rafmacalaba/datause-encoder-data huggingface dataset:
**Table 9: If the truth is 10,000, how much would owners report?**

||Reve|nue|Exp|enses|
|---|---|---|---|---|
||KMS|SLMS|KMS|SLMS|
|Mean|6744|8204|12254|11417|
|10th percentile|4500|6000|10000|8000|
|25th percentile|5000|7000|10000|10000|
|50th percentile|7000|8000|12000|10000|
|75th percentile|8000|10000|15000|13000|
|90thpercentile|10000|10000|15000|15000|

**Table 10: Comparison of Reported Sales to Directly Monitored Sales** For 58 KMS firms receiving visits every 2 days

||Mean|S.D.|Median|25th|75th|
|---|---|---|---|---|---|
|Hours of transactions observed in month|8.2|1.8|8.0|7.0|9.3|
|Reported Sales for September|37090|26205|30360|17150|50462|
|Estimated Sales based on observed<br>transactions and weekly hours reported|53630|39224|44405|22723|71712|
|Implied % underreporting|30.8||31.6|24.5|29.6|

28
reached 90 or more. Total event counts by governorate in Gaza fall within a narrower range, from 30 at the lowest to almost 100 at the highest.<sup>10</sup> Not only does the distribution of outcomes tend to be more condensed in Gaza than in the West Bank, but those outcomes are on average more severe. If the Facebook survey tends to pull estimates toward the more severe end of the scale relative to the PPCS, the measurable differences produced by this process are likely to be smaller if the PPCS estimates are already at that end of the scale.

People in Gaza are also plausibly more likely to be interested in responding to a survey on the effects of the May 2021 violence on Palestinians, suggesting that the sample of respondents who selected into the Facebook survey is likely to be more representative of the underlying population in Gaza than it is in the West Bank. May 2021 saw escalations of violence in both Gaza and the West Bank, but Gaza experienced more severe violence and more extensive damage.<sup>11</sup> This greater exposure may have resulted in broader motivation to complete a World Bank Facebook survey seeking to understand the effects of the escalation. In fact, out of the sample of respondents who began the Facebook questionnaire, people in Gaza were over 25 percent more likely to complete the survey than were people in the West Bank.

Finally, all respondents to the Facebook survey received the conflict exposure questions, while only respondents in Gaza received these questions in the PPCS. Since the conflict exposure module preceded the mental health modules, some of the greater similarity in Gaza for these modules may be due to the regional differences in questionnaire structure.

How robust are the main results to different ways of controlling for observable characteristics?

# Achieved samples

As is often the case with online surveys, the achieved sample for the Facebook survey differed from the population on several observable characteristics (see Table 5). Respondents to the Facebook survey were more likely than the general population to live in the West Bank, more likely to be male, more likely to report working in the past week, and much more likely to have completed secondary or higher education. The distribution of observable characteristics in the PPCS sample was more closely (although not perfectly) aligned with that of the population.

**Table 5: The achieved sample for the Facebook survey is farther than the PPCS sample from the population distribution**

|**Indicator**|**Facebook sample**<br>**(unweighted)**|**PPCS sample**<br>**(unweighted)**|**Population**|
|---|---|---|---|
|_Share who live in the West Bank_|69.4|56.4|62.0|
||(1.3)|(2.6)||
|_Share who are female_|24.5|51.8|49.4|
||(1.2)|(0.5)||

> 10 Based on publicly available <u>ACLED data for the West Bank and Gaza from May 6 to 21, 2021, accessed around</u> December 2022 and filtered to the following event and sub event types: Battles, Explosions/Remote violence, Riots, Violence against civilians, and Excessive force against protesters. See also Raleigh et al (2010). 11 See for example Mehvar et al (2021) and World Bank Group et al (2021).

12
APPENDIX bonds with a much longer maturity, since average maturities were much longer then. From 1942 on, we rely on the 10-year government total bond return series provided by the CRSP database, which builds on a comprehensive collection of US Treasury bond prices.

## <u>UK government bonds, monthly, since 1815</u>

The return index on long-term UK government bonds is from GFD and was compiled from Neal (1990) for 1815-1823, from The Times (1824-1844), from the periodical The Banker’s Magazine (1844-1852), and from the Central Statistical Office of the British government (1853 onwards). Whenever possible, we use an index of 10-year bonds, but in the 19<sup>th</sup> century, the available indices also contain bonds with a much longer maturity, since average maturities were much longer compared to today.

## <u>S&P US Stocks Total Return Index, monthly, since 1815</u>

From 1815 to June 1962, we use the US stock market returns index provided by Schwert (1990). From July 1962 onwards, we use the S&P US Stocks Total Return Index from Standard & Poor’s retrieved from GFD.

## <u>FTSE UK Stocks Total Return Index, monthly, since 1815</u>

This index is from GFD and compiled from the following sources: Rostow and Schwartz (1953) which contains data on Bank of England Shares and the East Indies Company from 1815 to 1850, Hayek (1935) from 1851-1867, and Smith and Horne (1934) from 1874 to 1922. Additional sources are the periodical Banker’s Magazine (1907-1933), The Economist (1933-1962), and The Financial Times from 1950 onwards. For more recent decades, the data comes from the Central Statistical Office of the British government for the years 1939-1988 and from Eurostat from 1989 until today.

## <u>UK inflation/CPI series, monthly</u>

The monthly UK CPI series comes from the Bank of England’s (BoE) historical dataset sheet called “A Millennium of UK Data”, Version 3.1, by Thomas and Dimsdale (2017). From 1914 onwards, we use the spliced monthly consumer price index. For the period before WW1, we use the spliced monthly wholesale/producer price index. These series are those recommended by the BoE.

## <u>US CPI series, monthly</u>

The monthly “Historical Consumer Price Index for All Urban Consumers” is from the US Bureau of Labor Statistics and starts in 1913. For the period before that we rely on series gathered and cited by Reinhart and Rogoff (2009), in particular from the Historical Statistics of the United States. Note that we only have ten USD bonds in our sample that were issued before 1913.

## <u>US Corporate bond total return index, monthly, since 1900</u>

The US corporate bond return series is spliced by combining the returns on the S&P 500 AAA Investment Grade Corporate Bond Index from GFD until 1984 with data from the Bank of America Merrill Lynch US Corp AAA Total Return Index thereafter, which we retrieved from the Federal Reserve Bank of St. Louis database.

# **References Appendix A** (those not cited in the main paper):

British Central Statistical Office (various issues) _Annual Abstract of Statistics_ . London: CSO. Hayek, Friedrich A. (1935) _Prices and Production._ London: Routledge.

A2
BOX 1.4 TEXT REFERENCE FOR THIS BOX Many new accounts in India—but many dormant ones too In August 2014 the Indian government launched the Pradhan Mantri Jan Dhan Yojana scheme for comprehensive financial inclusion with the goal of opening a bank account for every household. To encourage new accounts, the scheme offered attractive features such as zero balances, overdraft facilities, and free life insurance. By the end of January 2015 it had led to the opening of 125 million new bank accounts; as a point of comparison, a 2013 survey had found that fewer than 400 million people in the country had an account.<sup>a</sup> But the scheme has attracted criticism for expanding the public sector’s role in banking— more than 97 percent of the new accounts are at public banks. In addition, 72 percent of the accounts show zero balances.<sup>b</sup> This may be in part because many new account holders may not yet have had an opportunity to use their accounts—especially since the accounts were not set up for an explicit purpose, such as to receive wages or government transfer payments. Moreover, only 39 percent of all account holders in India own a debit or automated teller machine (ATM) card, and using an account might be inconvenient and time-consuming if every transaction requires using a bank teller.

- a. Calculated based on the assumption that 47 percent of people age 15 and above have an account at a financial institution (Intermedia, Financial Inclusion Insights database, http://finclusion.org/datacenter/).

- b. Data are from the Indian Ministry of Finance’s website for the Pradhan Mantri Jan Dhan Yojana scheme (http://www. pmjdy.gov.in).

> THE GLOBAL FINDEX DATABASE **26**
prevented them accessing non-farm jobs, particularly regular non-farm jobs. This raises the question of how non-farm employment growth might help to reduce poverty.

One possible explanation for our findings from the region-level fixed effects specification centers around the distinction between the average “incidence” of non-farm employment and the marginal incidence of such employment. Tables 5a-5c reveal that on average the poor are highly under represented among those with non-farm jobs. The average odds of regular non-farm employment are particularly low for the poorest quintile of the rural population (0.027 relative to 0.072 in 2004 – Table 5a). These patterns are echoed in the analysis of non-farm employment probabilities summarized in Tables 6 and 7. However, what such quantitative patterns fail to reveal is how these odds would change as the non-farm sector grew. Lanjouw and Ravallion (1998) consider this question in the context of public spending programs in rural India. They note the possibility of early capture by the rich at the onset of a particular public spending program which gradually becomes diluted and more pro-poor as the program continues to grow: the rich get first “dibs” on the public program, but then become satiated, and subsequent expansion of the program trickles down to the less well off. In such a setting the _marginal_ incidence of the public program may be much less regressive than the _average_ incidence (see Lanjouw and Ravallion 1999 for further discussion).

Lanjouw and Ravallion (1999) propose a method for estimating marginal incidence based on cross-sectional variation at the region level. Essentially they propose estimating five models, corresponding each to a particular quintile of the overall rural per capita consumption distribution, in which the region (and quintile) – specific incidence of a program is regressed on the overall state-level average incidence of the program. A different parameter estimate obtains for each quintile, and can be interpreted as the marginal odds of program participation (see Lanjouw and Ravallion, 1999, for details). We apply this method to assess the marginal odds of non-farm employment in rural India, using the 2004 NSS survey data.

Table 10 presents our results. We consider first, in Table 10a, the incidence of regular non-farm employment. On average, just under 4% of working adults in the poorest quintile are employed in the regular non-farm sector. This compares to over 9% of the overall adult working population, and translates into average odds of regular non21
cap in India, and the introduction of a cap on high‐cost short‐term credit in the United Kingdom in 2015.

The selection of case studies is geared towards analyzing different variations of recently implemented interest rate caps and based also on data availability. Table 2 uses the taxonomy developed in Section 3 to classify the caps used in those countries along their key characteristics.

**Table 2: Summary of the country case studies**

||**Kenya**|**Zambia**|**Cambodia**|**WAEMU**|**India**|**UK**|
|---|---|---|---|---|---|---|
|**Scope**|||||||
|Narrow|||●||●|●|
|Broad|●|●||●|||
|**Number of ceilings**|||||||
|Single ceiling|●||●|||●|
|Multiple ceilings||●||●|●||
|**Type**|||||||
|Relative cap|●|●|||●||
|Absolute cap|||●|●||●|
|**Methodology**|||||||
|Multiple of benchmark|||NA|NA|●|NA|
|Fixed spread|●|●|NA|NA||NA|
|**Benchmark**|||||||
|Central bank rate|●|●|NA|NA||NA|
|Average market rate|||NA|NA|●|NA|
|**Binding**|||||||
|Below market rate|●|●|●|●|●||
|Above market rate||||||●|
|**Fees**|||||||
|Regulated|●|●*|||●|●|
|Not regulated|||●|●|||
|<br>* The application of cap allows for<br>well as insurances to be charged ov|among other<br>er and above|s, an arrange<br>the effective|ment fee and s<br>annual rate o|urvey fee and<br>f interest.|a commitm|ent fee – as|

_Source:_ Own compilation 21
E. **Top 10 industries**<sup>**with highestrate of job**</sup> reallocation

|Industry<br>re|Job<br>Sh<br>allocatioemp<br>nrate|arein<br>loymen<br>t|
|---|---|---|
|Wholesale<br>||28|
|trade<br>|18.7<br>|.<br>|
|Machinery<br>|14.6<br>|1.2<br>|
|Basicmetals<br>|12.8|0.7|
|Otherbusiness<br>||22|
|activities<br>|12.5<br>|.<br>|
|Metalproducts<br>|11.8<br>|1.4<br>|
|Construction<br>|11.5<br>|5.5<br>|
|Leather<br>|11.4<br>|1.2<br>|
|Retailtrade<br> <br>|11.3|5.1|
|Rubber<br>&<br>||07|
|plastic<br>|10.2|.|
|Electric<br>|81|14|
|equipment|.|.|

Note: The raking<sup>isbasedonindustrieswhosesharein</sup> total employment<sup>isat least 0.5%.</sup> Source: Central Bureau<sup>of Statistics;Authorscalculations.</sup> Apart form the<sup>changeintheindustrystructure, aless visiblebutequally</sup> important change of<sup>the job structureis under way inCroatia.Within industrieslower</sup> productivity jobs<sup>arebeingdestroyedandsimultaneouslypresumablehigher</sup> Such productivity jobs,<sup>oftenrequiringdifferent,higherskills,arebeingcreated.</sup> of industries<sup>includewholesaletrade, manufacturing</sup> dynamic, restructuring machinery, manufacturing<sup>of basicmetals,andotherbusinessactivities(Table10,</sup> Panel E). Again, it is noteworthy,<sup>the restructuringsectors includeboth</sup> manufacturing and<sup>serviceindustries.</sup> The presumption<sup>thatjobreallocationinvolvestheeliminationofless</sup> productive jobs<sup>andthesimultaneous creationof moreproductivejobscannotbe</sup> However, substantiated directly<sup>for Croatia, as necessaryinformationis lacking.</sup> international evidence<sup>providesstrongsupportto thehypothesisthat job reallocation</sup> tends to bring about<sup>substantialproductivitygains.For example,Fosteret al.(2002)</sup> argue that "a substantial<sup>fractionof aggregateproductivity growth is associatedwith</sup> the reallocation of<sup>outputsandinputsfromlessproductivetomoreproductive</sup> individual microeconomic<sup>units."(p.2)Forexample,"intheU.S.,roughlythirty</sup> percent of productivity<sup>growth(measuredaseithermultifactor or labor productivity)</sup> over a ten-year horizon<sup>isaccountedfor by moreproductiveentering plants displacing</sup> _-26-_
# When Goal-Setting Forges Ahead but Stops Short<sup>_∗_</sup>

Asad Islam<sup>_†_</sup> Sungoh Kwon<sup>_‡_</sup> Eema Masood<sup>§</sup> Nishith Prakash<sup>_¶_</sup> Shwetlena Sabarwal<sup>_∥_</sup> Deepak Saraswat<sup>_∗∗_</sup>

## _JEL Codes_ : D9, I20, I25, O15, O55. Keywords: Goal-Setting, Recognition Rewards, Student Performance, and Zanzibar.

> _∗_ We appreciate helpful comments and suggestions from Nathan Fiala, Caroline Hoxby, Tarun Jain, Matt Lowe, Michael Manove, Nirajana Mishra, Dilip Mookherjee, Priya Mukherjee, Abhiroop Mukhopadhyay, Philip Oreopoulos, Daniele Paserman, Gautam Rao, Michael Kremer, Stephen Ross, Frank Schilbach, Samer Al Samarrai, Inaam Ul Haq and seminar and conference participants at Boston University, University of Connecticut, and 14th Annual Conference on Economic Growth and Development, Norms and Behavioral Change Conference (2019), and 17th Midwest International Economic Development Conference. This work was made possible through the leadership and support of Abdulla Mzee Abdulla and Khalid Wazir from the Ministry of Education and Vocation Training (MoEVT) in Zanzibar. We are also grateful for the support of Huma Ali Waheed and Kaboko Nkahinga from the World Bank. Tumainiel Ngowi provided excellent field support. Funding from the Results in Education for All Children (REACH) Trust Fund is gratefully acknowledged. Declarations of Interest: None.

> _†_ Monash University. `asadul.islam@monash.edu` .

- Korea Institute of Public Finance. `sokwon@kipf.re.kr` .

- §World Bank. `emasood@worldbank.org` .

- University of Connecticut, IZA, HiCN, GLO, and CReAM. `nishith.prakash@uconn.edu` .

> _∥_ World Bank. `ssabarwal@worldbank.org` .

_∗∗_ University of Connecticut. `deepak.saraswat@uconn.edu` .
school when in fact they are not. Thus, self-reported data attenuate program impacts for the CCT arm and give the impression that UCTs outperform CCTs in terms of increasing enrollment. Teacher reports purge the data of the bias caused by this differential misreporting and reveal the true program impacts. As we will see below, the evidence of program impacts on school attendance and test scores are also consistent with the finding that the program impacts on enrollment are higher in the CCT arm than the UCT arm.

_Attendance (intensive margin)_ We now turn to examining the intensity of attendance for those enrolled in school in 2009 and the first term of 2010. The school ledgers from Round 3 provide term by term information on the number of days the students were present for each day the school was in session.<sup>32</sup> Table V presents the results on attendance rates using single difference regressions. The attendance rate in the control group among those enrolled in school ranges from a high of 85% in Term 2 of 2009 to a low of 69% in Term 3 of 2009. Interestingly, the overall attendance rate for 2009 is 81% in the control group, right around the attendance requirement in the CCT arm. Attendance on the intensive margin is uniformly higher in the CCT arm than the control group. The overall attendance rate for 2009 is 8.0 pp higher than the control group, which translates into approximately four school days per term or more than ten school days over the entire 2009 school year. In the UCT arm, impact estimates are mostly positive, but none of them are statistically significant. Program impacts in the CCT arm are higher than the UCT arm during Term 1 in both 2009 (13.9 pp vs. 6.3 pp; p-value=0.13) and 2010 (9.2 pp vs. -3.8 pp; p-value=0.01). Term 1 coincides with the lean season in Malawi, when food is scarcest and the number of malaria cases reaches its peak.<sup>33</sup> Thus,

> 32 As we showed in Appendix Table B.1, the baseline propensity of those lost to follow-up to attend school in 2009 does not differ by treatment status (Column (2)).

> 33 In 2001, the prevalence of malaria parasitaemia among non-pregnant females, ages 15-19, was 24% (Dzinjalamala 2009). The same figure was 47% in school children. Malaria is a frequent cause of absenteeism in school, resulting in poor scholastic performance on the part of the student.

21
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# _5.2 Catastrophic health care payments_

Illness can induce a sizable and unpredictable shock to a household’s living standards (Wagstaff and van Doorslaer 2003). In order to explore the financial impact of healthcare expenditures on households, we examine the extent of catastrophic expenditure on healthcare. This involves measuring the extent to which health costs incurred exceed or fall short of different threshold levels, i.e. the degree of ‘catastrophe’ experience by a household, and the impact on poverty measures<sup>13</sup> .

Table 8 presents the incidence ( _headcount_ ) and the intensity ( _gap_ ) of catastrophic out-of-pocket payments. The _headcount_ is the percentage of individuals whose health care costs, expressed as a proportion of income, exceed a given discretionary fraction of their income, _z_ ; the _mean gap_ is the average amount by which payments as a proportion of income exceed the threshold _z_ . The incidence and intensity of the occurrence, though, are related through the mean positive gap (MPG) which is defined as the gap over the headcount<sup>14</sup> . The sensitivity of the analyses to different threshold levels is tested.

**<u>Table 8 Catastrophic impact of out-of-pocket payment – at various threshold levels</u>**

||**Out-of-pocket health expenditure**||**Thresh**|**old level z**||
|---|---|---|---|---|---|
||**(as % of tot expenditureper capita)**|**5%**|**10%**|**15%**|**25%**|
||Headcount|36.55%|20.79%|12.58%|5.12%|
|**Albania**|Mean gap|3.58%|2.19%|1.36%|0.52%|
||Mean positive gap|9.79%|10.53%|10.81%|10.16%|
|**Bi**|Headcount|7.83%|3.10%|1.29%|0.35%|
|**osna and**<br>**Herzegovina**|Mean gap|0.47%|0.21%|0.12%|0.04%|
||Mean positive gap|6.00%|6.77%|9.30%|11.43%|
||Headcount|5.84%|1.14%|0.70%|0.15%|
|**Montenegro**|Mean gap|0.23%|0.12%|0.07%|0.04%|
||Mean positive gap|3.94%|10.53%|10.00%|26.67%|
||Headcount|23.83%|12.22%|7.64%|3.52%|
|**Serbia**|Mean gap|2.28%|1.44%|0.97%|0.46%|
||Mean positive gap|9.58%|11.76%|12.67%|13.12%|
||Headcount|44.73%|26.32%|15.35%|6.73%|
|**Kosovo**|Mean gap|4.59%|2.87%|1.86%|0.83%|
||Mean positive gap|10.26%|10.90%|12.08%|12.29%|

The table shows that in Albania, for instance, as much as 5% of the sample recorded out-of-pocket payments (as proportion of income) that exceeded 25% of their pre-payment income, with an average degree of 0.5%. Decreasing the threshold level to 10% raises the proportion of the population with catastrophic payments to almost 21%, while the mean gap rises to 2%. As expected, both the incidence

> 13 To do so, we follow the methodology outlined in the World Bank’s _Quantitative Techniques for Health Equity Analysis— Technical Note # 18_ and _Quantitative Techniques for Health Equity Analysis—Technical Note # 19_

> 14 The headcount, _H,_ only captures the incidence of any catastrophes occurring, while the gap, _G,_ also captures the intensity of the occurrence. They are related through the _mean positive gap_ which is defined as

> _MPG_ = _G ._ Because this implies _G_ = _H_ * _MPG_ , it means that the overall ‘mean catastrophic gap’ equals the fraction with _H_

> catastrophic payments times the mean positive gap.

17