rafmacalaba/datause-encoder-data huggingface dataset:
# SELECTED BIBLIOGRAPHY

- Arcelus, Francisco and Meltzer, Allan H. "The Markets<sup>for Housing and Housing Services."</sup> Journal of Money. Credit and Banking 5, No. 1, Part I (February 1973):<sup>78-99.</sup>

- Armington, Catherine and Odle, Marjorie. "Small Business-How<sup>Many Jobs?" Brookings Review</sup> 1, No. 2, (Winter 1982): 14-17.

- Bosworth, Barry P.; Carron, Andrew S.; and Rhyne, Elisabeth H. The<sup>Economics of Federal</sup> Credit Programs. Washington: Brookings Institution,<sup>1987.</sup>

- Buckley, Robert M. and Gross, David J. "Selective Credit Policies<sup>and the Mortgage Market."</sup> Journal of Money, Credit. and Banking 17, No. 3 (August<sup>1985): 358-70.</sup>

- Calomiris, Charles W.; Hubbard, R. Glenn; and Stock, James H.<sup>"Growing in Debt: The 'Farm</sup> Crisis' and Public Policy." Brookings Papers on Economic Activity 1986,<sup>No. 2: 441-79.</sup>

- Davis, Richard G. "An Analysis of Quantitative Credit Controls<sup>and Related Devices." Brookings'</sup> Papers on Economic Activity 1971, No. 1: 65-104.

- DeFina, Robert H. "The Link Between Savings and Interest Rates:<sup>A Key Element in the Tax</sup> Policy Debate." Business Review Federal Reserve Bank<sup>of Philadelphia</sup> November/December 1984: 15-21.

- Dhrymes, Phoebus J. and Taubman, Paul J. "An Empirical Analysis<sup>of the Savings and Loan</sup> Industry." In Study of the Savings and Loan Industry, edited<sup>by Irwin Friend, pp. 67-181.</sup> Washington: U.S. Government Printing Office, 1969..

- Gale, William G. "Economic Effects of Federal Credit Programs."<sup>American Economic Review</sup> 81, No. 1 (March 1991): 133-52.

- Hamburger, Michael J. and Zwick, Burton. "The Efficacy of<sup>Selective Credit Policies: An</sup> Alternative Test." Journal of Money. Credit. and Banking<sup>11, No. 1 (February 1979):</sup> 106-110.

- Hodgman, Donald R. "Selective Credit Controls." Journal of<sup>Money. Credit. and Banking 4, No.</sup> 2 (May 1972): 342-59.

- LeBlanc, Michael and Hruboveak, James. "The Effects of Tax<sup>Policy on Aggregate Agricultural</sup> Investment." American Journal of Agricultural Economics<sup>68 (November 1986): 767-77.</sup>

- McPherson, Michael S. "The Demand for Higher Education."<sup>In Public Policy and Private</sup> Higher Education, pp. 143-96. Edited by David W. Breneman<sup>and Chester E. Finn, Jr.</sup> Washington: Brockings Institution, 1978.

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**Figure 9 Reserves as percent of GDP, by country, 2007 and 2015**

<!-- Start of picture text --> Reserves to GDP<br>by country<br>2007 2015<br>Constructed by the authors using Lane and Feretti (2007)<br>AREBHRCOM DJIDZAEGYIRQJORKWTLBNLBYMARMRTOMNQATSAUSDNSYRTUNWBGYEM<br>2<br>1.5<br>1<br>.5<br>0<br><!-- End of picture text --> _Source_ : Authors, based on Lane and Feretti 2015.

After presenting the structure and trends of NFA in Arab countries, it is important to see how such variables are correlated with macroeconomic determinants. Table 2 shows the interaction between NFA and a series of policy variables, including whether a country has a more developed financial system, an SWF, or an intermediate exchange rate regime (a r <mark>egime between a fixed and floating one)</mark> . Countries that are more financially developed and countries with SWFs are more likely to have a better allocation of their assets. Yet, countries with an intermediate exchange rate regime have more uncertainty in their exchange rate policy than countries with fixed peg or flexible regimes, which can adversely affect their external positions. Table 2 highlights these findings. For most of the variables (for both assets or liabilities), countries with an SWF, with a more developed financial system, and without an intermediate exchange rate regime are financially more integrated with the rest of the world. This finding was the key motivation behind including these policy variables in our empirical estimation.

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generally consistent with the evidence in Bender, Bloom, Card, Van-Reenen, and Wolter (2018) for Germany, and suggests that observed skills of top executives are systematically associated with advanced management practices.

# **5.4.2 Managerial skill and the division of revenue shocks**

Having shown that management z-scores tend to be higher among firms with a greater share of highly-skilled top executives, we now examine whether and how managers matter for the distribution of exogenous shifts in revenue. To do so, we split our estimation sample according to whether the proportion of managers with a degree was above or below the median in 2006 (or in the first year of observation in case of firms that entered the market later than 2006).<sup>15</sup><sup>_,_16</sup> Tables A8 and A9 in the Appendix provide some summary statistics for each of the two subsamples, with the sample split based on a definition of top managers according to occupational category in Table A8 and as the 5 percent highest earners in Table A9. In either case, we see that, on average, firms with high-skilled managers are larger, as measured by sales and employment, and also export and invest more, than firms with low-skilled managers. The former category of firms also tend to employ more skilled workers and pay them better along all dimensions of worker compensation.

When estimating (3) on each of the two subsamples, the results reported in Tables A10 and A11 in the Appendix show that the baseline results on the effects of unexpected demand shocks on firm performance generally apply to both subsamples. Regardless of the method used for identifying top managers, the forecast error has a significant effect on exports and sales for both categories of firms, which suggests that our strategy for identifying unexpected demand shocks is valid also for these more restricted subsamples.

Having established the general validity of our identification strategy, our main objective in this part of the analysis is to re-estimate the results in Table 4 using the above explained sample partition, in order to examine whether and how the within-firm distribution of the revenues from unexpected demand shocks is related to managerial skills. The resulting estimates for the two alternative sample partitions are shown in Tables 5 and 6, respectively.

> 15When identifying managers by occupational category, the classification of firms as being managed by highskilled vs low-skilled managers is made according to the first year in which a managerial occupation is observed.

> 16Whether we identify managers by occupational category or by their position in the wage distribution, the share of managers with a degree is zero for more than half of the firms in our sample in 2006, and is thus zero also for the median firm. This implies that all firms in which no manager has a degree are classified as firms with low-skilled managers, whereas all firms in which at least one manager has a university degree are classified as firms with high-skilled managers.

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On evaluating all variables at their sample means and using the estimates in column (1) of Table 3 and column (5) from Table 4, and using the OLS elasticity of elasticity of the initial headcount index with respect to the initial survey mean of -1.085, one finds that the mean convergence effect is -0.047, while the direct effect of poverty is 0.026 and the poverty elasticity effect is 0.016. The mean convergence effect is almost exactly cancelled by the combination of the two ―poverty effects,‖ which are roughly equal in size.

Naturally, different data points and parameter estimates give different magnitudes for this decomposition, though all share the feature that the two poverty effects work in opposition to the (conditional) mean convergence effect. Evaluating the decomposition at a higher initial headcount index increases the poverty elasticity effect while reducing the other two components. The estimates using only the consumption surveys give a higher direct effect of poverty, as do the estimates from the subsample with three surveys; in the latter case the poverty convergence elasticity is larger due to both a lower mean convergence component and the higher direct effect.

# **9. Conclusions**

The most interesting thing about the fact that we do not see poverty convergence in the developing world is what it tells us about the underlying process of economic growth and its impact on poverty. The lack of poverty convergence—despite mean convergence and that growth generally reduces poverty—suggests that something about the initial distribution is offsetting both the ―advantage of backwardness‖ and the ―advantage of growth.‖ That something turns out to be poverty itself. The paper‘s findings point to three distinct consequences of being a poor country for subsequent progress against poverty. The usual neoclassical convergence effect entails that countries starting with a lower mean, and so (typically) a higher poverty rate, grow faster and (hence) enjoy faster poverty reduction than otherwise similar countries. Against this, there is an adverse direct effect of poverty on growth, such that countries with a higher initial incidence of poverty tend to experience a lower rate of growth, controlling for the initial mean. Additionally, a high poverty rate makes it harder to 22
# 6. Conclusion

This paper evaluates the _Vaso de Leche_ (VL) feeding program in Peru. In doing so, we ask that if a community-based multi-stage targeting scheme such as that of the VL program is progressive, is it possible that the program can achieve its nutritional objectives? This is done by linking public expenditure data with household survey data to assess the targeting, and then to model the determinants of nutritional outcomes of children to see if VL program interventions have an impact on nutrition.

We confirm that the VL program is reasonably well targeted to poor households and to households with low nutritional status. Despite official targeting criteria based not on income status, rather on the presence of young children and pregnant and lactating women in the household, approximately 50 percent of the poor received VL benefits, while less than 20 percent of the non-poor were beneficiaries. Further, in Probit estimates we find that households in the poorest quintile of the population are 24 percent more likely than households in the richest quintile to receive VL transfers. In terms of the values of transfers, over 60 percent (possibly up to 75 percent) of the allocated VL budget goes to the poor. Tobit models of the determinants of household transfer values also indicate that the poor benefit more, as a one percent decrease in the income level of a household corresponds to a four percent increase in the value of the VL transfer, _ceteris paribus._ While this targeting is progressive, especially for one which is commodity based, it is less so than other programs including the interministerial fund, FONCOMUN (Fondo de Compensacion Municipal). At this time a cash transfer program is being piloted in Peru.

The impact of food subsidies beyond their value as income transfers, however, is limited by the degree to which the commodity transfers are infra-marginal. Using the 2002 Public Expenditure Tracking Survey, we find that transfers of milk and milk substitutes from the VL program are infra-marginal for approximately half of the households that receive them. Thus, it is not entirely surprising that we fail to find econometric evidence of the nutritional objectives of the VL program being achieved. In the models of child standardized heights, we use VL expenditures to the community instead of household participation as an explanatory variable, solving the issue of endogenous household choice. When we account for endogenous program placement with fixed effects and instrumental variables models, we find no impact of the VL program expenditures on the nutritional outcomes of young children - the group to whom the program is targeted.

Thus, despite being reasonably well targeted to the intended beneficiaries as well as to the poor and malnourished, the VL program falls short of its primary objective to improve the nutritional status of young children _33_
PARALLEL EXCHANGE RATES IN DEVELOPING COUNTRIES Lesons from Eight Case Studies Miguel A. Kiguel The World Bank and Stephen A. O'Connell Swarhinore College We are extremely gateful to Saul Lizondo who played a pivotal role in the preparation and management of the research project, prepared background mateial for this dft and spent long sessions discussing the fmdings We also thank David Bevan, Vittorio Corbo, Robert Flood, Steven Kamin, and seminar participants at Swarthrnor College, Oxford University, and the World Bank for helpful comnments Nita Ohei provided excellent research assistance.
# **I. Introduction and literature review**

Teachers are not only a major determinant of student learning, but also absorb a large amount of public resources. The supply of teachers has attracted much attention in academic research as well as in public policy debate. Dolton (2006) gave a comprehensive review of the existing empirical work on teacher supply issues, including entering teaching as the first job, staying in teaching, changing jobs and exiting teaching, and re-entering teaching. A majority of the existing literature on teacher supply is on developed countries, such as the US and the UK, where teacher supply issues have received a high level of attention, matched with rich micro and time-series data. One major research area is the determinants of the choice for teaching as an occupation, and particularly the effects of the differentiated earnings of teaching and non-teacher occupations.

Manski (1985)’s early work examined the relationship between academic ability, earnings, and the decision to become a teacher through analysis of data from a national sample of college graduates in the US. He found that the frequency of choice of teaching as an occupation is inversely related to academic ability. In the meantime, the earnings of teachers tend to rise only slightly, if at all, with academic ability. An econometric analysis suggests that in the absence of a minimum ability standard, increases in teacher earnings would yield substantial growth in the size of the teaching force but minimal improvement in the average academic ability of teachers. The average ability of the teaching force can be improved and the size of the teaching force maintained if minimum ability standards are combined with sufficient salary increases.

2
# TABLES

|TABLE : ALL INDUSTRY.<br>............................................<br>21|
|---|
|TABLE 2:BREAKDoWN STRUCrURE OF GVIO<sup>AT 1989 PRICEs.....</sup><br>.......... 23|
|TABLE3: BRANCH STRUCrURE oFGVIO<sup>AT 1989 PRIcES.....</sup><br>..............<br>24|
|TABLE 4: NET_OurPuT_ATCURRErrPRICES<sup>.......</sup><br>......................<br>26|
|TABLE S: CHMCALINDUSTRY<br>........................................<br>27|
|TABLu 6: ALTERNATIVE<br>DATA TO MEASURE INrLATION.....<br>...............<br>28|
|TABLE 7: PRICE INDmxs EXTRACTED<br>FROM INDUSTRTAL<br>OUTPTr VALuz DATA. 29|
|TABLE 8: ENERcY PRODUCTnvTY<br>IN CHNESE INDUSTRY, OFFICIAL DATA,<br>19781987 .......................................<br>30|
|TABLE 9: OFFICL.L ENERGY DATA FOR CHNESE<sup>INDUSTRY BY BRANCH,</sup><br>1980-'98S..................................<br>31|
|TABLE10: GVIODATAFOR1SBRANCHES<sup>...............................</sup><br>32|
|TABLE 11: REvISED CALCULATION:<sup>ANNUAL PERCENT INCREASE</sup><br>IN<br>ENERGY PRODUCTIVrrY BASED ON PHYSICAL ENROY<sup>CONSUMPTION</sup><br>AND<br>PUBLISHD GVIO DATA ............................<br>34|
|TABLE12:ENERGY DATA FOR STATE SECTOR<sup>INDEPENDENT</sup><br>UNrrS,<br>40BRANCHES, 1980AD1985..<br>35|
|TABLE 13: ROUGH CONVERSION<br>OF SOE ENERGY DATA TO 15 BRANCH<sup>FoRmAT . 37</sup>|
|TABLE14:TRIAL CALCULATION<br>OF ENEROY CONSumpONAND<br>PRODUCTIVrY FOR COLLECTIVE<br>INDUSTRY<br>.<br>.<br>38|
|TABLE 15: PERCENT CHANGE IN INDUSTRIAL<br>ENERGY PRoDucTIVrrY FOR 15<br>BRANCHES, 1980-1985 ...<br>..........<br>..................<br>40|
|TABLE16:DATA ON "ENERGYSAvwNGS"<br>(1980-1985).<br>...<br>..41|
where ;= ( ;)/®( ;)and ;=[( ;+ 4) ;4+1]. Also, we ha e: Vin = Elm] = E Ind(¥;=0)4 + Pu itp ; E 1 peep (> at Pai Ind(¥;> 0) Cu (= Gt Pah ( F-1P = £, +~—— C3) + Pu itp H pn.v22 (i( i) - OC i) +)aiay (7+) ( idx %u ( 7+1) ( iat 21-®( i))+( 7+ i) Ca) _i, (Cit a) ( )+1-O( j))aiat ( i®C i)<sup>+ida</sup> = sE, Os ( @( i+ i)az 2(.1—®( j))+ iCi (i) it 4), where ;=( ?+4+1) ( 4).

> Ind(i;> 4)-[— ¢+%j][-1+ 07] _i, H-CiCd +O d)G ( aa ou —(( d+ ¢ ( )-CF+2)(a) op 1—© EHR 4) drilou)Xi/ Oy ( i)/Ou Using the moments calculated abo e, we can erify that eq. (5) do not hold for the obit model.

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<!-- Start of picture text --> Employment<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>2006 2007 2008 2009 2010 2011 2012<br>m= Domestic m Exporting<br><!-- End of picture text --> <!-- Start of picture text --> Foreign Ownership (%)<br>Be<br>30<br>25<br>20<br>15<br>10<br>5<br>fe)<br>2006 2007 2008 2009 2010 2011 2012<br>TM Domestic m Exporting<br><!-- End of picture text --> <!-- Start of picture text --> Turnover (GELs)<br>12000000,<br>10000000<br>8000000<br>6000000<br>4000000<br>2000000<br>0<br>2006 2007 2008 2009 2010 2011 2012<br>= Domestic mExporting<br><!-- End of picture text --> <!-- Start of picture text --> L/K<br>OO 2<br>0.02.<br>0.0015<br>0.001<br>0.0005<br>0<br>2006 2007 2008 2009 2010 2011 2012<br>= Domestic mExporting<br><!-- End of picture text -->
# **Figure 2: Correlation between Saving Rate and the Six Macro Determinants**

a: Saving rate and per capita GDP, percent b: Saving rate and per capita GDP, quarterly change <!-- Start of picture text --> 12% 13.0<br>10%<br>8% 12.5<br>6%<br>4% 12.0<br>2%<br>0% 11.5<br>-2%<br>-4% 11.0<br>2002q1 2004q1 2006q1 2008q1 2010q1<br>Saving rate (left axe)<br>log GDP per capita (right axe)<br>billion AMD<br><!-- End of picture text --> <!-- Start of picture text --> 0.06<br>0.05<br>0.04<br>0.03<br>0.02<br>0.01<br>0<br>-0.01<br>-0.02<br>2002q1 2004q1 2006q1 2008q1 2010q1<br>Quaterly change in Savings rate<br>Quaterly change in log GDP per capita<br>billion AMD<br><!-- End of picture text -->

# c: Saving rate and real exchange rate, percent

# d: Saving rate and time deposit rate, percent

<!-- Start of picture text --> 12% 5.0 12% 10%<br>10% 10%<br>4.9 9%<br>8% 8%<br>4.8 6% 8%<br>6%<br>4% 7%<br>4% 4.7<br>2%<br>2% 6%<br>4.6 0%<br>0% 5%<br>-2%<br>4.5<br>-2%<br>-4% 4%<br>2002q1 2004q1 2006q1 2008q1 2010q1<br>-4% 4.4<br>2002q1 2004q1 2006q1 2008q1 2010q1<br>Saving rate (left axe)<br>Saving rate (left axe) ln REER (right axe) Time deposit rate % (right axe)<br><!-- End of picture text -->

# e: Saving rate and openness, percent

# f: Saving rate and public deficit, percent

<!-- Start of picture text --> 12% 1.2<br>10% 1.1<br>8% 1.0<br>6% 0.9<br>4% 0.8<br>2% 0.7<br>0% 0.6<br>-2% 0.5<br>-4% 0.4<br>2002q1 2004q1 2006q1 2008q1 2010q1<br>Saving rate (left axe) OPEN (right axe)<br><!-- End of picture text --> <!-- Start of picture text --> 12% 0%<br>10%<br>-1%<br>8%<br>-2%<br>6%<br>4% -3%<br>2%<br>-4%<br>0%<br>-5%<br>-2%<br>-4% -6%<br>2002q1 2004q1 2006q1 2008q1 2010q1<br>Saving rate (left axe)<br>Public deficit % GDP (right axe)<br><!-- End of picture text --> Source: Ministry of Finance and authors’ calculations. All data are seasonnally adjusted using an HP filter.

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# **Table 4: The Effect of Large surprises in US Monetary Policy on Emerging Economies’ Financial Variables**

||E|xchange rate|s||Equity prices|||Bond yields||
|---|---|---|---|---|---|---|---|---|---|
||k=1|k=2|k=3|k=1|k=2|k=3|k=1|k=2|k=3|
|2yr Treasury yield|0.020**|0.024**|0.030***|-0.003|-0.017|-0.023|0.039**|0.037*|0.032*|
||[2.31]|[2.33]|[3.40]|[-0.28]|[-1.36]|[-1.56]|[2.59]|[2.04]|[1.82]|
|2yr T-yield* Dummy||||||||||
|Large Tight|0.040***|0.043***|0.033***|-0.134***|-0.168***|-0.145***|0.040|0.073**|0.100***|
||[5.22]|[4.43]|[3.89]|[-10.49]|[-13.74]|[-7.83]|[1.50]|[2.50]|[3.30]|
|2yr T-yield* Dummy||||||||||
|Large Ease|0.021***|0.021**|0.013|-0.022|-0.036|-0.059**|0.044*|0.071**|0.068**|
||[3.32]|[2.32]|[1.62]|[-1.18]|[-1.60]|[-2.73]|[1.98]|[2.48]|[2.39]|
|Constant|-0.092***|-0.063***|-0.057***|0.555***|0.621***|0.662***|-0.126*|-0.173**|-0.201***|
||[-5.70]|[-3.42]|[-4.13]|[13.31]|[13.10]|[12.66]|[-1.81]|[-2.62]|[-3.16]|
|Observations|1,300|1,300|1,300|1,194|1,200|1,197|1,049|1,052|1,053|
|R-squared|0.095|0.083|0.068|0.079|0.098|0.085|0.054|0.067|0.065|
|# of countries|20|20|20|19|19|19|17|17|17|

_Notes:_ Dependent variables are percent change in exchange rate, equity prices, or bond yields, between the day of FOMC announcements and k days after that, as per Equation 1. The independent variable is percent change in 2-year Treasury yield between t and t-1, t being the day of the FOMC announcements. Large Tight is a dummy that takes a value 1 when there is a large increase in 2-year Treasury yield between t and t-1, i.e. the increase in yield exceeds the average daily change by 2 standard deviations; and 0 otherwise. Large Ease is a dummy that takes a value 1 when there is a large decline in 2- year Treasury yield between t and t-1, i.e. the decline in yield is larger than the average daily change by 2 standard deviations. Regressions include country fixed effects. Robust t statistics are in parentheses. *, **, *** indicate that the coefficients are significant at 10, 5, and 1 percent level.

# **4. Extensions**

In this section, we carry out extensions to the analysis presented above. In the previous section, we established that the US monetary surprises have a significant spillover on emerging economies. Here we ask whether the extent of spillover is economically large from the emerging economies’ perspective. We also ask whether emerging economies are equally sensitive to the surprise policy announcements in other advanced economies, such as the euro area, Japan or UK. Finally, we extend the time horizon considered from 1 to 3 days to 5, 10 and 15 days and ask whether the spillover persisted over a longer period. In the latter, admittedly though, it may be difficult to attribute the impact on emerging economies solely to the US policy announcements over longer horizons, since the likelihood of occurrence of other factors affecting the financial markets in emerging economies increases.

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Table A.5: Sought company to visit the ADC during the intervention period

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|**A: Sought company from**|**someone**||||
|Own Voucher<br>BAF Voucher|0.160***<br>[0.035]<br>0.328***<br>[0.040]|0.161***<br>[0.035]<br>0.328***<br>[0.040]|0.160***<br>[0.034]<br>0.334***<br>[0.040]|0.173***<br>[0.035]<br>0.356***<br>[0.039]|
|Observations|612|607|603|603|
|Endline control mean|0.034|0.034|0.034|0.034|
|p-value: Own = BAF|0.001|0.001|0.001|0.000|
|**B: Sought company from **|**husband **|**or mothe**|**r-in-law**||
|Own Voucher|0.037*|0.038*|0.038*|0.040*|
||[0.021]|[0.021]|[0.021]|[0.022]|
|BAF Voucher|0.026|0.027|0.026|0.032*|
||[0.018]|[0.018]|[0.018]|[0.019]|
|Observations|595|590|586|586|
|Endline control mean|0.016|0.016|0.016|0.016|
|p-value: Own = BAF|0.668|0.660|0.641|0.783|
|**C: Sought company from **|**someone **|**other th**|**an husband or **|**mother-in-law**|
|Own Voucher|0.084***|0.086***|0.090***|0.099***|
||[0.028]|[0.028]|[0.028]|[0.028]|
|BAF Voucher|0.265***|0.267***|0.276***|0.291***|
||[0.039]|[0.038]|[0.039]|[0.038]|
|Observations|595|590|586|586|
|p-value: Own = BAF|0.000|0.000|0.000|0.000|
|Endline control mean|0.022|0.022|0.022|0.022|
|Balancing controls|No|Yes|Yes|Yes|
|Other balancing covariates|No|No|Yes|Yes|
|Village FE|No|No|No|Yes|

_Notes:_ Balancing controls are the baseline variables used to balance randomization, i.e., whether a woman is currently using family planning, her years of education, whether she wants another child, and the number of peers she mentions in the social networks module. Other baseline covariates comprise baseline values of woman’s age, indicator variables for Hindu, SC-ST, OBC, whether the woman works, wears ghunghat, and had ever visited a family planning clinic, her mobility score and asset index. Variable definitions are in Online appendix. Robust standard errors are in brackets.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01.

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