rafmacalaba/datause-encoder-data huggingface dataset:
disbursing and receiving payments (e.g. Aker, et al., 2013; Babatz, 2013), and increase transparency between the sender and receiver, thereby reducing leakage (Muralidharan, Niehaus, and Sukhtankar 2014). Evidence also suggests that digital transfers empower women within their households (Docquier, F., L. Lowell, and A. Marfouk, 2009). However, shifting cash payment into accounts is not without challenges. When making the switch, governments must take steps to guarantee a reliable and consistent digital payments experience. This includes making up-front investments in payments infrastructure, consumer protection and financial literacy programs to ensure that payments recipients know how to access and use their accounts (Klapper and Singer, 2017).

It is important to note that financial inclusion and access to finance are different issues. Some people choose not to use financial services even when they are affordable and available. Others want to use financial services but are prevented from doing so by any number of factors, including high costs, a lack of documentation needed to open accounts, or unavailability of banking services. Policy interventions should only target people who are involuntarily excluded from the formal financial system.

The rest of the paper is organized as follows. Section 2 introduces the survey instrument and breaks down account application rates by time and individual characteristics such as gender, income, age and education. Section 3 discusses reasons for not applying for an account and who are the unbanked. Section 4 discusses the introduction of JDY accounts and differences between adults who applied for an account within the past year and those that applied earlier for a traditional account. Section 5 offers a detailed overview of costs incurred in the application process and Section 6 concludes.

# **2. Access to Financial Services in India**

Financial exclusion remains widespread in India and the wider region. South Asia, home to about 625 million adults without an account, hosted about 31 percent of the world's unbanked population in 2014 (Demirguc-Kunt, et al., 2015).<sup>7</sup> Between 2011 and 2014 account penetration increased in India from 35 percent to 53 percent. In absolute numbers, this growth

> 7 Global Findex data is available at: https://www.worldbank.org/globalfindex.

4
# C.1.2 Resource Use

## C.1.2.1 Land

The data for the total land and commercial land endowments come from the GTAP Integrated Global Land Use Data Base (Lee et al. 2009) and GTAP Global Forestry Data Base (Sohngen et al. 2009b). We dene the initial amount of commercial land as the sum of crop land, pasture land, and managed (accessible) forest land areas. The initial amount of natural land is dened as the area of unmanaged (inaccessible) forest land. Other land areas, such as builtup lands, grasslands, savannah, shrublands, desserts, and barren lands, are not included in the current version of the model. The data for initial allocation of natural lands come from Antoine et al. (2008, p.8, Table 3).

Following past literature on land access modelling (Gouel and Hertel 2006, Golub et al. 2009) we assume that marginal natural land access cost per hectare is given by: In equation (C.2), the variable _c_<sup>_u_</sup> _t_<sup>referstothemarginalaccesscost,im-</sup> plied by the marginal valuation of non-reserved natural land. The parameter _ξ_ 1<sup>_N_determinesthelong-runelasticityofnaturallandaccesscostswithrespect</sup> to cumulatively accessed hectares, which eventually becomes innite as the remaining non-reserved natural land is exhausted. The parameter _ξ_ 2<sup>_N_governs the</sup> size of the short-term adjustment costs. We assume that additional costs per hectare of converting natural land to protected land are given by In equation (C.3), the parameter _ξ_ 0<sup>_R_referstothelong-runtime-invariant</sup> costs of protecting land. The parameter _ξ_ 1<sup>_R_governsthesizeoftheshort-term</sup> adjustment costs. We set the value of the marginal access cost, _c_<sup>_u_</sup> _t_<sup>_,_equalto</sup> the shadow price of agricultural lands dedicated to food crops (see equation C.8 below). The parameter values dening natural land access cost function (C.2), and natural land protection cost function (C.3) are calibrated based on FAO (2010) data to match deforestation rates in 2004 and ensure stable rates of natural land access and protection.

14
**Table** **<u>3: Descriptive statistics at the household level, by credit constrained status</u>**

||**Total**|**Constrained**<br>|**in informa**<br>|**l sector C**|**onstrained i**<br>|**n semi-forma**<br>|**l sector**|
|---|---|---|---|---|---|---|---|
|||**Yes**|**No**||**Yes**|**No**||
|**_Household characteristics_**||||||||
|Female headed hhd=1|0.28|0.34|0.21|***|0.32|0.20|***|
|Number of children (0-14)|2.09|1.95|2.27|***|1.99|2.31|***|
|No. female adults|1.31|1.28|1.35|**|1.29|1.37|***|
|No. male adults|1.10|1.02|1.22|***|1.06|1.19|***|
|Age head|46.32|48.45|43.42|***|47.31|43.95|***|
|No. years of schooling head|3.21|2.76|3.83|***|2.89|4.00|***|
|**_Asset endowments_**||||||||
|Total land owned|0.65|0.53|0.81|***|0.55|0.88|***|
|% of high quality soil|0.36|0.35|0.38|**|0.36|0.37||
|% of medium quality soil|0.15|0.14|0.15||0.15|0.15||
|% of wetland|0.07|0.07|0.07||0.07|0.08||
|% of irrigated land|0.05|0.05|0.04|*|0.05|0.04||
|Total borrowed, USD|23|15|33|***|15|41|***|
|Sale value of hhd assets, USD|56|36|83|***|43|87|***|
|Sale value of livestock, USD|163|124|217|***|139|222|***|
|**_Social capital_**||||||||
|No. of adult children in village|0.36|0.44|0.24|***|0.38|0.32|*|
|Hd born in the village|0.71|0.69|0.73|***|0.69|0.75|***|
|Hd displaced|0.47|0.47|0.47||0.46|0.49|*|
|<br>Member of farm cooperative|0.22|0.20|0.25|***|0.19|0.30|***|
|<br>Listen to the news at least once a week|0.69|0.62|0.79|***|0.65|0.79|***|
|Head/Spouse holds political office|0.10|0.08|0.13|***|0.08|0.15|***|
|Relative holds political office|0.21|0.18|0.24|***|0.18|0.27|***|
|Distance to the market, min|371|366|377||365|383||
|Distance to the road, min|276|279|272||283|261||
|**_Input use and agricultural productivity_**||||||||
|<br>Used chemical fertilizer|0.17|0.14|0.21|***|0.13|0.25|***|
|Used manure|0.73|0.70|0.77|***|0.70|0.81|***|
|Used pesticides|0.13|0.11|0.15|***|0.11|0.17|***|
|Used improved seed|0.26|0.23|0.29|***|0.24|0.30|***|
|Used extension advice|0.19|0.17|0.22|***|0.16|0.26|***|
|Male family labor days|136|140|129||143|117|***|
|Female family labor days|326|363|274|***|333|307||
|<br>Used hired labor|0.43|0.36|0.53|***|0.38|0.56|***|
|Hired labor days|146|139|151||144|148||
|<br>Total labor days|488|495|482||493|480||
|Yield per ha in USD|444|419|477|***|428|481|**|
|**_Income and its sources_**||||||||
|Involved in agriculture|0.97|0.97|0.98||0.97|0.98||
|Involved in agricultural wage labor|0.35|0.40|0.28|***|0.38|0.27|***|
|<br>Total yearly agricultural income, USD|115|116|113||116.|111||
|<br>Involved in nonfarm wage labor|0.20|0.18|0.23|***|0.18|0.24|***|
|<br>Total yearly off-farm income, USD|480|337|629|***|319|780|***|
|Off-farm self-employment activities|0.23|0.19|0.28|***|0.20|0.31|***|
|<br>Totalyearly profit,USD|649|369|915|*|372|1077|**|
|**TotalObservations**|3600|2079|1521||2542|1058||

**Source:** Own computation from WB survey 2011. **Note:** * significant at 10%; ** significant at 5%; *** significant at 1%

20
focus on collecting unconsolidated financial information to avoid double counting issues that may overestimate the footprint of the state. When only consolidated information is available, we document that in the consolidation variable and indicate in the respective subsidiaries to avoid any overestimations.

The steps to construct the BOS Global Database financial and governance modules are the following:

- 1) For each country, identify the set of unique company identifiers in the SOE registry (using their BvD ID numbers) and export the list of firms of interest to the ORBIS interface.

- 2) Retrieve the key unconsolidated variables defined in section 2 using the ORBIS interface as of 2019 for the firms of interest, to be consistent with the ownership links created as of 2019.

- 3) For firms identified through supplementary sources, allocate a unique identifier using a similar structure as ORBIS and use complementary databases (as EMIS, Pitchbook, and those described in section 3) to retrieve the variables of interest (e.g., sector, year of incorporation) and financial unconsolidated information as much as possible for employment, revenues, net after tax profits/ losses.<sup>40</sup>

- 4) In case that a specific company is found both in ORBIS and in the SOE Supplementary Database, but where there are information discrepancies, use the official data collected through the SOE Supplementary Database.

- 5) Although the main focus of financial data is to obtain unconsolidated financial statements for all companies, when we can only find consolidated information despite the use of supplementary databases, the financial information is added to the database and indicated as C1/C2 (consolidated) in the consolidation code variable and mark as n.a. (not available) for the subsidiaries to indicate that those are covered under the financials provided by the parent company.

- 6) To differentiate between corporatized and non-corporatized SOEs, create a variable (Corporate type) which specifies whether a company is corporatized or not based on the analysis of the national legal forms and/or the company’s incorporation act.

- 4.1.3 Merging the ownership module with Financial, Economic Performance, and Governance Variables

The final stage consists of merging the ownership information with financial, economic, and governance variables through the unique company identifier. For this purpose, we developed another algorithm to transpose all ownership links in the form of a matrix that combines different layers of state participation into a single row. In that way, the ownership links can be merged with the SOE registry using the unique firm identifier (BvD ID identifier). Figure 8 shows an example of the ‘ownership tree flipping’ algorithm’s input with three levels of ownership. In our example, entity 1, which is a public authority, owns entity 2 at 74.8%, which in turn owns 10.26% of entity 3.

> 40 The unique identifier of each company follows the same structure XX00000j, where XX corresponds to the ISO 2-digits code to denote the country of operation of the company, and j is a unique value provided by ORBIS to identify the firm.

24
to different priorities in government spending. While the first source is mainly driven by economic growth, we expect democracy to be a main driver of the second source. As mentioned, we were unable to gather good data for relative government spending for the given period. Only for more recent years the Government Finance Statistics of the IMF include sufficient information on these issues.

Neither the public expenditures’ path of causation nor the channel of private spending can be investigated here due to data restrictions. We presume that increases of private expenditures on health or education can be decomposed into increases of income and increases due to different priorities as well. There are different possible explanations for changes in priorities: It certainly plays a crucial role how much income remains after the satisfaction of basic requirements such as housing and nourishment. Moreover a high level of education might foster expenditures on health and education, and additionally the returns of health and education spending partly determine the level of the spending. Without sufficient expenditure data, we must rely on the use of proxies like income itself or literacy and on the theoretical argumentation that underpins our empirical analysis.

# **3.2 Descriptive statistics**

First, it is worthwhile to take a look at the densities of life expectancy and literacy for democracies and autocracies separately (Figures 1 and 2). We use kernel density estimators for this purpose and apply boundary corrections at 0 and 100 in the case of the literacy rate and at the minimum and maximum values in the case of the life expectancy. While for democracies both for life expectancy and literacy the mass of the distribution tends to the right hand side, there seems to be a group of autocracies with a low level and another one with a high level of life expectancy and literacy each. The same pattern can be observed in Tables 7 to 14 in the appendix where we classified 18
# KENYA WOMEN AND FOOD SECURITY

|||Pa|r|
|---|---|---|---|
|A.|Intr|oduction<br>........................................|1|
|B.|Wome|n's Roles in the Rural Sector<br>**.....**<br>**.....**|2|
||1.|Overall time-use patterns ..........<br>..<br>.|2|
||2.|Women as smallholder farmers ....................|4|
|||a. Overview............<br>.......|4|
|||b. Women's contribution to smallholder||
|||agriculture..................|5|
|||i. Marketing................|6|
|||ii. Storage..........<br>.......|7|
||3.|Women as income earners ...|7|
|||a.<br>Farm<br>and off-farm income ...|7|
|||b.<br>Remittances...................<br>.**.**|11|
||4.|Women as family caretakers and providers<br> <br>||
|||offood..<br>...<br>00..|11*....000*.|
|||a.Overview.<br>**....**<br>**..**<br>**o............**|**11**|
|||b.. Secondary crops and gathered food...<br>**..**|12|
|C.|criti|cal<br>..<br>Iss..ues.o....|13|
||1.|Intra-household<br>allocation of and control||
|||over resources|13|
||2.|The impact of cash-cropping on nutrition.......|15|
||3.|Access to services and resources...o........ o..|18|
|||a. Agricultural extension.....................|18|
|||b. Home economics extensiont...<br>s.......o....|. 19|
|||5c-<br>Technology.....--.-.o_.......<br>o..<br>P...<br>o.....|21|
|||d.<br>Crei...e.dit<br>...<br>o<br>...<br>o<br>....|22|
||4.|Women-headed households.......... .........|23|
||5.|The role of women's groups...o...u.oos........-|26|
|D.|Enha|ncing Food Security: Recommendations<br>......-...|29|
||1.|<br>Improving access to agricultural and||
|||<br>nutrition<br>extension<br>information....<br>**.....**<br>**.**<br>**....**|29|
||2.|<br>Improving access to technology..................|32|
||3.|<br>Improving access to crdit....e... ... o..o._...|32|
||4.|<br>Improving access to and control over||
|||<br>i<br>n<br>comens<br>contcted**..........**<br>**.**<br>**....**<br>o**..........**<br>**..**<br>**o**|33|
|Bibl|iograp|hy...o..oo<br>.<br>.o<br>......<br>oo....|J6|
|List|<br>of|persons<br>contcedt<br>a..c<br>t<br>ed..**..**o<br>..<br>.o|40|
at lower levels of PCE, recall impacts are larger than at higher. The pattern bears more similarity to that of recall of acute events than of expenditures.

Table 6 repeats the basic regressions in Table 3 to show that the response heterogeneity by income visible in the figures is, indeed, born out statistically. In these analyses, we restrict ourselves to the observational data and augment the estimating equation with the inclusion of household PCE and household PCE interacted with recall period. The estimates largely confirm the results from the figures in the observational data. For all our outcome variables, the recall effect is greater for the poor than for the rich. These effects are uniformly significant and suggest, for instance, that at the household level, an increase in (log) PCE by 1 increases the size of the recall effect by close to 0.4 doctor visits per month. The duration estimates at the household level are particularly large. Since log PCE varies between 5 and 10 in our sample, our estimate suggests that monthly surveys may underestimate the total time spent sick by members of a household by as much as 18 days a month. The labor impact of sickness shows a similar pattern with declines in the importance of the recall effect at higher levels of PCE, although in this case the estimate is less precise.

# <u>Further Robustness Checks</u>

The recall period affects reports of morbidity and health seeking as well as the labor impacts of sickness. We investigate several ancillary specifications to check whether (a) these results could be driven by specific age populations, (b) specific respondents or, (c) whether the differential impact between the rich and poor could be picking up the effect of attributes correlated with PCE, notably education. For brevity, only the education results are presented in Appendix Table 2.

_Age Impacts._ One-third of our sample is below the age of 15. To check whether this recall effect is driven by high morbidity and differential recall for sickness among children, we restricted the sample to those above the age of 15 and find no difference in the estimated parameters. The estimates in the observational data for number of acute illnesses, the probability of a single acute illness, the labor impacts of acute illnesses and doctor visits are respectively 0.55 (0.57), 0.13 (0.12), 0.024 (0.029) and 0.13 (0.14) in the full and adult-only samples, the latter in parenthesis. For the labor impacts of acute illnesses where the differences are somewhat larger, the interaction between adults and the recall impact is not significant (p-value = 0.859).

_Respondent Impacts_ . Every effort was made to retain the same primary respondent in all surveys, weekly and monthly. We code an indicator variable for whether the respondent was the designated primary respondent to check for respondent bias. In the case of the weekly surveys, this is aggregated up to the percent of weeks in the relevant month that the respondent was the designated primary respondent. The data suggest that 16
4 These measures represent a substantial improvement on commonly used measures. The usual tools for summarizing price-distorting policy trends in a country or region (see, e.g., Anderson and Masters 2009) are measures of the unweighted or weighted mean nominal rate of assistance (NRA) and consumer tax equivalent (CTE), the standard deviation of NRAs, and in a few instances the weighted mean NRA for exportable versus import-competing covered products.<sup>1</sup> Authors often need to report more than one measure to gain an appreciation of the nature of the policy regime. For example, indicators of dispersion of NRAs give some idea of the additional welfare losses that come from greater variation of NRAs across industries within the sector (Lloyd 1974). Further, if import-competing and exportable sub-sectors have NRAs of opposite sign, they need to be reported separately because those policies would offset each other in calculating the aggregate sector NRA.

While those various indicators are useful as a set, it is often helpful to have a single indicator to capture the overall trade or welfare effect of an individual country’s regime of agricultural price distortions in place at any time, and to trace its path over time and make cross-country comparisons. To that end, the scalar index literature is very useful. The pioneering theoretical work is by Anderson and Neary (summarized in their 2005 book), with an important partial equilibrium contribution by Feenstra (1995). The theory defines an ad valorem trade tax rate which, if applied uniformly across all tradable agricultural commodities in a country will generate the same reduction in trade, or in welfare, as the actual cross-product structure of distortions.<sup>2</sup> In recent years, several empirical papers have provided series of estimates of scalar index numbers for individual countries. Irwin (2008) uses detailed tariff data to calculate the Anderson–Neary Trade Restrictiveness Index for the United States in 1859 and annually from 1867 to 1961. Kee, Nicita and Olarreaga (2009) estimate partial-equilibrium indexes for 78 developing and developed countries for a single point in time (mid-2000s). Lloyd, Croser and Anderson (2010) estimate indexes for

> 1 The OECD (2009) measures similar indicators to the NRA and CTE, called producer and consumer support estimates (PSEs and CSEs). The main difference, apart from the CSE having the opposite sign to the CTE, is that the NRA and CTE are expressed as a percentage divergence from undistorted (e.g., border) prices whereas the PSEs/CSEs relate to the divergence from actual (distorted) prices. 2 Other indexes define an ad valorem trade tax rate which, if applied uniformly across all tradable products, will generate the same government revenue (Bach and Martin 2001), or the same real national income and general equilibrium structure of the economy (Anderson 2009a), as the actual cross-product structure of distortions.
To understand these results, it is useful to start with the second group, which relies on within-country data using a single country as its focus (and thus faces the standard external-validity problem). Here, the typical measure of spatial concentration is either the level or the rate of growth of regional GDP per capita (and, in some cases, the region-industry share of employment). Regarding the measure of trade openness, it must be noted that half of the papers in this group use Mexican data and the trade liberalization episode associated with NAFTA to identify the change in policy. The finding of spatial divergence in the Mexican case is easily explained by the observation that liberalization shifted economic activity to regions bordering the US. Because these regions were relatively more industrialized and richer than the rest of Mexico prior to liberalization, it is not surprising that trade exacerbated regional inequalities in general. The instructive exception in this group is Sanguinetti and Martincus (2009), who find that those Argentinian manufacturing sectors that received the largest tariff reductions in the 1985– 94 period tended to have employment that grew faster in regions that are not usually associated with traditional sites of manufacturing activity in and around the main port and largest city, Buenos Aires. This result is also important because unlike most of the nonMexican papers in this group, Sanguinetti and Martincus use changes in tariff rates and do not depend on such endogenous measures of trade openness as trade-to-GDP ratios. The same cannot be said for the vast majority of the papers in the first group. Starting with Rosen and Resnick (1980) and Ades and Glaeser (1995), the standard measure of trade openness is the trade-to-GDP ratio. As pointed out by Rodriguez and Rodrik (2000), using an outcome variable such as trade-to-GDP (or imports-to-GDP) is inappropriate if one wants to go beyond general correlations and explore the causal