rafmacalaba/datause-encoder-data huggingface dataset:
In order to maximize the number of observations, and focus on the macroeconomic determinants of US monetary policy, we estimate the model at a monthly frequency, even if this limits the availability of country-specific determinants of emerging market credit-worthiness. For many of the countries in our sample, data on consumer prices, reserves and the money supply are available monthly, while quarterly or annual data are available for GDP and foreign debt. We interpolate the latter variables where necessary.

In specifying the effects of US monetary policy, we include in the long-run relationship the levels of US short rates (the US Treasury bill rate<sup>12</sup> ), the 10-year Treasury bond rate, and the interest rate spread between high and low risk US corporate bonds. The latter variables, as well as being influenced by US monetary policy, may also capture global risk appetite. We interact the US T Bill rate with the country’s debt to GNI ratio, since as discussed in section II, non-linearities are likely to be important.

We have argued that it is important to divide the sample into crisis and non-crisis periods. A country i is in a non-crisis period t if in that period it is not suffering a crisis, even if other countries are. But it is also possible that the existence of crises at t in other countries—and their number—may increase the interest rate for country i. A significant coefficient on the number of other countries in crisis may be evidence of contagion effects, since, if positive, it would come over and above the country’s own fundamentals and global monetary conditions.<sup>13</sup>

> 12Arora and Cerisola (2000) suggest that the federal funds rate is more appropriate, but we find the TB rate to be a better explanatory variable of EM spreads.

> 13 Eichengreen, Rose and Wyplosz (1995) find that the occurrence of crises elsewhere tends to increase a country’s likelihood to experience a crisis.

14
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21

*] Both the Sargan statistic and the "difference" Sargan statistic are asymptotically distributed as Chi-square under the null hypothesis of validity of their respective instruments. The number of degrees of freedom of the Sargan test is equal to the number of overidentifying restrictions of the system estimator. The number of degrees of freedom of the “difference” Sargan test is given by the number of additional restrictions in the system estimator with respect to the difference estimator, that is, the difference between the number of overidentifying restrictions of the system estimator and that of the difference estimator. 2 However, if the test rejects the null hypothesis of no second-order serial correlation but fails to reject the null of absence of third-order serial correlation, we conclude that the original error term is MA(1) and, therefore, use the moment conditions appropriate to this case. As shown below, in the private saving regressions we find in some cases evidence that the error term is MA(1) and, in others, that it is serially uncorrelated; for purposes of comparability across regressions we adopt the conservative strategy of choosing the instrument sets for the regressions as if the error term were always MA(1). In the case of national saving, we find that the error term can be well characterized as serially uncorrelated, and we choose the instrument set accordingly.

*3 Deaton (1977) finds the same result but proposes a somewhat different explanation: to the extent that the increase in inflation is unanticipated, a larger saving rate may result from confusing the increase in the overall price level with a rise in the relative prices of certain goods.

*4 Tt seems plausible that measured real interest rates are more closely related to intertemporal decisions in the industrial countries (where the sample mean of the real interest rate is positive) than in developing countries (where it is negative).

5 The sources of central government data are different from those of public sector data. The former cover many more countries but fewer years (from 1970 on only). For comparability, all the regressions in the table use the same set of countries, and hence the central government-based saving regressions possess fewer observations. © The second exception is that M2/GNP has a positive and significant coefficient only in the regressions corresponding to the central government definition of the public sector. *7 Using instead the variance of the forecast error to measure uncertainty yielded similar results. *8 Other year dummies, when added to the regressions, were likewise insignificant. ° Other experiments using instead the Hodrick-Prescott decomposition yielded qualitatively similar results: larger coefficients on the temporary than on the permanent components of both variables (the latter was in fact negative for the terms of trade), but in this case all significantly different from zero.
Policy Research Working Paper 9071

# **Abstract**

This paper combines remote-sensed data and individual child-, mother-, and household-level data from the Demographic and Health Surveys for five countries in Sub-Saharan Africa (Malawi, Tanzania, Mozambique, Zambia, and Zimbabwe) to design a prototype drought-contingent targeting framework that may be used in scarce-data contexts. To accomplish this, the paper: (i) develops simple and easy-to-communicate measures of drought shocks; (ii) shows that droughts have a large impact on child stunting in these five countries—comparable, in size, to the effects of mother’s illiteracy and a fall to a lower wealth quintile; and (iii) shows that, in this context, decision trees and logistic regressions predict stunting as accurately (out-of-sample) as machine learning methods that are not interpretable. Taken together, the analysis lends support to the idea that a datadriven approach may contribute to the design of policies that mitigate the impact of climate change on the world’s most vulnerable populations.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at eskoufias@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Figure 2b: Poverty Dynamics - Synthetic vs. Actual Panel Data by Sub-population Groups, Nicaragua 2001 - 2005 <!-- Start of picture text --> Poor in 2001 and Poor in 2005 Poor in 2001 and Non-poor in 2005<br>100 40<br>80<br>30<br>60<br>20<br>40<br>10<br>20<br>0 0<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40<br>Actual Actual<br>Non-poor in 2001 and Poor in 2005 Non-poor in 2001 and Non-poor in 2005<br>40 100<br>80<br>30<br>60<br>20<br>40<br>10<br>20<br>0 0<br>0 10 20 30 40 0 10 20 30 40 50 60 70 80 90 100<br>Actual Actual<br>Lower Bound Upper Bound<br>Estimation Estimation<br>Estimation Estimation<br><!-- End of picture text --> Data source: EMNV, 2001 - 2005 Note: Results are constrained to the panel sample of households whose heads are between 25 and 65 years old. Results are weighted using household-level survey-sampling weights. The 45 degree line shows actual panel mobility. Upper bound estimations are based on 50 repetitions.

36
<!-- Start of picture text -->
|—___#—<br>| iim<br>| _—__/—<br>) 100 200 300 400<br>Amount kept<br><!-- End of picture text -->

<!-- Start of picture text --> 113<br>2/3<br>) 100 200 300 400<br>Amount kept<br><!-- End of picture text --> Table 10. Building Trust to scale warrantage,

||;<br>Confident <sup>in</sup><br>storing (Own<br>A<br>village)|;<br>Ownvillage<br>memberwould<br>;<br>:<br>returnwallet|_<br>Incentivized<br>trust|Confidentin<br>storing<br>UNelgnpONTINg<br>village)|Neighbouring<br>villagemember <br>would return<br>wallet|.<br> Co**n**fidencein<br>i stitutions<br>.<br>index|
|---|---|---|---|---|---|---|
|PanelA: Householdrandomization|(1)|<br>(2)||(3)|(4)|(5)|
|Access towarrantage,|0.024|0.084*|-13.326|-0.017|0.018|-0.057|
||(0.039)|(0.049)|(23.823)|(0.043)|(0.056)|(0.285)|
|Observations|312|318|121|307|479|318|
|Control mean|0.556|0.673|203.8|0.582|0.496|-0.133|
|PanelB: Village randomization (Sample|: Non-lotterypartici|pantsand Contro|l lotterypartic|ipants)|||
|Hightreatment village|-0.125|-0.012|9.735|-0.061|0.075|0.683|
||(0.087)|(0.054)|(28.088)|(0.083)|(0.064)|(0.514)|
|Observations|424|434|73|421|399|434|
|Lowtreatmentvillagemean|0.608|0.699|189.1|0.601|0.464|-0.362|

Notes: In Panel A, the sample is restricted to lottery participants. In Panel B, the sample is restricted to all control households and non-lottery participants. Standard errors are clustered by village and presented in parentheses. *, **, *** indicate significance at the 1, 5, and 10 percent respetively.
|_Comlang ij_|1.791***|3.696***|1.838***|3.106***|
|---|---|---|---|---|
||(0.378)|(1.019)|(0.405)|(0.882)|
|_Pol stabilityj_|0.617**|0.529**|||
||(0.248)|(0.212)|||
|_Gov qualityj_|||-0.106|0.619|
||||(1.248)|(1.757)|
|_BIT ij_|-0.744|-0.424|-0.531|-2.102|
||(1.034)|(1.466)|(1.069)|(1.521)|
|_PTA ij_|-0.443|0.932|-0.705|2.461|
||(1.081)|(1.184)|(1.061)|(1.870)|
|_BIT*Dist ij_|0.00026***|3.17e-05|0.000245**|0.000243|
||(9.74e-05)|(0.00023)|(0.000104)|(0.000165)|
|_PTA*Dist ij_|-2.02e-05|-0.000198|3.51e-05|-0.000465**|
||(0.000109)|(0.000153)|(0.000106)|(0.000223)|
|_BIT*SS ij_|0.296|3.517***|0.202|2.560**|
||(0.797)|(1.216)|(0.804)|(1.178)|
|_PTA*SS ij_|1.698**|0.336|1.248|0.951|
||(0.832)|(0.967)|(0.802)|(1.169)|
|Constant|-16.90***|-7.360*|-15.60***|-17.84***|
||(3.558)|(4.418)|(3.906)|(6.079)|
|Home FE|Y|Y|Y|Y|
|Sector FE|Y|Y|Y|Y|
|Observations|2,508|2,508|1,852|1,852|

Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1

**Table 8.** IEAs, domestic institutions, and FDI. Hurdle negative binomial regressions

||(1|4)|(1|5)|(|16)|
|---|---|---|---|---|---|---|
|VARIABLES|Logit|NB|Logit|NB|Logit|NB|
|_GDPpcj_|-0.00668|-0.0682|0.280|0.350|0.221|0.478***|
||(0.165)|(0.222)|(0.245)|(0.232)|(0.190)|(0.147)|
|_Populationj_|0.612***|0.580***|0.424***|0.755***|0.490***|0.449***|
||(0.104)|(0.136)|(0.118)|(0.201)|(0.0979)|(0.139)|
|_Distance ij_|-0.00027***|-6.15e-06|-0.00026***|-8.76e-05|-0.0003***|-0.00019|
||(8.38e-05)|(0.000103)|(8.62e-05)|(0.000111)|(8.48e-05)|(0.000119)|
|_Trade ij_|6.95e-08*|-2.02e-08**|1.07e-07***|-2.83e-08*|8.52e-08**|-2.25e-08**|
||(3.72e-08)|(8.44e-09)|(3.89e-08)|(1.57e-08)|(3.80e-08)|(9.98e-09)|
|_Comlang ij_|1.630***|2.820***|1.695***|2.647***|1.469***|3.519***|
||(0.387)|(0.574)|(0.397)|(0.568)|(0.396)|(0.702)|
|_Pol stability_|0.629*|1.411**|||||
||(0.323)|(0.623)|||||
|_Inst quality_|||-0.806|4.167**|||
||||(1.685)|(1.644)|||
|_Rule of law_|||||0.0779|0.293***|
||||||(0.0564)|(0.0955)|
|_BIT ij_|-0.324|1.410|-0.492|2.333|0.0163|4.088***|

22
||||Tadle<br>R current Health Exoendlture.IvSo<br>Publk C|3 cant.<br>urcee<br>Private<br>Share f Ot|h f Pi|<br>|
|---|---|---|---|---|---|---|
||||NON<br>Social Security Other Public Out-of-|<br>o u-<br>Pit<br>Oh<br>|are o rvat<br>|<br>Share of Formol<br>|
|Countrv|Year<br>|Currency<br>|<br> <br>t<br>Sudeet<br>udoet<br>Pocket|rvae<br>ter<br>of-Packet in<br>Inwrance Private<br>Total<br>Total|Inuronce in<br>Total|Public Insurance<br>ln Total|
|Mozabique|19l5|ueticat|--<br>-<br>MA<br>S<br>ACC0rdi,n to ource, donor contributions have<br>in recant yeors;amountnot knom.<br>lst recovery revnu s. In 1967. in the face<br>difficulties,<br>*<br>**,**<br>**nvmore**<br>coq<br>O<br>rhensive cost r<br>Introduced, consistins of tl) a flet one-time<br>cmnwultations (11) p ynent for outpatint<br>dru<br>Inpatient**fee**in urban hoapitals.|.-<br>-<br>754.7<br>7.4<br>been substntial<br>of growi,n finncial<br>ecovery schemwes<br>fee for outpetient<br>gs,**_w*_**(Mii)**a**daily|--|--|
|Niger|1964|<br>FICA<br>|4,455<br>-<br>4.0450<br>300I<br>*Includes: (f) Externl<br>assistance, 2,500; Cii)<br>(Off) Ninistry of Finance 1,090 (of<br>(iv) Others, 105.<br>binclucjs:<br>(i) Druss. 2,800;fii)Other. 200.|*-<br>--<br>11,500<br>35.2X<br>Niniatiy of Higher Education, 350;<br>uhichO1N0for foreign evacuations);|||
|igeria|1965<br>|Nair&<br> <br>**(mill**ions)|177.2<br>--<br>592.1<br>696<br>% 9b<br>aincludes:<br>(1)Stateexpenditures.<br>436.6; (1i)<br>bstimsted fr<br>OverandDenton(1968),<br>pare.<br>5<br>103.1 x 16 x 42.37 (population x11t1xavera<br>cssua<br>that only the respondents to NU0 qusti<br>s<br>**OCrnts***<br>colum In Table 3.|**74 9C**15431<br>45.3<br>Local governmentauthorities,<br>155.5.<br> <br>.14endTable 5.3;<br>ge eenditure).<br>ommare provide care;|||

**(mill** ions) 6 9 LA aincludes: (1) State expenditures. 436.6; (1i) Local government authorities, 155.5. bstimsted fr Over and Denton (1968), pare. 5.14 end Table 5.3; 103.1 x 16 x 42.37 (population x 11t1 x average eenditure). cssua that only the respondents to NU0 qustiommare provide care; s **OCrnts*** colum In Table 3. Senegal 1961 FCFA 6.890.4 _175.7_ 9,479.9 6,919.9b 4 **15 6**<sup>***C**</sup> -- 27,622.4 25.1X 15.0X .061 (il li fons) CIncludes: (1) Special budget for civil servants, 1,201.2; (11) External contributors. 4,530.9; (ifi) Paratatal expenditures. 1,52S.1; (iv) Local governent expenditure, 1,1791. bincludes: (i) Coummity participation, 425.0; (1i) leport of pharoceuticals, 6,494.9. Cincludes: (I) Hopital Principal, 2.294.0; (ii) Private doctors, 562.8; (111) Private clnics, 1.300.0.
source. PAYE directly taxes personal emoluments including wages, salaries, overtime pay, leave pay, commissions, fees, bonuses, and any other payments from employment. However, there are some exempt emoluments, such as pension benefits and gratuities. The design of PAYE tax in Zambia is largely progressive, with a certain proportion of earnings below the defined tax threshold of ZAR4,500 (approximately US$250) taxed at a rate of zero. The top rate for PAYE in 2022 was 37.5 percent, compared to a Southern African Development Community (SADC) average of 30.1 percent and an OECD average of 56 percent.

Zambia introduced VAT as a principal consumption tax on goods and services in 1995. VAT in Zambia is imposed on the final consumers of both imported and locally manufactured standard rated goods. Goods and services are classified into three broad categories for VAT purposes. These include zero- rated supplies, where input VAT incurred is claimable while output VAT is charged at 0 percent. The other class of supplies is exempt supplies, where input VAT incurred is non-claimable and output VAT is not chargeable. The remainder of inputs are taxed at a standard rate of 16 percent of sale value. This standard tax rate is similar to the SADC average.

Filing of tax returns is the principal means by which a taxpayer’s tax liability is determined by the Zambian Revenue Authority. VAT and PAYE returns contain information about formal firms’ revenue and employment levels each month. Further background information about firms is also available, such as their locations, sectors, and registration dates, which can be used to estimate the age of the firm (see examples of VAT and PAYE returns in Appendix A).

Firms that filed VAT and PAYE returns in 2020 were largely based in urban areas and were mainly involved in wholesale/retail trade and manufacturing. Almost two-thirds of firms were in Lusaka (the capital and by far the largest city). Almost half of firms were involved in wholesale and retail trade, and another 14 percent were involved in manufacturing (these were by far the two most common types of business activities). This is a somewhat similar composition of firms to the survey sample. A noteworthy difference is that the bulk of firms that file VAT and PAYE returns have fewer than five employees (and these firms were 12
_Note: The table summarizes estimates of revenue losses from reduced dividend payments for the 5 most important recipient countries in 2014. The table presents an estimated revenue impact of resetting withholding tax rate with a specific country to the domestic level of 15%._ _Data Source: Central Bank_ In table 2b<sup>29</sup> we report estimated effects for the year 2012 to allow for a comparison of effects before the dramatic drop in dividend outflows as a result of ban on dividend repatriation following the currency shock in late 2014. Revenue implications in 2012 are more dramatic. However, the relative efficiency — the total effect as a share of the direct effect — is similar across both years, we estimate that roughly 80% of any mechanical increases would have ended up as additional government revenue.

_Table 2b: Estimated revenue loss from reduced withholding on dividends (2012)_

|**Country**|**Flow in 2014**<br>**_Total non‐_**<br>**_portfolio_**<br>**_dividend flow_**<sup>**30**</sup><br>**_(Million US$)_**|**Differential**<br>**_of qualifying_**<br>**_rate with_**<br>**_standard_**<br>**_withholding_**|**Direct Effect**<br>**_Tax revenue_**<br>**_foregone_**<br>**_Million US$)_**|**Total Effect**<br>**_Tax revenue_**<br>**_foregone_**<br>**_Million US$)_**|**Efficiency**|
|---|---|---|---|---|---|
|**Cyprus**|948.02|0.10|94.80|77.33|0.82|
|**Netherlands**|516.64|0.10|51,66|42,36|0.82|
|**Russia**|113.37|0.10|11.34|9.62|0.85|
|**Austria**|63.30|0.10|6.33|5.55|0.88|
|**Switzerland**|47.05|0.10|4.70|4.24|0.90|
|**Mean**|337.67|0.10|33,76|28,70|0.85|

_Data Source: Central Bank_ We repeat this exercise for the main recipients of interest flows and royalty payments in 2014, applying the same approach. Given that we cannot differentiate between flows potentially qualifying for a lower rate, we only refer to the higher standard rate stipulated in each treaty.

_Table 3: Estimated revenue loss from reduced withholding on interest (2014)_

|**Country**|**Flow in 2014**<br>**_Interest_** **_(Million_**<br>**_USD)_**|**Differential**<br>**_with standard_**<br>**_withholding_**|**Direct Effect**<br>**_Tax revenue_**<br>**_foregone_**<br>**_Million USD)_**|**Total Effect**<br>**_Tax revenue_**<br>**_foregone_**<br>**_Million USD)_**|**Efficiency**|
|---|---|---|---|---|---|
|**Cyprus**|613.65|0.13|79.77|21.72|0.27|

> 29 We also present tables for royalty and interest flows in 2012 in the Annex.

> 30Assumed to be 80 % of total flow.

17
<!-- Start of picture text --> Income-related pension + guaranteed pension<br>Annual pension Monthly pension<br>in price-related in SEK (2007) ;<br>base amounts<br>3.07 10 310<br>2.72 9135 -<br>2.13 7153 ‘<br>1.90 6 3] Unmarried<br>Married ti<br>i!<br>Guaranteed pension.<br>1<br>0<br>Annual pension in price-related 1.14 1.26 272 3.07<br>base amounts<br>Monthly pension in SEK (2007) 3829 4 237 9135 10310<br>Income-related pension<br><!-- End of picture text -->
# Supplementary Appendix

# Why Do Fiscal Multipliers Depend on Fiscal Positions?<sup>∗</sup>

Raju Huidrom, M. Ayhan Kose, Jamus J. Lim, and Franziska L. Ohnsorge

## This appendix presents some additional results. <u>IT IS NOT FOR PUBLICATION.</u>

|Figure A.1|Unconditional Fiscal Multipliers|
|---|---|
|Figure A.2|Unconditional Impulse Responses|
|Figure A.3|Distribution of Fiscal Positions|
|Figure A.4|Ricardian and Interest Rate Channels in Separate Models: Fiscal<br>Multipliers|
|Figure A.5|Ricardian and Interest Rate Channels: Impulse Responses|
|Figure A.6|Ricardian and Interest Rate Channels: Fiscal Multipliers|
|Figure A.7|Ricardian and Interest Rate Channels: Impulse Reponses and Fiscal<br>Multipliers|
|Figure A.8|Ricardian Channel Only: Fiscal Multipliers|
|Figure A.9|Baseline Model: Robustness|
|Table A.1|Data Sources|
|Table A.2|Distribution of Fiscal Positions|
|Table A.3|Comparison of Fiscal and Business Cycle States|

> ∗ Huidrom: International Monetary Fund; rhuidrom@imf.org. Kose: World Bank, Prospects Group; Brookings Institution; CEPR; and CAMA; akose@worldbank.org. Lim: Essec Business School; jamus.lim@essec.edu. Ohnsorge: World Bank, Prospects Group; and CAMA; fohnsorge@worldbank.org.

28