in an indicator of underemployment).

It is important to emphasize that while our identification strategy allows for rigorous estimation of heterogeneity in the effect of adolescent childbearing depending on individual, household and municipal characteristics, it is beyond the scope of this analysis to pinpoint the mechanisms that drive such heterogeneity. Hence our findings should be considered with much caution; for example, as indications of promising areas for further qualitative and quantitative work.

# **5. Alternative explanations and robustness checks**

As discussed in the previous sections, validity of the proposed identification strategy hinges on the assumption that women, who upon becoming pregnant decide to abort, are not mistakenly included into the control group. This may happen if a woman who would choose to abort suffers a miscarriage before she is able to actually act on her decision.

Inclusion of these women into the control group is likely to result in overestimating negative effects of bearing a child as a teenager. Women who choose to have an abortion are likely to come from more favored backgrounds. Ashcraft and Lang (2006) show it with the US data; the same is likely to hold for Mexico – although due to underreporting of abortions we cannot demonstrate it with the 17
After dropping observations outside the region of common support, the final sample consists of 6,084 total labor market observations on 2,675 men, and 5,330 observations on 2,260 women. These individuals are divided into three cohorts. The oldest cohort consists of those born from 1940 to 1963, the middle cohort covers those born from 1964 to 1972, and the youngest cohort contains those born from 1973 to 1980. The IFLS survey asks the youngest cohort to report their performance in the junior secondary final examination.<sup>10</sup> Hence, for this most recent cohort, a direct measure of scholastic ability is available. Descriptive statistics for all variables are given in Appendix 1.

All estimates are separated by sex, because men and women exhibit different labor market participation patterns and they select different vocational education majors. As shown in Figure 3, 63.8% of men choose a technical or industrial major, while 56.0% and 28.9% of women are enrolled in business management and tourism majors, respectively.

<!-- Start of picture text --> Figure 3. Choice of Vocational Majors<br>Technical and industrial Business management Tourism Others<br>Females<br>Males<br>0% 20% 40% 60% 80% 100%<br><!-- End of picture text --> _Note: calculated from the National Labor Force Survey (_ Sakernas _)_

> 10 The examination is designed to be nationally comparable by the Ministry of National Education. We standardize the scores by year of junior secondary graduation to take into account possible quality changes in the exam over time.

9
# **Box 7: The 123PRSP Model: Distributional Effects of Macro-Policies and Shocks in Zambia**

Devarajan and Go (2003) employ the 123PRSP model to examine the distributional impact of macroeconomic shocks and policies in Zambia.

Zambia is a landlocked economy that depends heavily on mineral exports (particularly copper). This renders the country very vulnerable to external price shocks. Over the 2001–3 period, two risk factors threatened macroeconomic stability in Zambia. The first shock stemmed from expenditure pressures associated with the presidential election in late 2001, and the second from the decline in the world price of copper (the major export for the economy) induced by a slowdown in world demand.

One scenario assumes a 15 percent increase in government consumption in 2001 followed by another 10 percent increase in 2002. Assuming that foreign borrowing is kept at a constant fraction of GDP with respect to the base case and that investment adjusts to the available total savings, model simulations show a small increase in GDP the first year that disappears the following year. The first-round multiplier effects of a rise in government consumption are offset by the crowding-out effect of high fiscal deficits in subsequent periods. The results also show small increases in consumption and household incomes.

Another simulation combines the expenditure shock with a decline in the copper price by 20 percent the first year, 15 percent the second year, and 10 percent the third year. The results (see table below) show that these shocks would lead to a significant drop in GDP growth of about 0.5 percent the first year, 1.4 percent the second year, and 1.3 percent the third year. Relative to the base case, household income falls about 2 percent the first year, cumulating to about 6 percent the last year. Even though all household groups are affected negatively, the results show that the poorest are the hardest hit.

## **Impact of Shocks in Government Expenditures and Copper Prices (Percentage deviation from the reference run, unless otherwise stated)**

||**2001**|**2002**|**2003**|
|---|---|---|---|
|**Real GDP**(% +/-)||||
|Expenditure shock|0.6|–0.1|0.1|
|Expenditure +TOT|–0.5|–1.4|–1.3|
|**Real exchange rate of exports(depreciation>0)**||||
|Expenditure shock|0|0|0|
|Expenditure +TOT|7.9|7.9|7.9|
|**Household income (real)**||||
|Expenditure +TOT||||
|Decile 1 (poorest)|–4.6|–8.0|–9.8|
|Decile 2|–4.2|–7.4|–9.2|
|Decile 3|–4.0|–7.0|–8.8|
|Decile 4|–3.6|–6.4|–8.2|
|Decile 5|–3.4|–6.1|–7.8|
|Decile 6|–2.6|–5.0|–6.6|
|Decile 7|–2.4|–4.7|–6.3|
|Decile 8|–2.7|–5.1|–6.7|
|Decile 9|–2.5|–4.8|–6.4|
|Decile 10 (richest)|–1.7|–3.7|–5.3|
|Total|–2.1|–4.4|–5.8|

_Source_ : Devarajan and Go 2003.

41
Note that the analysis of returns to unobserved skill presented in the previous section relies on the plausible assumption that within skill (education and experience) groups, the distribution of unobserved skill has not changed over time. Because of the potential for workers to move between formal and informal jobs, a similar assumption for formal and informal worker groups would not be tenable.

If, as these results suggest, the growing informal-formal gap has been driven by increases in the returns to unobserved skill, technical change may be the ultimate cause of some of the informal-formal dynamics. Changes in technology employed in formal salaried jobs may have increased the demand for workers with complementary skills. If these skills are unobserved in survey data (but observed by potential employers), those with unobserved skill may have been sorted into formal sector jobs, expanding the gap between informal and formal jobs, even while the share of informal employment has increased.

# **8. Conclusion**

The variance decomposition analysis presented in this paper shows that that the returns to unobserved skill decreased in Argentina in the 1980s and then increased during the 1990s, during the same period that the returns to education increased. The changes in the 1980s are compatible with a stagnation of the level of technology in Argentina during the decade, paired with educational upgrading of the workforce. In other words, the drop in returns may reflect an increasing supply of those skills, combined with falling demand.

We interpret the simultaneous timing of the increase in the returns to unobserved skill and education in the 1990s as circumstantial evidence that skill-biased technical change is driving both phenomena. Demand for relevant skills outpaced the parallel increase in supply of those skills in the workforce with improved education.

The variance decomposition also demonstrated that in the 1990s the growth in the inequality of the upper half of the earnings distribution was mainly caused by rising returns to college education. In contrast, the growth in the inequality of the lower half of the distribution can mainly be explained by increasing returns to unobserved skills. This finding offers a possible explanation for the growth in the wage gap between informal and formal salaried workers in the 1990s. Competing explanations to SBTC do not seem to be able to explain these phenomena. Also, changing returns to education do not account for the changes in relative 22
why Monteiro and Assunção (2012) may not have found similar impact of the program on formalization rates as Fajnzylber et al. (2011).

The third block of results shows the first-stage estimates when firms that started their business in October 1996 are dropped from the sample. The coefficients are positive, a bit unstable, and statistically significant only for relatively larger bandwidth size. Since dropping firms reduces power, the fourth block shows estimates with dummy variables for firms created in October 1996 (see Barreca et al. 2015). In fact, the point estimates are similar to those in the previous block, but more precisely estimated.

One should bear in mind some indication of imbalance in observed characteristics of firms created before and after November 1996 when interpreting the positive and statistically significant impact of the program on the formalization rates shown in the fourth block of table 4. The more one moves away from the threshold, the more likely the estimates will be confounded by unobserved factors.

A final robustness check consists in running placebo tests setting the cutoff in different points in time. The tests involve checking whether the discontinuity observed around November 1996 exclusively captures the effect of the program or is some other factor responsible? A factor not investigated in this paper, but that could be correlated with firms’ creation.

**Table 5 – Placebo Tests**

||3-month|4-month|5-month|6-month|7-month|8-month|
|---|---|---|---|---|---|---|
||||_Cutoff = No_|_vember 1993_|||
|D|-0.024|0.057|0.091|0.069|0.059|0.079|
||(-0.24)|(0.64)|(1.16)|(0.93)|(0.86)|(1.22)|
|N|724|787|876|920|1012|1039|
|_Mean_|||_Cutoff = No_|_vember 1994_|||
|D|0.15|0.17**|0.21***|0.20***|0.20***|0.19***|
||(1.62)|(2.12)|(2.98)|(3.04)|(3.24)|(3.45)|
|N|1120|1166|1351|1416|1544|1622|
|_Mean_|||_Cutoff = No_|_vember 1995_|||
|D|0.018|0.0098|0.070|0.083|0.067|0.043|
||(0.23)|(0.14)|(1.19)|(1.48)|(1.31)|(0.93)|
|N|1198|1287|1570|1670|1887|2020|
|_Mean_|||||||

Note: ***, **, *statistically significant at 1, 5, and 10 percent. These are spline linear regressions in which y is regressed on a constant, a dummy that is 1 for firms created after the threshold and 0 otherwise (D), the assignment variable defined in months (Z) and an interaction term between the ‘after’ dummy and the assignment variable (DZ) to allow for different trends in each side of the threshold. Robust t-statistics in parentheses. In 1995, the proportion of eligible firms aged 24 to 32 months (control) was 22.3 percent. In 1994, the proportion was 20.9 percent for firms 36 to 44 months old, and in 1993 it was 26.4 percent for firms 48 to 56 months old.

15
Social Exclusion and the Gender Gap in Education<sup>1</sup> Maureen Lewis<sup>2</sup> Marlaine Lockheed<sup>3</sup> World Bank Center for Global Development

> 1 The authors would like to thank Bilal Sadiqqi for his research assistance. This paper draws from Lewis and Lockheed (2006) and Lewis and Lockheed, Eds. (2007).

> 2 Maureen Lewis. The World Bank, 1818 H Street, N.W., Washington D.C. 20433

> 3. Marlaine Lockheed. Center for Global Development, 1776 Massachusetts Ave., N.W. Washington, D.C. 20036
returns are recorded for low and middle-income countries. This update includes new country estimates and updated estimates for 42 countries.

Average returns to schooling are highest in the Latin America and the Caribbean region and for the Sub-Saharan Africa region (Table 4). Returns to schooling for Asia are at about the world average. The returns are lower in the high-income countries of the OECD. Interestingly, average returns to schooling are lowest for the non-OECD European, Middle East and North African group of countries.

During the last 12 years, average returns to schooling have declined by 0.6 percentage points (see Annex Table A4). At the same time, average schooling levels have increased. Therefore, and according to theory, everything else being the same, an increase in the supply of education has led to a slight decrease in the returns to schooling.

Overall, women receive higher returns to their schooling investments (Table 5 and Figure 6). But the returns to primary education are much higher for men (20 percent) than for women (13 percent). Women, however, experience higher returns to secondary education (18 versus 14 percent).

# **III. A More Selective Approach**

Returns to education compilations, as presented above, have been attacked in the literature (see Bennell 1996), although not for the right reasons (see Psacharopoulos 1996). The real reason one should be skeptical about indiscriminate rate of return compilations, and in spite of the efforts of the compilers, is that in the original works the estimates are rarely fully comparable. There are two main sources of non-comparability: data sample coverage and methodology.

Ideally, a rate of return to investment in education should be based on a representative sample of the country's population. But in reality this is the exception rather than the rule. This is problematic when the estimated rates of return are based on a survey of firms - rather than households - because firm-based samples are highly selective. In order to control survey costs, such samples focus on large firms with many employees. Second, the questionnaire is typically filled by the payroll department rather than by the individual employee. Typically, this approach leads to the use of samples concentrated only in urban areas.

Another problem occurs when rate of return estimates are based on samples that include civil servants. This is a problem because public sector wages typically do not reflect market wages. Of course, in many countries - although fewer now than in the past - the majority of university graduates end up in public sector employment. The concentration of graduates in public sector employment is identified as a problem in growth studies (see, for example, Pissarides 2000). However, civil service pay based rate of return estimates are useful in private calculations regarding the incentives set by the state to invest in education - and opt for employment in the public sector.

2
through contracts. The level of corruption in the country is also taken into consideration. The index uses a scale from 1 (maximum freedom) to 5 (minimum freedom). The sample used in this paper includes countries with scores between 1 and 4.

Descriptions, as well as sources, of both firm-specific and macroeconomic variables and definitions of financial data used in the analysis are provided in Appendix A.

(Insert Appendix A about here)

# _4.4. Data Correlation_

Table 3 provides correlation matrices for all variables used in the analysis. There are no notable significant relationships in the firm-specific data. The only variables that exhibit significant correlation are some of the macro variables. The correlation of several macroeconomic variables is significant and is generally an issue in many international studies. As a result, empirical examinations using different specifications, including select macroeconomic variables and the subsequent addition of problematic variables, are used to provide robustness to the results given the potential empirical biases based on the correlation between the macroeconomic independent variables.

(Insert Table 3 about here)

# **5. Results**

# _5.1. The Impact of FPI on Capital Issuance_

Intuitively appealing is the fact that in DPR nations, variables such as leverage and cash are significantly positively related, and risk and profitability are significantly negatively associated with capital issuance. In LDPR nations, there are fewer significant firm level variables suggesting that access to capital has less to do with these characteristics and more to do with country-level influences and 15
# **3. INDIVIDUAL COUNTRY EXPERIENCES**<sup>**1 0**</sup>

# **3.1 Indonesia**

_The situation in Indonesia is controversial. On one hand, there is no striking indication of a country-wide credit crunch on the basis of movements in monetary and credit aggregates; on the other, strong sectoral shifts in the flows offunds occurred, likely leading to bottlenecks in the availability offinancial resources and to localized credit crunch._ Chart 12 reports the value of returned checks as a percentage of checks presented for clearing, generally a leading indicator of bankruptcies, and the mean value of returned checks, which we would expect to grow when the composition of returned checks shifts from household expenditures to corporate payments. Both variables sizably increased in the second half of 1997, suggesting widespread and severe corporate financial distress.

# **Chart I1**

GROWTH RATE OF REAL LOANS<sup>1</sup> ' <!-- Start of picture text --> 7.8<br>5076<br>10<br>97.03 97!04 975 97!05 V 97!07 97!08 9760 71 97!10 1 971.11 80 0 98.3<br><!-- End of picture text -->

- 10 Unless otherwise specified, all data are obtained from either the International Monetary Fund's IFS or from the respective countries' Central Bank's publications and Websites. The sample period used and the variables employed are determined by the availability of data from these sources.

Growth rates are computed on year-on-year basis and deflated by the CPI, unless otherwise indicated.

16
**Figure 9: Location of commercial teff farms in 2013/4 with smallholder kebeles and their yields in 0-25, 25-50, 50-100, and 100-150 km distance bands**

<!-- Start of picture text --> Legend<br>Kebele boundary 2014<br>Yield (q/ha) by quintile<br>1 - 8<br>9 - 13<br>14 - 18<br>19 - 25<br>26 - 41<br>Region boundary<br>Buffer around Teff farm (14)<br>Distance (km)<br>25<br>50<br>100<br>150<br><!-- End of picture text --> 34