rafmacalaba/datause-encoder-data huggingface dataset:
Overall, there are only two differences between Saath’s individual and group liability loans: first, the interest rate, which increased from 18% to 24% (declining balance) per year; and second, the required total monthly installment amounts (principal plus interest), which varied slightly month to month in the individual liability model but was constant in the group liability setting. In our main analysis on loan repayment and savings discipline, we will focus only on individuals who converted to group liability loans, exploiting the timing of their switch. By doing so, our empirical design accounts for any self-screening based on the increase in interest rates between the two loan contracts.

# **4 Data Collection and Summary Statistics**

In this study, we use data from Saath Microfinance’s administrative software systems. The loan data cover outstanding loans every month from April 2005 through March 2009. Since the change in the type of loan contract occurred in November 2007, the data contain over two years of monthly data on individual liability loans and over one year on group liability loans. Data are available electronically from only 2 out of 4 Saath Microfinance branches, Behrampura and Vasna, and we focus on these. They are the two largest branches, and the oldest, accounting for the vast majority of Saath’s clients.

The data were maintained for accounting purposes, recording cash flowing in and out of each branch. They are therefore of very high quality. They do not, however, contain information on the terms of each loan, such as maturity dates, installment amounts, and amounts outstanding. These data were recorded by loan officers in client passbooks and administrative ledgers. As a result, we are unable to look at overdue amounts, prepayments, and other similar measures.<sup>6</sup> While the data on loans cover April 2005 to March 2009, the savings data were only availthey were not aware of this change in compensation structure before it occured. Nevertheless, to isolate the focus of our study on contract structure, we exclude months after March 2009 in the analysis.

> 6The loan data come from three software systems which Saath Microfinance has used at different points in time. Each of the two branches in our dataset used a separate system until early 2008, when the current system was introduced in both branches. Because client identifiers were not carried over from one software system to another, we had to rely on using client names to track individuals over time. These names were unique since they included first, middle, and last names. In identifying clients across systems, 80% of the clients had exact name matches, while 14% had to be matched by hand due to name spelling errors. The remaining 6%, on the other hand, could not be matched to the current software system. It is likely that these clients have withdrawn their membership with Saath Microfinance and therefore have not borrowed under the group liability setting, since Saath migrated information from the previous to the current software system only for existing members.

11
# **Table 4: Land market restrictions and wages**

IV Regression Results

|||Real|Annual wag|e||
|---|---|---|---|---|---|
||1|2|3|4|5|
|% Area Under LDO|-2.030|-2.328|-1.873|-1.808|-2.109|
||(2.93)***|(2.74)***|(2.43)**|(2.60)***|(2.92)***|
|Area LDO*Travel Time|0.328|0.347|0.262|0.298|0.329|
||(2.73)***|(2.80)***|(1.70)*|(2.43)**|(2.71)***|
|Marginal Effects (at median)||||||
|% Area Under LDO|**-1.41**|**-1.67**|**-1.38**|**-1.25**|**-1.49**|
|Log(slope)|no|no|Yes|no|no|
|Area controls|Yes|Yes|Yes|Yes|Yes|
|District Fixed Effect|Yes|Yes|Yes|Yes|Yes|
|Individual controls|Yes|Yes|Yes|Yes|Yes|
|Sectoral Dummies|Yes|Yes|Yes|Yes|Yes|
|**IV Diagnostics**||||||
|Weak Identification Test||||||
|Kleibergen-Paap wald F|30.32|19.71|38.22|31.31|28.52|
|Stock-Yogo 5% maximal IV rel. bias|9.53|9.53|9.53|9.53|9.53|
|Hansen's J Statistics (Overidentication test)|0.25||0.84|0.52|0.32|
|P-Value|0.62||0.36|0.47|0.57|

Robust t statistics in parentheses. Standard errors corrected for clustering * significant at 10%; ** significant at 5%; *** significant at 1% Col1: instruments: Malaria Endemicity* % area under inland water, Malaria Endemicity*% of Area above 1000 feet of elevation, difference in slope, difference in slope*malaria Endemicity*inland water Col2: drops % of area above 1000 feet elevation from instrument set Col3: same instruments as in column 1, but adds log(slope) as an additional control Col4: same instruments as in column 1, use Plasmodium Falciparum cases as malaria control instead of Plasmodium Vivax cases. Col5: same instruments as in column 1 but has average years of education in the DSD as a control instead of % of labor force with education level primary or above.
mal firms.<sup>14</sup> Firm data accounting for the informal sector would allow to assess resource misallocation in this sector, which represents around 6 percent of the manufacturing sector as a whole ( _Haut Commissariat au Plan_ ). Intuitively, informal firms face more distortions than in the formal sector. Notably, these firms have incentives to remain small in order to avoid detection. In addition, informal firms face higher credit constraints.<sup>15</sup> Busso et al. (2012) modify the HK framework to include the informal sector and apply the model to Mexico. They find an astonishing 200 percent increase in TFP from a full liberalization compared to 138 percent found in Inklaar, Lashitew, and Timmer (2015) applying the HK procedure to Mexico without the informal sector.<sup>16</sup>

# **5.3 Robustness: Alternative Measure of Market Distortions and New TFP Gain Calculation**

As a robustness exercise we compute market distortions and associated productivity gains using Bartelsman, Haltiwanger, and Scarpetta’s 2013 methodology, which as previously mentioned, relies on a much less extensive theoretical framework than HK. The idea here is that low levels of market distortions would better ensure that more resources will be allocated to more productive firms, allowing them to grow in size. The approach uses a decomposition of labor productivity for a given industry initially presented in Olley and Pakes (1996): Where _Y_ and _L_ represent respectively real output and labor for a given sector. The symbol ( _ω_ ) denotes the weighted average of firm labor productivity with employment shares used as weights ( _θi_ = _Li/_<sup>�</sup> _i_<sup>_Li_).Thiscanbewrittenasthe</sup> unweighted average labor productivity (indicated by the upper bar) plus the covariance between firm size (in terms of employment) and firm labor productivity. We observe first that the more this covariance is higher the more the level of productivity increases. Second, a positive covariance indicates that more productive firms are also larger. (Bartelsman, Haltiwanger, and Scarpetta, 2013) develop a model where greater distortions to resource allocation lead to lower covariances.

In contrast to the previous empirical analysis, this methodology does not require data on capital at the firm level. This allows us to be able to use a larger share of each survey (eliminating the previous issue of representativeness). Thus, WBES2007 and WBES2013 contain 325 (about 55% of the initial sample) and 149 (around 70% of the initial sample) observations respectively.

> 14The WBES2013 finds that firms consider the informal sector as one of the top obstacles they face.

> 15Although this does not enjoy a full consensus in the empirical literature, see for instance Kaplan et al. (2011).

> 16More precisely, 138 is a number obtained by the authors using results from Inklaar, Lashitew, and Timmer (2015).

17
of 6 years when transaction costs increase from 0.5% to 2%. In comparison, a similar increase in transaction costs would increase the holding period of investors by about 190 trading days in the dataset used in this paper. The empirical results are closer to the calibrated values in Lo, Mamaysky and Wang (2006) who predict a similar change in holding periods as in this paper. The results in this section suggest that models that incorporate potentially exogenous liquidity or trading needs are more likely to be representative of actual investor behavior. The results also offer a potential explanation for the discrepancy between the empirically observed liquidity premium and the one predicted by the models in which the holding periods are endogenously determined as in Vayanos (1998) and Constantinides (1986).

# **4.2. Robustness Checks**

To make sure the results are robust to underlying stock characteristics, I include book-tomarket, size and momentum characteristics in the hazard regressions. As before, to get a more intuitive interpretation of the results, each year I segment stocks into quintiles based on these stock characteristics. Dummy variables are created and take on a value of one if a stock in the dataset falls into one of the five groups. These characteristics are calculated based on the information available at the beginning of the month in which a sale is made. Table 5 summarizes the results from hazard regressions using these characteristics. The transaction costs measure remains significant after I control for stock characteristics, while the economic and statistical significance of stock characteristics is reduced once I control for liquidity. On average, households tend to hold value and small stocks longer. Relationship between momentum and holding period appears to be U- shaped, but it is more significant at the high return end. A stock belonging to the highest momentum quintile is 1.4 times more likely to be sold conditional on no sale up to that point in time.

The disposition effect (Shefrin and Statman 1985), the tendency of individual investors to hold on to losing stocks too long and to sell winners too quickly, has been shown to be a significant driver of trading behavior in a variety of contexts for both individual and institutional investors. If the disposition effect is the main driver of a decision to buy/sell (Grinblatt and Kellaharjou 2001), then the holding period and the 17
investing space as either an alternative or complement to microfinance (e.g. Chowdhry, 2010; Ayayi, 2012). However, in practice there are few concrete examples that show how such contracts could be structured, or pilots of how they would work in practice with microenterprises in developing-country settings.<sup>1</sup> We design a model contract for making micro-equity investments in existing microenterprises. We face several difficulties in mapping typical angel finance / venture capital funding models to the microenterprise context. First, the target enterprises are at least partially informal. They rarely use external accountants, and may not keep formal records. Most of their business transactions are in cash. Second, the target businesses are owner managed. There is no market for the sale of the business, either through an IPO or through a sale to a larger business. That presents a serious constraint on investor exit options. We address these issues by basing contract payments on revenues, which investors find somewhat easier than profits to monitor, and by having the firm buy back the equity investment through additional revenue-based payments or through fixed monthly payments.<sup>2</sup> The contract calls for monthly payments comprised of two components: a fixed amount that at least partially repurchases the equity investment, and a specified percentage of relevant monthly revenues that provides the investor’s return and any part of the initial investment not returned in the fixed payments. The percentage of revenues to be paid by the firm is calculated from four key parameters: 1) the projected revenues of the firm after the equity-financed investment during the investment period (36 months in our case); 2) the expected return on investment for the equity investor; 3) the share of the capital (equity) investment bought back through the revenue-sharing mechanism; and 4) the proportion of base-year, pre-investment revenues that are excluded from the calculation. The contract allows the parties to flexibly shift risk from the entrepreneur to the investor by varying the contract parameters. In particular, increasing either of the last two

> 1 One organization that conducted early pilots in this space was inVenture. However, Singh and Ingawale (2014) describe how the organization quickly pivoted from trying to provide equity funding to small firms to providing a text-based daily record-keeping system that could be used to help record transactions in firms, with the idea that this might be used by others interested in making such investments.

> 2 Several investment organizations operating in Africa make limited use of royalty-based quasi-equity contracts similar to the one we implement in Sri Lanka. See annual reports of Business Partners Limited (www.businesspartners.co.za/en-za.), GoFin (www.grofin.com/), and SEAF (seaf.com), and the discussion of these in Kulasinge et al. (2018). The minimum investments by these entities is typically many times larger than the investments made by KCG Equity in our study.

3
higher unemployment rates. However, the conclusion that high unemployment rates failed to decline for a long time seems robust.

Two developing regions are missing in figure 8, despite the fact that they both have successful globalizers in their ranks. East Asia and the Pacific is the region with the best record in this respect. But in all its successful globalizers unemployment rates remained very low for decades, until the crisis of the late 1990s. The Middle East and North Africa region, on the other hand, does not include many success stories. While Tunisia is one of them, a high and stable unemployment rate is one of the few weak points in its otherwise remarkable performance. However, the case of Tunisia is more in accordance with figure 8 than its stubbornly high unemployment rate suggests. This is because the survey instruments that were used to measure unemployment over the last few decades did not use comparable questions to assess the willingness to work. An assessment of comparable estimates actually reveals a steady, if slow, decline in the unemployment rate since the 1980s (Rama, 1998).

In the long run, open economies do not appear to have higher unemployment rates, as shown by figure 9. If anything, their unemployment rates are lower. The comparability issues raised at the beginning of this section could cast doubts on the interpretation of this figure. But to the extent that country- and period-specific biases are independent from the degree of openness of the economy, the pattern described by the figure should be basically correct. This pattern is one of high dispersion of unemployment rates in more closed economies, and convergence towards a relatively narrow range in more open economies.

**Figure 9. Unemployment Rates and Openness to Trade**

<!-- Start of picture text --> 35<br>30-<br>-. 25<br>L'20-<br>D 15<br>a-<br>E<br>10<br>5<br>0<br>0 50 100 150 200 250 300<br>Foreign trade In % of GDP<br><!-- End of picture text --> _Source:_ Average unemployment rates over five-year periods are from Rama and Artecona (2002). Data on the trade-to-GDP ratio are from the World Bank.

19
**Table 1: Descriptive statistics by treatment status and year**

||201|0|201|4|20|17|20|20|
|---|---|---|---|---|---|---|---|---|
||Treatment|Control|Treatment|Control|Treatment|Control|Treatment|Control|
|Percent water|3.90|0.48|2.25|0.73|1.99|4.12|1.72|0.73|
|Percent settlement|18.40|44.07|22.04|52.54|20.78|46.97|35.08|49.39|
|Percent bare ground|7.41|7.26|10.32|8.72|7.88|4.36|7.48|3.15|
|Percent forest|63.40|37.29|55.86|27.36|59.17|29.78|45.53|25.18|
|Percent aquaculture|0.60|1.45|3.18|4.36|0.40|1.69|0.33|3.87|
|Percent agriculture|6.29|9.44|6.35|6.30|9.79|13.08|9.86|17.68|
|Mean lights index|-|-|0.13|0.33|0.31|0.59|0.49|0.71|
|Mean population|5.26|8.35|5.68|9.34|6.06|10.10|6.22|11.04|
|Percent national park|0.00|29.06|-|-|-|-|-|-|
|Percent wildlife preserve|29.38|0.00|-|-|-|-|-|-|
|Mean distance to road (km)|0.29|0.11|-|-|-|-|-|-|
|Observations|1,511|413|1,511|413|1,511|413|1,511|413|

Source: authors’ calculations based on a random sample of pixels in the Cox’s Bazar district. For each pixel, we determine land-use / land cover (LULC) classified maps using Landsat remote sensing imagery for the district in the years 2010, 2014, 2017, and 2020. We determine the population using Worldpop unconstrained individual country and UN adjusted data for 2010-2020. The night lights score for each pixel comes from the VIIRS Night Lights Index data for 2014-2020. Static statistics only appear in the first and second columns. The classification of pixels into national parks and wildlife preserves comes from Protected Planet’s World Database of Protected Areas map. We derive the pixel’s geodesic distance to the nearest main road using the Google Maps API. The distance buffer, which we use to assign pixels to treatment and control groups, is determined by the pixel’s minimum geodesic distance to the nearest camp border. Treated pixels are 0-5 km from the nearest camp, and control pixels are 10-15 km from the nearest camp 17
—@—batch1 —@ebatch3 —s— matched comparison <!-- Start of picture text --> 4<br>|<br>2 LYi\.<br>fo N<br>Le] Ss<br>2008/2008 2008/2010 2010/2011 2011/2012 2012/2013<br>(4y3m) (Sy3m) (6y3m) (7¥3m} (2y¥2m}<br><!-- End of picture text -->

<!-- Start of picture text --> 4<br>|<br>2oo<br>oa —<br>2008/2009 92008/2010 2020/2011 2011/2012 2012/2013<br>(1y8m) (2y3m) (3y3m) (4y3m) (5y8m)<br><!-- End of picture text -->

<!-- Start of picture text --> —@—batch1 —@ebatch3 —s— matched comparison<br><!-- End of picture text --> <!-- Start of picture text --> H '<br>2<br>oO ne ——— =<br>2006/2005 2009/2010 2010/2011 2011/2017 2013/2013<br>(48m) (5y3m} (63m) (78m) (8y3m)<br><!-- End of picture text --> <!-- Start of picture text --> 3 \ :<br>i]a'<br>o :H H\ ee<br>2008/2009 2009/2010 2010/2011 2011/2012 2012/2013<br>(1y8m} (2y8m} (3y3m} (4y3m} (5y3m}<br><!-- End of picture text -->
Costa Rica, the Dominican Republic, Ecuador, El Salvador, Guatemala, Haiti, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, and Uruguay.<sup>15</sup> The data set on poverty lines gathered through official sources is the major contribution of this paper to the estimation process of regional comparable poverty estimates. The pioneering work of Ravallion, Datt, and van de Walle (1991) gathered a compilation of 33 non-official national poverty lines for the whole world, while Ravallion, Chen, and Sangraula (2009) used data on 88 poverty lines extracted from the World Bank’s program of country _Poverty Assessments_ that have been carried out since 1990 worldwide. In contrast, this paper comprises the most comprehensive and up-to-date database on official poverty lines for LAC countries. We collect more than 80 _subnational_ urban poverty lines distributed in 18 countries in LAC, accounting for regional disparities within countries by capturing subnational heterogeneities in the standard of living.

_3.2 Income distribution from harmonized household surveys_ In order to estimate poverty measures that are comparable across countries, we need to increase the cross-country comparability of the welfare measures. Therefore, the second source of information is the SEDLAC data set,<sup>16</sup> which is used as the primarily source of comparable welfare aggregates across the LAC countries and represents approximately 518 million individuals, or 85 percent of LAC’s population in 2013.

The main objective of this comprehensive data set of household surveys is to increase crosscountry comparability of a range of socioeconomic measures, including household total income, from more than 300 household surveys within 18 countries in the LAC region from the 1970s to the present. Following the practice of most countries in the region, in this paper we measure poverty based on household per capita income at the individual level. Given that the main purpose of the SEDLAC project is to enhance cross-country comparability of household surveys, these

> Economic Research (IPEA, for its acronym in Portuguese) several years ago, which are updated on a yearly bases based on the CPI (IPEA 2014). 15  Cuba and the República Bolivariana de Venezuela are not included in this study due to the lack of microdata from SEDLAC project in both countries and the lack poverty lines in the case of Cuba. The estimation of the international poverty lines is robust to the inclusion of the República Bolivariana de Venezuela. 16  Bourguignon (2015) presents a detailed evaluation of the SEDLAC data set. SEDLAC is a harmonized database of LAC’s households’ surveys compiled by the poverty group at the World Bank in partnership with the Center for Distributive, Labor, and Social Studies (CEDLAS, for its acronym in Spanish) at the Universidad Nacional de La Plata in Argentina.

8
capital input have been mean corrected; therefore, the first-order output coefficients are elasticities evaluated at the sample mean.

**Table 2: Estimates of labor requirement function**

||D|ependent variab|le: number of e|mployees, in log|s|
|---|---|---|---|---|---|
|Variable|(1)|(2)|(3)|(4)|(5)|
|Intercept|0.513|0.174|1.645|1.616|1.432|
||(8.83)|(0.33)|(1.23)|(1.20)|(1.18)|
|Ln (Sales)|0.375|0.365|0.253|0.253|0.322|
||(9.21)|(8.43)|(5.19)|(5.17)|(6.88)|
|Ln (Customers)|0.337|0.350|0.535|0.538|0.426|
||(4.61)|(4.70)|(7.14)|(7.19)|(5.71)|
|Ln (Service area)|0.112|0.109|0.078|0.084|0.091|
||(7.05)|(6.71)|(4.45)|(4.48)|(5.56)|
|Ln (Distribution network)|-0.013|-0.014|-0.022|-0.028|-0.005|
||(-0.27)|(-0.28)|(-0.45)|(-0.57)|(-0.12)|
|Time|-0.076|-0.077|-0.058|-0.051|-0.060|
||(-8.18)|(-8.08)|(-5.77)|(-4.00)|(-6.53)|
|Time*Private firms dummy||||-0.017<br>(-0.95)||
|Ln (GNP per capita)||0.039|-0.142|-0.140|-0.109|
|||(0.64)|(-0.88)|(-0.85)|(-0.73)|
|Private firms dummy|-0.305|-0.311|-0.410|-0.323|-0.405|
||(-6.46)|(-6.22)|(-8.03)|(-3.17)|(-8.32)|
|Country dummies<sup>†</sup>|No|No|Yes|Yes|Yes|
|Log likelihood|-249.92|-249.73|-180.00|-179.49|-133.11|
|R-squared|0.89|0.89|0.92|0.92|0.93|
|Number of firms|110|110|110|110|108|
|Observations|470|470|470|470|451|

Notes: t-ratios obtained by using heteroskedasticity-consistent standard errors are in parentheses. In all cases we are estimating a translog form. To save space, second order terms are not shown. In all models the Cobb-Douglas specification is rejected against a translog at the 1% level.

In the model in column (5) Ln (Sales) was replaced by Ln (Sales + Network losses). † In models (3) to (5) the country dummies are significant at the 1% level.

In all models, the first-order output coefficients are significant at the usual levels of confidence and have the expected signs regarding economic behaviour—an increase in outputs is associated with an increase in the use of labor. The coefficient on kilometres of distribution network is never significant. The values of the estimated coefficients suggest that labor use is mainly driven by the electricity sold and the number of customers.

The coefficient of the time trend is negative and significant at the 1% level in all six models; the average rate of labor productivity growth is in the range of 5.1% to 7.7%, 6