rafmacalaba/datause-encoder-data huggingface dataset:
|**Table 8**|**. Objective Finan**|**cial Access Variables**|**by Firm Characteris**|**tic**|
|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|
|**Variable**|**Loan**|**Credit constraint**|**Sales credit**|**External finance**|
|Small|0.467***|-0.412***|0.187***|0.338***|
||(0.041)|(0.043)|(0.035)|(0.030)|
|Medium|0.779***|-0.721***|0.320***|0.572***|
||(0.063)|(0.068)|(0.049)|(0.038)|
|Large|0.973***|-0.852***|0.427***|0.747***|
||(0.072)|(0.073)|(0.078)|(0.045)|
|Mature|0.097**|-0.063*|0.055|0.023|
||(0.040)|(0.035)|(0.055)|(0.037)|
|Older|0.287***|-0.200***|0.079|0.034|
||(0.066)|(0.057)|(0.063)|(0.048)|
|Multi|0.032|-0.128***|0.023|0.007|
||(0.051)|(0.036)|(0.047)|(0.048)|
|Manuf|-0.089|0.196***|-0.031|-0.015|
||(0.062)|(0.048)|(0.058)|(0.048)|
|Exporter|0.412***|-0.261***|0.084*|0.204***|
||(0.064)|(0.053)|(0.049)|(0.044)|
|Foreign|-0.101|-0.024|-0.026|-0.157***|
||(0.070)|(0.049)|(0.050)|(0.043)|
|Govt|-0.361***|0.199**|0.042|-0.118|
||(0.119)|(0.082)|(0.099)|(0.094)|
|Constant|-0.325***|-0.071|0.070|-1.039***|
||(0.104)|(0.091)|(0.063)|(0.070)|
|Number of observations<br>* Significant at the 10 percen<br>_Source:_Authors' estimations<br>_Note:_Standard errors (in par<br>Model:_FC_=_b_0 +_b_1Small<br>_b_9Foreign +_b_10Govt +_e_.|34,916<br>t level. ** Significa<br>based on data fro<br>entheses) are rob<br>+_b_2Medium +_b_3|35,663<br>nt at the 5 percent level.<br>m World Bank Enterpris<br>ust to heteroskedasticity<br>Large +_b_4Mature +_b_5|36,746<br>*** Significant at the 1<br>e Surveys (2006-10).<br>and clustered on cou<br>Older +_b_6Multi +_b_7|36,746<br>percent level.<br>ntries.<br>Manuf +_b_8Exporter +|

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Table A12: Relationship between energy management and energy intensity in value terms (split by energy intensity for manufacturing and knowledge intensity for service firms).

||(1)<br>High EI|(2)<br>Moderate EI|(3)<br>Low EI|(4)<br>High KI|(5)<br>Low KI|
|---|---|---|---|---|---|
|Energy|-0.015|0.053|-0.002|-0.049|0.568|
||(0.460)|(0.199)|(0.243)|(0.263)|(0.467)|
|General|-0.346|0.060|0.159|0.220|-1.283<sup>_∗_</sup>|
||(0.884)|(0.333)|(0.358)|(0.358)|(0.737)|
|No. Employees|0.860<sup>_∗∗_</sup>|0.910<sup>_∗∗∗_</sup>|1.052<sup>_∗∗∗_</sup>|1.261<sup>_∗∗∗_</sup>|1.044<sup>_∗∗_</sup>|
||(0.420)|(0.249)|(0.306)|(0.234)|(0.419)|
|Capital Stock|0.529<sup>_∗∗∗_</sup><br>(0.195)|0.333<sup>_∗∗∗_</sup><br>(0.088)|0.447<sup>_∗∗∗_</sup><br>(0.132)|||
|Log(Sales)|-1.534<sup>_∗∗∗_</sup>|-1.732<sup>_∗∗∗_</sup>|-1.673<sup>_∗∗∗_</sup>|-1.972<sup>_∗∗∗_</sup>|-1.619<sup>_∗∗∗_</sup>|
||(0.310)|(0.173)|(0.280)|(0.194)|(0.313)|
|3-digit sector|✓|✓|✓|✓|✓|
|Age|✓|✓|✓|✓|✓|
|Country|✓|✓|✓|✓|✓|
|Observations|584|1684|656|1293|754|
|Adjusted _R_<sup>2</sup>|0.190|0.236|0.286|0.188|0.444|
|df<br>~~m~~|44|47|54|44|53|

Standard errors in parentheses Capital, labor, and output enter in logs. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 54
Australia (-0.045%). The impact of a negative Chinese real GDP shock is less marked for India (-0.018%). Interestingly, for the Philippines, the impact of a negative Chinese shock is positive.

In the last three panels of Figure 3, we display the GIRFs for a one percent decline in China’s GDP on three commodity price indices. China’s increasingly commodity-intensive growth path manifests itself in the increasingly negative impact of Chinese negative growth shock on commodity prices. Similar to the GIRFs observed above, the impact of Chinese negative GDP shock is considerably more visible under the 2013 trade matrix structure. China is the world’s largest consumer of industrial metals (-1.33% drop in a long term) and the second-largest consumer of oil (-1.14%). China’s impact on agricultural prices rose significantly after year 2000, although the impact on prices in both the short-term and the long-term is much more muted compared to the impact on oil and metal prices.

Figure 4 shows the GIRFs for a one percent decline in China’s real GDP growth rate using the trade weights of 2013 at a 68% confidence interval using a bootstrapping method. The figures show that the GIRFs of US, Euro, Japan, Indonesia, the Republic of Korea, Malaysia, Singapore and Thailand are significant at a 68% confidence interval.

Figure 5 compares the distribution of GIRFs with different trade weights. When we use the trade weight matrix of 1985, the distribution of GIRFs stays close to the zero line for all of the sample countries. However, with 2013 trade weights, we observe significant downward shifts for the US, the Eurozone, Japan, Indonesia, Malaysia, Singapore and Thailand. For Australia, India and the Republic of Korea, the dominant portion of the distribution has also shifted downward. However, for the Philippines and New Zealand, the effect of a Chinese shock is not very clear. We have summarized the patterns in Table 8.

Lastly, we also note that oil, metal and agricultural price indices are consistently all negatively significant.

What is the response of other domestic variables? Figures 6 and 7 report the GIRFs of three other variables, _Dp_ (rate of inflation), _eq_ (real equity price), and _reer_ (real effective 26
Notably, poverty status was not associated with survey completion. An important contributor to nonresponse over the course of the survey period seems to have been differential network coverage between geographic areas and disruption to network coverage within geographic areas over time. Having multiple options to call respondents by recording an alternative contact number helped significantly. This suggests that survey designs that use existing household mobile phones might yield higher response rates than survey designs that hand out mobile phones. The downside of this strategy is representativeness in areas with low household ownership of mobile phones.

Larger incentives in the form of pre-paid calling credit did not work to encourage survey completion. In fact, participation rates were slightly lower for those who received the greater incentive. The underlying mechanism of this result is not clear. Given that the design of the study did not incorporate a group without an incentive payment, it is not possible to say whether offering some incentive increased participation versus offering no incentive at all.

In terms of implementation, a critical feature of this study was partnering with the National Bureau of Statistics. The NBS provided the staff, knowledge and experience that enabled the sample selection and successful distribution of phones. Being able to tap the NBS’s experience in collecting data in South Sudan—given obvious logistical and operational challenges in the country—was a tremendous asset to the SSEPS project.

Overall, the SSEPS experience is encouraging as to the potential for mobile phone-based surveys while at the same time offering a cautionary tale about the potential hurdles to the approach. In particular, variability in the functioning of the mobile network, which was not anticipated by the project team beforehand, turned out to be a substantial problem. At the same time, selection due to non-response turned out to be less of a concern than expected, as it does not appear to have markedly affected survey results in this case. The overall success of the project in an extremely difficult environment suggests that further efforts at mobile data collection in developing countries are worth exploring.

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The results of such shifts suggest the following improvements in health indicators and, while data on impact is sparse, the implication is that similar shifts away from military spending and into social development would have similar impacts:

- 1) If for example, the quality of the bureaucracy were to improve from 1 to 3.7, results from Rajkumar and Swaroop (2002) predict that a 10% increase in health expenditure could lead to a 4.5% reduction of under-five mortality.  In our case, the hypothesized increase of 33.5% in public health expenditures would lead to a 15% decline in under-five mortality (from 89 deaths per 1000 live births to 76 (WDI, 2006)).

- 2) If the CPIA were to improve from 2.5 to 3.25, results in World Bank (2004) predict that a 10% increase in health expenditures could lead to a 6.95% decline in maternal mortality ratio.  Thus, a 33.5% increase in public health expenditures could conceivably lead to a 23% reduction in maternal mortality ratio (from 450 deaths per 100,000 live births to 346.5, (WDI, 2005)).

These predictions are out of sample estimates and must be used with caution. Still, it is not unreasonable to assume that 25% of the unexplained military spending could be diverted to productive development activities and realize the improvements above. These numbers demonstrate how little movement is necessary by both actors to find a more productive peace and the very real opportunity cost in terms of human development foregone to ongoing, high levels of military expenditure.

# **5.  Extensions:  Changes to the Payoffs**

While many of the parameters are given by the CPA, such as oil-revenue sharing arrangements and the nature of the referendum, not all of the payoffs are set in stone.

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using the Washington Group Short Set on Disability.<sup>11</sup> Sharing a home is common, likely as an economic coping mechanism, with on average of 1.3 families living in the same house. Housing quality is low, with most houses in some state of disrepair or incomplete construction.

Table 1 also illustrates descriptive statistics for the treatment versus control households to assess balance across groups, where the third column presents a mean difference test. Respondents across the study arms are largely balanced by age (with an average age of 34 years), marital status (84% married), and incidence of a disability (Panel A). There is a slight imbalance in the share of respondents who are female, with the proportion slightly higher in the control group. Moreover, average household size is just above five members, with nearly three children on average, and these are balanced between treatment and control, as is the number of families per residential unit (Panel B). Refugee households in the sample face challenging and precarious housing conditions, but these characteristics are generally balanced across groups (Panel C). For example, only 66% of households have access to piped water, 22% have functional windows, and 44% completed floors. While the majority of refugees plans to stay in the same housing unit (92%), a large share have moved shelter in the last year (with an average of 0.44 and 0.47 moves in the control and treatment groups, respectively).

Across all baseline characteristics presented in the table, the p-value on the hypothesis that all differences are zero is 0.102 (using a Chi-squared test), not quite significant at traditional confidence levels. This finding together with the fact that there are few economically meaningful differences across treatment arms in terms of respondent, household and shelter characteristics – out of the 18 covariates examined only two exhibit a statistically significant difference at 95% confidence – indicates that the study randomization generated largely comparable groups of households.

> 11We use the recommended cut-off (level 3) to classify respondents as being disabled or not based on six domains: seeing, hearing, walking, cognition, self-care, and communication.

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PAYING THE PRICE FOR UNRELIABLE POWER SUPPLIES: GENERATION OF ELECTRICITY BY PRIVATE FIRMS IN AFRICA

**Table 4  The comparative costs of self-generated and publicly supplied electricity, and the effect of  own generation on the marginal cost of electricity, in Africa**

|Country|Average<br>variable cost<br>of own<br>electricity (A)|Average capital<br>cost of own<br>electricity (B)|Average total<br>cost of own<br>electricity<br>(C=A+B)|Price of kWh<br>purchased from<br>public grid (D)|Weighted<br>average cost of<br>electricity<br>(E=∂C+(1-∂)D)|
|---|---|---|---|---|---|
|Algeria|0.04|0.11|0.15|0.03<br>*|0.05|
|Benin|0.36|0.10|0.46|0.12<br>*|0.27|
|Burkina Faso<br>†|0.42|0.32|0.74|0.21<br>*|0.23|
|Cameroon<br>†|0.41|0.04|0.46|0.12<br>*|0.16|
|Cape Verde<br>†|0.46|0.04|0.50|0.17<br>*|0.26|
|Egypt, Arab Rep.|0.04|0.26|0.30|0.04<br>*|0.12|
|Eritrea|0.11|0.03|0.13|0.11|0.12|
|Kenya|0.24|0.06|0.29|0.10|0.14|
|Madagascar|0.31|0.08|0.39|—|—|
|Malawi|0.46|0.03|0.50|0.05<br>*|0.09|
|Mali|0.26|0.26|0.52|0.17|0.21|
|Mauritius|0.26|0.35|0.61|0.14<br>*|0.25|
|Morocco|0.31|0.32|0.62|0.08<br>*|0.15|
|Niger<br>†|0.36|0.04|0.41|0.23<br>*|0.26|
|Senegal|0.25|0.09|0.34|0.16|0.18|
|Senegal<br>††|0.28|0.40|0.68|0.16|0.30|
|South Africa|0.18|0.36|0.54|0.04|0.05|
|Tanzania|0.25|0.04|0.29|0.09|0.13|
|Uganda|0.35|0.09|0.44|0.09|0.14|
|Zambia|0.27|0.18|0.45|0.04|0.06|

† Tourism industry (hotels and restaurants sector) only.

†† Survey of informal sector ∂ Share of total electricity consumption coming from own generation

- Data not reported in the enterprise surveys (obtained from the public utilities).

— = data not available.

The second column of table 4 reports the first term of equation (4), the estimated average capital cost of self-generated electricity, adjusted by the frequency of power outages. As might be expected, the average capital cost of self-generated electricity is higher in countries with a reliable power supply (Egypt, Mali, Mauritius, Morocco, South Africa), and in the informal sector, which uses inefficient lowcapacity generators (Senegal).

The third column of table 4 shows the average total cost of self-generated electricity, calculated as a sum of the average capital cost and the average variable costs. Overall, the average total cost of selfgenerated electricity is about five times the price of electricity purchased from the public grid and can be as much as ten times more (in Malawi, South Africa, and Zambia). Eritrea is the only country in which the average total cost of self-generated electricity is comparable to the price of purchasing electricity from the public grid.

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Estrades, Maliszewska, Osorio-Rodarte, and Seara E Pereira. (2021) Figure 11. Output in volume, percentage change relative to business-as-usual scenario, 2035 <!-- Start of picture text --> 2 2.5<br>2<br>1.5<br>1.5<br>1<br>1<br>0.5<br>0.5<br>0<br>0<br>-0.5 -0.5<br>Tariffs alone RCEP ROO liberalization Productivity kick<br>Productivity kick (%)<br>Tariffs alone, RCEP, and rules of origin liberalization scenario (%) Wearing apparel and leather Textiles Electrical equipment and machinery Other transport equipment Petroleum, coal products Chemical, rubber, plastics Motor vehicles and parts Tourist services Metals Non metallic minerals Air transport Other extraction Wood and paper products Meat products Food and beverages Crops Other manufactures Utilities Fossil fuels Trade Water transport Financial services and insurance Road transport Construction Animal products Business services Communications Basic pharmaceuticals Public administration and services Total agriculture Total natural resources Total manufacturing Total services Total<br><!-- End of picture text --> Source: World Bank staff calculations using ENVISAGE model The top expanding sectors by countries differ, as well as the most negatively impacted sectors (see Table 3). In some countries such as Japan, Australia and New Zealand, Korea, and Lao PDR, the increase in output in some sectors drives resources from other sectors, which contract. Also, in some countries the contraction in output is related to the expansion of the sectors in other countries. This is for example the case of textiles, which expand significantly in China, Japan, and Korea, and contract in Australia and New Zealand, Lao PDR, Philippines, and Thailand. In Australia and New Zealand and Thailand, agriculture and food products are among the sectors that expand the most, while in Malaysia and Philippines, some services sectors are among the top expanding sectors. In the rest of the countries, the sectors that expand the most are manufacturing sectors. These results are found in the full scenario with the productivity kick. In the other scenarios, the impact on output by sector is smaller, and the ranking of most affected sectors by country differs.

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# **Table 4. COVID-19 impacts on the likelihood of being employed across different employment types**

||Recent entry<br>|into labor markets<br>|
|---|---|---|
||Recent entry into<br>employment<br>(1)|Recent entry into<br>unemployment<br>(2)|
|**Panel A: Female**|||
|COVID|0.003***<br>(0.001)|-0.003***<br>(0.001)|
||[0.019]|[0.000]|
|Pre-pandemic average|0.025|0.010|
|**Panel B: Male**|||
|COVID|-0.002*|-0.002**|
||(0.001)|(0.001)|
||[0.151]|[0.053]|
|Pre-pandemic average|0.029|0.012|
|Test:βm= βf(p-value)|0.000|0.249|

Notes: ***, ** and * denote significance at 1%, 5% and 10%, respectively. Parentheses indicate standard errors, while square brackets indicate the False Discovery Rates (FDR). Data are taken from Sakernas 2016–2020 (both February and August rounds). The main variable of interest in the above results is the COVID dummy, which takes on the value of 1 if the time period is August 2020. Pre-pandemic averages are population averages of the employment outcomes throughout 2016–2019 (both February and August rounds). Regressions are of the 15–64-year-old population, consisting of 1,614,217 male and 1,640,149 female individuals. Control variables include age, squared age term, marital status, household size, education level dummies, training experience dummy, and urban/rural dummy. All specifications include year, province and month fixed effects.

# **_6.2 Effects on Job Quality_**

First, we look at how the COVID-19 pandemic affects the likelihood of being in formal and informal employment by sector. Table 5 distinguishes formal and informal employment in the primary (agriculture), secondary (industry), and tertiary (services) sectors. The six columns sum to the total share of employed individuals. Generally, the results suggest that both women and men are more likely to shift towards informal employment, as seen by the significantly positive net effects on informal employment across all sectors. However, the sectoral shifts are somewhat different between men and women. Women have a higher likelihood of turning to informal agriculture and services, while men are more likely to turn to informal industries (the differences between women and men are statistically significant at the 10 percent level). This shift from formal 31
# **5.1 Graphical Illustration**

We first use graphical illustrations to build intuition (see Appendix Section J). We first model a shift in demand from a set of homogeneous (representative) consumers receiving a transfer in partial equilibrium. The equilibrium price rises in a perfect-competition setting if the supply curve is upward sloping or in a monopoly setting. The price rise shifts a portion of the welfare gains afforded by the transfer from consumers to producers.

Next, we allow for SNAP and non-SNAP consumers. Under perfect competition and upward-sloping supply, we show that SNAP-consumer surplus increases from increasing SNAP benefits, and non-SNAP-consumer surplus decreases because of higher prices. Producer surplus increases because of higher prices. Deadweight loss arises as a result of SNAP consumers buying marginal units they value at less than the marginal cost for producers. Under monopoly and constant marginal cost, SNAP-consumer surplus increases, non-SNAP-consumer decreases, and producer surplus increases. Deadweight loss now results from the monopolist further restricting output to non-SNAP consumers, which may be partially or more than offset by a decrease in deadweight loss when the monopolist sells more to SNAP consumers. Hence, the change in deadweight loss is theoretically ambiguous.

# **5.2 Pass-through Formulas**

We extend upon the unit-tax pass-through formulas derived in Weyl and Fabinger (2013). Our extensions allow for analyses of price and quantity responses to a heterogeneous demand shock (some consumers receiving a demand shock but not others). The results allow us to gain intuition over the mechanisms of the pass-through using sufficient statistics.

Let market demand for SNAP-eligible goods _Q_<sup>_D_</sup> ( _p, b_ ) be a function of price _p_ and SNAP benefits per population _b_ . Let supply _Q_<sup>_S_</sup> ( _p_ ) be a function of _p_ . Market demand is composed of demand by SNAP recipients _Q_<sup>_D,S_</sup> ( _p, b_ ) and non-SNAP recipients _Q_<sup>_D,NS_</sup> ( _p_ ); the latter quantity does not directly depend on _b_ . We first present the pass-through formula derived from the equilibrium condition in the case of perfect competition (see Appendix Section K for all steps of the derivation): This formula can be thought of as extending the unit-subsidy pass-through elasticity, 1 _−_ <u>1</u> , which is higher when demand is relatively more elastic than supply.<sup>35</sup> The 1+<sup>_−εD_</sup> _εS_

> 35The unit-subsidy pass-through elasticity is given by 1 minus the cost pass-through rate, <u>1</u> . The 1+<sup>_−εD_</sup> _εS_

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payoffs may take much longer to materialize than what can be captured by our six-year panel for Chile, thus further research using longer panel datasets will be better placed to capture the impacts of past weak performance on subsequent plant innovation choices.

# **6. Conclusion**

Innovation is not only potentially very costly but can also expose plants to significant survival risks as the launch of new products may result in lower than expected sales. At the same time, innovation is a potentially powerful source to allow longer plant survival in the marketplace. Focusing on Chilean manufacturing plants, this paper shows that product innovation reduces the probability of plant death under certain circumstances. Risk plays an important role for the innovation-survival link in that only innovators that retain diversified sources of revenues and innovators facing lower market risk benefit in terms of longer survival. By contrast, we do not find the technical risk of innovation leads to differential impacts in terms of survival. In particular singleproduct plants that innovate are at greater risk of exiting than non-innovating plants. These are not irrational decisions since plants reap positive payoffs from such innovations, but those payoffs are not always higher than those from more cautious types of innovations suggesting that constraining factors impede these plants from engaging in more cautious types of innovations.

Our findings have several policy implications. First, our results show that while there are positive micro-level effects of innovation on the survival of some manufacturing plants, these do not hold for plants engaging in more risky types of innovation. This suggests an additional reason for underinvestment in innovations from the point of view of the plant, beyond the usual fears of not appropriating all the benefits from innovation. Second, while cautious innovation is the most desirable innovation strategy to obtain a survival benefit, it may not be feasible for all types of plants nor in all types of industries. Policy actions may be required to improve for example the capacity of small plants to engage in cautious innovation given the desirability of small plant survival in terms of securing employment. Where risky innovation is the only possibility, an adequate policy mechanism that avoids moral hazard problems would be required that provides a guarantee to help failed innovation while setting the right incentives.

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