rafmacalaba/datause-encoder-data huggingface dataset:
-9-

**Figure 1: Tunisia: plausible futures for the**<sup>**H1IV/AIDSprevalencerate and lossesIn GDP**</sup> **(2000-2015)**

<!-- Start of picture text --> 2%IX0%<br>1J150%<br>MO1~% - 11<br>8 50.0%<br>0.0% ~ 00% I 2L9<br>0.0% 50# 10.0% 150% 20.0%<br>HIV/AIDS prevalence rate In year 2015<br><!-- End of picture text --> The same type of analysis was conducted for<sup>the 9 countries included in the study.For each</sup> country we summarize the distribution of the<sup>outputvariablesofinterestby reportingthemean,</sup> standard deviation, and the minimrum and<sup>maximurnvalues(seeTable1).</sup> Graphics of the full distributions are provided in Appendix E. All<sup>distributionsdeviatefrom the normaldistribution,and</sup> therefore one needs to be careful when interpreting<sup>the means.</sup>
Appendix Table 10 Probability of Firms Using Credit Program: Split by Period

||Appli<br>|cations to <br>|Credit Program<br>|
|---|---|---|---|
||(1)|(2)|(3)|
||April|May|June to December|
|_Panel A: Analysis using Predicted Default Risk_||||
|Risk|0.260|0.129|0.122|
||(0.017)|(0.029)|(0.026)|
|Increase in Sales Dummy|0.041|0.124|0.059|
||(0.005)|(0.008)|(0.007)|
|Decrease in Sales Dummy|0.048|0.132|0.046|
||(0.005)|(0.008)|(0.007)|
|Use Employment Program|0.034|0.066|0.008|
||(0.003)|(0.005)|(0.004)|
|Dependent Variable Mean|0.096|0.324|0.231|
|Dependent Variable Std. Dev.|0.295|0.468|0.421|
|Number of Firms|62,018|62,807|62,761|
|R<sup>2</sup>|0.035|0.028|0.018|
|Industry FE|Yes|Yes|Yes|
|Municipality FE|Yes|Yes|Yes|
|_Predicted Default Probability_||||
|Firms with Previous Loans|0.084|0.084|0.084|
|_Panel B: Analysis Using Spread_||||
|Spread _Ex Ante_|0.002|-0.001|0.001|
||(0.000)|(0.001)|(0.000)|
|Increase in Sales Dummy|0.043|0.128|0.057|
||(0.007)|(0.011)|(0.009)|
|Decrease in Sales Dummy|0.053|0.131|0.050|
||(0.007)|(0.011)|(0.009)|
|Use Employment Program|0.037|0.066|-0.009|
||(0.004)|(0.006)|(0.006)|
|Dependent Variable Mean|0.120|0.372|0.233|
|Dependent Variable Std. Dev.|0.325|0.483|0.423|
|Number of Firms|37,613|38,213|38,164|
|R<sup>2</sup>|0.036|0.029|0.023|
|Industry FE|Yes|Yes|Yes|
|Municipality FE|Yes|Yes|Yes|
|_Predicted Default Probability_||||
|Firms with Previous Loans|0.106|0.107|0.107|

This table reports probit estimations of the probability of a firm with previous loan applying a government program on a set of firm-level characteristics. Panel A defines risk using the predicted default probability from the default probability model reported in Table 1, Column 8; Panel B defines risk using the spread between the interest rate of the loans a firm received and the risk-free rate. Firms with previous loans are those that either have bank credit outstanding in December 2019 or receive a bank loan over the period 2012–2019. In Column 1, the dependent variable is equal to one if the firm applies to the program during April and zero otherwise, in Column 2 the dependent variable is equal to one if the firm applies to the program during May and zero otherwise, in Column 3 the dependent variable is equal to one if the firm applies to the program between June and December and zero otherwise. Increase (decrease) in sales dummy is equal to one if sales growth is greater (lower) than or equal to 2% (-2%), and zero if sales growth is between - 2% and 2%. Use credit program and employment program are dummy variables equal to one for program participation and is zero otherwise. Use both programs is a dummy variable equal to one for credit and employment program participation, and is zero otherwise. The table uses the firms with previous loans of the credit program eligible firms sample. Some observations are dropped when estimating the model with fixed effects. Standard errors, shown in parentheses, are clustered at the industry and municipality level.

28
# **4 Determinants of Subjective Well-Being**

Subjective well-being is obviously not only related to employment status. A multivariate approach is necessary. Income, health, age, education, and civic status are all likely to directly affect an individual’s well-being. As the GHQ caseness score is an ordinal measure (it is not a continuous variable), a linear probability model is not the best tool to estimate its determinants<sup>6</sup> . An ordered probit regression is used to complement the results of the OLS regression, and the results from both methods will be discussed below. The results on the main determinants of SWB are in regression table 2, which shows the ordered probit estimation. Table A.3 in the appendix shows the results from the OLS regressions. Three specifications are presented for both cases, in which the explained variable is subjective well-being.

The three OLS equations estimated are shown below. _Si_ stands for labor market status; _Yi_ for income; _Educi_ for educational achievement, the reference category being no diploma. _Healthi_ stands for the health status, the reference being excellent health. The reference dummy for civic status is never married and _Xi_ stands for the control of all previous variables.

In the first specification, labor market status is the only regressor, for which the reference category is ”Employed”. The coefficients indicate that the self-employed are slightly but significantly happier than the employed, when other life variables are <u>not</u> controlled for. The unemployed are the least happy. In the OLS regression, they have around 1 _._ 2 points less of well-being than the employed. The coefficients of the ordered probit indicate that Unemployed are 40% less likely to have a score of 12 than the employed, when controlling for other factors. In the second specification controls are added for the list of individual characteristics mentioned above. In the third specification, children dummies, year and region fixed effects are included. The results in all three specifications are in line with those found in Clark (2003) using the first seven waves of the BHPS.

> 6because the distribution of the disturbance term is not normal anymore - standard errors and t-stats are thus invalid. An ordered probit increases the fit and provides reliable z-stats, instead of the t-stats.

9
<!-- Start of picture text --> Selected Generic 10-Year Government Bond Yield<br>180 {1 Sep, 2008 = 100}<br>-<br>waa A=erinf [~A, mS VA<br>40<br>20<br>0<br>o3/o1/20 08 10/01/2008 11yo1/2008 12/01/2008 o1/o1yf2009 o2/o1/2009 03/01/2009<br>—— Bri | Chile —— Chins ——— Hungary = India= Indonesia Mexico ——— Peru US ome UK<br>Source: BloombergLP, Detastream<br><!-- End of picture text -->

<!-- Start of picture text --> Net Bond Fund Flows (#6 of GDP)<br>o.2<br>0.0 eee Coreeeeerteeeetnes :<br>-0.2 ‘ ae & " Rol oe) oe % oe ca % e i. PE EP oo<br>ae,_ 0.4hd a Wey’“Fea \OO fji Sa<br>-1.0<br>ems i, mmmET hmm (ccfi] ffi, ce SP SS,<br>-12<br>Source: Emerging Portfolio Funds Research<br>Note: Flows by country: a5 3% of GDP<br>SAR excludesSri Lanka: MMA excludes Morocco: 554 encludes Kenya<br><!-- End of picture text -->
|P002538|1990|1995||12 Closed|Sao Tome and Principe|
|---|---|---|---|---|---|
|P002538|1990|1996||14 Closed|Sao Tome and Principe|
|P002538|1990|1997||15 Closed|Sao Tome and Principe|
|P002540|1989|1989||1 Closed|Sao Tome and Principe|
|P002540|1989|1990||2 Closed|Sao Tome and Principe|
|P002540|1989|1991||4 Closed|Sao Tome and Principe|
|P002540|1989|1992||7 Closed|Sao Tome and Principe|
|P002540|1989|1993||10 Closed|Sao Tome and Principe|
|P002540|1989|1994||12 Closed|Sao Tome and Principe|
|P002540|1989|1995||13 Closed|Sao Tome and Principe|
|P002540|1989|1996||15 Closed|Sao Tome and Principe|
|P002542|1992|1992||1 Closed|Sao Tome and Principe|
|P002542|1992|1993||2 Closed|Sao Tome and Principe|
|P002542|1992|1994||4 Closed|Sao Tome and Principe|
|P002542|1992|1995||7 Closed|Sao Tome and Principe|
|P002542|1992|1996||9 Closed|Sao Tome and Principe|
|P002542|1992|1997||11 Closed|Sao Tome and Principe|
|P002542|1992|1998||12 Closed|Sao Tome and Principe|
|P002542|1992|1999||16 Closed|Sao Tome and Principe|
|P002542|1992|2000|6/15/2000|19 Closed|Sao Tome and Principe|
|P002542|1992|2001|5/21/2001|21 Closed|Sao Tome and Principe|
|P002548|1991|1991||1 Closed|Sao Tome and Principe|
|P002548|1991|1992||4 Closed|Sao Tome and Principe|
|P002548|1991|1993||6 Closed|Sao Tome and Principe|
|P002548|1991|1994||8 Closed|Sao Tome and Principe|
|P002548|1991|1995||9 Closed|Sao Tome and Principe|
|P002548|1991|1996||11 Closed|Sao Tome and Principe|
|P002548|1991|1997||13 Closed|Sao Tome and Principe|
|P002548|1991|1998||15 Closed|Sao Tome and Principe|
|P002572|1978|1985||0 Closed|Sudan|
|P002573|1978|1985||0 Closed|Sudan|
|P002573|1978|1986||0 Closed|Sudan|
|P002573|1978|1987||0 Closed|Sudan|
|P002574|1978|1985||0 Closed|Sudan|
|P002574|1978|1986||0 Closed|Sudan|
|P002574|1978|1987||0 Closed|Sudan|
|P002574|1978|1988||0 Closed|Sudan|
|P002577|1979|1985||0 Closed|Sudan|
|P002578|1980|1985||0 Closed|Sudan|
|P002578|1980|1986||0 Closed|Sudan|
|P002578|1980|1987||0 Closed|Sudan|
|P002578|1980|1988||0 Closed|Sudan|
|P002578|1980|1989||6 Closed|Sudan|
|P002580|1980|1985||0 Closed|Sudan|
|P002580|1980|1986||0 Closed|Sudan|
|P002580|1980|1987||0 Closed|Sudan|
|P002580|1980|1988||0 Closed|Sudan|
# **Appendix A.  The Monte Carlo Experiments**

The Monte Carlo experiments use 10,000 replications of the model: _w f_ = α + β ln ( _x n_ ) +γ ln _n_ + _u_ , where α = 1.4, β = -0.06, γ = -0.06 and each series is 1000 observations. Parameter values match the results using data from the benchmark diary, in column (8) of Table 3. To implement the experiments, total consumption, _x_ was partitioned into food consumption, _xF_ = _x_ ⋅ _wF_ and nonfood consumption, _xNF_ = _x_ − _xF_ and three different types of errors were added to food ( _vF_ ) and non-food ( _vNF_ ) and the error-ridden total expenditure and food share variables were ~ ~ ~ ~ ~ reconstructed as _x_ = _xF_ + _xNF_ and _w_ = _xF_<sup>~</sup> _x_ , before the food Engel curve regressions were reestimated.

In case (1) the measurement errors were independent of any of the variables in the model and of each other, with _vF_ ~ _N_ (,0 σ _v_ 2 _F_ ), and _vNF_ ~ _N_ (,0 σ _v_ 2 _NF_ ). The errors, σ _v F_ and σ _v NF_ took each of nine values, ranging from 0, 0.05, 0.1, 0.15, ..., 0.35, 0.4.

> In case (2) the errors were correlated with true values, _vF_ =φ ln _xF_ + ε and _vNF_ =ψ ln _xNF_ + ε where ε ~ _N_ ()1.0,0 and the values used for φ and _ψ_ were 0, -0.05, -0.1, -0.15, ..., -0.35, -0.4.

In case (3) errors were correlated with household size, _n_ : _vF_ = λ ln _n_ + ε and _vNF_ =η ln _n_ + ε where ε ~ _N_ ()1.0,0 and the values used for _λ_ and _η_ were 0, -0.05, -0.1, -0.15, ..., -0.35, -0.4.

16
planning as also found for the main results on cumulative births. Again, this evidence points to the direct role of family planning access on age differentiated fertility patterns as opposed to indirect effects of health services.

# **5 Conclusion**

Despite substantial interest among policy makers in family planning programs, especially in high fertility, low-income settings, the debate about their effectiveness has so far been lacking in reliable empirical evidence. This is partly due to the methodological challenges involved in controlling for the potential nonrandom program placement of family planning programs. Although experimental data provide a theoretically “clean” way to address these concerns, their application to fertility is complex given the time span over which fertility decisions are made and therefore, in practice, only survey data are usually available.

This paper studies the effects of family planning on fertility in Ethiopia and to address potential nonrandom program placement uses a set of novel instruments: the rankings of area characteristics (as opposed to the levels). We argue that such rankings of area characteristics are likely reflective of policy makers’ actual decision process when allocating family planning programs, while not affecting fertility directly. They are intuitive and easy to generate from readily available secondary data like a census or even from the primary data set itself, enabling easy replication of the methodology in other settings.

The results suggest that access to family planning reduces the total number of children born for Ethiopian women without education. The reduction in completed fertility is large at more than one child. This is more than twice the effect reported in other studies, typically from more developed settings. Moreover, the actual impact is likely larger as the results are arguably underestimates. As detailed in this paper, our approach is conservative in attributing no access to women who have some access later in the study period. This biases our impact estimates downward. No effect of access to family planning was found among women with some education, suggesting that family planning may act as a substitute for education in reducing fertility.

There are two likely explanations for the large estimated effect. First, as Ethiopia’s fertility is high compared to other study countries, such as Colombia, the marginal effects of increasing access to family planning are likely higher. Second, education levels are extremely low in Ethiopia and certainly lower than in the settings examined in other studies. If family planning acts as a substitute for education, previous 22
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27
Learning versus Stealing: How Important are Market-Share Reallocations to India’s Productivity Growth?<sup>_∗_</sup> Ann E. Harrison<sup>†</sup> , Leslie A. Martin<sup>‡</sup> and Shanthi Nataraj<sup>§</sup> May 1, 2011

**Abstract**

Recent trade theory emphasizes the role of market-share reallocations across firms (“stealing”) in driving productivity growth, while the older literature focused on average productivity improvements (“learning”). The authors use comprehensive, firmlevel data from India’s organized manufacturing sector to show that market-share reallocations did play an important role in aggregate productivity gains immediately following the start of India’s trade reforms in 1991. However, aggregate productivity gains during the overall period from 1985 to 2004 were driven largely by improvements in average productivity, which can be attributed to India’s trade liberalization and FDI reforms.

> _∗_ We thank Mr. M.L. Philip, Mr. P.C.Nirala, Dr. Praveen Shukla, and Mr. M.M.Hasija at the Ministry of Statistics and Programme Implementation for their assistance in understanding the Annual Survey of Industries data collection process; Rana Hasan, Karl Jandoc, and Steve O’Connell for discussions regarding the firm-level data; and Pauline Grosjean and Ben Crost for providing us with their district-level concordances, which formed a basis for ours. This material is based upon work supported by the National Science Foundation under Grant No. 0922332. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily

†Department of Agricultural and Resource Economics, University of California, Berkeley, and NBER.

‡Department of Agricultural and Resource Economics, University of California, Berkeley.

§RAND Corporation.

1
poverty line would have to be slightly higher than $3 per person per day using 2011 PPP values. Similarly, if we want to obtain the same poverty rate with the new moderate poverty line as the one obtained with the currently-used moderate poverty line of $4 per person per day (i.e., 24.3 percent), the new moderate poverty line would have to be approximately $5 per person per day.<sup>27</sup> However, according to Jolliffe and Prydz (2015), this approach presents two fundamental limitations. First, by fixing the poverty rate, we would be giving more weight to the LAC poverty headcount based on 2005 PPPs than the one based on 2011 PPPs. However, since the poverty rates would be different across countries, we would end up preferring the 2011 PPPs for country-specific comparison. Second, by keeping the poverty line fixed, we would not maintain the same standard of living in 2011 as in 2005.

One relevant question is whether these new regional poverty rates differ considerably with respect to the current LAC poverty rates based on 2005 PPPs. The difference between the poverty rate with the previous poverty line based on 2005 PPPs and the revised numbers in the first four rows of Table 3 is the result of two factors: (i) changes in the value of the regional poverty lines and (ii) changes in the per capita income distribution due to temporal and spatial adjustments, which are explained by changes in the CPI and PPP values, respectively. If the nominal value of the new moderate poverty line were the same as the previous extreme poverty line (i.e., $2.5 per person per day), LAC’s extreme poverty rate in 2013 would have been approximately 7.8 percent at 2011 PPP values instead of 11.5 percent at 2005 PPP values. Similarly, if the nominal value of the new moderate poverty line were the same a currently-used $4 per person per day, LAC’s moderate poverty rate in 2013 would have been approximately 17 percent at 2011 PPP values instead of 24.3 percent at 2005 prices.<sup>28</sup>

> 27  It is worth mentioning that if we wanted to obtain the same poverty rate as the one obtained with the $4.4 per person per day (27.5 percent, see footnote 24), this new line would have to be approximately $5.5 per person per day.

> 28  $4 per person per day was the moderate line used for measuring moderate poverty in LAC. However, Gasparini, Cicowiez, and Escudero (2013) show that the actual mean of national poverty lines in 2005 PPPs is $4.4 per person per day. Therefore, if the nominal value of the new moderate poverty line were $4.4 per person per day, LAC’s moderate poverty rate at 2011 PPP values would have been approximately 20.2 percent instead of 27.5 percent at 2005 PPPs in 2013.

15
In spite of these caveats, we also use the Frankel-Romer instruments, as they will go a long way in reducing the simultaneity bias.<sup>24</sup> In particular we interact the FrankelRomer instruments with the skill level of the occupation and use them as instruments for our trade and FDI measures. Table 7 reports the results of this exercise. Generally speaking the earlier results from Table 6 remain valid. We still find a negative impact of trade and FDI net inflows on the gender wage gap for low skill occupations, both in the poorer and richer countries.<sup>25</sup> Also the earlier finding that there is a negative impact of trade on the gender wage gap for high skill occupations in richer countries is confirmed. In particular the coefficient for the sectoral trade variable is now also negative (although insignificant), confirming that simultaneity bias is especially important for the sectoral trade data, presumably because high gender gaps imply cost-savings and increase trade intensity with an upward bias in the OLS estimate. Interestingly, however, the significant positive impact of FDI on high skill occupations in Table 6 becomes now significantly negative for richer countries. Hence, FDI may still increase the gender wage gap, but only for high skill occupations in poorer countries. This finding is consistent with the earlier explanation that high skill occupations may be complementary to trade in low skill goods in developing countries.

Also the estimated coefficients for the trade and FDI variables are generally larger when instrumented (compare Tables 6 and 7). This result may reflect the attenuation bias because of measurement error in Table 6, as well as simultaneity bias.<sup>26</sup> In the poorer countries a 10% point increase in trade (as a % of GDP) lowers the low skill gender gap by 1% point. In richer countries a similar increase in trade intensity would reduce the gender gap by 2% point, both in low and high skill occupations. The impact of FDI on the gender gap is particularly large – a 1% point increase in FDI (as a % of GDP) would lower the gender gap by 3-4% points, except for the high skill occupations in poorer countries.

> 24 Dollar and Kraay (2001) have used lagged levels of trade as an instrument, assuming that any timevarying omitted variables are uncorrelated with lagged levels of openness. This strategy also has its limits because it does not take into account any serial correlation in growth (shocks).

> 25 There is a positive and insignificant coefficient on the sector trade variable only for low skilled occupations in poorer countries.

> 26 Simultaneity bias will lead to an upward bias if gender gaps imply cost-savings and therefore increase trade and FDI net inflows.

23
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