rafmacalaba/datause-encoder-data huggingface dataset:
adults are often excluded from formal banking services because they face more barriers to account access.

Specifically, when asked why they do not have an account, the largest share of unbanked adults without an account at a financial institution cite a lack of money among their reasons. Yet a lack of money is not the only reason, and most respondents give more than one answer. They include that bank services are too expensive; that banks are located too far from where the respondent lives; that a family member has an account (this latter answer is often more common among women than men); and that they lack the documentation, such as a government-issued ID, needed to open an account. Distrust of the financial system also plays a role for unbanked adults around the world. Finally, religion can be a barrier, particularly in Arab economies where adults might prefer Sharia-compliant banking services.

Mobile money accounts can overcome some of these barriers, specifically distance and cost. Yet when unbanked adults in Sub-Saharan Africa were similarly asked why they did not have a mobile money account, one of the top reasons they gave was that they did not have a mobile phone. This points to a parallel access issue related to digital connectivity via a mobile phone, a computer, or other digital device with internet access. In addition, many unbanked adults say that excess distance to a mobile money agent is a barrier to mobile money account ownership. Though the goal may be a seamless digital experience where money is sent and received digitally, in reality, mobile money typically includes cash-in/cash-out (CICO), especially in predominantly cash-based economies. For this, agents are necessary.

Yet as digital financial services become more popular, a larger share of consumers are exposed to financial risks from fraud and phishing scams, over-indebtedness in digital credit, and incomplete or incorrect information on the fees and costs of financial products.<sup>6</sup> A lack of financial experience among unbanked or newly banked adults exacerbates these risks. In fact, about one in three of all mobile money account owners in Sub-Saharan Africa say they cannot use their account without help from a mobile money agent or from a friend or relative. Such lack of financial and digital independence may expose these users to increased risks. Safely

> 6 For a review of studies on consumer protection, see Innovations for Poverty Action, “Consumer Protection Initiative: Research Initiatives” (dashboard), https://www.poverty-action.org/program-area/financialinclusion/consumer-protectFion-initiative.

15
| POLICY RESEARCH WORKING PAPER 1751

# Summary findings

Bond presents a tariff index that uses constant-elasticityof-substitution aggregators of tariff line data to calculate how preferential tariff reductions affect both prices and average tariff rates.

A simple general-equilibrium model with sectorspecific factors of production can be combined with the tariff indices to calculate how a preferential trade arrangement affects sectoral output, factor prices, and general welfare. The general equilibrium model is simple enough that it can be calculated on an Excel spreadsheet, and is flexible enough to be used with different ranges of available domestic data.

Bond presents an example of the model, simulating the effects of free trade agreements between Chile and MERCOSUR countries and between Chile and NAFTA countries.

which allows large amounts of detailed tariff line data to be aggregated for use with domestic production data that is available only at a more aggregated level.

Bond finds that results using the tariff aggregators may differ substantially from those derived using simple averages of tariff reductions. Reductions in import prices using the index were 10 to 30 percent larger than those calculated using a simple average of tariffs. Ignoring the information available in tariff line data could lead to a substantial overestimate of the average tariff rate on imports after a preferential reduction.

The tariff index could be extended to incorporate the role of quantitative restructions. The general equilibrium nmodel could be used to consider the effects on domestic production of allowing reallocation of capital between industries over time.

Calculations for the case of Chile show that the index is simple to calculate because of its recursive structure,

- - This paper a product of the International Trade Division, International Economics Department represents the results of a study funded by the Bank's Research Support Budget, "An Operational Model for Evaluating Preferential Trading Arrangements" (RPO 679-77). Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Jennifer Ngaine, room NS-060, telephone 202-473-7947, fax 202-522-1159, Internet address trade@wworldbank.org. April 1997. (42 pages)

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even_ **_if_** _the presentations are less than fully polished. The papers carry_ **_the_** _names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent._

## Produced by the Policy Research Dissemination Center
Table 7 Poverty and Export Participation Instrumental Variables

||L|and Share|s|In|come Shar|es|
|---|---|---|---|---|---|---|
||Model 1|Model 2|Model 3|Model 1|Model 2|Model 3|
|IV – Linear Model|||||||
|Coefcient|_−_1_._89|_−_1_._50|_−_1_._51|_−_1_._68|_−_1_._78|_−_1_._79|
|Standard error|(0_._45)<sup>_∗∗∗_</sup>|(0_._91)<sup>_∗_</sup>|(0_._90)<sup>_∗_</sup>|(0_._35)<sup>_∗∗∗_</sup>|(1_._20)|(1_._20)|
|_R_<sup>2</sup>|0.15|0.21|0.21|0.15|0.21|0.21|
|IV – Probit|||||||
|Coefcient|_−_5_._75|_−_5_._28|_−_5_._33|_−_5_._11|_−_6_._04|_−_6_._10|
|Marginal efect|_−_2_._08|_−_1_._91|_−_1_._93|_−_1_._81|_−_2_._12|_−_2_._14|
|Standard error|(0_._40)<sup>_∗∗∗_</sup>|(0_._89)<sup>_∗∗_</sup>|(0_._88)<sup>_∗∗_</sup>|(0_._33)<sup>_∗∗∗_</sup>|(1_._03)<sup>_∗∗_</sup>|(1_._02)<sup>_∗∗_</sup>|
|_R_<sup>2</sup>|0.13|0.18|0.18|0.11|0.15|0.15|
|Observations|6734|6734|6734|6734|6734|6734|
|Export Price Index|No|Yes|Yes|No|Yes|Yes|
|Food Markets|No|No|Yes|No|No|Yes|
|Household characteristics|Yes|Yes|Yes|Yes|Yes|Yes|
|District characteristics|No|Yes|Yes|No|Yes|Yes|

Note: Instrumental variable estimates of poverty on export participation. Poverty is defined as a dichotomous variable of whether the household is poor or not. Export participation is measured with the share of land devoted to export crops (coffee, tea, cotton, pineapples, passion fruit) and with the share of income generated by sales of these crops. The linear model is estimated with IV and the IV-Probit model is estimated with control function methods. Standard errors (within parenthesis) are cluster-corrected. Based on the Uganda National Household Survey 1999/2000.

37
Figure C.2: Productivity-Elasticity of Distortions in Chile <!-- Start of picture text --> 0.6<br>G<br>0.4<br>G G<br>G G G G Estimated Value<br>G G G G Linear Fit<br>G G G<br>G<br>G<br>0.2<br>1990 2000 2010<br>Year<br>TFPR−TFPQ elasticity<br><!-- End of picture text --> Note: The gure illustrates the regression coecient between _log_ ( _TFPR_ ) and _log_ ( _TFPQ_ ) for the period 1984-2011. The dots correspond to the point estimates from Chile's ENIA (Encuesta Nacional Industrial Anual) for 1984 through 1996. We dene _log_ ( _TFPR_ ) and _log_ ( _TFPQ_ ) as in Hsieh and Klenow 2009. The solid line illustrates a linear t on the estimated values projected on to 2011. We assume that reforms stabilize in 2011, and the productivity-elasticity of idiosyncratic distortions remain constant a the 2011 level. The initial steady state is represented by the elasticity estimate for 1984.

in the behavior of the investment rate from the pattern exhibited by the average growth acceleration carries consequences for the calibration of the average idiosyncratic distortion in the economy, controlled by the parameter _ZIt_ in the idiosyncratic distortion prole. This parameter was set to reconcile the growth in the capital output ratio in the model with that of the data. In China's acceleration this could be achieved parsimoniously, due to the somewhat monotonic rise in the capital-output ratio throughout the transition. This is not the case under Chile's cyclical behavior of the investment rate. For this reason, we decided to abstract from seeking to match the behavior of the capital output ratio, and preserve the value of _ZI_ in 1984 and in 2011, the initial and terminal points of the transition, to attain a common capital-output ratio.

# C.2 Development Dynamics

Figure C.3 shows the development dynamics under Chile's calibrated reforms. Although the model can almost fully account for the overall growth in _TFP_ from the 48
The striking fact, revealed by these numbers, is that the Gini-based global inequality extraction ratio today is at about the same level as in the early 19th century. If we use Bourguignon and Morrison’s historical data, it could be said to have remained stable over the last two hundred years with the exception of a moderate tick up around 1850-1870 (Figure 2).<sup>15</sup> With BFLZ (2009) data, the Gini inequality extraction ratio in 19<sup>th</sup> century is somewhat higher, and accordingly is higher too than it is today. If we use the Theilbased inequality extraction ratio, today’s values are significantly lower than those of the early 19<sup>th</sup> century, but after 1910, there is little downward movement (Figure 3). For the last one hundred years, we do not see any compelling evidence that there was a decline in the global inequality extraction ratio, measured either by the Gini or Theil index. What has happened is that the actual global Gini has risen _pari passu_ with the maximum feasible Gini thus broadly keeping the inequality extraction ratio constant (see Figure 4).<sup>16</sup> Combining this with the earlier finding that the composition of global inequality has shifted from being “caused” by internal factors, like domestic income distribution, to "external" like differences in mean country incomes, we can conclude that the main “inequality extractors” today are not (within)-national elites, but an elite which is basically composed of the citizens of rich countries.

> 15 The result  also shows why the Bourguignon-Morrisson estimate of Gini 50 for global inequality in 1820 is implausibly high. Using their own global mean income of $PPP 652, it can be shown that the maximum feasible Gini could be only 54 points and that the inequality extraction ratio would have to be greater than 90 percent. This cannot be totally rejected but seems implausibly high in light of the results  for the subsequent years.

> 16 The "extraction" levels are quite different depending on whether we use Gini- or Theil-based measures. However, our concern here is not with the levels as such but with changes in the levels and in that respect both Gini- and Theil-based extraction  ratios are in agreement for the period of  the last one hundred years. Note that the Theil index is much  more  sensitive to the extreme values than the Gini. Thus the maximum feasible Theil increases faster as α goes up than  the maximum feasible Gini. It can be readily verified from equations (3) and (4) that the slope of Gini-based Inequality Possibility Frontier is 1/α<sup>2</sup> while the slope of Theil-based Inequality Possibility Frontier is 1/α. Thus the former is more concave (see Figure 4).

19
residual. This is in line with standard<sup>national accounting</sup> practices. Table A1.9 gives a summary<sup>view of the national</sup>

# accounts.

Table A.9 National Accaunts (Cu rent Prices)

|||1987|1988|Source|
|---|---|---|---|---|
|H Oflo<br>|Other Factor IncomeBudget<br>|-93.1<br>|1t666.4<br>|(Fiscal<br>data)<br>Fil<br>dt|
|NFlo<br>|SEsFactor Inco e<br> <br>|2.309.8<br>|3,416.6<br>4|(sca<br>aa)<br>RifL|
|NVAp<br>N TI|Private<br>Sector Value Added<br>Indirect<br>Taxes|50,710.5<br>6,618.9|86,63.9<br>11,255.6|(esus)<br>(Fiscal<br>data)|
|NSub|SubsidiesC-)|1,247.9|2,229.6|(MA data)|
|HGDP|aP|58,300.0|100,573.0|(Stardard<br>Tables 3)|
|N**INt**<br>|Total<br>Imports<br>|13,078.4<br>|22,097.2<br>|(SWdata)<br>|
|HXt|Total Exports|12.147.4|**.4,644.1**|(SoP data)|
|NRG<br>|Resource Gap<br>|931.0<br>|-2,546.8<br>|(Calculated)<br>Fil d|
|NCb|BiudetConsuaption<br> <br>|5,320.0<br>|8,814.7<br>|(sca ata)<br>|
|HCp|Private<br>Conumption|39,062.7|65, 164.9|(Resid&tl)|
|NCt|TotalConsumption|4,382.7|73.979.6|(Calculated)|
|N IOld<br>|Pubtic Irneti ent<br>|7,786.6<br>|10,990.4<br>|<br>|
|1lb<br>|Guidet<br>Investnt<br>|4,205.8<br>|6,321.2<br>|(Fiscal<br>data)<br> <br>|
|Hlo<br>|SEEs Investnt<br> <br>|3,555.6<br>|4,624.6<br>|(Fiscal<br>data)|
|N**Id**<br>|Bankinsector Investme<br>(Public)<br> <br>|2.2<br>|44.6<br>||
|HIp<br>N It|Private<br>Investment<br>Total<br>In1et|7,061.7<br>14,J48.3|13,056.2<br>24,046.6|(Calculated)|

Source: See explwetfon in text nn right met colun.

# **Al.S.** Private Sector

The flow of funds methodology<sup>is based on double entry</sup> accounting. In all other accounts<sup>the private sector variables are</sup> obtained residually. The inherent<sup>consistency of the methodology</sup> ensures that the private sector's budget constraint is automatically satisfied. The private<sup>sector accounts can therefore</sup> be read as a check of the historical<sup>data in terms of the quality</sup> of consistency.

29
early marriage, female genital mutilation, gendered social stigmas and gender‐based violence) are likely to prompt migration (Ferrant et al., 2014).

Gender is a crucial part of understanding the processes of migration among Indonesians, and the recent history of migration is likely to have both a discouraging effect (fear of ill-treatment) or an encouraging effect (better opportunities than informal labor) for women. Indonesia is one of the largest origin countries of migrants in the world. Opportunities to work abroad attract Indonesians because of a lack of domestic labor demand and higher potential earnings in destination countries (World Bank, 2017). Concerning liquidity constraints, using data from Indonesia, Bazzi (2017) shows that positive rainfall and price of rice stocks are associated with larger international migration flows. He shows that this phenomenon is linked to the role of liquidity constraints in the decision-making process of migration. Emigration was largely female before the introduction of migration restrictions towards multiple countries in the Middle-East, North- and East-Africa and Pakistan (Makovec et al., 2018). Indeed, female migrants were facing risks throughout their migration journey, leading the Indonesian government to implement these moratoriums as an attempt to address the cases of illtreatment (ranging from physical and sexual abuse, forced labor and unpaid wages) facing Indonesian women working abroad (World Bank, 2017). Makovec et al. (2018) show that migration restrictions towards Saudi Arabia increased the share of informal employment in Indonesia, formal labor markets being unable to absorb the excess labor supply.

# 3. Data and methods

## 3.1. Data

This study draws from household data collected for the impact evaluation of the Government of Indonesia’s _DESMIGRATIF_ program. The _DESMIGRATIF_ program aims to provide a holistic approach to promote safe international migration from Indonesia, based on the four pillars of: (i) better migration information and services, (ii) productive enterprises, (iii) community parenting, and (iv) financial cooperatives for potential migrants, return migrants, and families of migrants. Because of the multidimensional nature of the program, the baseline survey—used in this study—contains rich household data. The survey was carried out in October 2018 and covers 176 villages, spanning across 6 provinces and 58 districts. The data is not nationally representative. Selected villages have higher shares of international migrants, consistent with the Ministry of Manpower’s intention to allocate the 7
In this estimating equation,<sup>�</sup> _p_<sup>_Treatedip_0istheweightedsumoftreated</sup> women identified by woman _i_ in the control group at baseline, where weights correct for each woman’s position within the photobook. Weighting in this way allows us to interpret _η_ as the effect from each additional baseline connection with a treated woman.<sup>19�</sup> _gip_ 0 controls for the overall number of women in _p_ the study to whom a woman is connected at baseline, again using weighted sums of network connections. We control for location fixed effects, marital status, household size, number of children, age, network size, education, and the lagged outcome variable.

Our interest in estimating Equation 2 is twofold. First, we are interested in whether there are spillovers from the program and, if so, which types of network links are most likely to transmit spillovers. Second, we use estimated spillover effects to calculate adjusted average treatment effects. Doing so allows us to provide estimates and confidence intervals for the effect of the program accounting for network-based spillovers to give a sense of the magnitude of the bias in our main effects.<sup>20</sup> We use the high-frequency SMS data to qualitatively evaluate general equilibrium effects and to estimate the effect of the COVID-19 lockdown on sales revenues. However, response rates to the SMS surveys are lower than response rates for our in-person surveys.<sup>21</sup> We correct for this non-response bias using a two-step procedure. First, we use a LASSO procedure to select the set of baseline covariates, including baseline outcomes, that best predict responding to the SMS survey in the first month of the experiment. We then regress a binary indicator for responding during the first month of the SMS survey on

> 19We calculate weights as follows. Let _wp_ be -1 multiplied by the coefficient in Table A11 Column (1) that corresponds to the page on which an identified woman appears. Let _ws_ be -1 multiplied by the coefficient in Table A11 Column (2) that corresponds to the position on the page where an identified woman appears. We compute the woman’s weight as _w_ = 1 + _wp_ + _ws_ such that women appearing on the first page in the upper left corner have a weight of 1 and women appearing elsewhere in the photo book get up-weighted to adjust for respondent fatigue.

> 20Our confidence intervals account for uncertainty in the traditional intent to treat effect as well as the estimated spillover effect which we compute using the delta method.

> 21See Table A3 for balance in the SMS survey and Table A4 for a description of the correlates of SMS attrition.

19
allocation mechanism that divides the water among the parties by percentage rather than a fixed amount may reduce possible inter-state tensions when future water flows in the basin are uncertain. Other authors have also pointed to various types of variability management schemes (De Stefano et al. 2012). In situations of drought, for example, authors have discussed the utility of stipulations such as consultations, stricter irrigation procedures, water allocation adjustments, specific reservoir releases, and data sharing (McCaffrey, 2003; Turton, 2003; Drieschova, Giordano, and Fischhendler 2008). For flood issues authors have generally argued that the mere existence of a flood-control treaty may confer the necessary resilience (Bakker 2009) while others have stressed a specific flood-control mechanism such as warning systems, channelization schemes, and levees, among other schemes, as the necessary stipulations needed to confer resilience on river basins (Arnell 2002; Drieschova, Giordano, and Fischhendler 2008).

Perhaps even more important in addressing the uncertainty of climate change is the ability to predict the availability and accessibility of water flow data for river basins in near real time. In fact, the above discussed treaty mechanisms and stipulations are most efficiently negotiated and devised when reliable data are available. In this manner, negotiators and water policy makers are able to make educated decisions pertaining to which stipulations to include or which mechanisms should take priority. They are able to anticipate possible shocks or extreme events and design appropriate institutions. In the next section we describe a number of these existing methodologies, used to anticipate future flows, and then discuss the BWI.

# **EXISTING RUN-OFF ESTIMATION METHODOLOGIES**

Existing methods that assess global climate change in basins typically calculate river flow predictions in basins by calculating a percent change from a long-term normal base time period compared to predictions provided by the Intergovernmental Panel on Climate Change (IPCC) General Circulation Model(s) (e.g. Alcamo et al. 2003; Milly et al. 2005; Palmer et al. 2008). These methods have several shortcomings. First, global models tend to be complex and require many inputs. Second, global models are useful for global comparisons of basins; however policy work on specific treaties benefit greatly from basin specific applications. Third, the timely nature of the results does not provide continuous updates from the most current observations. Many experts see integrated monitoring systems that include remote sensing and GIS data helpful in 6
# **Business Training and Female Enterprise Start-up, Growth, and Dynamics: Experimental Evidence from Sri Lanka**<sup>*****</sup>

Suresh de Mel, _University of Peradeniya_ David McKenzie, _World Bank_ Christopher Woodruff, _University of Warwick_ _Keywords:_ Business training; female self-employment; randomized experiment; business startup; trajectory of treatment effects.

_JEL codes:_ O12, J16, L26, M53.

> * We thank the World Bank’s Gender Action Plan and Research Support Budgets for funding for this project, Matthew Groh for valuable research assistance, and Alan de Brauw and various seminar audiences for helpful comments. For logistical support on the project, we thank the Sri Lankan Business Development Council for delivering the training program and especially the Kandy Consulting Group Pvt) Ltd for (once again) providing exceptional service conducting baseline and follow-up surveys and providing logistical assistance in the implementation of treatments.
Annex 4: Charts <!-- Start of picture text --> Probability of Insufficient Debt<br>Coverage Under Different Scenarios<br><!-- End of picture text --> <!-- Start of picture text --> f 80% Interest Rate = 10.5%<br>uo 70%<br>-1 60%<br>.. 50%<br>430%<br>b 2 0 %<br>10%<br>- revenues only- -revenues & maintenance, uncorrelated<br>of INFRISK<br>Information Probability<br>* Input Data distribution of<br>* Parameters main variables<br>Company Lender Government<br>IRR, NPV Debt Coverage Social benefits of<br>Ratios | l project net of subsidies<br>No NNo Yes1<br>L--4~~~~ ------ ~ ~~ -<br>Transaction.<br><!-- End of picture text -->

Flow chart diagram of _INFRISK_ 32