rafmacalaba/datause-encoder-data huggingface dataset:
9

# **Authorship**

On multi-author papers, we give each author equal credit for the paper insofar as EconLit allows us to. This is in contrast to Rubin and Chang (2003) who gave authors of papers with two authors only half a paper‘s credit each, and authors of papers with three authors only one third a paper‘s credit. Unfortunately, EconLit lists only the first author when the original list includes more than three or four authors.<sup>13</sup> In such cases, authors other than the first get no credit in our study as in Rubin and Chang‘s; the EconLit user has no alternative in such cases.

The author data required a good deal of cleaning. Many authors appear in different forms, sometimes with different spellings. The top 250 authors in terms of publication counts were all checked manually for multiple spellings and forms, and the scattered records aggregated; the same manual cleaning was applied to other authors who ranked highly on one or more of the indicators. The trickiest person proved to be Matthias von der Schulenburg, whose name appeared in no less than ten different forms.<sup>14</sup> The funniest was Adam Wagstaff, who after years of writing with Eddy van Doorslaer became ―Adam van Wagstaff‖ in one paper.

# **Affiliation**

The EconLit author-address field gives the author‘s address (affiliation) at the time of publication of the article (actually at the time of proof-correction). The field is empty until the early 1980s, so the results below are for the period 1981-2010. The information in the authoraddress field needed some manual cleaning, and some explanation of what was done is in order. Some clarification is also in order on the issue of joint authorship.

Each unit record (one per publication) generated multiple author-address records depending on the number of authors and whether or not they shared an affiliation. The maximum number of lines in the author-address field is equal to the number of authors listed. If a publication has, say, two authors working at two different institutions, we count the publication twice—both institutions get full credit for it. If the two authors share the same address, the

> 13  Rubin and Chang (2003) report this happens when the list exceeds three, but several recent EconLit records have four authors. 14 For those anxious to dig up Prof von der Schulenburg‘s work, here are the ten permutations: J. M. Graf von der Schulenburg, J. Matthias von der Schulenburg, J. Matthias Schulenburg, J. Matthias Graf von der Schulenburg, J. Matthias Graf von Schulenburg, J. Matthias Graf van der Schulenburg, J. Matthias Graf v.d. Schulenburg, J. Matthias Graf v. d. Schulenburg, J. Matthais Graf van der Schulenburg, and J. M. Graf v.d. Schulenburg. Prof von der Schulenburg‘s records were cleaned because he was in the top 250 on the basis of publication count.
7 We present Gini coefficients, a popular measure of inequality<sup>2</sup> , to describe how income inequality evolved in our selected sample of countries (Table 5). All our measures-except for urban Ethiopia-are based on real household expenditures per adult equivalent.<sup>3</sup> The surveys were designed to enable comparisons over time within a country, though due to different survey designs caution is warranted in making comparisons across countries. Nonetheless, the differences in the degree of income inequality in our sample of countries are striking. At one extreme, Zimbabwe has a highly unequal distribution (a Gini ratio of over 0.6)4, reflecting unequal land distribution, a result in part of its colonial past. Income distributions in Ghana and Uganda, are far more egalitarian.

In terms of evolution, the general picture is one of very little change in _overall_ income inequality in these countries. Reforms and growth have clearly not led to a significant deterioration in consumption inequality, as popular belief would hold (Forsyth, 2000). Nevertheless, these aggregate measures of inequality can be misleading. They may in fact mask a great deal of distributional change, an issue we review further in section IV below.

> 2 Recall that the Gini ratio varies from 0 (perfect income equality) to I (perfect inequality). The higher the value, the greater the inequality.

> 3 While the actual measures are based on expenditures, we use the terms 'income' and 'consumption' interchangeably.

> 4 Intuitively, the Gini index of a population represents the expected income difference between two randomly selected individuals or households. From Table 1 we know that in Zimbabwe real average per capita consumption in 1996 amounted to US$461. The corresponding Gini index is 0.64 (Table 5). Thus, in 1996 the per capita consumption of any two randomly selected Zimbabweans differed on average by US$295 (= 0.64*US$461)-a clear indication of high inequality given that average per capita consumption is only US$461.
# Characteristics of the new poor

By predicting levels of income for all households under the two alternative scenarios, the model enables us to examine the characteristics of those who became poor due to COVID-19, i.e., people who would not have been poor in FY20 had there been no crisis. The new poor differ from those poor apart from COVID-19 in several ways (Table 4). While 38.4 percent of the “always” poor reside in urban settings, around 45.8 percent of the new poor due to COVID-19 lived in urban areas. The new poor are more likely to reside in male-headed, younger, less educated, and smaller households than those always poor. The new poor are also disproportionately concentrated in households with heads who are informal workers (74.7 percent of the new poor), compared to all other households.

Table 4: Household characteristics by poverty status with and without COVID

|Characteristics|All|Poor with<br>and<br>without<br>COVID|New<br>poor<br>with<br>COVID|Exited<br>poverty<br>with<br>COVID|Non-poor<br>with and<br>without<br>COVID|
|---|---|---|---|---|---|
|Households in urban areas|42.1|38.4|45.8|25.3|43.1|
|Mean household size|5.0|6.0|5.6|5.8|4.7|
|Mean dependencyratio|0.9|1.3|1.1|1.4|0.7|
|Household head is male|88.0|92.2|95.0|89.8|86.6|
|Mean age of household head|48.7|45.2|42.6|42.8|49.9|
|Household head with basic education or lower|49.2|59.6|66.8|78.6|46.2|
|Household head is informal worker|44.3|51.8|74.7|72.5|41.0|
|Household head is not working|22.9|17.1|9.6|14.6|25.0|

## Source: Authors’ calculations.

To assess the relative importance of these household characteristics in the likelihood of becoming poor due to COVID-19, Figure 6 presents the odds from a logistic regression for the probability of becoming poor for households exhibiting specified characteristics versus all other groups (Annex B presents the regression results). An odds ratio above 1 indicates that a household with a particular characteristic is more likely to become poor due to COVID. We find two main features increasing the likelihood of becoming poor: living in metropolitan areas and working in the informal sector. The most prominent feature is informality, with households where the head was engaged in the informal sector being 2.2 times more likely to become poor due to the pandemic.

The role of compensatory cash transfers As discussed, the simulation models two channels of impact that play in opposite directions. On the one hand, there is a negative shock to labor income, while on the other hand there is an expansion in cash transfers. This sub-section quantifies the relative importance of the two channels. The overall estimated drop in income of about 1.7 percent stems from a 3.1 percent decline in labor income and a 1.4 percent increase in non-labor income (Table 5). The growth in non-labor income reflects the 13
called "strong ignorability") assumption, namely that outcomes are independent of participation given the observable covariates. Instrumental-variables methods require an alternative conditional independence assumption, namely the exclusion restriction that the IV is uncorrelated with outcomes given participation and the control variables.

In some (rather rare) cases, evaluations of ALMP's have used randomized assignment. In the case of training programs, two examples are the US Job Training and Partnership Act (see, for example, Heckman et al., 1997), and the US National Supported Work Demonstration (studied by Lalonde, 1986, and Dehejia and Wahba, 1999, amongst others). For wage subsidy programs, randomized evaluations have been done by Burtless (1985), Woodbury and Spiegelman (1987) and Dubin and Rivers (1993)all for targeted wage subsidy schemes in the US; indeed, Woodbury-Spiegelman and Dubin-Rivers studied the same experiment by the Illinois Department of Employment Security in the mid-1980s.

Randomization is the theoretical ideal in that it balances the distributions of all (observed or unobserved) covariates between the treatment and control groups. If everyone who was given access to the training automatically took it up and access was assigned randomly then we would have no difficulty estimating the impact on those treated by comparing measured outcomes with those observed for the randomized-out control group. Under weak conditions, an unbiased estimate of mean impact for those treated can then be obtained by taking the mean difference in the outcome measure (employment say) between the treatment and control groups. This is equivalent to the regression coefficient of the outcome measure on a dummy variable for which group one belongs to (treatment/control). This provides an unbiased estimate of impact because this dummy variable is exogenous given randomization and full compliance.

_5_
A striking case of the scope for non-transparent and anti-competitive behavior in health goods procurement is documented in the Bank’s Direct Implementation Review of five health projects in India (India DIR) conducted in 2007. Inappropriate handling of procurement such as last-minute changes to contract provisions accepted from certain bidders; contract specifications altered to enable a certain bidder to win the award; alerting of certain bidders of impending bids prior to the advertising period; rejection of all lowest-price bidders rejected; kickback negotiations; and decisions made about bidders with lack of supporting documents (WB Department of Institutional Integrity 2007).

To detect and address procurement irregularities a transparent and established process, hard indicators, oversight, and accountability for performance are crucial. A study of procurement irregularities in hospital payments for selected homogeneous inputs in four countries in Latin America provides a stark illustration. The ratio of the highest to lowest prices paid was 15 to 1 for saline and 36 to 1 for cotton (Figure 5) (Gray-Molina, Pérez de Rada, and Yañez 2001) While overpayment may result from incompetence, this example of extreme wastage in procurement is explainable only by either gross mismanagement or corruption (Di Tella and Savedoff 2001). In Ghana interviews with officials and the public suggested that 21 percent of procurements in government hospitals were corrupt, and that 18 percent of the value of contracts was required in kickbacks to public officials (World Bank 2000).

# **FIGURE 5. MEDICAL SUPPLY PRICE DIFFERENTIALS ACROSS HOSPITALS IN FOUR LATIN AMERICAN COUNTRIES, 1997–1998**

<!-- Start of picture text --> 40<br>Bolivia (1998)<br>35<br>Argentina (1997)<br>30 Colombia (1998)<br>Venezuela (1998)<br>25<br>20<br>15<br>10<br>5<br>0<br>Saline solution Cotton Dextrose Penicillin<br>Source: Di Tella and Savedoff (2001).<br>Ratio of highest to lowest price<br><!-- End of picture text --> Results from surveys of physicians and nurses in Argentina, Colombia, and Venezuela indicate that corruption within facilities leads to overpayment of suppliers, and that the lack of sanctions and the low probability of getting caught are what makes it possible 23
# **REFERENCES**

Andrews, F. M. (1984). Construct validity & error components of survey measures: a structural modelling approach. _Public Opinion Quarterly_ , 48, pp. 409–442.

- Beegle, K., De Weerdt, J., Friedman, J., & Gibson, J. (2012). Methods of household consumption measurement through surveys: Experimental results from Tanzania. _Journal of Development Economics_ , 98.1, pp. 3–18.

- Bound, J., Brown, C., & Mathiowetz, N. (2001). In J. Heckman & E. Leamer (Eds.), _Measurement error in survey data_ (3707–3843), Elsevier: Amsterdam.

- Christiaensen, L., Lanjouw, P. Luoto, J., & Stifel, D. (2012). Small area estimation-based prediction methods to track poverty: validation & applications. _Journal of Economic Inequality_ , 10.2, pp. 267–297.

- Douidich, M., Ezzrari, A., Van der Weide, R., & Verme, P. (2013). Estimating quarterly poverty rates using labor force surveys: a primer, World Policy Research Working Paper No. 6466.

- Eckman, S., Kreuter, F., Kirchner, A., Jaeckle, A., Tourangeau, R., & Presser, S. (Forthcoming). Assessing the mechanisms of misreporting to filter questions in surveys. _Public Opinion Quarterly._

- Elbers, C., Lanjouw, J. O., & Lanjouw. P. (2003). Micro-level estimation of poverty & inequality. _Econometrica_ , 71.1, pp. 355–364.

- Herzog, R. A., & Bachman, G. J. (1981). Effects of questionnaire length on response quality. _The Public Quarterly,_ 45.4, pp. 549–559.

- Hess, J., Moore, J., Pascale, J., Rothgeb, J., & Keeley, C. The effects of person-level versus household-level questionnaire design on survey estimates & data quality. _The Public Opinion Quarterly_ , 65.4, pp. 574–584.

- Houssou, N., & Zeller, M. (2011). To target or not to target? The costs, benefits, & impacts of indicator-based targeting. _Food Policy_ , 36.5, pp. 627–637.

Johnson, W. R., Sieveking, N. A., & Clanton III, E. S. (1974). Effects of alternative positioning of open-ended questions in multiple-choice questionnaires. _Journal of Applied Psychology_ , 59.6, pp. 776–778.

- Kraut, A. I., Wolfson, A. D., & Rothenberg, A. (1975). Some effects of position on opinion survey items. _Journal of Applied Psychology_ , 60, pp. 774–776.

- Kreuter, F., McCulloch, S., Presser, S., & Tourangeau, R. (2011). The effects of asking filter questions in interleafed versus grouped format. _Sociological Methods & Research_ , 40.1, pp. 88–104.

- Mathiassen, A. (2013). Testing prediction performance of poverty models: empirical evidence from Uganda. Review of Income & Wealth, 59.1, pp. 91–112.

16
# **Table 3: Typology of SOFIs instruments**

|**Typology**|**Benefits**|
|---|---|
|**(i) Direct lending** **(also known as first-tier, retail).**Direct<br>provision of finance to the ultimate beneficiary. Finance can<br>be a regular loan, leasing, or factoring.|Targeted approach, when ultimate beneficiary or<br>location is too expensive for private financial<br>intermediaries to serve.|
|**(ii) On-lending** **(also known as second-tier, wholesale,**<br>**apex).**On-lending to financial intermediaries for their<br>direct provision of finance to the ultimate beneficiary.<br>Finance can be a loan, leasing, or factoring.|Lower cost and risk management burden. Limited<br>scope for political interference and market<br>distortion. Can promote competition among<br>private sector participating institutions and lead to<br>higher demonstration effect.|
|**(iii) Partial credit guarantee schemes (also known as**<br>**risk sharing facilities).**Offering of partial credit<br>guarantees that partially offset loan losses by private<br>financial intermediaries upon the ultimate beneficiary’s<br>default (e.g. infrastructure projects, MSMEs, gender<br>targeting etc.).|Leverage public resources. Alleviate enterprise<br>collateral constraints, reduce project risk, and<br>financial intermediary risk aversion.|
|**(iv) Grants.**Direct or indirect (through third parties)<br>provision of grants.|Achieve socially desired objectives, ensure<br>equitable income distribution.|
|**(v) Other financial products**, such as venture capital and<br>equity financing.|Typically deployed to develop segments of the<br>capital markets. Targeting seed start-up and early<br>stage enterprises.|
|**(vi) Non-financial products.**Offering advisory services,<br>capacity building, training programs to financial<br>intermediaries or ultimate beneficiaries, or market creation<br>services such as reverse factoring platforms.|Strengthen financial intermediaries or ultimate<br>beneficiaries, typically complemented by<br>financing.|

# **4 Sample and classification**

The survey (see Appendix 1) covers a broad range of financial institutions that are government-owned and operate in ECA.<sup>8</sup> Government ownership is defined as ownership above 50 percent (e.g. plus 1 share with voting rights). The sample includes 41 institutions distributed across 21 countries (see Appendix II). Quantitative information is gathered at three points in time: 2007, 2011 and 2015. It is worth mentioning that some results should be interpreted with caution, as the sample representation in terms of sector assets or state ownership in the sector varies considerably.

> 8 It excludes SOFIs that were nationalized during the GFC (or other recent crises) and the authorities’ policy intention is to privatize them in the short to medium term.

9
# Table 7: Direct and Indirect MDB Mobilization Effects

||**Direct + Indire**|**ct Effects**|**Indirect Effect**|**s**|
|---|---|---|---|---|
||Size (%GDP)<br>including<br>MDB loan<br>(1)|Banks<br>including<br>MDB loan<br>(2)|Size (%GDP)<br>excluding<br>MDB loan<br>(3)|Banks<br>excluding<br>MDB loan<br>(4)|
|MDB cs,t|0.2952***<br>(0.043)|2.6323***<br>(0.275)|0.0651***<br>(0.021)|0.5328**<br>(0.207)|
|Size (%GDP) cs,t-1|0.2151***<br>(0.069)||||
|Banks per Loan cs,t-1||0.2205***<br>(0.029)|||
|Size (%GDP) cs,t-1|||0.2252***<br>(0.072)||
|Banks per Loan cs,t-1||||0.2394***<br>(0.031)|
|Observations|25,272|25,272|25,272|25,272|
|R-squared|0.407|0.675|0.405|0.672|
|Sector-country FE|Yes|Yes|Yes|Yes|
|Sector-year FE|Yes|Yes|Yes|Yes|
|Country-year FE|Yes|Yes|Yes|Yes|
|Average MDB|0.0478|0.0478|0.0478|0.0478|
|Average Dep. Var.|0.109|0.112|0.103|1.503|

_Notes_ : The table presents the estimates of equation 1, without additional lags of MDBcs,t. Columns 1-2 report total mobilization effects (direct + indirect); columns 3-4 report indirect mobilization effects only. The dependent variables are: (i) the size of syndicated loans (% of GDP, columns 1-3), and (ii) the average number of banks per loan (columns 2-4). In columns 1-2 the outcome variables include the amount lent by MDBs’ partners and the number of those partners, respectively. In columns 3-4 the outcome variables exclude the amount brought by MDBs’ partners and the number of the banks. MDBcs,t is a dummy equal to 1 if there is at least one MDB providing a syndicated loan in country-sector _cs_ at time _t_ . The bottom rows show the sample averages of the MDBcs,t and outcome variables. Standard errors clustered at country level are in parentheses: * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01.

31
28

# Trade creation and diversion

Tables 2, 3, and 8 and Figures 19-22 show the decomposition of export changes by destinations and sectors in absolute and relative terms with respect to the baseline in 2030. As can be seen, the simulation results confirm that FTAs tend to increase export flows toward signatory countries: exports within the CPTPP will increase by 27% as compared to the baseline scenario in 2030, TPP12 – by 15.4% RCEP – by 11.7%, FTAAP – by 18.1%.

The importance of FTA markets as destination for its members’ exports is growing both at the aggregate and country level (see Tables 2, 3 and 8) as exporters from signatory countries gain more competitive advantage in the member markets compared to the non-members, as well as exports from non-members become relatively less competitive. On the aggregate level, signatory countries’ share in total members’ trade will expand from 14.8% to 18.3% under the CPTPP; from 35.7% to 39.5% under the TPP12; from 45.4% to 48.3% under the RCEP; from 59.9% to 64.7% under the FTAAP. Therefore, exports from nonmembers tend to divert from member markets countries. However, the changes in trade for all considered FTAs are projected to be significantly larger, both in dollar and percentage terms, that any trade diversion observed by non-members. For example, CPTPP exports within the treaty will go up by $185 billion, while non-members exports to the CPTPP markets will go down by $63 billion. Gains in total exports of FTAs members are also much higher compared to exports losses of non-members (see Tables 6 and 7), exceeding them by more than five times. As a result, all FTAs are expected to boost the global exports, with larger FTAs having greater positive impact on global trade.

## **Table 2. Exports of FTA Members by destinations (FTA Members vs Non-members markets) compared to the baseline in 2030**

|||**CPT**|**PP**|**TP**|**P12**|**RC**|**EP**|**FT**|**AAP**|
|---|---|---|---|---|---|---|---|---|---|
|||**Members**|**Non-**<br>**members**|**Members**|**Non-**<br>**members**|**Members**|**Non-**<br>**members**|**Members**|**Non-**<br>**members**|
|**Members’**<br>**exports in**<br>**2030**|$ billion<br>share,<br>%|869.0<br>_18.3_|3,882<br>_81.7_|3,207.1<br>_39.5_|4,907.4<br>_60.5_|5,867.4<br>_48.3_|6,288.8<br>_51.7_|10,998.2<br>_64.7_|6,011.1<br>_35.3_|
|**Change to**|$ billion|184.9|-57.3|427.6|-104.8|616|-27.7|1,685.3|-225.8|
|**baseline**<br>**in 2030**|in<br>shares|_3.5_|_-3.5_|_3.9_|_-3.9_|_2.9_|_-2.9_|_4.8_|_-4.8_|
||in p.p.|||||||||

Source: Authors’ estimates (*share in total FTA members’ exports)
# TABLE C.1: Estimation of rent, ACCRA

||Simple||Job densit|y|Interactio|n|Tenure||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Directly affected by food|-0.215*||-0.198*||||-0.042||
||(0.119)||(0.112)||||(0.061)||
|Flood risk perception||-0.274**||-0.253**||||-0.149**|
|||(0.111)||(0.106)||||(0.073)|
|Job density|||-0.814***|-0.789***|-0.564|-0.406|-0.511***|-0.479***|
||||(0.274)|(0.276)|(0.356)|(0.300)|(0.137)|(0.138)|
|Number of rooms|0.420***|0.396***|0.391***|0.369***|0.384***|0.345***|0.409***|0.397***|
||(0.121)|(0.118)|(0.114)|(0.111)|(0.112)|(0.110)|(0.069)|(0.072)|
|Number of rooms squared|-0.052***|-0.048***|-0.048***|-0.045***|-0.048***|-0.042***|-0.038***|-0.036***|
||(0.013)|(0.013)|(0.013)|(0.012)|(0.013)|(0.012)|(0.007)|(0.007)|
|Unimproved house|-0.390**|-0.378**|-0.310*|-0.302*|-0.311**|-0.323**|-0.463***|-0.461***|
||(0.184)|(0.175)|(0.157)|(0.153)|(0.153)|(0.153)|(0.106)|(0.109)|
|Unimproved roof material|0.443***|0.438***|0.487***|0.481***|0.485***|0.471***|0.140***|0.140***|
||(0.099)|(0.099)|(0.096)|(0.097)|(0.095)|(0.098)|(0.046)|(0.045)|
|Unimproved wall material|-0.357***|-0.356***|-0.337***|-0.337***|-0.325***|-0.329***|-0.223***|-0.215***|
||(0.118)|(0.118)|(0.118)|(0.118)|(0.118)|(0.117)|(0.047)|(0.051)|
|Unimproved foor material|0.053|-0.018|-0.044|-0.107|-0.089|-0.155|-0.136|-0.153|
||(0.279)|(0.297)|(0.234)|(0.253)|(0.220)|(0.246)|(0.095)|(0.113)|
|Unimproved drinking water source|-0.110|-0.114|-0.076|-0.081|-0.082|-0.092|-0.051|-0.043|
||(0.097)|(0.093)|(0.097)|(0.093)|(0.097)|(0.093)|(0.048)|(0.049)|
|Unimproved toilet facility|-0.027|-0.039|-0.078|-0.088|-0.077|-0.077|-0.085|-0.071|
||(0.110)|(0.097)|(0.111)|(0.100)|(0.109)|(0.095)|(0.075)|(0.075)|
|Not collected solid waste|0.121|0.097|0.097|0.076|0.103|0.094|-0.017|-0.022|
||(0.110)|(0.104)|(0.110)|(0.108)|(0.111)|(0.106)|(0.058)|(0.059)|

_Continued on next page_