rafmacalaba/datause-encoder-data huggingface dataset:
Ang, Andrew, and Geert Bekaert, “Stock Return Predictability: Is It There?,” _Review of Financial Studies,_ 20 (2007), 651–707 _._ Amromin, Gene, and Steven Sharpe, “From the Horse’s Mouth: Gauging Conditional Expected Stock Returns from Investor Survey,” AFA 2006 Boston Meetings Paper.

Backus, David, Patrick Kehoe, and Finn Kydland, “International Real Business Cycles,” _Journal of Political Economy_ , 100 (1992), 745–775.

Barberis, Nicholas, “Investing for the Long Run when Returns Are Predictable,” _Journal of Finance,_ 55 (2000), 225–264.

Brandt, Michael, and Pedro Santa-Clara, “Dynamic Portfolio Selection by Augmenting the Asset Space,” _Journal of Finance_ , 61 (2006), 2187–2217.

Campbell, John, “Stock Returns and the Term Structure,” _Journal of Financial Economics,_ 18 (1987), 373–399.

Campbell, John, Yeung Chan, and Luis Viceira, “A Multivariate Model of Strategic Asset Allocation,” _Journal of Financial Economics,_ 67 (2003), 41–80.

Campbell, John, and Robert Shiller, “The Dividend-Price Ratio and the Expectations of Future Dividends and Discount Factors,” _Review of Financial Studies,_ 1 (1988), 195–228.

Campbell, John, and Luis Viceira, “Consumption and Portfolio Decisions when Expected Returns are Time-Varying,” _Quarterly Journal of Economics,_ 114 (1999), 433–495.

~~--------,~~ _Strategic Asset Allocation: Portfolio Choice for Long-Term Investors._ (New York, NY: Oxford University Press, 2002).

Campbell, John, and Motohiro Yogo, “Efficient Tests of Stock Return Predictability,” _Journal of Financial Economics,_ 81 (2006), 27–60.

Chacko, George, and Luis Viceira, “Dynamic Consumption and Portfolio Choice with Stochastic Volatility in Incomplete Markets,” _Review of Financial Studies,_ 18 (2005), 1369–1402.

42
|**Table 13e. Impact of Tariff Red**<br>|**uctions**<br>Overall<br>_change from_|_base year, %_|||||||
|---|---|---|---|---|---|---|---|---|
||average|Northwest|Central|South|Volga|Urals|Siberia|Far East|
|**Social Welfare Indices (ev/GDP)**|||||||||
|sigma=0|-0.2|0.1|-0.3|-0.3|-0.9|-0.3|-0.2|0.1|
|sigma=1|0.1|0.6|0.2|0.2|-0.4|0.1|0.0|0.4|
|sigma=4|0.3|0.8|0.4|0.4|-0.1|0.4|0.2|0.5|
|sigma=+inf|0.4|0.8|0.5|0.5|0.0|0.5|0.2|0.5|
|ev/C|0.7|1.6|0.8|0.8|0.0|1.2|0.4|1.0|
|**Aggregate trade**|||||||||
|Regional terms of trade (% change)|1.4|2.0|1.5|0.9|0.9|0.4|0.4|1.0|
|Regional exports (% change)|0.2|0.3|0.3|0.0|0.4|0.0|-0.4|0.2|
|Real exchange rate (% change)|2.1|2.4|2.3|2.1|2.0|1.5|1.5|1.7|
|International exports (% change)|5.5|7.2|10.1|7.5|7.3|1.5|5.7|7.6|
|**Return to primary factors**(% change)|||||||||
|Unskilled labor|1.0|1.7|1.3|1.1|0.1|1.0|1.2|1.2|
|Skilled labor|1.8|2.3|2.1|2.0|1.4|1.5|1.9|2.1|
|Aggregate capital earnings|1.8|2.1|2.0|1.8|1.7|1.2|1.2|1.5|
|Regional mobile capital|2.4|3.7|2.9|2.1|2.1|2.1|2.0|2.8|
|Energy sector resources|2.4|3.4|6.1|3.6|3.5|1.9|2.3|2.5|
|Specific capital in domestic firms|-3.5|-4.4|-4.1|-3.0|-4.3|-1.6|-2.0|-4.2|
|Specific capital in multinational firms|10.6|4.7|7.2|17.3|14.2|21.2|19.5|13.4|
|**Factor adjustments**|||||||||
|Unskilled labor (% changing sectors)|1.2|1.4|1.1|1.1|1.5|1.0|1.3|1.9|
|Skilled labor(% changingsector)<br>Source: Authors' calculations.|1.0|1.0|1.0|0.7|1.1|0.7|0.9|1.5|

52
# References

Afrouzi, H., M. Halac, K. Rogoff, and P. Yarad. 2023. “Monetary Policy Without Commitment.” NBER working paper 31207.

Altansukh, G., R. Becker, G. J. Bratsiotis, and D. R. Osborn. 2017. “What Is the Globalisation of Inflation?” Journal of Economic Dynamics and Control 74 (January): 1- 27.

Antolín-Díaz, J., and Rubio-Ramírez, J. F. 2018. “Narrative sign restrictions for SVARs.” American Economic Review 108 (10), 2802-29.

Aucremanne L., and L. Wouters. 1999. “A Structural VAR Approach to Core Inflation and Its Relevance for Monetary Policy.” Bank for International Settlements, Basel.

Auer, R.A., Levchenko, A. A. and P. Sauré. 2019. “International Inflation Spillovers through Input Linkages.” Review of Economics and Statistics 101 (3): 507-21.

Baffes, J., M. A. Kose, F. Ohnsorge, and M. Stocker. 2015. “The Great Plunge in Oil Prices—Causes, Consequences, and Policy Responses.” Policy Research Note 1, World Bank, Washington, DC.

Ball, L., D. Leigh, P. Mishra. 2022. “Understanding U.S. Inflation During the COVID Era.” BPEA Conference Drafts, Brookings Paper on Economic Activity, Washington, DC.

Baumeister, C., and G. Peersman. 2013. “Time-Varying Effects of Oil Supply Shocks on the US Economy.” American Economic Journal: Macroeconomics 5 (4): 1-28.

Baumeister, C., D. Korobilis, and T.K. Lee. 2020. “Energy Markets and Global Economic Conditions,” Review of Economics and Statistics. 104 (4): 828-44.

Baumeister, C. and J.D. Hamilton. 2019. “Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks.” American Economic Review 109 (5): 1873-1910.

Bekaert, G., E. Engstrom, and A. Ermolov. 2021. “Macro Risks and The Term Structure of Interest Rates.” Journal of Financial Economics 141(2): 479-504.

Blanchard, O.J. and J. Galí. 2007. “The Macroeconomic Effects of Oil Price Shocks: Why are the 2000s so different from the 1970s?” In International Dimensions of Monetary Policy (pp. 373-421). University of Chicago Press.

Bu, C., J. Rogers and W. Wu. 2021. “A Unified Measure of Fed Monetary Policy Shocks.” Journal of Monetary Economics 118: 331-49.

Calvo, G.A., 1983. “Staggered prices in a utility-maximizing framework.” Journal of Monetary Economics 12 (3): 383-98.

Carriero, A. and F. Corsello and M. Marcellino. 2019. “The Global Component of Inflation Volatility.” CEPR Discussion Paper No. DP13470.

Charnavoki, V., and J. J. Dolado. 2014. “The Effects of Global Shocks on Small Commodity- Exporting Economies: Lessons from Canada.” American Economic Journal: Macroeconomics 6 (2): 207-37.

20
respectively, which implies that a one standard deviation decrease in affinity raises aid by about 200 percent, while being a former colony implies aid flows about five times as big as observed for comparable disasters.

[Table 4 : Panel: Tobit Analysis]

# **5.2 Part II: Individual Donor Analysis**

In the previous section, we implicitly assumed that the factors driving bilateral aid decisions were the same across donors. In this section, we determine the factors driving aid for each donor separately, and directly test the restrictions imposed in the panel analysis presented before. For expositional convenience, we limit our analysis to the five major donors in our sample - the US, Japan, Germany, the UK and Norway, which alone represent more than the 40% of total humanitarian aid - and confront their aid patterns to those of private donors. With scarce disaggregate data on private donations, total nongovernmental donations is the only proxy for "private" donations generally available in the FTS data. While this variable is a useful benchmark for the country specific results, its aggregate nature makes the interpretation of the estimated coefficients rather difficult.

Table 5 below reports the coefficients for the Probit models estimated for each donor and, in the last column, the Wald test for the equality of coefficients across them. All donors are more likely to intervene in emergencies characterized by a higher death toll and a larger number of people affected, although these effects are only partially significant for Japan and private donors.

With respect to our socioeconomic controls, all donors are more likely to intervene in favor of less populated potential recipients, even though this effect is not significantly different from zero for the US. This effect is similar to what is found in the aid literature (Berthélemy and Tichit, 2004), and, as discussed in the previous section likely reflects donors’ evaluations of the recipient’s capacity to deal with the disaster.<sup>22</sup> While Japan seems marginally more likely to provide aid to more densely populated countries, the opposite is true for Norway. Only private donors and the UK are more likely to help poorer countries. The positive and highly significant coefficient on the oil indicator found in the panel regression applies only to the US the UK and Norway, who are 24, 35 and 39 percentage points more likely to help oil exporting than other countries, respectively.

Among the five major donors analyzed, Norway is the one showing the highest responsiveness to policy performance indicators. In particular, one standard deviation change in trade openness, as measured by total trade value over GDP, reduces Norway’s likelihood to provide aid by 14 percentage points. The same negative response is displayed by private donors (16% decrease). Norway also appears to be hesitant to donate to ethnically fractionalized countries.

Moving from the least to the most ethnically fractionalized background reduces the likelihood to receive aid from Norway’s by a remarkable 38 percentage points<sup>23</sup> .

13
gradient found in 1993 had largely vanished by 2004. Simultaneously with rising landlessness, the distribution of land holdings became more equal across levels of living.<sup>37</sup> Figure 7 is not consistent with our model’s prediction that post-reform landholding should have a positive wealth gradient. However, our model ignored heterogeneity in land quality. On a closer inspection we find that the poor have not seen gains in their land quality. This is evident from Figure 8, which gives the share of annual crop land that is irrigated. We see marked gains over time at all levels except among the poorest. The same pattern was also found when we looked at the distribution of the share of annual crop land that the commune authorities rated as high quality (details available from the authors). A plausible explanation for these differences is the initial inequality in non-land wealth, given credit market failures. Those with wealth were naturally in a better position to invest in their land.<sup>38</sup> The 2004 survey allows us to study how land was acquired.<sup>39</sup> Figure 9 shows how the incidence of land transactions varies with consumption. The totals include all means of acquiring or disposing of land that are allowed under the land laws (purchase, sale, inheritance, bequest, exchange). We also identify purchase and sale. We see that the upper consumption groups were more likely to take up the new opportunities for land-market transactions. These data are clearly not consistent with the view that the poor were selling their land to the rich and remaining poor. (It is likely that some of the transactions observed at middle consumption levels are for those who were previously poor.) This is further supported by Figure 10, which shows how each household’s land-holdings in 2004 were initially acquired. We see that the poor in 2004 had relied heavily on reclaiming land, and hardly at all on market transactions for obtaining land; the purchase of land was more likely for the rich.

> 37 The changes in the distribution of annual land show a very similar pattern.

> 38 This echoes Taylor’s (2004) observations from field work in the Mekong Delta.

> 39 The relevant questions were not asked in prior surveys.

25
# Does Patient Demand Contribute to the Overuse of Prescription Drugs?

Carolina Lopez and Anja Sautmann and Simone Schaner<sup>_∗_</sup> JEL Codes: I12, I18, O12 Keywords: malaria treatment, demand for prescription drugs, healt care overuse, doctor-patient interaction, Mali

> _∗_ We are grateful to Pascaline Dupas, Rema Hanna, seminar participants at the University of Toronto, Loyola Marymount University, MIT, USC, Rice University, UCSD Rady, the Barcelona Summer Forum, the BREAD pre-conference, the Stanford Rosenkranz Global Health Policy Symposium, McGill, Brown, Microsoft Research, FGV Sao Paulo, PUC Rio, the University of Cape Town, the University of Gothenburg, and the Stockholm School of Economics for many helpful comments. We thank the staff of IPA Mali and especially Alassane Koulibaly for expert field coordination. This project would not have been possible without the advice and support of Dr. Seydou Doumbia and Dr. Issaka Sagara (Malaria Research and Training Center, University of Bamako) as well as Dr. Seydou Fomba (Programme National de Lutte contre le Paludisme (PNLP)). We thank the Ministry of Health and the participating community clinics for allowing us to conduct this research. The study was funded by ESRC/DFID Development Frontiers Award ES/N00583X/1. The research protocol was approved by the IRB of La Facult de Mdecine de Pharmacie et d’Odontostomatologie de Bamako (FMPOS) (protocol No. 2016/47/CE/FMPOS) and registered in the AEA RCT registry (AEARCTR-0001707). The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. All errors are our own. Carolina Lopez: Brown University, carolina <u>lopez@brown.edu.</u> Anja Sautmann: World Bank, asautmann@worldbank.org. Simone Schaner: University of Southern California, BREAD, and NBER, schaner@usc.edu.
Figure H.2. Rights and Services – Fuzzy RD Plots with Local-linear Fitted Polynomial <!-- Start of picture text --> Rights and Services (Index) SISBEN Access<br>-200 -150 -100 -50 0 50 100 150 200 -200 -150 -100 -50 0 50 100 150 200<br>Arrival (days from June 8, 2018) Arrival (days from June 8, 2018)<br>Sample average within bin Polynomial fit of order 1 Sample average within bin Polynomial fit of order 1<br>Subsidized Healthcare Financial Products<br>-200 -150 -100 -50 0 50 100 150 200 -200 -150 -100 -50 0 50 100 150 200<br>Arrival (days from June 8, 2018) Arrival (days from June 8, 2018)<br>Sample average within bin Polynomial fit of order 1 Sample average within bin Polynomial fit of order 1<br>Transfers from Government<br>-200 -150 -100 -50 0 50 100 150 200<br>Arrival (days from June 8, 2018)<br>Sample average within bin Polynomial fit of order 1<br>4 .5<br>3.5<br>3 .4<br>2.5<br>.3<br>2<br>1.5 .2<br>SISBEN Access<br>1<br>Rights and Services (Index) .1<br>.5<br>0 0<br>.4 .3<br>.35<br>.25<br>.3<br>.25 .2<br>.2 .15<br>.15<br>.1<br>Financial Products<br>Subsidized Healthcare .1<br>.05 .05<br>0 0<br>.25<br>.2<br>.15<br>.1<br>Transfers from Government .05<br>0<br><!-- End of picture text --> _Notes:_ Each graph depicts the bias-corrected estimator 200 days around June 8, 2018, when the RAMV census closed, and the mean squared error (MSERD) optimal bandwidths. Confidence intervals are at the 95% significance level.

69
# **4. Empirical Strategy**

We estimate the equation (1) below for female-owned and male-owned firms separately to explore the heterogenous relationship between mobile money use and investment.

the heterogenous relationship between mobile money use and investment. I I I 0 1M M I 2 3M 4 5 6M 7E 8 9 10 11 ii ii ii ii ii ii = ββ + ββ 12 MMIIM 13MM + ββ SSMMSSII14+ ββI IIM1 IIMM + ββ2 LLM LLM (1)LL + ββ AAMMII ii ii ii ii ii + ββ MMIIMM+ ββ EEMMII + ββ FFM II + ββ TTM MMII + ββ LLMMPPIIMM + ββ EEMMEEIIMMII ii ii ii ss ll ii + ββ LLM II + ββ BBMMIIBB + ββ MMI + δδ DD + δδ DD + εε where I I I is a measure of investment by firm . M M I is a set of variables that capture mobile money use. To account for as many confounding factors as possible several firm-level MM MMIIM MM controls are employed. These include firm size ( ); manager experience (M ); labor productivity ( ); firm age ( ); part of a multi-establishment firm (M ); export SSMMSSII IIM IIMM orientation (E ); foreign ownership ( ); offered formal training ( ); experienced LLM LLM LL AAMMII MMIIMM power outages ( ); used electric generators ( ; had a loan or line of credit ( ); EEMMII FFM II TTM MMII has a bank account ( ); use of their own website (I ). The estimation includes sector twoLLMMPPIIMM EEMMEEIIMMII) LLM II digit (ISIC) sector ( ) and within country location ( ) fixed effects. is the standard error term BBMMIIBB MMI with the usual desirable properties. Weights and the stratification design of the survey are utilized ss ll ii DD DD εε for all the estimates in this study. Given that the survey was stratified, by design, the standard errors are clustered by firm size, sector, and location.

Investment is captured by two variables. The first is a binary variable that takes a value of 1 if the firm purchased any fixed assets such as machinery, vehicles, equipment, land, or buildings were purchased (new or used) in the previous fiscal year. The second variable is the amount of expenditure on the fixed assets (in logs). The main explanatory variables capture the use of mobile money (M M I ). This includes a binary variable that takes on a value of 1 if a firm has used mobile money for any transaction, MMIIM MM and 0 if it has not. Four additional binary variables are used to capture four types of purpose of mobile money use – payment of employees, payments to suppliers, payments of utility bills, and receipt of payments from customers.

There is a concern of endogeneity given the possibility of simultaneity between mobile money use and investment - it may be that firms that invest are more likely to use mobile money. While this is difficult to obviate, given that we estimate equation (1) separately for female-owned and male12
|**Forum**|**Year**<br>**Originated**<br>|**Number**<br>**of**<br>**Members**<br>|**Purpose/Objectives**<br>|
|---|---|---|---|
|SEACEN(SouthEast<br>AsianCentralBanks)|1980s|13|Initiallyatrainingandresearch<br>organization;nowsubstantive forum<br>fordiscussionofcentralbanking<br>issues.Includesmajor Southeast Asian<br>economies,aswellassomesmaller<br>Asia-Pacific Countries.<br>|
|SEANZA (SouthEast<br>Asia,NewZealand,<br>Australia)|1956|17|Largestoftheregionalpolicyforain<br>termsofmembership.Forumfor<br>exchangebetween centralbanksand<sup>a</sup><br>vehiclefortrainingincentralbanking.|
|Source:**IMF,**APEC andAS|EAN|||

# **ASEAN's Response to the Crisis**

Despite the fact that the ASEAN swap<sup>arrangementdescribedabovehad been in</sup> place for 20 years prior to the crisis for<sup>the purposeof providing liquidity in the event of</sup> just such a crisis, ASEAN was surprised<sup>by the events of the crisis and failed to provide</sup> any meaningful assistance. Soesastro<sup>(1998)providesa bleakassessmentof ASEAN's</sup> response to the crisis saying that the crisis-affected<sup>countrieshad to resortto theIMF or</sup> resolve the problem on their own (as did<sup>Malaysia) because ASEAN was "not in a</sup> position to do anything".<sup>33</sup> ' In fact, the ASEAN<sup>finance ministersmet for the first time in</sup> March 1997 and voiced their expectation<sup>that the positiveoutlook for Asia would</sup> continue. Even after the crisis was in full<sup>swing,ASEAN's response was weak. The</sup> existing ASEAN swap arrangement was<sup>woefullyinadequate(US$200million at the</sup> time) and was reportedly never used. The<sup>ASEANfinanceministers met again in</sup> December 1997 in Kuala Lumpur to discuss<sup>the crisis andpossible responsesbut</sup> reportedly did not make any kind of financing<sup>arrangementto help ASEANmembers.'3 4</sup> The official view of ASEAN's role in response<sup>to thecrisiswas summarizedin</sup> 1998 by Rodolfo Severino, ASEAN Secretar<sup>-General,whooutlinedfour levels of</sup> response by ASEAN members at that time:'3

- _National Level_ - Individual countries<sup>affected by the crisiswere undertaking</sup> reforms, with varying degrees of success,<sup>relatedto closer supervisionand tighter</sup> discipline of banks, greater transparency in<sup>financialtransactions,promotionof</sup> more competition, enactment of bankruptcy<sup>laws andstronger commercialcodes,</sup> and strengthening of the rule of law and<sup>the judiciary.(Some of theseare</sup> described in detail in Table 5.3, "Progress<sup>at the National Level in Financial</sup> Sector Restructuring".)

- _Bilateral Level -_ ASEAN's government<sup>leaders offeredoneanothermaterialand</sup> financial assistance and advice. For example,<sup>Thailandofferedriceand the</sup> Philippines offered medicine, Singapore<sup>guaranteed letters of credit issued by</sup> Indonesian banks, and Malaysia extended<sup>to Indonesiaa standby creditof</sup> US$250 million with Singapore offering<sup>asimilar facility.</sup>

55
25 Table 1: Expected Signs of the Parameters

||Yd|Xm|ib<br>eD$/P|
|---|---|---|---|
|cp*|Q11><br>0|a02>O<br>013>0|a14>0<br>a15>°|
|ml*|021>0|C22<O<br>a23<0|a24<0<br>025>0|
|v*|031>0|032<0<br>033>0|C34<0<br>3 5<sup><0</sup>|
|b*|N41>0|042<0<br>C43<0|C44>0<br>C45<0|
|k*p <br>Impa<br>low|1a51>0<br>ct of a S|oSa>O<br>a53<0<br>Table 2 :<br>imple Debt-Reduc<br>on Main Macroe|054<0<br>a55<0<br>tion<br>and an Increase<br>conomic Variables|
||-<br>d(e|DSg-1)<br><0|d(eOD$p)>0|
|y||<br>+|<br>+|
|ip||+|+|
|ig||+|0|
|OR$||+|+or-|
|aLg||||
|OLp||+|+|
|e0J|||+|
|cp|||+|

The Impact of a Simple Debt-Reduction and an Increase in Foreinn Capital Inflow on Main Macroeconomic Variables
|Temporarily away from<br>work|Although you did not work last<br>week, do you have a job or your<br>own business to which you will<br>soon return?|Although you did not work last<br>week, do you have a job or your<br>own business from which you are<br>temporarily away and to which you<br>will soon return?|
|---|---|---|
|Unemployed|During the last week, did you<br>search for a job or try to establish<br>your own business or farm?|Did you search for a job or try to<br>establish your own business or<br>farm in the last week or the last<br>four weeks?|

Note: Translated by the authors from the questionnaires of the EPHPM 2015 and 2018.

A comparison of the 2015 and 2018 employment outcomes for rural women is reported in Table 7. Assuming negligible changes in female labor force participation between the two years, we can compare the results from the two surveys to draw some inferences on how changes in the questions affected the measurement of female employment. The comparison between the two survey years shows that removing the explicit exclusion of housework did not change how women reported employment in response to the initial question. The addition of the follow-up question which lists different types of underreported activities increases the share of women who report paid employment, but only by 2.1 percentage points. In addition, it is also probable that it increased the share of women who report having unpaid employment (25 percent of the women who reported an employment activity in the follow-up question then identified it as unpaid work). The share of women reporting unpaid work increases from 5.8 percent to 7.2 percent. With these changes, the share of employment increases from 37.4 percent to 41.2 percent.

Table 7. Participation rates in 2015 and 2018, women in rural areas

||**2015**|**2018**|
|---|---|---|
|**Employed**|30.3|29.6|
|**Employed - follow-up question**||2.1|
|**Unpaid work**|5.8|7.2|
|**Employed but temporarily out**|1.3|2.3|
|**Employed(total)**|**37.4**|**41.2**|
|**Unemployed (1 week)**|1.6|1.4|
|**Unemployed (1 month)**||1.0|
|**In labor force**|**38.9**|**43.6**|

Source: Author’s tabulations using the Encuesta de Hogares de Propósitos Múltiples (EPHPM) 2015 and 2018. Note: The table reports the percentage of women living in rural areas who are 18 and over.

Another change is the expansion of unemployed to include those who searched for work in the past month, not just the past week. After all of these changes, the estimated labor force participation rates for rural women from the 2018 survey are only marginally higher than they had been in 2015 (from 38.9 to 43.6 percent), suggesting that they did not fully solve the issue of underreporting. A total of 24.5 percent of all rural women (accounting for 43 percent of those out of the labor force in 2018) reported that they did not search for work because they were too busy with household tasks. While childcare and domestic 23