rafmacalaba/datause-encoder-data huggingface dataset:
systematically different perceptions of and attitudes to objective shocks (Bertrand and Mullainathan, 2001). In the section that follows, we therefore investigate the relationship between subjective crisis impact and coping strategies used by households.

# **3. Household coping strategies and subjective impact**

The subjective impact of the crisis goes beyond lower aggregate consumption and may depend on the coping strategies adopted. In fact, 13 percent of those who reported that their household was affected "a fair amount" and 9 percent of those reporting "a great deal" also answered that they did not reduce their consumption. One-quarter of these households experienced job loss and one-half had lower wages, but they were able to maintain consumption using private and public safety nets as well as increasing their earnings. The LiTS II allows an examination of formal and informal coping strategies, beyond adjustments in consumption and labor supply: the survey collected information on a wide range of coping mechanisms (as declared by respondents), although the list is not exhaustive. For example, respondents were not asked if they reduced education expenditures<sup>6</sup> .

# **3.1. Coping strategies: Descriptive statistics**

# **_Typology and summary statistics_**

Following Lokshin and Yemtsov (2004), we combine coping strategies into “active”, “safety nets” and “passive” strategies. We further distinguish between public and private safety nets, and between cutting expenditures on necessities (staple foods and health) and other expenditure, thus analyzing how households differed in terms of their use of 5 broad coping categories:  _Active strategies_ . These include getting a second job or increasing working hours to compensate for reduced wages, enrolling in further education because of a lack of job opportunities, selling assets or moving to a new location.

 _Safety nets_ . Households can draw upon public safety nets (state benefits from government) and private safety nets (borrowing money from relatives, friends or a bank).

> 6 However, World Bank crisis response surveys in five countries show no evidence of this (see World Bank, 2011).

10
_(In the Dangers of Decentralization 5_ local governrrents are free to set certain taxes, they all chose the same rate because either they follow ministry of finance "guidelines" or they all hit a low ceiling imposed by the ministry.

1.21 Another difficollty with tax and expenditure decentralization ratios is associated with the role and the weight of government "satellites." The satellites are often important in many countL Aes, particularly at the lacal level, but vary greatly from country to countiy and over time. In certain countries or cities, such local public services as water, public transportation, and public housing are provided directly by local governments, and related income and expenditures _appear_ in local governments' accounts; in other countries or cities, _ecactly_ the same services are provided by government-controlled entities (such as utilities, concessionaires) and **reiated** income and expenditures do _not appear_ in local government statistics The same reality is reflected in different measures of decentralization. Another example of this bias in many fornmer socialist countrie; is local public services that were provided by central governrn it-controlled satellites andi have now been shifted to local government-controlled satellites. This significant increase in decentralization will not be reflected in current measures of decentralization.

1.22 These examples illustrate the many dimensions of decentralization and show the difficulty of producing simple estimates of complex realities. Thus, publishcd expenditures and tax decentralization ratios based on local government accounts can deviate from true ratios, often by a very large margin, and one that differs over tirme and space. This limits the usefulness of studies that try to establish a link-often considered a causal link between the degree of tax o. expenditure decentralization and some economic magnitude, such as the total size of government (verifying the Leviathan hypothesis), or the level of development, or even economic growtth (Reid and Winkler, 1991).

1.23 This paper will attempt to describe and analyze some of the dangers associated with decentralization. Most of the analysis will refer to the "pure" decentralization of fiscal federalism theory, that is, a system in which pure subnational govcrnments raise pure local taxes and undertake pure local expenditures, without the benefit of central government transfers. This is not a very realistic model, but it is the one used in the pro-decentralization theury, and it is useful for analytical purposes. In clr.ssical Musgravian fashion, the paper will examine the dangers of decentralization from the successive viewpoints If allocation (section 2), redistribution (section 3), and stabilization (section 4). The paper will then try to relax some of the questionable assumptions of the decentralization model to go beyond the centralization-decentralization dichotomy (section 5).
Meza, 2002; Åstebro, 2003; Coelho, de Meza and Reyniers, 2004). Conversely, Caliendo, Fossen and Kritikos (2010) shows that a lower risk aversion is not connected at all with entrepreneurial survival.

If one takes into account the (often dominant) psychological attitudes discussed above (especially a desire to be independent, a desperate search for autonomy caused by frustration in the previous job, a fear of becoming unemployed), entry mistakes and excess entry can be further justified. In fact, the observed occurrence of these entry mistakes suggests an attitude which can be defined as a ‘try and see’ bet. In this view, new founders, mainly driven by a personal search for autonomy and job satisfaction, ‘visit’ a sectoral niche searching for business chances; later, they find out whether their entry decision was right or wrong and may decide to exit. Accordingly, market churning, turbulence and early failure, observed at a more aggregate level of analysis, see Section 2, emerge as normal and expected features of industrial dynamics.

These findings lead to the conclusion that several heterogeneous entry processes are simultaneously at play in the economy and that ‘opportunity entrepreneurs’, those bringing about innovation and economic growth, should be distinguished from ‘revolving door’ start-ups doomed to early failure and generating only precarious and temporary jobs.

Obviously enough, this distinction is _a fortiori_ crucial when we focus on the DCs, where ‘entrepreneurship’ and ‘self-employment’ often generate informal and very transient activities not so very different from ‘disguised unemployment’. In the following section an attempt will be made to correlate entrepreneurial characteristics with new firms’ actual potentials in terms of economic growth and job creation; special attention will be devoted to evidence from the DCs, when available.

# **4.** **<u>The post-entry performances of entrepreneurial firms</u>**

Since entrepreneurs are driven by both progressive and regressive determinants and are intrinsically heterogeneous, the post-entry performance of newborn firms and their eventual contribution to economic development may be very diverse as well. In what follows, after a theoretical discussion dealing with the macroeconomic constraints to new firms’ growth (Section 4.1), we survey those empirical microeconomic studies which have 14
**Table S3.5: Effect sizes on financial behavior and heterogeneity of treatment effects by delay in measurement of treatment effects**

||(1)<br>Financial<br>behavior|(2)<br>Short term<br>subsample|(3)<br>Medium term<br>subsample|(4)<br>Long term<br>subsample|
|---|---|---|---|---|
|RCT|-0.061**<br>(0.026)|0.148<br>(0.102)|-0.085***<br>(0.027)|-0.073*<br>(0.038)|
|TOT|0.043*<br>(0.025)|-0.221**<br>(0.078)|0.043<br>(0.032)|0.062<br>(0.049)|
|Short term|0.089**<br>(0.039)||||
|Medium term|-0.006<br>(0.018)||||
|1/SE|-0.000<br>(0.000)|-0.005**<br>(0.002)|-0.000<br>(0.000)|0.000<br>(0.000)|
|Intensity /week|0.004***<br>(0.001)|0.006<br>(0.007)|0.002<br>(0.002)|0.004***<br>(0.001)|
|Duration|-0.000|0.010**|0.000|0.000|
||(0.001)|(0.005)|(0.001)|(0.001)|
|Low inc. clients|-0.044***<br>(0.014)|-0.046<br>(0.087)|-0.041**<br>(0.020)|-0.045**<br>(0.019)|
|Years of schooling|-0.021***<br>(0.005)|-0.103**<br>(0.047)|-0.011<br>(0.008)|-0.021**<br>(0.009)|
|Low/lower inc. econ.|-0.122***|-1.127***|0.034|-0.156***|
||(0.041)|(0.318)|(0.055)|(0.058)|
|Mandatory|-0.041**|-0.076|0.003|-0.056***|
||(0.019)|(0.097)|(0.047)|(0.021)|
|Teach. moment|0.090***|0.202*|0.009|0.109***|
||(0.028)|(0.108)|(0.032)|(0.024)|
|Constant|<br>0.332***|<br>1.634**|<br>0.235**|<br>0.332***|
||(0.077)|(0.624)|(0.101)|(0.119)|
|R<sup>2</sup>|0.204|0.457|0.073|0.319|
|n (Studies)|90|18|24|53|
|n(Effect Sizes)|349|42|143|164|

Notes: Results from OLS meta-regression with robust standard errors clustered at the study-level. Dependent variable is effect size (Hedges’ g) on financial behavior. Standard errors in parentheses. ***, ** and * denote significance at the 1%, 5% and 10% level.

56
_24_ _Price Support at Any Price?_

**Tab.4.1 Alternatives for price supporting Simulation experiments:** POLICY COSTS

||Fast path<br>reduc<br>(EU pr. by 20|/output<br>tion<br>00, quotas)|More pen<br>|sioners<br>|Jobs prom<br>farm<br>|otion for<br>ers<br>|
|---|---|---|---|---|---|---|
||2000|2010|2000J|2010|20001|2010|
|_In $ billions(91 prices):_<br>(1) Cost for taxpayers|1.00|0.68|0.00|0.00|2.91|1.96|
|(2) Cost for consumers|1.56|1.81|0.00|0.00|0.00|0.00|
|**Total cost (1)+(2)**<br>(excl.social security)|**2.56**|**2.49**|**0.00**|**0.00**|**2.91**|**1.96**|
|Increase of social<br>security expenditures|-0.06|-0.09|0.10|0.23|-0.00|0.01|
|Producer Subsidy<br>Equivalent (PSE) in %|28.8%|23.8%|0.0%|0.0%|0.0%|0.0%|

_Source: Model POLAGR, simulation Jan.1995_ As both table 4.1 and figure 9 show, the level of costs in the JOB PROMOTION FOR FARMERS experiment remains similar to the price supporting costs. However, the shape of the line is quite different: the investment experiment assumes a 15 year investment program. After the end of the program (in 2010), expenditure falls to zero. In contrast, the price support only freezes the expenditure level (the structural production surpluses are small thanks to output control, but costs for consumers remain high). The MORE PENSIONERS <!-- Start of picture text --> Alternatives to price support<br>Policy cost ($ billions)<br>No O,m<br>-<br>6<br>5 prnxoton for farmers<br>3<br>More pensioners<br>2 /<br>I /<br>1 1 1 2 0<br>Figure 9<br><!-- End of picture text --> experiment entails a relatively small budgetary cost".

15 Relatively small budgetary costs result from a low ratio between an average pension for farmers and average wage (below 40% in 1993). If the increase of this ratio is necessary to encourage old farmers to retire, the cost for social security will increase.
## _14_ **_Polish Fiscal_** _Crisis_

The inflation bias in the national accounts is clearly visible: substantial increas in stocks are implausible, especially for 1989-90. How much the inflation bias amounts to would depend on precisely how GDP was calculated.

# **VI.** PRoFrrs, TAX **REVENUE** AND WAGE BEHAVIOR

In a separate paper (Schaffer 1992) I describe how, at the start of the government's stabilization/liberalization program (January 1990), the enterprise se_tor was hit with large profit tax liabilities resulting from the infPation bias. This fiscal crunch contributed to the decline in real wages - to come up with the money to pay their profit taxes, enterprises held down wager in. rder to boost cash flow - and thus to the large output fall that was observed in earlv 1990. Here I want to argue that the inflation bias played a critical role in both the profit collapse anki the lack of wage restraint observed in 1991.

Table 6 summarizes the direct effect of changes in the inflation bias on profit tax revenues."1 We do this by dividing profit tax liabilities of the enterprise sector according to the estimated contribution of the inflation bias to HCA profits. These estimates suggest that tax revenues from the inflation bias were very large in 1989 and 1990; about 10-11% of GDP in 1989 and 6-7% of GDP in 1990. The contribution of the inflation bias to profit tax revenues fell sharply in 1991, amounting to a loss in revenues of about 3-4% of GDP.

Table 6: Taxation of the inflation bias as a % of nominal GDP

||1988|1989|1990|1991|
|---|---|---|---|---|
|Assumed IL = 1 month|7.9|10.7|6.4|3.5|
|Assumed IL = 2 months|7.8|10.5|7.1|3.6|
|Assumed IL = 3 months|7.8|10.4|7.4|3.7|

But the decline in the inflation bias had a further, indirect effect on profit tax revenues by allowing an increase in unit labor costs. Ceteris paribus, a decreas,e in the inflation bias increases net cash flow (cash flow after taxes paid out of profit). But ceteris is not paribus, and increased net cash flow meant more scope for wage increases. The improvement in net cash flow resulting from the decrease in the burden of inflation bias taxation could be transferred to workers in the form of wages. The increase in amortization allowances had a similar indirect effect.

11. Profit tax in 1989 is adjusted so that **the** inclusion of **the** popiwek in profit **tax** in thut year alone is netted **out.**
# **4 Evidence on the diffusion of green technologies as revealed by ECTs**

We now turn to the evidence on patterns of adoption and diffusion of these green technologies across firms and countries drawing on the textual analysis of earnings conference calls transcripts. As discussed above, we follow a growing literature seeking to infer firm-level technology adoption using general-text classification methods for identifying technical-relevant keywords associated with specific technologies; and by counting the number of times these words are mentioned in the quarterly earnings conference calls that publicly listed firms host with financial analysts. The rationale behind this approach is that earnings calls are a venue in which senior management responds directly to questions from market participants about the firm’s prospects. They consist of a management presentation followed by a Q&A session, which requires management to comment on subjects they might not otherwise have voluntarily proffered. Thus, the extent to which the adoption of green technologies feature in ECTs serves as a proxy of their importance for both the firm’s management and market participants.

The evidence in Figure 3 reveals that green technologies have featured more prominently in ECTs and firms since 2015. In 2015, green technologies were mentioned in about 5 percent of ECTs and 8 percent of firms. By 2021, the last year of our sample period, green technologies were mentioned in 10 percent of ECTs and by 18 percent of firms. This increase in the relevance of green technologies in quarterly earnings conference calls was especially marked after 2019, rising from about 6 percent to 10 percent of ECTs and from 12 to 18 percent of firms. Panel A in Figure A1 in the Appendix reveals that a similar pattern is observed when looking at the economic relevance of firms mentioning green technologies in their ECTs, which increased from around 20 percent of sales in 2019 to almost 35 percent of sales in 2021. This suggests that firms mentioning green technologies in ECTs tend to be relatively large, a pattern to which we will return below.

Looking across WIPO green sectors in Figure 4, we observe that this marked increase observed after 2019 was accompanied by a rise in the relative importance of technologies from the PMP sector, whose relative importance became similar to those from the energy sector. Mentions of technologies from the construction sector accounted for the majority of mentions in 2021, but their relative importance observed a downward trend since 2014. Figure 5 reveals that a few technologies drove these trends. In the PMP sector, carbon capture experienced a marked increase after 2019. In the energy sector, we observe a significant increase in the relative importance of Wind Turbines and Li-ion Batteries after 2019. In the construction sector, we ob8
Trade Effects of the New Silk Road: A Gravity Analysis

# **Suprabha Baniya, Nadia Rocha, Michele Ruta**<sup>**1**</sup>

_Keywords:_ Belt and Road Initiative, Transport Infrastructure, GIS Analysis, Trade Flows, Time Sensitivity, Input‐output Linkages _JEL Codes:_ F14, F15, R41 1 Suprabha Baniya, Clark University <u>(SuBaniya@clarku.edu), Nadia Rocha, World Bank (nrocha@worldbank.org),</u> Michele Ruta, World Bank (mruta@worldbank.org). We would like to thank Alvaro Espitia for his excellent research assistantship during the project. We are also grateful to Caroline Freund, Francois de Soyres, Lionel Fontagne, Mauricio Mesquita Moreira, Alen Mulabdic, Siobhan Murray, and seminar participants at University of Bologna, GTAP conference, IADB, Rand Corporation, World Bank for helpful comments. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
All of these issues plague censuses, population registers and administrative data, as well as surveys. Thus it is critical for collectors of data to ask specific questions and users to be aware of the differences. Ideally, both place of birth and citizenship status should be asked about. However, many undocumented migrants will refrain from participation in the survey for fear of identification when faced with citizenship questions, which will bias the data collected. If a choice needs to be made, place of birth is preferable.

# **_Variation in census dates and missing censuses_**

Despite the attempts by multilateral institutions to achieve coordination, countries choose when to conduct a census or a survey. Even though the standard procedure is to conduct the censuses in the middle of the round, say in 2010, and every ten years, many exceptions exist to both norms. For example, the census dates in France were 1962, 1968, 1975, 1982, 1990, 1999 and 2006. Such large variation can lead to difficulties in comparison across countries, especially in global databases.

The more serious problem is that many countries fail to conduct a survey or a census at a given time or fail to include relevant questions to identify migrants. Censuses are both expensive and demanding in terms of human resources, which make them less attractive activities in poorer countries. Wars, civil conflict or simple public opposition might prevent data collection. There might be political opposition since surveys and censuses collect data on potentially controversial subjects such as ethnicity, language and religion. Even if collected, data might not be released in detail or in a timely manner. Or 12
Learning from Developing Country Power Market Experiences: The Case of Philippines <!-- Start of picture text --> GWh<br>70,000 45%<br>Spot market<br>(GWh) 60,000 40%<br>16% 35%<br>50,000<br>30%<br>40,000 25%<br>30,000 20%<br>Bilateral 20,000 15%<br>contracts 10%<br>(GWh) 10,000 5%<br>84%<br>0 0%<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Bilateral contracts (GWh) Spot market (GWh) Relative size of spot market (%)<br>Figure 5‐1 Relative size of power submarkets in<br>Figure 5‐2 Evolution of traded energy volumes in contracts and spot<br>Philippines during 2017, as percentage of total traded<br>market in Philippines.<br><!-- End of picture text --> Figure 5‐1 Relative size of power submarkets in Philippines during 2017, as percentage of total traded energy.

Source: Own elaboration based on data collected by the World Bank.

Source: Own elaboration based on data collected by the World Bank.

Contracted electricity volumes are driven by load growth (around 85‐95% of customer loads are permanently contracted by most Discos). Maturity is usually long‐term (typically 20 yrs.) for coal‐fired power plants, and short to medium‐term (e.g. 5‐10 yrs.) for peaking plants and also for other existing plants (e.g. coal, geothermal and hydro). On the other hand, absent a spot market, peaking plants in Mindanao are bilaterally contracted with a typical maturity of 20 years. Regulated contract prices are based on actual generation costs.

Energy traded in the Philippine spot market (hour‐ahead) stood at 16% of total traded energy in 2017. The spot market shrank over the past decade, partly due to abuse of market power and the tendency to vertical integration of Gencos and Retailers. As a result, retailers and market participants in general have relied in bilateral contracts outside the spot power market. Hence, the spot market has been relegated to a bare minimal existence with inadequate power to influence investment and operational decisions. Bilateral contract prices dominated by generation tend to be high and have more influence than spot prices on retail tariffs. However, bilateral contract prices are not publicly available since these contracts are privately agreed by the parties (and approved by the regulator in the case of contracts for supplying captive customers).

Spot electricity prices in Philippines have been rather volatile over the past decade. Spot prices have fallen by 55% in real terms since the inception of the power market (from 7,154 PhP/MWh in 2006, to 3,205 PhP/MWh in 2017; or 116 USD/MWh in 2006 to 65 USD/MWh in 2017, a 44% decrease when expressed in 2015 USD). However, the annual spot price evolution highlights price hikes in 2010 and 2013, and a price slump in 2009 to 45 USD/MWh (see Figure 5‐3 and Table 5‐2). Average spot prices reached 190 USD/MWh during 2013, and peak spot prices reached 618 USD/MWh in the same year, due to what was later determined to be the result of anti‐competitive behavior by market participants who withheld capacity from the market.

33