rafmacalaba/datause-encoder-data huggingface dataset:
This is an important point as the _methodologically consistent_<sup>procedure for</sup> fixing the non-food basket does not produce a _welfare_<sup>_consistent_ranking. That is,</sup> we know from basic consumer theory that method I, using<sup>a Laspeyres index of food</sup> and non-food inflation rates based on actual consumption<sup>shares of the poor, should</sup> _overstate_ the welfare impact of those at the poverty line because<sup>it does not allow for</sup> consumers response to changing relative prices by changing their<sup>consumption</sup> patterns. As is well known, the Laspeyres index will exceed<sup>the "exact" inflation</sup> rate from an expenditures function since the increase in the amount<sup>of money</sup> expenditures needed to reach the same level of utility is lower<sup>when one allows for</sup> substitution across commodities (e.g. buying relatively<sup>less of items whose prices</sup> increased).

Method II (using the poverty basket food share) also overstates<sup>poverty</sup> increases, not only (as in Method I) because it does not allow<sup>substitution but also</sup> because it does not use the actual consumption bundle of<sup>households at the poverty</sup> line. But method II is perhaps defensible as the food weight in<sup>the price index</sup> represents the actual consumption pattern of some group in poverty<sup>(although a</sup> group considerably below the poverty line).

However, poverty line inflation rates, such as that of method III,<sup>that expand</sup> the 1996 inflation line to 1999 by more than the amount<sup>of method II are creating a</sup> poverty line at which the welfare of those at the poverty line in<sup>1999 is</sup><sup>_higher,_</sup> perhaps substantially higher, than those at the poverty line<sup>in 1996. The repetition of</sup> difference in the percentage points of poverty between the<sup>two methods is: 0.2*79*0.4*0.63=3.98,</sup> which is quite close to the actual difference between the two methods<sup>in table 4 of 20.33-16.27=4.06.</sup> 20
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to the realization, but that fail to foresee the precise economic growth of a country. In recent years, evidence shows a wide gap in inaccuracies of a variety of forecasters in predicting the economic growth of several countries (Figure 1).

<!-- Start of picture text --> Panel A. Concurrent-Year Forecast Horizon Panel B. Next-Year Forecast Horizon<br>18 18<br>12 12<br>6 6<br>0 0<br>-6 -6<br>Releasing period (month) Releasing period (month)<br>90th percentile Median 10th percentile<br>GDP growth HFE GDP growth HFE<br>(percentage points) (percentage points)<br>Jan 2005 Jul 2006 Jan 2008 Jul 2009 Jan 2011 Jul 2012 Jan 2014 Jul 2015 Jan 2005 Jan 2007 Jan 2009 Jan 2011 Jan 2013 Jan 2015<br><!-- End of picture text --> FIGURE 1. HISTORICAL FORECAST ERRORS OF REAL GDP GROWTH RATES FOR A VARIETY OF COUNTRIES Source: Consensus Economics, Global Economic Prospects, and World Development Indicators. Note: Monthly observations over January 2005–August 2015 period. Forty-one economies with real GDP growth forecasts are considered: Argentina, Australia, Bulgaria, Brazil, Canada, Switzerland, Chile, China, Colombia, Czech Republic, Germany, Euro area, Spain, Estonia, France, the United Kingdom, Hong Kong SAR-China, Croatia, Indonesia, India, Italy, Japan, the Republic of Korea, Lithuania, Latvia, Mexico, Malaysia, Netherlands, Norway, New Zealand, Peru, Poland, Romania, the Russian Federation, Singapore, Slovak Republic, Sweden, Thailand, Turkey, the United States, and Venezuela. Historical forecast error is defined as predicted minus realized value. A positive HFE value indicates overprediction. The next-year horizon historical forecast errors (Panel B) shows that the country growth point forecasts made during January 2007–December 2008 were mostly The most likely outcome—mode—of world GDP growth is taken from predictions made in a structural model that includes inputs from experts on country economic growth (World Bank 2016). However, note that the looking-forward conditional variance and skewness of world GDP growth are assumed not observed. The ex-ante information on GDP growth and its risk factors uses forward-looking information covering the scope of annual predictions spanning the period of 2005–2017. The BCEF method recovers predictive densities of global growth over the full monthly period of October 2005–August 2015 with three annual horizons: 17
participation of the training in the control group does not lead to an underestimation of program impacts.

# **5. Main Results: Impacts on Self-Employment in the short and medium-term**

Table 2 provides the main findings on impacts on self-employment in the short and medium run. The main outcomes include self-employment during the last 7 days (Panel A) and self-employment history between graduation and endline (Panel B). Based on an employment history module included in the medium-term follow-up survey, a binary variable is constructed to capture whether respondents were self-employed at any time during 6-months spells between 2011 and 2013 (Table 2, panel B).

Estimates in Panel A show that entrepreneurship education led to a small increase in selfemployment among program participants approximately one year after graduation (in 2011). The ITT estimate for self-employment during the last 7 days shows a 3 percentage point increase in the probability of being self-employed. For those students who completed the entrepreneurship track (education and coaching), the LATE estimate reveals a 4 percentage point increase in the likelihood of being self-employed in any activity in the last week. Since the rate of selfemployment is low in the control group, these small absolute effects lead to relatively large effect sizes. In fact, a 3 percentage point increase in self-employment is equivalent to a 68 percent increase over the self-employment rate in the control group. These effects on self-employment are slightly weaker when excluding self-employment activities that do not operate year-round (column (2)).

The small positive effects observed in the short-term are not sustained in the longer term. When considering similar outcomes from the endline survey collected for years after graduation (in 2014), ITT and LATE estimates show that there are no lasting impacts from the entrepreneurship track on self-employment among graduates.<sup>15</sup>

> 15 When testing equality of the 2011 and 2014 treatment effects with respect to self-employment, we cannot reject that the impacts are equal for both years. Still, the only significant results are for 2011. In addition, most of the intermediary outcomes are not significant any more in 2014, providing a consistent set of results pointing to no lasting impacts, as further described below.

14
13 decile to 47.5 percent in the tenth decile. The highest increase is in the last decile with 47.5 percent within the seven years of this study. Overall, mean per capita expenditures increased by 34 percent over the seven year period.

However, overall income distribution as shown by the Gini coefficient worsened slightly. In 1985/86 the Gini coefficient was 0.387, the value increased to 0.45 in 1992/93. The table also shows that in 1985/86 there were 35.8 million people (or 43 percent) in poverty as defined by households in which individuals live on less than N 395 per annum or almost a dollar a day. In 1992/93 the population in poverty was 34.7 million individuals or 34.1 percent.

**Table 4.1: Distribution of Per Capita Expenditure by Population Deciles (Constant 1985/86 Naira Per Annum)**

|**Decile**|**1985/86 PCE**|**1992/93 PCE**|**Percentage Change**|
|---|---|---|---|
|First five percent|118.11|70.24|_-40.5_|
|Second five percent|175.22|140.5|-19.8|
|Second Decile|229.47|210.94|-8.1|
|Third Decile|289.98|304.58|5.0|
|Fourth Decile|351.86|404.13|14.9|
|Fifth Decile|421.40|505.68|20.0|
|Sixth Decile|512.03|633.59|23.7|
|Seventh Decile|624.13|806.09|29.2|
|Eighth Decile|769.02|1050.90|36.6|
|Ninth Decile|998.29|1424.91|42.7|
|Tenth Decile|1688.69|2489.99|47.5|
|Mean PCE|592.81|792.64|33.7|
|Moderate Poverty Line|395.41|395.41||
|(2/3 mean PCE in 1985)||||
|Core Poverty Line|197.71|197.71||
|(1/3 mean PCE in 1985)||||
|Moderate Poverty(%)|31.0|20.5|-10.5|
|Core Poverty (%)|12.0|13.6|+1.6|
|Non Poverty(%)|57.0|65.1|+8.1|
|Gini Index|0.387|0.450|16.0|

Source: NCS, 1985/86 and 1992/93.

Table 4.2 shows the distribution by decile of mean per capita household food expenditure. Except for the bottom two deciles, all others indicate an increase in real food expenditures over the seven year period. Overall, there was 41.7 percent increase in mean food expenditure compared to a 34 percent increase in mean per capita total expenditure.

Table 4.1 shows mean per capita expenditure by deciles in both periods. It is clear from the table that except for the first two deciles, incomes have increased in all deciles from 5.0 percent in the third decile to 47.5 percent in the tenth decile. Although the Average per capita expenditure (PCE) has increased by 34 percent nationally, it is important to note that income distribution has worsened with a Gini coefficient of 0.387 to 0.45. Figure 4.1 shows that the proportion of total expenditure by each decile for both periods. It is notable that every decile has a lower share in 1992/93 compared to 1985/86, except the tenth decile. This is also reflected in
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Before turning to the results on trustworthiness in column 6, it is worth mentioning two interesting features of the results in column 5. First, there are stark differences in the behavior of the poor and non-poor in the amount sent in the first round of the trust game. Non-poor subjects sent roughly 809 riels or a little over one-quarter of their endowment. Poor subjects sent only 524 riels or a little over one-sixth of their endowment. Thus nonpoor subjects sent over 50 percent more, on average, than did poor subjects. It is not clear, if this difference between poor and non-poor is due to greater trust by the non-poor subjects or a greater sense of altruism. The amount sent in the first round of the trust game may be confounded by altruism—the “investor” may be motivated at least in part by a desire to send a gift to his or her trustee. The fact that non-poor sent more in the altruism game (as shown in column 3) suggests that greater altruism may have played a role in this difference. Indeed, our altruism measure is highly correlated in a regression model with trust and trustworthiness.<sup>14</sup> Of course including or excluding altruism in these models leaves the effect size and standard errors associated with the LEAP program unchanged.

A second interesting feature of the trust models in column 5 is that the amount sent by the investor is quite low compared to standard results in the trust-game literature. Johnson and Mislin (2011) provide a meta-analysis of over 200 trust-game studies that were conducted over dozens of countries. They found that on average subjects in the “investor” role sent about half of their endowment. The subjects in this study sent only about 20 percent of the endowment on average. Even the non-poor subjects sent only a little over one-fourth of their endowment. Thus at least according to these results the subjects in our study appear to be considerably less trusting than subjects in previous trust-game studies conducted around the world. In principle, then, there is room for LEAP to improve levels of trust.

Turning to the effects of the program on trustworthiness in column 6, again the program did not produce significant increases in trustworthy behavior in the full sample or either of the two subsamples. In this case there is little concern over low-powered tests, because the

> 14 Altruism strongly correlates with trustworthiness – both unconditionally and conditional on the amount received in the first place.

20
institutions could affect the investment decisions on charging stations. A similar strategy is used in Corts (2010) to study the investment decision of flex-fuel charging stations. The identification assumption is that the lagged institutional EV stock is unlikely to be correlated with concurrent demand shocks to individual buyers. Our results hold when we use the second lag of the institutional EV stock.

To address potential policy endogeneity due to unobserved local demand shocks, we use a cityborder regression design, similar in spirit to studies such as Holmes (1998); Dube et al. (2010); Hagedorn et al. (2015); Barwick et al. (2017). The design exploits the fact that local policies change sharply across city borders, but other demand factors such as transportation costs, access to dealer stores, and consumer preference are likely to be similar among neighboring cities.<sup>32</sup> In practice, we group adjacent cities into a cluster of multiple cities. Among the 150 cities, we create 34 clusters with each cluster having 4.4 cities. Each cluster includes at least one city that is among the top 40 EV cities (e.g., a major city). To control for time-varying local unobservables common in each cluster, we include cluster-time fixed effects, and cluster-brand-year fixed effects. Cluster-brand-year fixed effects control for demand shocks that are specific to each brand in a cluster for example due to the change in dealer presence or consumer preference for a brand among the cities in a cluster. In a robustness check, we restrict the clusters to include only bordering cities with average household income less than 20% different from the major city in the cluster. This leaves 31 clusters with 106 cities. The results are very similar to those with 150 cities.

# **4 Estimation Results**

We first present estimation results for the main specifications and then results from robustness checks.

## **4.1 Parameter Estimates**

Table 4 reports the estimation results of the demand model in Equation (1) from eight specifications. All the regressions include consumer price, the total number of charging ports, the dummy variable for exemption from driving restriction, the dummy variable for green plate policy, and three key vehicle attributes: vehicle size (length by width), power over weight, and

> 32 Subsidies are tied to the city of residence.

19
# Figure 5. Evolution of global GDP: 2007:3–2009:4

<!-- Start of picture text --> A. Role of credit shock<br><!-- End of picture text --> <!-- Start of picture text --> 5<br>0<br>-5<br>-10<br>-15<br>-20<br>Actual Counterfactual<br>-25<br>2007:3 2007:4 2008:1 2008:2 2008:3 2008:4 2009:1 2009:2 2009:3 2009:4<br><!-- End of picture text --> <!-- Start of picture text --> B. Contributions of credit and productivity shocks<br>2.5<br>2.0<br>Credit<br>1.5 Productivity<br>1.0<br>0.5<br>0.0<br>-0.5<br><!-- End of picture text --> 2007:3 2007:4 2008:1 2008:2 2008:3 2008:4 2009:1 2009:2 2009:3 2009:4 Source: Helbling et al (2011).

Note: Panel A compares the results of counterfactual simulations for the global GDP factor during the GFC. The solid line represents the actual global GDP factor and the dashed line represents the counterfactual when the global credit shock is set to zero during the period considered. Panel B compares the contributions of credit and productivity shocks to cumulative global GDP growth based on the counterfactual simulations. The bars represent the median difference. A positive (negative) bar captures how the decrease in the global GDP factor would have been smaller (greater) in the absence of the respective shocks. Credit is defined as the aggregate claims on the private sector by deposit money banks and is obtained from the IFS. Labour productivity is defined as real GDP per hours worked and is obtained from the OECD.

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- 29 -

# Monetization

4.8 If the Government were to attempt to monetize the residual public sector borrowing requirement, given the level of private investment, domestic inflation would be 8.2 percent in 1996 after reaching levels of **8.1** percent in 1992 and 10.1 percent in 199 1(Table **IV)-** In the base projections, domestic inflation is 5.0 percent in 1994 and 1996, and 9.5 percent in 1991.

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In the model higher inflation increases nominal values of the domestic interest rates, the exchange rate and GDP growth (at factor costs) but leaves their real values unchanged. In contrast, the demand for currency, demand deposits and quasi-deposits all fall as inflation rises, given a fixed real deposit interest rate and the fixed real growth of GDP at factor cost. Since higher inflation causes the stock of high powered money to fall as a share of GDP but leaves the net foreign assets position and government's demand for credit unchanged, the credit to the private sector must contract to maintain macroeconomic consistency. Indeed, Table IV shows the stock of credit to the private sector at 25.1 percent of GDP in 1996 compared with 26.1 percent of GDP in the base projections.
Table N10: Effect of SNAP-benefit changes on prices and real sales, drug and merchandise stores

|Store Type|(1)|(2)<br>Dru|(3)<br>g|(4)|(5)|(6)<br>Mercha|(7)<br>ndise|(8)|
|---|---|---|---|---|---|---|---|---|
|Specifcation|OLS|IV|OLS|IV|OLS|IV|OLS|IV|
|VARIABLES|Log pric|e index|Log rea|l sales|Log price|index|Log re|al sales|
|Log benefts per population|0.00113|0.0250|-0.0155|0.198*|0.00567|0.0311*|0.00950|-0.0646|
||(0.00651)|(0.0251)|(0.0363)|(0.113)|(0.00408)|(0.0175)|(0.0236)|(0.0754)|
|Observations|445467|445467|445467|445467|385527|385527|385527|385527|
|R-squared|0.912|0.912|0.977|0.976|0.915|0.914|0.991|0.991|
|Prob _>_ F|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|Number of units|9281|9281|9281|9281|8032|8032|8032|8032|
|Number of clusters|48|48|48|48|49|49|49|49|
|First stage F-stat||21.167||21.167||14.903||14.903|

Notes: Robust standard errors are in parentheses, clustered by state. Control variables as well as store and *** ** * period fixed effects are included. p _<_ 0.01, p _<_ 0.05, p _<_ 0.1.

Table N11: Effect of SNAP-benefit changes on grocery store firm dynamics and market structure

|Sample|(1)|(2)|(3)<br>SNAP st|(4)<br>ores|(5)|(6)<br>All st|(7)<br>ores|
|---|---|---|---|---|---|---|---|
|VARIABLES|Authorization|Entry|Exit|Reallocation|Log count p.p.|Log est. p.p.|HHI|
|Log benefts p.p.|0.0319|0.0525|0.0481|0.101**|0.0778|-0.0237|0.00716|
||(0.0330)|(0.0362)|(0.0430)|(0.0487)|(0.0697)|(0.0817)|(0.00733)|
|Observations|149178|149178|149178|149178|144342|146562|146562|
|R-squared|0.497|0.438|0.398|0.526|0.978|0.968|0.980|
|Prob _>_ F|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|Number of units|3108|3108|3108|3108|3023|3070|3070|
|Number of clusters|49|49|49|49|49|49|49|
|First stage F-stat|22.822|22.822|22.822|22.822|22.878|22.846|22.846|

Notes: Robust standard errors are in parentheses, clustered by state. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Control variables as well as county and period fixed effects are included. Authorization refers to the authorization rate, that is, the number of newly authorized stores per month divided by the total number of SNAP-authorized grocery stores in each county. Entry and exit refers to the entry and exit rates, that is, the number of entering and exiting stores in a particular year divided by the total number of SNAP-authorized grocery stores in each county. Reallocation is the sum of entry and exit rates. Log count p.p. refers to the log of the total number of SNAP-authorized grocery stores per population in each county. Log est. p.p. refers to the log of the total number of grocery establishments per population in each county. HHI refers to a county-level measure of HHI, using the number of employees as the measure of firm size for the grocery industry.

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