rafmacalaba/datause-encoder-data huggingface dataset:
Table 4. Main regression results-OLS predicting log unitprice (each regression includes controls for purchased quantity, fixed effects for supervisory ministry, product market, location of the buyer, and the year of auction)

|=====================================================================================<br> <br>---------------------------------------<br>|======================================<br>Dependent variabl<br>--------------------------------------<br>log unitprice|====================<br>e:<br>--------------------<br>|===================<br> <br>-------------------<br>|====================<br> <br>--------------------<br>|
|---|---|---|---|---|
|(1) (2)<br>-------------------------------------------------------------------------------------|(3) (4)<br>--------------------------------------|(5)<br>--------------------|(6)<br>-------------------|(7)<br>--------------------|
|random phase length -0.0001*** -0.0001***|-0.0001*** -0.0001***|0.00002|-0.0001***|-0.0001***|
|(0.00000) (0.00000)|(0.00001) (0.00001)|(0.00002)|(0.00001)|(0.00002)|
|number of bids in the last 5 mins|0.018***||0.014***||
||(0.0003)||(0.001)||
|log % discount in last 3 mins|-0.086***|||-0.074***|
||(0.002)|||(0.004)|
|number of bidders 0.051***|0.061*** 0.092***|0.073***|0.061***|0.091***|
|(0.002)|(0.002) (0.004)|(0.004)|(0.002)|(0.004)|
|number of bidders squared -0.0004***|-0.001*** -0.002***|-0.001***|-0.001***|-0.002***|
|(0.0001)|(0.0001)(0.00000)<br>|(0.00000)|(0.00000)|(0.00000)|
|random phase length*number of bidders||-0.00002***|||
|random phase length*number of bidders sq.||0.00000***|||
|||(0.00000)|||
|random phase length*nr.of bids in last 5 mins|||0.00000***<br>(0.00000)||
|random phase length*log % discount||||-0.00002***<br>(0.00000)|
|-------------------------------------------------------------------------------------|--------------------------------------|--------------------|-------------------|--------------------|
|Observations 334,086 334,086|266,983 106,301|334,086|266,983|106,301|
|R2 0.612 0.617|0.634 0.629|0.617|0.634|0.629|
|Adjusted R2 0.612 0.617|0.633 0.627|0.617|0.633|0.627|
|Residual Std. Error 1.437 (df = 333641) 1.428 (df = 333639)<br>=====================================================================================<br>Note:|1.402 (df = 266535) 1.345 (df = 10585<br>======================================<br>|4) 1.428 (df = 33363<br>====================<br>|7) 1.401 (df = 2665<br>===================<br>*p<0|34) 1.345 (df = 1058<br>====================<br>.1; **p<0.05; ***p<0|

16
transition toward a future without oil. Most macroeconomic aggregates grow at real rates quite close to the rate of GDP at factor cost. The main exceptions are exports (which suffer from the decline in the oil sector) and private investment (which are hurt by the combination of high import intensity and depreciation).

**Figure 6:** Real sectoral GDP growth rates (2010-2025, percent)

<!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>-10<br>-20<br>Agriculture Industry - oil<br>Industry - non-oil Services - non-government<br>Services - government<br><!-- End of picture text --> <!-- Start of picture text --> Source: Authors’ calculations<br><!-- End of picture text --> On the fiscal side, government spending increases from 28 to 32 percent of GDP in 2025, mainly due to an increase in consumption caused by a rapid wage increase. Government revenues decline significantly to 21 percent of GDP. The fall in oil revenues, however, is mitigated by a rise in domestic non-oil taxes, both direct and indirect, relative to GDP.

GDP growth is accompanied by a relatively rapid expansion of employment. The unemployment is subsequently reduced from 19.4 percent (last country-wide assessment in 2005) to 13.5 percent in 2025. Real wages grow relatively evenly across the educational levels; between 4.1 and 5.0 percent per year on average. Wage growth is quite even across educational levels, reflecting the combined impact of differences in productivity and supply growth between labor types.

Poverty declines substantially and the other social indicators improve as well. In the baseline scenario, the poverty rate falls to about 24 percent in 2025, down from 51 percent measured by a household survey in 2005 (see Table 6). The under-5 mortality rate is reduced significantly from 128 to 67 per 1,000 live births. In the education sector, the performance is improved according to all indicators, i.e. promotion, repetition, drop-out and continuation rates. The only exception is a decrease in the gross primary completion rate, which, however, does not indicated worse performance, but rather that fewer older and out-of-cohort students enter primary school.

# 4.2 <u>Front-loading versus back-loading</u>

Under an alternative set of scenarios, the government is scaling up infrastructure spending even more than currently planned. It is assumed that an additional CFAF 2,000 billion (about USD 4 billion) are spent either over 3 years (2013 to 2015), equaling CFAF 667 billion additional investment each year, or over 7 years (2016 to 2022), amounting to CFAF 286 billion annually. We assume that the delay in 9
Mollerstrom et al. 2015, Durante et al. 2014). The papers in this literature that explicitly look at risk preferences examine how risk in the income generating process impacts subsequent nonrisky decisions. They do not look at risk as the decision of interest (Cappelen et al. 2013, Akbas ̧ et al. 2019). The consensus is that inequality from exogenous income differences increases preferences for redistribution. This relationship appears to be due to people’s preferences for increasing ex post equality when ex ante chances are not equal or “fair”. Fair is typically defined as income being based on choices or effort. Durante et al. (2014) is the most explicit about examining IOp, defined according to Roemer, as in our paper. And it is Durante et al. (2014) that most influences our design: we adopt their use of zip code to simulate IOp in the lab, and compare it with other kinds of income generating processes as they do. Our paper builds on Durante et al. (2014) in applying these techniques to focus on investment.

Finally, our work directly builds on a recent experimental study by Hillebrandt and Steinorth (2020). In their study, they compare investment choices between subjects who receive information about others’ wealth endowments, to a control that does not receive information. Endowment itself is held constant. Revealing other’s endowments makes poorer subjects take more risk and richer subjects take less risk. Unlike their paper, we use earned income, rather than wealth bequest. We also do not reveal actual wealth/income, and instead only share rank information. This eliminates any counteracting effects from earning level itself. Finally, we also use an exogenous circumstance as the determinant of inequality in the treatment group. This gives us a direct link to the theory on inequality of opportunity.

# **3. Experimental design**

With the experiment, we aim to answer two primary research questions: (1) What is the marginal impact of IOp on individual investment in a risky asset? (2) How does earnings rank interact with the source of inequality in the income generating process to impact investment decisions?

We conducted our laboratory experiment between February 2020 and July 2021 at the University of Maryland Department of Agricultural and Resource Economics - Symons Hall Experimental Laboratory (SHEL). There were a total of 499 subjects, out of which 240 completed 8
conditions from Section II). As a result, initial GDP per capita and DBPC/GDP may perform simila **r** oles in explaining real sector growth which, if Gertler and Rose are to be believed, should be linked to the post-intervention development of a country's financial system. We might, therefore, expect a negative partial correlation between postintervention financial sector development and either initial DBPC/GDP or initial per capita GDP. Inflation, the other initial condition from Section II, is, of course, also expected to have a negative partial correlation with financial development. With respect to initial conditions, therefore, explanatory variables will include the inflation rate, trade variables (EXP/GDP or IMPt/GDP), and DBPC/GDP or real GDP per capita, all measured in the year of the intervention.

More recent research on growth has emphasized the important role that institutions may play. In particular, both real GDP growth and investment rates have been linked to the quality of a country's institutions. The Gertler-Rose hypothesis suggests that any income growth, even if born of institutional development, should also be reflected in deeper financial systems. In the regressions that follow we would, therefore, expect a positive partial correlation between post-intervention financial sector development and institutional development. To measure institutional quality in the recipient country, we use indices created by Knack and Keefer based on indicators compiled by two private international investment risk services, the International Country Risk Guide (ICRG) and Business Environmental Risk Intelligence (BERI).<sup>3</sup> ' The Knack and Keefer indices are 31 Knack and Keefer (1995), pp. 225-6.

22
resources may be secondary to that played by parents’ education, because the low educated non-farmers have higher income.

In the parents’ generation, the results from the sensitivity analysis for mothers are very similar to those for the daughters discussed above (see Table 4B). This reinforces the conclusion that there has not been any significant change for women across generations. In sharp contrast to sons, for fathers, the sensitivity analysis shows that except for the group composed of educated farmers, the estimated effect can be accounted for by low to moderate selection on ability and preference. Thus, for boys, having nonfarmer parents did not constitute an advantage in parental generation.

# **Estimates from Minimum-Biased NIPW (MB-NIPW) Estimator**

The estimates from MB-NIPW are reported in Table 5.<sup>35</sup> The estimated effects are interesting, and in some cases different from the estimates in Tables 2 and 3. In childrenparents sample, the magnitudes of the estimates are broadly similar to the earlier estimates for daughters, but the magnitudes are larger for sons, especially for the low educated nonfarm households.

The MB-NIPW estimates for grandparents-parents sample show two things. First, the estimated effect of low educated nonfarm status for fathers is very low and not significant at the 10 percent level, implying that, for fathers, non-farm occupation of grandparents did not matter for educational attainment. This is in sharp contrast of a numerically larger effect found for the sons in this group. Interestingly, the irrelevance of occupational status is also observed for the educated subsample in parents’ generation: the estimates are very similar for the educated farmer and educated non-farmer groups. The non-farm occupation,

> 35We reemphasize here that these estimates are not comparable to the estimates in Tables 2-3 and the results of the sensitivity analysis. The sensitivity analysis pertains to the estimates for the full sample and refers to the average effects (ATE). The MB-NIPW estimates, in contrast, provide LATE; effects on the households that fall in the neighborhood of bias minimizing propensity score (0.50), but the advantage is that we have a point estimate.

33
imperfection - leads to more reliance on non-financial inflation hedges as investment in real estate and gold that are perfect substitutes of financial savings.

Short-term influence of real interest rates and financial development on real private savings are in line with theory and our earlier interpretation of long-term equation. Both variables are positively related to real private savings in the short term. This is in full conformity with the LCH.

Market openness has a significant effect on private savings. Exchange rate movement in the previous period reflects on real private savings in the current period. This is consistent with the analysis on the impact of short-term inflation. In general, devaluation of domestic currency in LDCs with limited capital mobility raises the public aversion to future uncertainties and trigger higher precautionary savings to mitigate future macroeconomic shocks. Another interpretation for the case of LDCs is that devaluation inflates the volume of the equivalence of foreign currency deposits in domestic currency. This has in turn led to a positive net effect for exchange rate devaluation on real private savings. Furthermore, the rise of foreign exchange rate triggers dissavings and preference to store of value in non-financial assets (See Hussein and Mohieldin 1997).

# **Conclusion**

A mix of the simple permanent income hypothesis as well as the life cycle hypothesis determinants are used to interpret real private savings behavior in Egypt. This study provides new empirical evidence related to savings behavior, financial development, and economic growth in Egypt. By using high quality and high frequency quarterly data covering 1991-2010, this study employed a robust vector equilibrium correction modeling strategy. The key long-term determinants of private savings in Egypt are the real interest rate and level of financial development. The data also show higher persistence of real private savings in the short run.

The real interest rate varies negatively with real private savings in the long term for the period under consideration, 1991 – 2010. This translates to the fact higher consumption is becoming more attainable at lower cost. Hussein and Mohieldin (1997) reached the same conclusion on the relation between private savings and real interest rates during 1960 – 1990. However, they referred to the fact that the financial repression of 1960s cannot be alleviated by an increase in the real interest rate as it further deepens the problem of excess liquidity of the banking system, which is indicative to the liquidity crisis in the late 1990s.

In the short term, real private savings vary positively with real interest rates, whereas financial development positively reflects on higher private savings on both horizons. This is a literal translation of the McKinnon -Shaw hypothesis on financial development and growth, which strongly holds for the case of Egypt.

20
Son Preference, Fertility and Family Structure. Evidence from Reproductive Behavior among Nigerian Women<sup>_∗_</sup> Annamaria Milazzo<sup>_†_</sup> JEL classification: D63, J13, J16, I10 Keywords: son preference, fertility, birth spacing, maternal mortality, polygyny, child fostering, Nigeria

> _∗_ I am grateful to Eliana La Ferrara for continued guidance and support, and to Alberto Alesina, Siwan Anderson, JeanMarie Baland, Sonia Bhalotra, Maristella Botticini, Denis Cogneau, James Fenske, Selim Gulesci, Rajshri Jayaraman, Sylvie Lambert, Adriana Lleras-Muney, Andreas Madestam, Massimo Marinacci, Nicola Pavoni, Martin Ravallion, and Dominique van de Walle for very helpful comments. I thank seminar participants at Bocconi University, Paris School of Economics, CRED at the University of Namur, 2012 CEPR/AMID Conference, IFPRI, IAST Toulouse, and the AEL Conference 2012 for comments and suggestions. The findings, interpretations, and conclusions expressed in this paper do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. All errors are my own.

> _†_ The World Bank. Email: `amilazzo14@gmail.com; amilazzo@worldbank.org`

1
# Table A1. Narrative sign restrictions

## A. Episodes of positive oil price shocks

|Date|Episode|Sign restriction<br>violation rate|
|---|---|---|
|1973.10-1974.3|Yom Kippur war|1.5|
|1978.12-1979.1|Iranian revolution|0.1|
|1980.10-1981.3|Iran-Iraq war|32.7|
|1990.8-10|Iraqs invasion of Kuwait|0.1|
|2002.12|Venezuela oil strike|0.1|

## B. Episodes of negative oil price shocks

|Date|Episode|Sign restriction<br>violation rate|
|---|---|---|
|1986.2<br>1998.2<br>2015.1|OPEC production target increase<br>Asian currency crisis and OPEC<br>production expansion<br>Surging U.S. shale oil production, receding<br>geopolitical risks|1.3<br>8.6<br>0.3|
|2020.3|COVID-19 pandemic, price war between<br>Saudi Arabia and Russia|79.8|

Note: This table indicates the list of historical events of large oil price shocks based on Hamilton (2011), Baffes et al (2015), Wheeler et al (2020), Kabundi and Ohnsorge (2020), and Antolin-Diaz and Rubio-Ramirez (2018). The last column presents the probability of draws that violate the narrative restrictions when the standard sign restrictions are imposed in identifying the oil price shocks.

51
**<u>Figure H2: Unconditional quantile regression of height-for-age z-scores on food security measures, expanded controls (Tanzania)</u>**

<!-- Start of picture text --> Per Capita Caloric Availability 1-SSR<br>.05 .15 .25 .35 .45 .55 .65 .75 .85 .95 .05 .15 .25 .35 .45 .55 .65 .75 .85 .95<br>Quintile Quintile<br>95% Confidence Interval OLS 95% Confidence Interval OLS<br>RIF-OLS RIF-OLS<br>1-SSEXR DDS<br>.05 .15 .25 .35 .45 .55 .65 .75 .85 .95 .05 .15 .25 .35 .45 .55 .65 .75 .85 .95<br>Quintile Quintile<br>95% Confidence Interval OLS 95% Confidence Interval OLS<br>RIF-OLS RIF-OLS<br>.4 .4<br>.2<br>.2<br>0<br>0<br>-.2<br>-.2<br>-.4<br>-.6 -.4<br>.4 .5<br>.2<br>0<br>0<br>-.2<br>-.4<br>-.6 -.5<br><!-- End of picture text --> 102
|Creativego|ods:Exp<br>Wo<br>|orts,by e<br>rld<br>|conomic<br>Deve<br>|group, 2<br>loping<br>|002and<br>Deve<br>|2011(in m<br>loped<br>|illionso<br>Trans<br>|fUSS)<br>ition<br>|
|---|---|---|---|---|---|---|---|---|
||2002|2odll|2002|2011|2002|20ll|2002|20ll|
|All Creative|||||||||
|Goods|198,240|454,019|73,890|227,867|123,169|222,597|1,181|3,555|
|Art Crafts|17,503|34,209|9,201|23,383|8,256|10,653|45|172|
|Audio-visuals|455|492|35|90|417|400|3|2|
|Design|114,694|301,262|53,362|172,223|60,970|127,239|362|1,800|
|New Media|17,506|43,744|4,412|14,607|13,071|28,918|23|219|
|Performing Arts|2,754|-|250|-|2,478|-|26|-|
|Publishing|29,908|43,077|3,157|8,106|26,061|33,650|690|1,321|
|Visual Arts|15,421|31,127|3,474|9,456|11,916|21,631|31|40|

# Creative goods: Exports, by economic group, 2002 and 2011 (in millions of USS)

~~<u><mark>[|</mark></u>~~
hypothesis.) It must also be assumed that the fact that an area turned against the government in the last election is not correlated with latent factors influencing schooling. The variation in spending attributed to this IV was found to significantly increase school attendance rates.

The third set of examples exploit discontinuities in program design, as discussed in section 6.<sup>58</sup> Here the impact estimator is in the neighborhood of a cut-off for program eligibility. An example of this approach can be found in Angrist and Lavy (1999) who assessed the impact on school attainments in Israel of class size. For identification they exploited the fact that an extra teacher was assigned when the class size went above 40. Yet there is no plausible reason why this cut-off point in class size would have an independent effect on attainments, thus justifying the exclusion restriction. The authors find sizeable gains from smaller class sizes, which were not evident using OLS.

Another example is found in Duflo’s (2003) study of the impacts of old-age pensions in South Africa on child anthropometric indicators. Women only become eligible for a pension at age 60, while for men it is 65. It is implausible that there would be a discontinuity in outcomes (conditional on treatment) at these critical ages. Following Case and Deaton (1998), Duflo used eligibility as the IV for receipt of a pension in her regressions for anthropometric outcome variables. Duflo found that pensions going to women improve girls’ nutritional status but not boys’, while pensions going to men have no effect on outcomes for either boys or girls.

Again, this assumes we know eligibility, which is not always the case. Furthermore, eligibility for anti-poverty programs is often based on poverty criteria, which are also the relevant outcome variables. Then one must be careful not to make assumptions in estimating who is eligible (for constructing the IV) that pre-judge the impacts of the program.

The use of the discontinuity in the eligibility rule as an IV for actual program placement can also address concerns about selective compliance with those rules; this is discussed further in Battistin and Rettore (2002).

As these examples illustrate, the justification of an IVE must ultimately rest on sources of information outside the confines of the quantitative analysis. Those sources might include

> 58 Using discontinuities in IVE will not in general give the same results as the discontinuity designs discussed in section 6.  Specific conditions for equivalence of the two methods are derived in Hahn et al. (2001); the main conditions for equivalence are that the means used in the single-difference comparison are calculated using appropriate kernel weights and that the IVE estimator is applied to a specific subsample, in a neighborhood of the eligibility cut-off point.

48
consumption, and labor activities) but also the other children in the household, thereby allowing us to study how the treatments may have affected the allocation of work and resources within families.

Finally, we obtained administrative records from the Secretary of Education that includes the enrollment records of every child in a public school and many private schools in the two localities. This data allows us to assess the effect of the treatments using every student that registered for the randomization, including those not claiming to attend one of the 68 schools selected for attendance data collection and surveying. Combined with the other outcome variables, this provides us with three concentric groups of students: all of the students who registered for the randomization (for which we only have administrative enrollment data), all of the students registered at the 68 schools selected for surveying (for which we have both administrative enrollment data and verified attendance data), and those students at the 68 schools who completed baseline and follow-up surveys (for whom we have administrative enrollment data, verified attendance data, and the information collected in the surveys).

# **D. Analytic Models**

We use three basic models to analyze the data. First, we use a simple difference estimator. Second, we also use a difference estimator that includes controls for individual and family characteristics. Third, we use an instrumental variables model to estimate externalities generated by the treatments within families and students friendship networks.

First, in order to validate the randomization, we use a simple difference model to make simple comparisons between different subsets of the sample without controlling for any covariates. These comparisons are intended to assess the comparability of different groups such as the research groups, registrants and non-registrants, etc. When used to compare a given treatment and the respective control group, for example, the specification takes the following form: To perform this estimate, the data sets containing the treatment group of interest and the respective control group are pooled. The variable<sup>_x_</sup> _ij_<sup>represents a particular characteristic of</sup>

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