rafmacalaba/datause-encoder-data huggingface dataset:
- King, Robert G. and Ross Levine (1993):  "Finance and Growth: Schumpeter Might Be Right," _Quarterly Journal of Economics_ 108, 717-38.

- Lane, Philip and Gian Maria Milesi-Ferretti (2008): The External Wealth of Nations Mark II: Revised and Extended Estimates of Foreign Assets and Liabilities, 1970-2004.” _Journal of International Economics_ 73, 223-50.

- Levine, Ross E. 2002.  “Bank-Based or Market-Based Financial Systems: Which is Better?” _Journal of Financial Intermediation_ 11, 1-30.

- Levine, R. 2005. Finance and Growth: Theory and Evidence.  In: Aghion, P and Durlauf, S.  (eds.), _Handbook of Economic Growth_ . Elsevier Science. The Netherlands.

- Levine, Ross and Zervos, Sara. “Stock  Markets, Banks, and Economic Growth,” _<u>American Economic Review</u>_ <u>, June 1998, 88, pp. 537-558.</u>

Roy, Andrew D. (1952). Safety First and the Holding of Assets. _Econometrica_ 20, 431-449.

- Schneider, Friedrich and Dominik Ernste (2000): “Shadow Economies: Size, Causes and Consequences.” _Journal of Economic Literature_ 38, 77-114.

Swiss Re (several years), SIGMA, Zurich, Switzerland.

World Bank (2007): “Finance for All: Policies and Pitfalls in Expanding Access” 19
# **Panel B. As a Share of Total Imports**

<!-- Start of picture text --> Imports of Intermediates under Advance Clearance (as share<br>of imports)<br>Decree N 010‐2009‐EF<br>20%<br>came into force ruling<br>18%<br>the implementation of<br>16%<br>Law N 1053.<br>14%<br>12%<br>10%<br>8%<br>Law N 1053 provides new<br>6% regulations to reform and<br>4% speed up the clearance<br>2%<br>0%<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br><!-- End of picture text --> 23
# **6. REFERENCES**

<mark>Azevedo, Joao Pedro; Atamanov, Aziz. 2014. Pathways to the middle class in Turkey : how have reducing poverty and boosting shared prosperity helped?. Policy Research working paper ; no. WPS 6834. World Bank Group. http://documents.worldbank.org/curated/en/2014/04/19354560/pathwaysmiddle-class-turkey-reducing-poverty-boosting-shared-prosperity-helped</mark> Beck, Demirguc-Kunt, and Levine 2007. Finance, Inequality and the Poor. <mark>Journal of Economic Growth Volume 12, Issue 1 , pp 27-49 Kluwer Academic Publishers-Plenum Publishers</mark> <u><mark>http://link.springer.com/article/10.1007%2Fs10887-007-9010-6</mark></u> Brunori, Paolo, Francisco H.G. Ferreira, Vito Peragine. 2013. Inequality of Opportunity, Income Inequality and Economic Mobility WPS6304 <mark>World Bank Group.</mark> <u><mark>http://elibrary.worldbank.org/doi/pdf/10.1596/1813-9450-6304</mark></u> Claessens, Stijn and Perotti, Enrico C. 2007., Finance and Inequality: Channels and Evidence, Available at SSRN: http://ssrn.com/abstract=998468 or <u>http://dx.doi.org/10.2139/ssrn.998468</u> de Mel, Suresh, Craig McIntosh, and Christopher Woodruff. 2013. "Deposit Collecting: Unbundling the Role of Frequency, Salience, and Habit Formation in Generating Savings." American Economic Review, 103(3): 387-92.

Demirguc-Kunt, Asli, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden. 2015. “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, DC.

Johnston, Don Jr., and Jonathan Morduch. 2008. “The Unbanked: Evidence from Indonesia.” World Bank Economic Review 22 (3): 517-537. - <u>http://www.nyudri.org/wp content/uploads/2011/10/unbanked.pdf</u> McKenzie, David, and Christopher Woodruff . 2008. “Experimental Evidence on Returns to Capital and Access to Finance in Mexico” World Bank Economic Review Volume 22 (3): 457 – 482. <u>http://elibrary.worldbank.org/doi/pdf/10.1093/wber/lhn017</u> Molinas Vega, José R.; Paes de Barros, Ricardo; Saavedra Chanduvi, Jaime; Giugale, Marcelo; Cord, Louise J.; Pessino, Carola; Hasan, Amer. 2012. Do Our Children Have a Chance? A Human Opportunity Report for Latin America and the Caribbean. World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/2374 License: CC BY 3.0 IGO.” Paes de Barros, Ricardo; Ferreira, Francisco H. G.; Molinas Vega, Jose R.; Saavedra Chanduvi, Jaime. 2009. Measuring Inequality of Opportunities in Latin America and the Caribbean. Washington, DC: World Bank; New York: Palgrave Macmillan. © World Bank. https://openknowledge.worldbank.org/handle/10986/2580 License: CC BY 3.0 IGO.

22
# **Table 7: South Asia**

||**P**<br>**I**|**rojected**<br>**mpact as**|<br>|**Projected**<br>**Impact as**|**Incremental**|**Projected**<br>**Impact as**||**Projected**<br>**Impact as**|**Incremental**|**Projected**<br>**Impact as**|<br>**Incremental**|**Projected**<br>**Impact as**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Incremental**<br>**a**|**% of**|**Incremental**<br>|**a % of**|**Impact:**|**a % of**|**Incremental**|**a % of**|**Impact:**|**a % of**|**Impact:**|**a % of**|
|**Country**|**Impact: Land**<br>**Area (sq. km)**<br>**C**<br>**T**|**oastal**<br>**otal**|**Impact:**<br>**Population**<br> <br>|**Coastal**<br>**Total**|**GDP (mil.**<br>**USD)**|**Coastal**<br>**Total**|**Impact: Agr.**<br>**Area (sq. km)**|**Coastal**<br>**Total**|**Urban Extent**<br>**(sq. km)**|**Coastal**<br>**Total**|**Wetlands (sq.**<br>**km)**|<br>**Coastal**<br>**Total**|
|**India**|**8,693**|29.33|**7,640,416**|28.68|5175.16|27.72|3,744|23.64|1,295|30.04|2,511|32.31|
|**Bangladesh**|**4,457**|23.45|**4,849,374**|16.01|2225.85|19.00|2,716|17.52|433|18.30|3,898|24.29|
|**Pakistan**|1,597|26.37|613,701|35.72|483.58|38.57|90|2.72|37|16.13|977|**61.38**|
|**Sri Lanka**|750|37.58|344,832|29.08|537.83|26.37|256|43.03|174|37.42|186|**55.46**|
**Table 5c: Effect of formalizing on attitudes in August 2010 follow-up survey**

||Trust in<br>Provincial<br>Government|Trust in<br>Municipal<br>Government|Trust in<br>National<br>Government|Trust in<br>the courts|Trust in the<br>Police|Confident in<br>police & courts<br>to settle<br>bus. dispute|Agrees<br> being good<br>citizen means<br>payingtaxes|Thinks<br>Govt. charges <br>business<br>too much tax|Feels Govt.<br> spends revenue<br>on important<br>items|
|---|---|---|---|---|---|---|---|---|---|
|Registered with the D.S.|0.634***|0.559**|0.227|-0.0387|-0.0993|0.0267|0.511***|0.517**|-0.0410|
||(0.234)|(0.234)|(0.221)|(0.188)|(0.238)|(0.203)|(0.188)|(0.238)|(0.232)|
|Lag included|Yes|Yes|Yes|Yes|Yes|No|No|No|No|
|Observations|369|369|369|369|369|369|369|369|369|
|Mean for controlgroup|0.360|0.310|0.620|0.811|0.567|0.744|0.767|0.444|0.378|

Notes: Registration with the D.S. instrumented with offer of the 10,000 Rs, 20,000 Rs, or 40,000 Rs registration treatments. Information only treatment group excluded from these regressions.

All regressions include controls for randomization strata, and for the lagged dependent variable if it was collected. Robust standard errors in parentheses *, **, and *** indicate significance at the 10, 5 and 1 percent significance levels respectively.

32
<!-- Start of picture text --> Figure 10a: Number of new firms around GEL Figure 10b: Number of new firms around GEL<br>30,000 cutoff – 2008 data (pre-reform) 30,000 cutoff – 2009 data (pre-reform)<br>Local Avg. + Polynomial in GEL 100 Intervals from Cutoff Local Avg. + Polynomial in GEL 100 Intervals from Cutoff<br>10000 20000 30000 40000 50000 10000 20000 30000 40000 50000<br>2008 Declared Revenue 2009 Declared Revenue<br>60 50<br>40<br>40<br>30<br>20<br>20<br>Number of new firms in 2007 Number of new firms in 2008 10<br>0 0<br><!-- End of picture text -->

# _4.1.2 Misreporting by new firms in response to the reform_

The second potential issue with using an RDD methodology is that the reform may provide an incentive for misreporting in the sense that some new firms with revenue above the cutoff that otherwise would have reported revenue above the cutoff may choose to report revenue below the cutoff to be eligible for the reform. That is, the number of new firms may drop above the cutoff as a result of the reform, implying that the comparison group may also be affected. This sorting around the cutoff could be an undesirable driver of some of the reform effect on registration and it could happen both around the GEL 100,000 and GEL 30,000 cutoff. We assess whether this sorting happens by measuring whether the number of new firms above the cutoffs declines significantly after the reform compared to pre-reform years.<sup>26</sup> Also, in the pre-reform period, the number of new firms just above the cutoff decreases slightly as declared revenue increases (Figures 9 and 10). If firms become less likely to register right above the cutoff after the reform, this pattern should be reversed, i.e. the number of new firms should increase with declared revenue in the neighborhood just above the cutoff in the postreform period, which is something we can test.

# _4.2 Studying the impact of the reform on tax compliance by previously registered firms_

In order to study the impact of the reform on tax compliance by previously registered firms, we use a RDD methodology that estimates a discontinuity in declared income around the GEL 30,000 mark. However, because of the misreporting by existing firms in pre-reform years, we are not able to use the RDD approach around the GEL 100,000 cutoff and thus are not able to study the impact of the reform on tax compliance for this group of firms. The RDD methodology around the GEL 30,000 cutoff examines whether firms below the cutoff that are eligible for the reform, as determined by revenue declared in 2009, declare significantly less revenue in 2010, 2011, and 2012 than firms just above the cutoff, in order to remain eligible for the reform. Figure 11 illustrates the jump in declared revenue at the cutoff that may arise due to the reform.

26 One further potential abuse dynamic is the artificial division of larger entities into several “small” businesses that operate below the eligibility threshold. Such a trend would not be captured by our analysis.

19
**Figure 5 - Reservation wage and predicted market wage**

<!-- Start of picture text --> Median predicted market wage<br>Low-skilled >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> High-skilled<br>200<br>100<br>0<br>Reservation wage gap (% of predicted market wage)<br>-100<br><!-- End of picture text --> _Source: Authors’ calculations._ In the third comparison, we compare individuals who, over the observed period, have had both employment and unemployment experiences, so that for them both market and reservation wage are available over a fairly short period of time. In such case, panel structure could be imposed; the utilization of fixed effects wipes out all unobservable factors which are potentially correlated with the reservation wage. The disadvantage is that we drop all unemployed individuals without employment experience over the observed period and those who appear only one in our sample, which reduces our sample to a quarter (see **Table 1** ), which may unquestionably inflict selection bias. From this viewpoint, results should be approached with caution. However, this is the only point where we could make use of the anyway scant temporal information. We assume asymmetry in the reservation wage gap, i.e. in the reservation wage formation depending on whether it follows or precedes the earning of the market wage.

Results are provided in **Table 5** . The first thing to note is that we obtain higher reservation wage gap compared to the between-case in **Table 4** . This may actually suggest that i) persons who have had any work experience may be systematically different than the entire cohort of unemployed (e.g. live in considerable shortage of living funds); and/or ii) unobservable factors (e.g. higher motivation to search intensively) – which are not accounted for in the between-variation calculation – may have played an important role. This point remains challenge for future work. Our focus, however, in **Table 5** is on another important aspect: individuals generally reduce their wage expectations only by about 2 20
Table 3: Comparing surplus from microfinance and savings to their impacts on consumption

|Source<br>Country|Outcome|Year|Estimate|(SE)|
|---|---|---|---|---|
|Consumption impacts of savings|||||
|Dupas et al. (2018)<br>MWI|Household expenditures|2011|$129|($260)|
|UGA|Household expenditures|2011|$37|($164)|
|Surplus from savings|||||
|Table 1<br>USA|Marshallian surplus|2005|$24|($1)|
|Table 1<br>PHL|Marshallian surplus|2007|$10|($8)|
|Consumption impacts of microfnance|||||
|Angelucci et al. (2015)<br>MEX|Food expenditures|2011|$66|($248)|
|Attanasio et al. (2015)<br>MNG|Household expenditures|2009|$25|($14)|
|Augsburg et al. (2015)<br>BIH|Household expenditures|2010|-$111|($56)|
|Banerjee et al. (2015a)<br>IND|Household expenditures|2008|$26|($95)|
||Household expenditures|2010|-$188|($198)|
|Crépon et al. (2015)<br>MAR|Household consumption|2009|-$68|($71)|
|Kaboski & Townsend (2011)<br>THA|Household consumption|2002|$139|($39)|
|Surplus from microfnance|||||
|Table 2<br>MEX|Marshallian surplus|2007|$16|($8)|
||Marshallian surplus|2009|$7|($3)|
|Kaboski & Townsend (2011)<br>THA|Equivalent variation (Lower bound)|2002|$4||
||Equivalent variation, present value|2002|$69||

_Notes:_ Experimental estimates of the impacts of savings and microfinance on consumption and estimates of saver and borrower surplus from microfinance, per $100 of deposits and loan amount, are reported in this table, with standard errors in parentheses. The construction of estimates from other papers is described in Online Appendix A.

crofinance in Mongolia on household expenditures of $25 (with a standard error of $14) per $100 loan amount, significantly different from $0 but not from my estimates of Marshallian surplus from microfinance in Mexico. Their estimate is relatively precise despite a small number of observations for two primary reasons: they have a strong first stage, with treated households 51pp more likely to take a loan from the MFI than control households, and the loans were large, with an average loan size of two months of household consumption. The second exception, Kaboski & Townsend (2011), estimate impacts of microfinance in Thailand on household consumption of $139 (with a standard error of $39) per $100 loan amount. As they also find these large positive impacts persist over time, this estimate on its own implies much larger welfare gains than their structural model; in Online Appendix E, I demonstrate that because the impact of microfinance on consumption does not discount uncertain returns to risky investments, it may be biased upwards as a measure of consumer surplus.

With this context, I then ask if we should think of the magnitudes of Marshallian surplus 37
tions can be additionally imputed by regressing research95 on research03. The first column of Table 2 shows the results of this regression. The _R_ -squared of 0.91 indicates a good fit for the imputation. Having filled these nine data points, we now have data for 95 countries for research95. That is still not enough. The next closely related variable is technical journal output in 1995 (techjour95). The second column of Table 2 indicates that this regression has an averagely good fit, with an _R_ -squared of 0.70. The variable, techjour95 is statistically significant at 1%. Therefore, the two-step regression process makes available data on research95 for 120 countries.

A similar two-step regression process is used to impute missing research03 data via the regressions shown in columns 3 and 4 of Table 2. The last three columns in Table 2 impute data for computer95, computer04 and tariffs and NTBs for ’95 (tntb95) using, respectively, GDP per capita for the two computer variables and tntb05 as regressors.<sup>8</sup> After completing the imputations we have available data for 120 out of the 128 countries. Unavailability of data on the regressors prevents imputing missing data for the remaining 8 countries. The factor analysis is based on the sample of these 120 countries. The authors’ working paper provide details on the countries for which variables are imputed and the imputed values.

# **3.2 Is It Worth Doing Factor Analysis on the KAM Data?**

The main objective of the factor analysis is to understand whether countries have advanced their positions over the 10-year period in terms of (i) the absolute measures of the factors, and (ii) their factor score ranks vis-a-vis other countries. Before proceeding with factor analysis and the computation of factor scores, it is important to understand whether and how much we can gain from undertaking a factor analysis. The cross-country data on the 12

> 8For imputing missing country values we use not only available data across countries but also for the ten regions including the world. There is additional information in these aggregated regions which can be brought to bear on the imputations.

16
14 provide the utility with sufficient financial ability to deal with the water losses and hence not provide utilities with an incentive to reduce them. As total revenues per capita are useful to determine whether utilities have some financial ability, the actual available financial space is determined by the difference between revenues and costs. It is quite well possible that the total revenues per capita are very high but so are the costs per capita and hence the margin available to deal with water losses are limited. We hence included a dummy variable (OCCRDUMMY) that measures a positive or negative difference between total revenues and total operation and maintenance costs<sup>4</sup> per capita per year. We expect this variable to be showing an inverse relationship with water losses. When total revenues exceed total operation and maintenance cost will result in more financial ability and hence lower water losses and vice versa.

Finally, we include a variable that according to Garcia and Thomas (2001) reflects the opportunity cost of water losses as measured by the cost of increasing production reflected in higher energy costs (in most systems resource cost of drinking water are negligible). These authors point out, that reductions in NRW are constrained by high repair costs. In their paper they note that utilities have two options when they both produce and distribute water – which is the case in most utilities. The first option is to keep production constant while reducing NRW through more frequent repair and maintenance. The second option is to increase production while keeping the water network rate of return constant. In case the labor and maintenance cost involved in repair and maintenance is higher than the cost associated with increased production (energy and resource costs), the second option will be the preferred one. When using the IBNET sample, we find that the cost of repair is significantly higher than that of energy costs in most utilities. We assume that the higher the opportunity cost of water losses (LN_WATERLOSS), the lower water losses will be. This variable is somewhat similar to that used of Saez-Fernandez et al. (2011) and the opportunity cost of water losses.

The last hypothesis states that the “environment” in which the utility operates affects the level of water losses. We do not have much access to environmental data at the utility level. Data on GNI or corruption are only available at the national level and hence captured in the fixed effects of the

> 4 No data is available on the total costs (including depreciation and financial costs) and hence we had to limit ourselves to operation and maintenance costs .
**Table 3. Emigration patterns by country group. 1990 and 2000**

||**Tot**|**al emigrati**|**on**|**Emigr**|**ation to O**|**ECD**|**Emigr**|**ation to no**|**n-OECD**|
|---|---|---|---|---|---|---|---|---|---|
||Stock<br>(million)|College<br>(%)|Women<br>(%)|Stock<br>(million)|College<br>(%)|Women<br>(%)|Stock<br>(milion)|College<br>(%)|Women<br>(%)|
|**Year 2000**||||||||||
|WORLD|111.9|25.7|48.7|59.3|35.3|51.0|52.6|15.0|46.1|
|OECD|32.2|30.4|50.4|29.1|31.0|50.8|3.1|24.4|46.6|
|HIGH|26.3|36.0|52.0|22.3|38.4|53.0|4.0|22.6|46.6|
|DEV|85.6|22.6|47.7|37.0|33.4|49.8|48.6|14.3|46.1|
|LOW|15.5|9.6|45.0|2.5|38.0|48.5|13.0|4.1|44.3|
|LDC|15.1|8.5|43.6|2.4|34.6|47.7|12.7|3.5|42.8|
|SIDS|4.3|35.6|54.6|4.0|37.0|54.9|0.3|17.7|51.7|
|USA|0.9|58.7|50.0|0.7|62.9|52.6|0.2|45.0|41.4|
|CANZ|1.5|57.1|54.0|1.4|57.6|54.3|0.1|46.9|47.9|
|EU27|20.0|32.3|52.0|17.7|33.1|52.4|2.3|25.6|48.8|
|GCC|0.6|20.3|37.3|0.0|65.2|39.7|0.6|16.5|37.0|
|LAC|15.6|25.1|50.2|14.0|26.4|50.1|1.6|13.9|51.6|
|SSA|10.5|11.5|45.2|2.2|43.1|47.5|8.3|3.1|44.6|
|CIS|19.2|26.3|54.6|2.4|42.1|58.2|16.8|24.0|54.1|
|INDIA|6.1|23.7|36.9|1.7|60.5|47.2|4.4|9.4|32.9|
|CHINA|3.9|27.9|51.8|1.7|46.7|53.0|2.3|14.0|51.0|
|MENA|9.1|21.8|37.9|4.2|29.9|43.0|4.9|14.7|33.5|
|**Year 1990**||||||||||
|WORLD|85.3|19.1|47.9|42.5|29.5|50.7|42.7|8.7|45.2|
|OECD|25.7|26.2|51.1|23.3|26.9|51.6|2.4|19.4|46.3|
|HIGH|23.4|29.1|52.0|20.5|30.5|52.8|2.9|18.9|46.4|
|DEV|61.9|15.3|46.4|22.1|28.6|48.7|39.8|7.9|45.1|
|LOW|13.1|7.2|42.9|1.4|33.7|45.6|11.7|4.0|42.6|
|LDC|13.0|6.5|41.4|1.4|30.2|45.1|11.6|3.7|40.9|
|SIDS|3.0|31.0|52.6|2.6|34.6|53.6|0.4|7.8|46.5|
|USA|0.8|51.4|50.4|0.6|53.8|53.0|0.2|43.3|41.3|
|CANZ|1.3|46.0|56.1|1.2|46.4|56.3|0.1|38.2|52.6|
|EU27|18.8|25.3|51.9|16.9|26.0|52.2|1.9|19.4|49.2|
|GCC|0.4|17.7|34.2|0.0|64.8|35.6|0.4|14.3|34.1|
|LAC|8.2|24.7|50.4|7.0|27.4|50.7|1.2|9.3|48.2|
|SSA|8.5|7.5|44.1|1.2|39.6|44.3|7.3|2.1|44.1|
|CIS|14.1|12.7|57.4|1.8|20.8|56.3|12.2|11.5|57.6|
|INDIA|5.5|12.4|35.1|1.0|45.5|47.0|4.5|5.2|32.5|
|CHINA|3.3|16.5|50.7|0.9|40.0|50.2|2.4|7.8|50.9|
|MENA|6.8|17.3|36.1|3.2|23.8|41.5|3.6|11.6|31.3|

Notes. Column ‘Stock’ gives the aggregate stock of emigrants in millions ; ‘College’ gives the percentage of high-skilled emigrants ; ‘Women’ gives the percentage of female emigrants.. For high-income (HIGH). developing (DEV) and low-income countries (LOW). we use the World Bank classification. Least developed countries (LDC) and small island developing states (SIDS) are defined by the United Nations. EU27: 27 countries of the European Union. USA: United States of America. CANZ: Canada + Australia + New Zealand; CIS: Commonwealth of independent States of the former USSR. MENA: Middle East and Northern Africa. SSA: Sub-Saharan Africa. Each country only belongs to one geographical group.
|**Appendix Table 1: The Registration Process for those**|**registering**<br>Perc|ent|
|---|---|---|
||Colombo|Kandy|
|_Had to provide the following:_|||
|Grama Sevaka certificate|11.1|95.7|
|Pradeshiya Saba or Municipal License|0.0|100.0|
|Public Health Inspection Report|0.0|4.3|
|Environmental Authority Report|3.7|0.0|
|Wildlife Conservation Department Report|3.7|0.0|
|Certificate of Technical Skills|0.0|4.3|
|Letter from property owner showing no objection|11.1|95.7|
|_Classify the registration process as:_|||
|Very Easy|85|17|
|Somewhat easy|15|57|
|Not that easyor verydifficult|0|26|
|_Total number of visits to institutions_|||
|Mean|2.11|5.9|
|Median|2|5|
|_Number of Days at D.S. between submission and rece_|_ipt of BRC_||
|Mean|2.4|8.1|
|Median|2|6|
|_Number of Days getting other documents processed_|||
|Mean|0.3|4.2|
|Median|0|3|

|**Appendix Table 2: Treatment doesn't affect which**|**firms survive**<br>Treatment<br>Group1|Treatment<br>Group2|Treatment<br>Group3|Treatment<br>Group4|Control|F-test of equality<br>p-value|
|---|---|---|---|---|---|---|
|Retail|0.21|0.20|0.22|0.24|0.24|0.960|
|Manufacturing|0.36|0.33|0.35|0.32|0.36|0.972|
|Services|0.43|0.48|0.43|0.44|0.40|0.913|
|Colombo|0.48|0.50|0.46|0.53|0.48|0.926|
|More than 2 paid workers|0.56|0.57|0.58|0.57|0.55|0.994|
|Says would register and sees benefit to doing so|0.48|0.47|0.51|0.48|0.46|0.979|
|Sales in last month at Baseline|217562|147252|185649|165991|183185|0.634|
|Doesn't keep records|0.48|0.58|0.54|0.59|0.52|0.613|
|Years of Education of Owner|10.97|10.48|10.82|11.38|10.52|0.125|
|Number of Paid employees|2.89|3.21|3.10|3.02|3.04|0.866|
|Registered at the Pradeshiya Saba|0.71|0.63|0.72|0.67|0.62|0.507|
|Profits in last month at Baseline<br>Note: Treatment Group 1 is the information and re<br>and 40,000 Rs respectively.|30176<br>imbursement|27919<br>only group, Gr|32851<br>oups 2, 3 and|33460<br>4 were offered|31220<br>10,000 Rs, 2|0.589<br>0,000 Rs,|

34