rafmacalaba/datause-encoder-data huggingface dataset:
Table S2.3: EFFECTS ON MATCHING IN TRANSFER NETWORKS

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|_Panel A:_|_Transfers_<br>ultrapoor|_received by Ul_<br>otherpoor|_tra-Poor Househ_<br>middle class|_olds from ..._<br>upper class|
|Treatment|0.048***|0.022|0.008|-0.009|
||(0.008)|(0.014)|(0.015)|(0.010)|
|Treatment_×_Endline|0.018|0.027|0.019|0.002|
||(0.013)|(0.018)|(0.023)|(0.013)|
|Mean in control|0.128|0.345|0.701|0.260|
|Adj. R-squared|0.439|0.415|0.327|0.407|
|Observations|13464|13464|13464|13464|
|_Panel B:_|_Transfe_<br>ultrapoor|_rs given by Ul_<br>otherpoor|_tra-Poor Househ_<br>middle class|_olds to ..._<br>upper class|
|Treatment|0.047***|0.021|0.030|0.005|
||(0.011)|(0.014)|(0.020)|(0.007)|
|Treatment_×_Endline|0.010|0.022|0.048|0.023*|
||(0.015)|(0.026)|(0.031)|(0.012)|
|Mean in control|0.108|0.252|0.394|0.078|
|Adj. R-squared|0.368|0.306|0.255|0.145|
|Observations|13464|13464|13464|13464|

**Source:** Author’s analysis based on original survey data.

**Notes:** The table reports ITT estimates based on specification (1) estimated with OLS. The sample includes observations from ultra-poor households. All specifications control for the baseline level of the outcome, survey wave and subdistrict (strata) fixed effects. Standard errors are clustered at the BRAC branch office level (unit of randomization). *** (**) (*) indicates significance at the 1% (5%) (10%) level. In Panel A, the dependent variables in columns 1, 2, 3 and 4 are dummy variables equal to 1 if the respondent reported that her household received any transfers from any ultra-poor, other poor, middle class or upper class households (within her community) respectively. In Panel B, the dependent variables in columns 1, 2, 3 and 4 are dummy variables equal to 1 if the respondent reported that her household gave any transfers to any ultra-poor, other poor, middle class or upper class households (within her community) respectively.

14
In terms of market share, for 56 out of the 58 countries in our sample the share of deposits captured by respondents exceeds 30 percent. Banks from France and Zimbabwe are not included in the calculations for deposit and payment barrier indicators because the market share of bank respondents in these countries is below 30 percent. When it comes to loans, the share represented by bank respondents exceeds 30 percent in 53 countries. In this case, the countries excluded from the sample are Germany, Nigeria, Romania, Swaziland, and Sweden. In 34 (29) countries the share of deposits (loans) exceeds 50%. On average across countries, the banks that responded to our sample account for 57 percent of the deposits and 53 percent of the loans in the countries in our sample, based on data from Bankscope.

# **3. The indicators**

This section presents our indicators of barriers to banking across countries. Tables 2, 3 and 4 present the country-level averages including descriptive statistics and Figures 1 through 16 show the cross-country variation graphically. Table 5 reports correlations across the different barriers. We separate our indicators based on the type of service: deposit, loan and payments. We report averages for each country calculated by weighing each banks’ responses by their share of deposits in total deposits of all sampled banks in the case of deposit and payment barrier indicators and by the share of loans for loan barrier indicators. Also, wherever possible, we try to distinguish between three different service dimensions: physical access, affordability, and eligibility. Physical access refers to the points of service delivery. Greater physical access means services are delivered in multiple and more convenient ways. Affordability refers to the costs in terms of minimum balances and fees that bank clients need to pay to obtain financial services, such as checking or savings accounts, consumer or SME loans, international payment transfers and use of ATM cards. Finally, eligibility refers to the criteria (in terms of documents or other 7
health (whether an individual was ill in the last month, and for how many days in the last month they were unable to perform their normal daily activities due to illness), reported ability to perform ordinary activities (doing vigorous activity, walking uphill, bending over or stooping, walking more than 1 km, walking more than 100 meters, or using a bath or toilet), anthropometrics (height, weight, middle upper-arm circumference, and z-scores for height-for-age, weight-for-age, weight-for-height, and body mass index-for-age), and—for those ill in the last month—the location where medical care was sought (no treatment sought, public dispensary, public hospital, public health center, private pharmacy, traditional healer, private dispensary/ hospital/ clinic, or mission dispensary/ hospital).<sup>9</sup> Among those seeking treatment, we further gathered data on health care financing methods (free treatment, loans, cash or assets, or health insurance). We also collected household-level data on expenditure on formal insurance<sup>10</sup> and—at endline—whether the household participates in the CHF.

# **4 Methods and Empirical Strategy**

## **4.1 Empirical specification**

We carried out follow-up surveys in 2011 and in 2012 to capture both short-term (1.5 years) and medium-term (2.5 years) impacts of the program. Given random assignment to treatment, we recover causal intent-to-treat estimates from the following empirical specification: where _i_ indexes individuals and _t_ indexes the survey round. _hit_ is a health-related outcome, _αi_ are individual fixed effects, _Ti_ =1 in a village assigned to treatment and zero otherwise, 2011 _t_ =1 at the time of the midline survey (July–September 2011) and zero otherwise, and

> 9Data on protective footwear were only collected for children aged 0–18, data on anthropometrics for children aged 0–5, and data on ordinary activities for those aged 60 and over. Individuals sick in the last month were asked to report the primary health provider and payment method for their main health problem.

> 10Insurance expenditure data are unfortunately not further disaggregated by type of insurance.

9
programs. Participants represented only 0.8% among the poorest households. The poorest households derived their main source of income from agriculture production (38%), trade (30%) and unskilled work (20%). They received comparatively less aid from relatives or friends from Haiti (11%) or from abroad (14%) than the richest (resp. 29% and 23%).

**Table 16. Households characteristics before the earthquake, by pre-earthquake** **<u>quintile of wealth</u>**

|Pre-earthquakequintile ofwealth|Poorest|2|3|4|Richest|
|---|---|---|---|---|---|
|Number of households|250|259|239|254|244|
|Location (commune)||||||
|Carrefour, Port-au-Prince and Delmas|0.04|0.10|0.21|0.24|0.30|
|Léogane, Gressier|0.28|0.24|0.16|0.11|0.07|
|Jacmel, Petit Goâve|0.31|0.16|0.13|0.06|0.08|
|Pétionville, Tabarre|0.03|0.12|0.15|0.23|0.32|
|Cité Soleil|0.03|0.15|0.21|0.31|0.18|
|Grand Goâve, Croix-des-Bouquets|0.32|0.22|0.14|0.06|0.05|
|Household size (median)|6|5|6|5|5|
|Housing characteristics||||||
|Electricity (lighting)|0.03|0.48|0.70|0.82|0.90|
|Toilet (WC)|0.00|0.00|0.01|0.06|0.37|
|Private water|0.00|0.06|0.13|0.63|0.86|
|Oven ownership|0.00|0.01|0.01|0.05|0.38|
|Television ownership|0.09|0.41|0.74|0.82|0.96|
|Fan ownership|0.04|0.14|0.41|0.47|0.85|
|Car ownership|0.00|0.03|0.01|0.02|0.17|
|Tools/Materials for production|0.66|0.34|0.09|0.14|0.08|
|Number of poultry owned (median)|7|9|6|5|8|
|<br>Number of goats owned (median)|3|3|4|2|3|
|Number of swines owned (median)|2|3|3|3|4|
|Number of cattle owned (median)|1|2|2|1|2|
|Number of sheeps owned (median)|2|8|2|2|-|
|Number of horses/donkeys owned (median)|1|1|1|2|1|
|Take part in cash-for-work program|0.008|0.015|0.017|0.016|0.00|
|Take part in food-for-work program|0.008|0.008|0.008|0.008|0.00|
|Take part in religious association (June)|0.28|0.32|0.24|0.22|0.24|
|Take part in non religious association (June)|0.19|0.08|0.10|0.08|0.10|
|Agricultural practice|0.66|0.32|0.13|0.06|0.05|
|Income sources||||||
|Agricultural production|0.38|0.15|0.04|0.02|0.00|
|Trade|0.30|0.32|0.40|0.43|0.42|
|Unskilled work|0.20|0.33|0.30|0.28|0.17|
|Skilled work|0.08|0.16|0.17|0.17|0.30|
|Transfer|0.02|0.01|0.04|0.04|0.05|
|Other income source|0.02|0.02|0.05|0.06|0.05|
|Transfer sent to relatives/friends in Haiti|0.14|0.17|0.25|0.26|0.29|
|Transfer received from relatives/friends in Haiti|0.11|0.11|0.20|0.22|0.23|
|Transfer received from relatives/friends abroad|0.14|0.12|0.15|0.21|0.34|

Source: Own computations using June 2010 (EFSA II) surveys. Notes: The sample is restricted to the households residing in the six strata that cover areas directly affected by the earthquake.

42
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# **Table 6. Testing distance-based stationary bandits**

||Full|Full|Full-High<br>War Casualty|
|---|---|---|---|
|Taiping *Year 1880|-0.491***<br>[0.089]|||
|Taiping *Year 1910|-0.469***<br>[0.085]|||
|Taiping *Year 1953|-0.541***<br>[0.093]|||
|Taiping *Year 1982|-0.466***<br>[0.084]|||
|Taiping *Year 2000|-0.451***<br>[0.089]|||
|Taiping * post year 1880||-0.500***|-0.608***|
|||[0.095]|[0.114]|
|Taiping *the number of decades since 1865||0.002|0.01|
|||[0.007]|[0.009]|
|Taiping * post 1880 *standardized distance to Nanjing|0.209***|0.209***|0.623***|
||[0.057]|[0.057]|[0.208]|
|Taiping *number of decades since end-Taiping *standardized distance to|-0.008**|-0.008**|-0.038***|
|Nanjing|[0.004]|[0.004]|[0.014]|
|Observations|1,862|1,862|1,820|
|R-squared|0.799|0.799|0.8|
|Number of prefectures|266|266|260|
|Control, prefecture FE, year FE|Y|Y|Y|

Note: The full sample contains the prefecture-year panel for 266 prefectures and seven time periods (from 1820 to 2000). The “full – high war casualty” prefectures contain the prefecture-year panel for 260 prefectures (after dropping 6 cities, Wuchang, Anqing, Songjiang, Jiangning, Suzhou, and Hangzhou which were heavily affected by the Taiping Rebellion) and seven time periods (from 1820 to 2000).

The time-varying control variables are the duration of treat ports, the duration of concessions and leased territories, the frequency of wars since year 1776. Also included are time dummies interacted with the following time-invariant controls: the distance to Yangzi River, to the coastline, and to the Grand Canal; the number of neighboring provinces; the pre-war level of land taxes per mu; the total number of palace graduates per million people from 1793-1820; silk and tea prefectures dummy; the four post designations classified by the Qing government in 1820.

Standard errors are robust, clustered at the prefecture level.

*** p<0.01, ** p<0.05, * p<0.1

51
<mark>A few studies have attempted to understand the reasons for differential levels of infrastructure across different cities in developing countries. Arimah (2005), for example, uses the</mark> _<mark>U</mark> nited Nations Human Settlements Programme_ ’s global urban indicators <mark>database to investigate the determinants of infrastructure</mark> spending across cities in developing countries. It finds that investments in infrastructure are low for cities in Africa and Asia, moderate in the Middle East and economies in transition, and comparatively high in the LAC region. The intercity variations depend upon the macroeconomic environment, urban growth rate; quality of governance; and the financial capacity of city governments.

# **2.2. Previous studies on determinants of infrastructure development in India at the state level**

Existing studies examining the factors influencing infrastructure investments in India include Mohanty et al. (2017), Pradhan (2017), Rasul and Sharma (2014), Ghate (2008), Pillai (2008), and Umdor & Panda (2007). Ghate (2008) develops theoretical models and proves that the fixed cost of accessing the modern sector, the initial level of infrastructure, the wealth of median voters, and corruption explain variation in infrastructure investment across states. Therefore, reducing the fixed cost of accessing the modern sector and reducing corruption and appropriate credit policy will attract investment in infrastructure. Mohanty et al. (2017) analyze factors affecting per capita infrastructure expenditure in 14 major Indian states for the 1991-2010 period. They find different levels of resource mobilization, per capita income, and population density explain the differences in infrastructure levels in these states. However, other factors such as public investment, political stability, and interdependence in infrastructure spending among states are also important factors. The present study differs from Mohanty et al. (2017) in multiple ways. First, our study tries to empirically find out the drivers of infrastructure development in stock form for three categories of infrastructure – physical, social, and financial, whereas Mohanty et al. examine the determinants of per capita infrastructure (flow) expenditure on economic and social infrastructure. The outcome of per capita expenditure leading to the real stock of infrastructure differs from state to state, given the heterogeneity of states in terms of efficiency, governance, geographical location, etc. Second, we make a composite index of infrastructure development for physical, social, and financial infrastructure by multiple indicators to reflect the real level of infrastructure development. Third, we explore more control variables and carry out multiple functions for the robustness of the results.

7
ILO. 2014. “Wages in Asia and the Pacific: Dynamic But Uneven Progress” in _Global Wage Report 2014/15 | Asia and the Pacific Supplement_ . Available from: <u>http://www.ilo.org/wcmsp5/groups/public/---asia/---ro-bangkok/---srobangkok/documents/publication/wcms_325219.pdf. Bangkok: International Labour Organization</u> Regional Office for Asia and the Pacific Regional Economic and Social Analysis Unit. Accessed 23 May, 2019.

Imbens, Guido and Jeffrey Wooldridge. 2009. “Recent Developments in the Econometrics of Program Evaluation.” _Journal of Economic Literature_ , 47, 5–86.

<mark>Kandpal, Eeshani, Harold Alderman, Jed Friedman, Deon Filmer, Junko Onishi and Jorge Avalos. 2016. “A</mark> Conditional Cash Transfer Program in the Philippines Reduces Severe Stunting”. _The Journal of Nutrition_ <mark>146 (9), 1793-1800.</mark> <mark>Klasen, Stephan and Simon Lange. 2016. “How Narrowly Should Anti-poverty Programs be Targeted? Simulation Evidence from Bolivia and Indonesia.” No. 213.</mark> _<mark>Courant Research Centre: Poverty, Equity and Growth-Discussion Papers.</mark>_ Labonne, Julien. 2013. “The Local Electoral Impacts of Conditional Cash Transfers: Evidence from a Field Experiment.” _Journal of Development Economics_ 104: 73-88.

Lalive, Rafael and Mattias Cattaneo. 2009. “Social Interactions and Schooling Decisions.” _The Review of Economics and Statistics_ 91(3): 457–477.

<mark>Macours, Karen, Norbert Schady and Renos Vakis. 2012. “Cash Transfers, Behavioral Changes, and Cognitive Development in Early Childhood: Evidence from a Randomized Experiment.”</mark> _<mark>American Economic Journal: Applied Economics</mark>_ <mark>4(2): 247-273.</mark> <mark>McKelvey, Christopher. 2011. “Price, Unit Value, and Quality Demanded.”</mark> _<mark>Journal of Development Economics</mark>_ <mark>,</mark> _<mark>95</mark>_ <mark>(2):157-169.</mark> <mark>Moradi, Alexander. 2010. “Nutritional Status and Economic Development in SubSaharan Africa, 1950–1980.”</mark> _<mark>Economics & Human Biology</mark>_ <mark>, 8(1): 16-29.</mark> <mark>Morris, Saul, Kalpana Beesabathuni and Derek Headey. 2018. An Egg for Everyone: Pathways to Universal Access to One of Nature's Most Nutritious Foods.</mark> _<mark>Maternal & child nutrition</mark>_ <mark>,</mark> _<mark>14</mark>_ <mark>, e12679.</mark> Mu, Ren and Dominique van de Walle. 2011. “Rural Roads and Local Market Development in Vietnam,” _Journal of Development Studies_ , 47(5): 709-734.

<mark>Muralidharan, Karthik, Paul Niehaus and Sandip Sukhtankar. 2017. “General Equilibrium Effects of (Improving) Public Employment Programs: Experimental Evidence from India.” Working Paper.</mark> <mark>Nazara, Suahasil and Sri Kusumastuti Rahayu. 2013. “Program Kelyarga Harapan (PKH): Indonesian Conditional Cash Transfer Programme.” International Policy Center for Inclusive Growth: Policy Brief No. 42.</mark> 37
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weighing the associated costs and benefits (Lang and Lundholm, 1993; Bamber and Cheon, 1998; Admati and Pfleiderer, 2000).

While there have been extensive empirical studies about the benefits of disclosure such as the effect of disclosure on the cost of external financing, there have been much fewer empirical studies on the costs associated with disclosure (Leuz and Wysocki, 2008). In addition, most existing studies are based on publicly traded firms in the U.S. where governance, markets, and institutions are more developed and efficient than those in developing countries. Thus, empirical evidence that directly examines both the benefits and costs of disclosure is scarce, while large-scale cross-country empirical evidence is almost non-existent. In this study, we employ a large cross-country firm level data set, which allows us to directly examine the benefits and costs, especially indirect political costs, of disclosure in a context where variations in institutions are substantial.

We analyze the World Bank Enterprise Survey (WBES) data from 2006 to 2014 for over 70,000 firms in 121 developing countries. We provide evidence that disclosure can be a double-edged sword: while audited firms exhibit a lower level of financial obstacles than unaudited firms, they also face a higher level of corruption obstacles. Furthermore, firms strategically disclose for higher net benefits: they are more likely to disclose if they have more growth opportunities measured by capital expenditure; they are less likely to disclose if they face a high level of competition. Perhaps surprisingly, auditing has an overall _negative_ impact on firm growth. However, the negative effect can be largely explained by the fact that most of the countries in our sample are under-developed countries where institutions are weak. Thus disclosure may be detrimental to firm development if they operate in an unsound business environment with poor governance. This conjecture is supported by our further empirical analyses: the beneficial effect of disclosure indeed increases as a country’s level of property rights protection improves.

We conduct extensive specification checks. In our empirical analysis, we recognize the potential endogenous nature of auditing and rely on two methods to address the non-random nature of auditing: 3
**Table 2: Incidence of Innovation in the last three years by Firm Size, and Sector**

||**Full**|**By Fir**|**m Size: Numb**|**er of Worker**|**s (excluding o**|**wner)**|
|---|---|---|---|---|---|---|
|**Type of Innovation**|**Sample**|**0**|**1-4**|**5-9**|**10-24**|**25+**|
|_All firms_|||||||
|Any Innovation|34.9%|25.5%|38.3%|43.5%|48.0%|58.5%|
|Product Innovation|18.0%|13.4%|19.5%|19.0%|26.9%|35.2%|
|Process innovation|6.6%|3.0%|7.7%|8.6%|12.9%|18.3%|
|Marketing innovation|26.6%|18.8%|29.3%|34.9%|32.8%|49.3%|
|Organizational innovation|10.6%|4.0%|9.9%|18.7%|22.8%|38.0%|
|Horizontal innovation|5.6%|3.2%|7.7%|3.8%|5.9%|14.5%|
|Vertical innovation|3.1%|1.8%|3.3%|4.1%|8.2%|4.2%|
|Spent money on R&D|1.9%|0.2%|1.8%|3.2%|5.9%|9.9%|
|Granted a patent ever|2.5%|1.4%|2.0%|4.6%|5.9%|7.0%|
|Number of observations|2865|1244|961|347|171|142|
|_Wholesale and Retail Trade_|||||||
|Any Innovation|33.8%|22.7%|35.3%|43.3%|46.8%|54.0%|
|Product Innovation|14.6%|11.6%|12.8%|17.3%|22.8%|28.0%|
|Process innovation|5.4%|1.9%|4.2%|9.3%|11.4%|18.0%|
|Marketing innovation|27.2%|18.0%|28.3%|36.7%|32.9%|48.0%|
|Organizational innovation|10.9%|3.6%|7.8%|19.3%|24.1%|40.0%|
|Number of observations|1000|361|360|150|79|50|
|_Manufacturing_|||||||
|Any Innovation|39.7%|30.7%|42.6%|51.2%|56.3%|74.2%|
|Product Innovation|23.0%|18.8%|24.9%|26.8%|31.3%|35.5%|
|Process innovation|9.1%|4.6%|11.0%|12.8%|18.8%|22.6%|
|Marketing innovation|29.8%|22.2%|32.8%|38.4%|40.6%|58.1%|
|Organizational innovation|10.5%|4.9%|11.3%|20.9%|18.8%|38.7%|
|Number of observations|949|410|390|86|32|31|

**Table 3: Characteristics of Product Innovation**

||**Full**<br>|**By Fir**|**m Size: Numb**|**er of Worker**|**s (excluding o**|**wner)**|
|---|---|---|---|---|---|---|
||**Sample**|**0**|**1-4**|**5-9**|**10-24**|**25+**|
|_All firms_|||||||
|New product for Sri Lanka|11.1%|1.7%|12.5%|3.7%|16.7%|28.6%|
|Invented by firm from own ideas|51.8%|43.6%|57.6%|51.9%|45.8%|53.6%|
|Invented by firm, based on ideas seen|||||||
|elsewhere|18.2%|19.4%|14.2%|25.9%|29.2%|14.3%|
|Mean share of 2007 sales from|||||||
|products introduced in last 3 years|45.9%|38.9%|43.0%|56.6%|55.6%|54.4%|

Note: Results are for the 247 firms which introduced a new product in the past three years

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