rafmacalaba/datause-encoder-data huggingface dataset:
Besides differing in the types of crops cultivated, the localities also differ in the availability of services and programs. Table 6a summarizes some of the locality characteristics for the 2002 and 2005 MxFLS samples.<sup>20</sup> The information is only available for those localities in the original sample. In 2005 there were households from 85 different localities since some of the households had moved to non-MxFLS localities. In most of the original MxFLS localities there was access to primary education but access to higher education was only readily accessible in a few localities. In many localities there were health services available, as about 75% of the localities had a public health clinic, but not all had such services locally. In the majority (about 75% in the 2002 sample and 99% in the 2005 sample), but not in all, qualifying households were able to access Oportunidades.<sup>21</sup> Table 6b shows some characteristics of the ENN sample localities. Although on average 76% of the households in the localities have electricity, only 27% have access to a sewage system.

# **5. Results**

Following we present the results from our analyses on the impacts of rainfall and GDD shocks on household consumption and on the health outcomes of children. To examine whether or not weather shocks impact household consumption, we estimate equation (1). We measure consumption by the logarithm of per capita expenditures on all non-health related items. We subtract health spending from the total expenditures since most health spending follows illness and thus is not welfare improving (Thomas et _al_ ., 2010). We also look at the impact of weather on food expenditures given that households may spend on different spending categories after weather shocks. The average share of food expenditures in our sample is 41% of total expenditures (without considering health expenditures). Included in the expenditures are the estimated value of goods consumed from own production and the value of goods received as

> 20 The information is more complete, although maybe more unreliable, for the 2002 sample.  In 2005 information was sought from both official and unofficial sources.  Information from official sources was used as the primary source of information and if no official information was available then the unofficial information was used instead. If more than one official source of information was used, and the information was conflicting, i.e. one source responding yes to the presence of a secondary school in the locality and the other responding no, the variable was coded as missing. Given this fact, in 2005 there are more observations with missing information than in 2002.

> 21 Oportunidades, originally named PROGRESA, is a conditional cash transfer (CCT) program aimed to alleviate current poverty through monetary and in-kind benefits.

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The second panel in Table 20 reports the results of the impact of different _market-enabling_ policies on the number of deposit accounts [see Beck and de la Torre (2006)]. These policies tend to provide incentives (or constraints) to financial institutions to operate more efficiently or to broaden their customer base. The one measure of these policies is the level of competition in the banking sector. Competitive pressures and the search for profits are key factors for institutions to innovate, expand their customer base, and offer accounts to underserved clients. Here, we follow Beck, Demirgüç-Kunt and Martinez-Peria (2008) and proxy competition with bank concentration, which we approximate by the share of deposits in the five largest banks. Column (12) presents the results. Consistent with the theory, higher concentration in the banking sector is associated with lower deposit account penetration, although this result is only significant at a 15% level. Next, we analyze the role of bank ownership which is a proxy for the threat of entry as well as quality of management. These data are from Barth, Caprio, and Levine (2004). Their government and foreign ownership measures are not statistically associated with measures of deposit penetration. In columns (15) - (23), we study the relationship between price and non-price barriers and financial outreach. We begin by analyzing the role of barriers to open a bank account. Our focus here is on five variables: KYC requirements, exceptions to these requirements, the existence of regulatory requirements for banks to offer basic or low fee accounts, the number of document required, and the minimum balance to open a checking account (as percentage of GDP per capita). The first three variables are from the _Financial Access Database (2009)_ ; the fourth is from World Bank (2008); the last one is from Beck, Demirgüç-Kunt and Martinez-Peria (2008). Broadly speaking, the signs of the coefficients support the idea that countries with lower barriers are associated with higher deposit penetration, although none of the coefficients are statistically significant. In columns (20) and (21), we assess whether policies to encourage people to save, such as tax incentives or annual fees, are associated with deeper deposit penetration. Neither variable enter significantly. Finally, in the last two columns we investigate whether regulatory policies aimed to foster financial inclusion are associated with a deeper account penetration. Specifically, we study the role of agents and postal banks. In the first case, we use and index of the extent to which agents are allowed to 30
# **III. The Need for SME Exchanges**

The importance of SMEs to the economy and their financing constraints are well known (see Table 3). SMEs account for a large part of emerging market economies and their job creation. A recent WBG report found that SMEs are the biggest contributor to employment (above 60 percent) in developing countries. Although their contribution to economic fundamentals varies across countries—from 16 percent of gross domestic product (GDP) in low-income countries to 51 percent of GDP in high-income countries—they are a high percentage of firms.<sup>5</sup> Their ability to get funding is key to economic growth and job creation.

For many SMEs, access to finance is a primary obstacle to growth. Recent International Finance Corporation (IFC) data show the financing shortfall for the 25–30 million formal SMEs in EMCs at around $1 trillion.<sup>6</sup> For the reasons discussed above, SMEs are a priority for many EMCs’ development and growth agenda. They have, and will increasingly have, space in the policy agenda.

||Table 3.<br>|The Importance of SMEs<br>|<br>|
|---|---|---|---|
|Country|SMEs as % of GDP|SMEs as % of Firms|SMEs as % of Employment|
|India|20%|—|40%|
|South Africa|52–57%|91%|61%|
|Taiwan|—|98%|78%|
|Brazil|20% (excluding<br>medium)|99%|60%|
|Poland|72%|>99%|69%|
|Turkey|60%|99%|76%|

_Source:_ WBG compilation of exchange information. _Note: — =_ not available.

# **_A. SME Financing: The Role of Exchanges_**

SME exchanges are one of many options for SME financing along a spectrum or ladder that is frequently linked to the life cycle or stage of growth of the SME. This ladder typically progresses from the startup phase, which is usually funded by entrepreneurs’ capital and family and friends, to an early stage where debt is taken on through bank loans. After this the shareholder structure broadens to include venture capital (VC), and in a growth phase, private equity (PE) financing is often sought. Capital market funding is usually seen as being at the end of the financing spectrum.

This funding life cycle is being increasingly compressed, particularly for high-growth companies in the innovative or technological sectors that are looking for capital or visibility at earlier stages. This is occurring as those companies observe the initial public offerings

> 5 Ayyagari, Beck, and Demirguc-Kunt, “Small and Medium Enterprises across the World: A New Database,” World Bank Group.

> 6 IFC Enterprise Finance Gap Database (2011).

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even negative, depending on the specification and methodology used. Moreover, the estimates of the multiplier are reasonably precise, with standard errors that are comparable to those obtained in studies of US military expenditures, as well as VAR‐based estimates. Across the various specifications and methodologies I consider, I consistently do not reject the null hypothesis that the multiplier is zero, and I can usually also reject the null hypothesis that the multiplier is equal to one.

There are numerous possible objections to this basic identification strategy. One immediate concern is that past project approval decisions are in fact correlated with contemporaneous shocks, because these shocks are persistent or otherwise predictable in some way. I address this concern by controlling for lagged growth, and by allowing for longer lags between project approval and actual disbursements. Another concern is that the timing of individual disbursements on World Bank projects approved in previous years is driven by contemporaneous shocks, even if the project approval decisions are not. I address this concern by constructing an alternative artificial measure of disbursements based on typical disbursement rates for similar projects in other countries that, by construction, do not reflect domestic shocks. Another potential issue with my identification strategy is that World Bank‐financed spending might crowd in or crowd out other forms of public spending, and so bias estimates of the overall government spending multiplier. To deal with this issue, in my core results I use changes in World Bank‐financed spending as an instrument for changes in total government spending in order to estimate spending multipliers.

Several caveats about the evidence in this paper are worth acknowledging at the outset. First, this paper shares with much of the literature the difficulty that empirically‐estimated government spending multipliers are not “deep” structural parameters but rather reflect the confluence of a wide variety of factors including preferences, technology, the nature of spending, and the eventual taxes that finance them. For this reason the term “multiplier” is perhaps best understood simply as short‐hand for the empirical correlation between plausibly exogenous changes in government spending and changes in output. Second, my results by design are based on data for a particular set of poor and/or small economies where World Bank lending is an important source of financing for public expenditure, and the effects of government spending on output in other countries outside my sample might very well be different. Finally, I emphasize that the empirical work here is designed only to assess the the short‐run impact on output of changes in public spending associated with changes in disbursements on World Bank loans. This question is of course related to, but distinct from, that of the long‐run growth impacts of foreign assistance more generally, which has been debated endlessly in the vast empirical aid‐growth 4
problem* = _β3 X + δ1 secretary* + δ1 council*+ λ1_ fire spot density+ _λ2 area +ε3_ with problem = 1 if problem* > 0 | problem = 0 otherwise

cov( _ε1_ , _ε2_ ) = ρ1 cov( _ε1_ , _ε3_ ) = ρ2 cov( _ε2_ , _ε3_ ) = ρ3 The first and second equations evaluate respectively the functional relationship between presence of environmental secretary (secretary) and presence of an active environmental council (council), against a number of explanatory variables. The set of explanatory variables consists of municipio population (the instrument), education and per capita income (reflecting the underlying demand for environmental quality), rural population share and biome classes.

The third equation then relates environmental problem perception (problem) to a common set of explanatory variables, excluding population size and including the presence of the secretary and the council. It lastly also contains fire spot density as an objective measure related to fire and smoke problem perception.

The probit model is estimated with a procedure developed by Terracol (2002) which uses the Geweke-Hajivassiliou-Keane (GHK) smooth recursive simulator.

All municipios (MCAs) are used for the analysis. This allows for accurate estimation and identification of the institutional presence equations, and tests whether absence of fires is related to non-perception of fires.

# **Estimation results**

We now turn to the regression results. Table 4 provides the summary statistics and Table 5 reports the output for the trivariate probit regressions for smoke and fire problem perception.

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Figure A3: PMT performance and survey quarter (N=4,500 per quarter) <!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>Undercoverage (%) Leakage (%) Eligibility (%) Poverty rate (%)<br>Q1 (N=4,500) Q1 (N=4,500) Q1 (N=4,500) Q1 (N=4,500)<br>Annual (N=4,500) Annual (N=18,000) Annual (N=21,756)<br><!-- End of picture text -->

# Figure A4: PMT performance and survey quarter (N=1,500 per quarter)

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>Undercoverage (%) Leakage (%) Eligibility (%) Poverty rate (%)<br>Q1 (N=1,000) Q2 (N=1,000) Q3 (N=1,000)<br>Annual (N=1,000) Annual (N=4,000) Annual (N=21,756)<br><!-- End of picture text --> Figure A5: PMT performance and survey quarter (N=250 per quarter) <!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>Undercoverage (%) Leakage (%) Eligibility (%) Poverty rate (%)<br>Q1 (N=250) Q2 (N=250) Q3 (N=250)<br>Annual (N=250) Annual (N=1,000) Annual (N=21,756)<br><!-- End of picture text --> 27
**Table 2: Trade policy uncertainty and trade - Baseline results extensive and intensive margins**

||(1)|(2)|(3)|(4)|(5)<br>|(6)<br>|
|---|---|---|---|---|---|---|
|VARIABLES|Pr|obability to ex|port||Logof export||
|Water|||-0.0581***<br>(0.0130)|||-0.470***<br>(0.112)|
|Tariff (log)|-0.545***|-0.444***|-0.560***|-3.538***|-2.982***|-3.607***|
||(0.0538)|(0.0496)|(0.0543)|(0.206)|(0.227)|(0.209)|
|Bound rate (log)||-0.131***<br>(0.0191)|||-0.670***<br>(0.159)||
|Observations|9,782,092|9,767,065|9,767,065|2,950,465|2,945,477|2,945,477|
|R-squared|0.376|0.376|0.376|0.340|0.340|0.340|
|Importer-Industry-|||||||
|2dig FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Exporter-Industry-|||||||
|2dig FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Country pair FE|Yes|Yes|Yes|Yes|Yes|Yes|

Robust SE in brackets clustered by country-pair. *** p<0.01, ** p<0.05, * p<0.1

**Table 3: Robustness to endogeneity – Sample of new acceding countries**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|VARIABLES|Pro|bability to ex|port||Logof exports||
|Water|||-0.495***<br>(0.0469)|||-4.840***<br>(0.343)|
|Tariff (log)|-0.879***<br>(0.124)|-0.371***<br>(0.0792)|-1.084***<br>(0.133)|-6.188***<br>(0.598)|-2.372***<br>(0.465)|-7.964***<br>(0.548)|
|Bound rate (log)||-0.836***<br>(0.0738)|||-5.815***<br>(0.418)||
|Observations|1,023,083|1,023,083|1,023,083|260,054|260,054|260,054|
|R-squared|0.367|0.371|0.369|0.339|0.341|0.341|
|Importer-|||||||
|Industry-2dig FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Exporter-|||||||
|Industry-2dig FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Country pair FE|Yes|Yes|Yes|Yes|Yes|Yes|

Robust SE in brackets clustered by country-pair. The estimates are based on the subsample of importers that acceded the WTO after 1995. *** p<0.01, ** p<0.05, * p<0.1 13
in 1997-98; (ii) E2, corresponding to entering in 1999-2001; (iii) E3, which relates to localities entering in 2002-2005; and (iv) E4, which identifies those localities entering after 2006. Early versus late enrollment comparison implies receiving more transfers, but if paired with an individual’s age it might also give different incentives based on the schooling conditionality, further for those individuals whose age is above the threshold for receiving PROSPERA’s transfers at the time their locality enters the program this also means that they will never be directly treated.

As poorer localities were in general treated first, we need to deal with the selection bias. Following Duflo (2001) and Parker and Vogl (2018), we begin by taking care of the selection problem by using different cohorts, which proxy an individual’s exposure to the program when interacted with the roll-out. Four cohorts are formed using individuals’ ages in 2001:

- (C1) 0-5 years, which corresponds to individuals who had not begun primary school when their locality enrolled, but if enrolled in 1997-98 they possibly benefited in early stages of their development.

- (C2) 6-10 years, which corresponds to individuals more sensitive to their locality’s date of enrollment because of the program’s conditionalities. For instance, if enrolled in the early groups (before 2002) they would still be in primary school when their locality entered the program. However, if enrolled later (e.g. 2006) they are likely to be in secondary.

- (C3) 11-16 years, which corresponds to individuals in critical schooling stages: if enrolled early (before 2002) they are likely in late primary or early secondary school years, but if enrolled late they are likely past this stage. Also this age range has a high likelihood of being employed by 2017-2018.

- (C4) 17-22 years, which is taken as a control group since regardless of year of enrollment they are past critical stages of schooling in terms of the conditionalities, as such they will never be directly treated.

The specification for the DiD estimation is: where _Yij_ is the 2015 outcomes for individual _i_ of locality _j_ , _Cki_ are the cohorts defined above, and _Eqj_ are the localities’ groups based on their year of enrollment (as defined above). Our parameters of interest are the _τ_ ’s, which correspond to the DiD estimates. For instance, in our results we will compare _τk,_ 1, _τk,_ 2, and _τk,_ 3, which give the effects of different levels of program exposure for a given cohort ( _k_ ). In this case, keeping as reference cohort C4 means that these _τ_ ’s control for selection bias using the reference cohort C4, which is not benefited by the program regardless of the enrollment date.

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# **Table 3. Women’s Empowerment and Gender Differences in Competitiveness**

Dependent variable =1 if participant chooses tournament in Round 3, =0 if instead chooses piece‐rate.

Linear probability model estimates in Columns 1‐3, Probit estimates in Column 4, standard errors reported in parentheses.

|||**LPM Estimates**||**Probit Estimates**|
|---|---|---|---|---|
||**(1) No**<br>**controls**|**(2) Experimental**<br>**controls**|**(3) Other**<br>**controls**|**(4) All**<br>**controls**|
|**Gender gap in control communities [**��**]**|.006|.037|.042|.045|
||(.056)|(.051)|(.051)|(.050)|
|**Program impact on boys [**��**]**|.016|.034|.034|.036|
||(.059)|(.050)|(.049)|(.048)|
|**Gender gap in treatment communities [**�����**]**|‐.047|‐.046|‐.047|‐.036|
||(.047)|(.043)|(.044)|(.048)|
|**Program impact on girls [**�����**]**|‐.037|‐.050|‐.054|‐.056|
||(.050)|(.044)|(.043)|(.042)|
|**Experimental controls**|No|Yes|Yes|Yes|
|**Other controls (socio‐demographics)**|No|No|Yes|Yes|
|**P‐value:**����|[.475]|[.206]|[.169]|[.135]|
|**R‐Squared**|.002|.178|.183|–|
|**Observations**|693|693|693|693|

**Notes:** *** (**) (*) indicates significance at 1% (5%) (10%) level. Each column corresponds to a separate regression. In all columns the dependent variable is a dummy equal to 1 if the participant chooses competitive tournament in round 3, and 0 if instead chooses uncompetitive piece‐rate. Linear probability model (OLS) estimates are reported in Columns 1‐3. Probit marginal estimates are reported in Column 4. Robust standard errors clustered by experimental session reported in parentheses (50 clusters). Column 1 only controls for a girl dummy, a treatment dummy, and their interaction. Column 2 additionally includes experimental controls: participant’s performance in round 1, improvement in performance from round 1 to round 2, a dummy equal to 1 if the participant believes to have ranked first during round 1 performance and 0 otherwise, and a dummy equal to 1 if the participant chooses to submit past performance in round 1 to tournament and 0 to piece‐rate. Columns 3 and 4 additionally control for participant‐level socio‐demographic characteristics: age, years of schooling, indicators for whether the participant is out of school, is married, and has children, total number of siblings, and number of sisters.
technologies, especially those where input fuel or energy can be stored (e.g., fossil fuels, nuclear, storage hydro power, biomass) are available when needed, intermittent resources such as solar, wind, and run of river hydro are not available as they vary hourly, daily and seasonally (differing plant availability factors). Second, even if a resource is available to produce electricity, it may not be used to meet the load because some other resources are more economical to produce power in a given hour (capacity utilization factor). Therefore, it would be more logical to compare different types of electricity generation sources or technologies based on the electricity they produce in a year. Based on the data maintained by the International Energy Association (IEA, 2017a), global electricity generation was 24,345 TWh in 2015, of which coal accounted for 39%, natural gas accounted for 23%, hydro accounted for 16%, nuclear accounted for 11%, and oil accounted for 4%. The remaining 7% was generated from renewable energy technologies including solar, wind, biomass, and geothermal (See Figure 3).

<!-- Start of picture text --> Renewables, 1,724,<br>7%<br>Hydro, 3,978, 16%<br>Coal, 9,538, 39%<br>Nuclear, 2,571,<br>11%<br>Natural gas, 5,543,<br>23%<br>Oil, 990, 4%<br><!-- End of picture text -->

**Figure 3. Global electricity generation mix in 2015 (TWh, %)**

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