rafmacalaba/datause-encoder-data huggingface dataset:
**Table 5-c: Bond Market Regression Results (2000-2006)**

||Model (19)|Model (20)|Model (21)|Model (22)|Model (23)|
|---|---|---|---|---|---|
||Private bond<br>market<br>capitalization|Turnover<br>private bond<br>market|Value of new<br>private bonds<br>to GDP|<br>Public bond<br>market<br>capitalization|Turnover<br>public bond<br>market|
|Log GDP per capita|1.2|0.192|0.168|0.197|-0.469|
||[0.074]***|[0.367]|[0.239]|[0.044]***|[0.586]|
|Offshore|2.424|-1.122|-0.44|0.492|-5.805|
||[0.372]***|[1.223]|[0.980]|[0.228]**|[3.353]*|
|Log fuel|-0.353|-0.071|0.149|-0.184|0.121|
||[0.070]***|[0.403]|[0.174]|[0.039]***|[0.770]|
|Log poverty gap|-0.932|0.953|2.228|-0.137|-0.579|
||[0.228]***|[0.968]|[0.560]***|[0.127]|[1.021]|
|Log population density|-0.066|-0.156|-0.003|0.133|-0.461|
||[0.032]**|[0.219]|[0.100]|[0.022]***|[0.300]|
|Log population size|0.168|0.084|-0.263|0.047|-0.534|
||[0.058]***|[0.224]|[0.141]*|[0.032]|[0.476]|
|GDP per capita *|-0.002|-0.032|0.024|0.001|0.196|
|population size|[0.005]|[0.018]*|[0.014]|[0.003]|[0.138]|
|Year-fixed effects?|Yes|Yes|Yes|Yes|Yes|
|Observations|242|55|173|280|52|
|Adjusted R-squared<br>Standard errors in brackets<br>* significant at 10%; ** signi|0.72<br>ficant at 5%; **|0.07<br>* significant at|0.24<br>1%|0.28|0|

**Table 5-d: Institutional Investors Regression Results (2000-2006)**

||Model (24)|Model (26)|Model (26)|Model (27)|
|---|---|---|---|---|
||Assets of|Assets of<br>insurance|Assets of|Assets of<br>instit.|
||mutual funds<br>to GDP|companies to<br>GDP|<br>pension<br>funds to GDP|investors to<br>GDP|
|Log GDP per capita|0.876|0.671|0.453|0.774|
||[0.073]***|[0.061]***|[0.101]***|[0.073]***|
|Offshore|-1.204|0.437|0.214|0.196|
||[0.653]*|[0.553]|[0.927]|[0.659]|
|Log fuel|-0.16|-0.131|-0.129|-0.171|
||[0.059]***|[0.051]**|[0.083]|[0.059]***|
|Log poverty gap|0.055|0.291|0.23|0.171|
||[0.134]|[0.115]**|[0.190]|[0.135]|
|Log population density|0.03|0.146|-0.163|-0.006|
||[0.066]|[0.057]**|[0.091]*|[0.065]|
|Log population size|0.232|0.054|0.05|0.054|
||[0.070]***|[0.060]|[0.096]|[0.069]|
|GDP per capita *|-0.006|0.001|0.008|0.002|
|population size|[0.009]|[0.008]|[0.013]|[0.009]|
|Year-fixed effects?|Yes|Yes|Yes|Yes|
|Observations|99|98|100|99|
|Adjusted R-squared<br>Standard errors in brackets<br>* significant at 10%; ** signi|0.64<br>ficant at 5%; **|0.63<br>* significant at|0.2<br>1%|0.58|

21
(Estimating in the form of equation (3), the coefficient on the log difference in the exchange rate ˆ is again not significantly different from unity;   .0995 with s.e.=0.010 and the R<sup>2</sup> rises to 0.991.) It is also of interest to see how well one can explain the changes between the 1985 and 2005 PPPs. Here there are further comparability problems, stemming from the differences between the World Bank’s methods and those of PWT.<sup>29</sup> Even so, the corresponding estimate of equation (3) has similar explanatory power (in obvious notation):<sup>30</sup> ˆ ln( _PPP_ 05 _i_ / _PPP_ 85 _i_ )   .0168  .0417 ln( _Y_ 05 _i_ / _Y_ 85 _i_ )  .0991 ln( _E_ 05 _i_ / _E_ 85 _i_ )   _i_ (.0111) (.0079 ) (.0025 ) (10) 2 _R_  .0981; _n_  54 Using instead the period 1985-1993, one obtains: Imposing the restriction that   1 the regression coefficient of ln( _P_ 05 _i_ /<sup>_P_</sup> 85 _i_ ) on ln( _Y_ 05 _i_ /<sup>_Y_</sup> 85 _i_ ) is ˆ <sup>ˆ</sup>  0.431 (s.e.=0.075; n=55; _R_<sup>2</sup> =0.488) and    .0194 (s.e.=0.104). So it appears that the DPE is even stronger over 1985-2005, despite the methodological differences between PWT and the World Bank’s methods. Figure 7 plots the data for this longer period (with fewer observations, and confined to 1985 benchmark countries). The switch point (at which _E_ ln( _P_ 05 _i_ /<sup>_P_</sup> 85 _i_ )  0) is at ln( _P_ 05 _i_ /<sup>_P_</sup> 85 _i_ ) =0.451 (s.e.=0.168), corresponding to an annual growth rate of 2.3%.

# **5. China’s controversial PPP revisited**

The new PPP for China from the 2005 ICP attracted much attention, given that it implies that the country’s GDP per capita at PPP for 2005 is 40% lower than we thought, at $4,091 rather than the prior estimate for 2005 of $6,760 (World Bank, 2008b). Just before the release of the 2005 ICP’s results, China’s price level index for 2005 was deemed to be 25%, up from 19% in 1993. The price surveys from the 2005 ICP implied a price index of 42%.

> 29 See Deaton and Heston (2010) for a description of the differences in methods.

> 30 The negative interaction effect with the initial (log) GDP was also evident using the 1985 PPPs, but for brevity this discussion is confined to the simpler version of the DPE.

15
significant number of studies, working papers, and guidance notes issued by development institutions and think tanks that document some of the experience with FMIS implementation and draw conclusions. A targeted google scholar search that is more inclusive with regards to working papers and gray literature identified 951 documents though widely ranging in relevance and rigor.

1.8 The aim of this study is to identify practical and operational issues that may arise during design, procurement, and implementation of FMIS systems and summarize lessons on what has worked through the triangulation of the various data points. Secondly, it is hoped that carefully outlining the various implementation steps in a framework will stimulate a conversation around the various dimensions and encourage practitioners to take a more programmatically coherent approach.

1.9 This report is an effort to make the vast World Bank experience with FMIS operations more readily accessible to practitioners engaged in such reforms to allow them to make better informed decisions or take calculated risks. Further, identifying critical dimensions through a framework could provide for an input for project design and the assessment of the programmatic coherence of the FMIS engagement in a country. It could also provide for an input into the development of M&E frameworks going forward, which are critical for documenting the FMIS contribution to larger PFM objectives. This report could serve as a reference point for practitioners in the World Bank and other development institutions, as well as government counterparts and their advisors leading such reform efforts. It could also be useful to contractors, consultants, and in-house information technology staff tasked with implementing an FMIS system.

# **Methods and Limitations**

1.10 The report draws on the FMIS literature, five purposefully selected case studies, and desk reviews of World Bank project documentation. These are discussed respectively in more detail below.

1.11 To identify the FMIS literature, a systematic search strategy was used drawing first on two search engines and secondly on forward and backward tracing of references from seminal reports. For academic literature, Web of Science<sup>7</sup> was used, which found five studies which were however of relatively low quality and none were of experimental or quasi-experimental in nature. Google Scholar was used to cast a wider net and capture reports issued by international finance institutions and think tanks as well as gray literature, guidance notes, various case studies, and relevant evaluations.

> 7 Web of Science is an online subscription-based scientific citation indexing service.

**15**

**15**
application of the expenditure rigidity in this section is taken from Battaile and Barrot (2013), which uses a classification framework from the literature to “tag” the BOOST expenditure data for several countries.

Applying measures of public expenditure force can improve basic fiscal monitoring as well as help manage pressing fiscal concerns such as rebuilding post-crisis policy space. The global financial crisis had major implications for the public finances of most countries around the world. Direct fiscal support was provided to the financial sector. Fiscal revenues declined through the operation of automatic stabilizers and due to lower asset and commodity prices, and many countries implemented discretionary fiscal stimulus. The consequent fiscal deterioration was particularly strong for advanced countries, where the increase in both government debt and contingent liabilities was unprecedented. Figure 11 shows the post-crisis fiscal deterioration in emerging and low income economies. Expenditure increases have outpaced revenue growth, even when the latter has been significant. Across country groups, fiscal policy is now aimed toward rebuilding policy space while strengthening the recovery and long-term growth prospects.

**Figure 11 Post-Crisis Fiscal Deterioration in Emerging and Low Income Economies** <u>(Moldovan Leu and percent)</u>

<!-- Start of picture text --> 15.0<br>MMR<br>HTI<br>GIN MOZ<br>NER 10.0 MWI KGZ<br>GMB<br>MRT KENTGO<br>KHM<br>TCD RWA<br>5.0 TZA ZAR<br>AFG<br>SLE<br>MLI CAF BFA BGD<br>BEN<br>NPL<br>UGA<br>0.0 TJK<br>-15.0 -10.0 -5.0 0.0 5.0 10.0 15.0<br>ETH<br>BDI<br>MDG<br>-5.0<br>GNB<br>ERI<br>-10.0<br>-15.0<br>Revenue (% of GDP)<br>Expenditure (% of GDP)<br><!-- End of picture text --> Source: IMF Fiscal Monitor 2014. Note: Above the red dotted line indicates the change in expenditure was greater than the change in revenue.

Linking insights on the dynamics of expenditure drivers with qualitative measures such as fiscal “rigidity” can be especially helpful to policy-makers concerned with unwinding fiscal stimulus. Following the global financial crisis, governments sought to avoid a major loss in output after the global financial crisis through fiscal stimulus and tax policy measures used to stimulate demand. As economies recover and get more solid private sector growth, fiscal policymakers are looking to unwind counter-cyclical expenditure measures. To carry out this adjustment, it is critical to know the extent to which expenditures are rigid or discretionary.
online appendix Table A7: Mask manufacturing exposure increases purchases of masks from manufacturers, decreases mask prices, and reduces COVID-19 infections when parallel pre-trends are imposed

||||_Depe_|_ndent variabl_|_e:_||
|---|---|---|---|---|---|---|
||log buy|er price|Ef mask Q fr<br>Ef|om manufacturers<br> mask Q|# paracet|amol receipts|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Mask manufacturing exposure_d ×_Pre_t_|-|-|-|-|-|-|
||-0.768|-0.678|1.076|0.853|0.047|-0.244|
|Mask manufacturing exposure_d ×_Apr_t_|(0.171)|(0.214)|(0.220)|(0.353)|(1.150)|(1.083)|
||[0.000]|[0.002]|[0.000]|[0.018]|[0.967]|[0.822]|
||-0.925|-0.753|0.511|0.370|-1.338|-1.473|
|Mask manufacturing exposure_d ×_May_t_|(0.190)|(0.229)|(0.161)|(0.231)|(1.029)|(0.995)|
||[0.000]|[0.001]|[0.002]|[0.113]|[0.193]|[0.139]|
||-0.890|-0.870|0.178|0.393|-2.127|-1.892|
|Mask manufacturing exposure_d ×_Jun_t_|(0.257)|(0.364)|(0.174)|(0.223)|(1.076)|(0.942)|
||[0.001]|[0.019]|[0.309]|[0.081]|[0.048]|[0.045]|
||-0.441|-0.395|0.423|0.278|-2.132|-1.853|
|Mask manufacturing exposure_d ×_Jul_t_|(0.222)|(0.244)|(0.126)|(0.185)|(1.142)|(0.987)|
||[0.051]|[0.110]|[0.001]|[0.136]|[0.062]|[0.061]|
||-0.735|-0.684|-0.156|-0.088|-1.894|-1.668|
|Mask manufacturing exposure_d ×_Aug_t_|(0.139)|(0.153)|(0.184)|(0.248)|(1.312)|(1.094)|
||[0.000]|[0.000]|[0.401]|[0.724]|[0.149]|[0.127]|
|Estimation method|OLS|OLS|OLS|OLS|Poisson|Poisson|
|Firm-product FE|X|X|||||
|Destination sub-District FE|X|X|X|X|X|X|
|Month FE|X|X|X|X|X|X|
|Residualized shocks||X||X||X|
|Controls x Month FE||X||X||X|
|# observations|3,937|3,937|506|506|780|780|
|# clusters (sub-Districts)|86|86|86|86|78|78|

# **F.3 Controls**

In Section 3.3, we presented impacts of mask manufacturing exposure using two specifications: first, one that omits controls interacted with month fixed effects and does not residualize mask manufacturing intensity on controls before constructing mask manufacturing exposure, and second, one that includes controls interacted with month fixed effects and residualizes mask manufacturing intensity on controls before A27
POLICY **RESEARCH WORKING PAPER** 2700

# **Summary findings**

Agenor examines whether output contractions associated with cyclical output fluctuations and economic crises have an asymmetric effect on poverty. He identifies four potential sources of asymmetry: expectations and confidence factors, credit rationing at the firm level (induced by either adverse selection problems or negative shocks to net worth), borrowing constraints at the household level, and the "labor hoarding" hypothesis. He also identifies some testable implications of these alternative explanations.

The author then proposes a vector autoregression technique (involving the detrended components of real output, the unemployment rate, real wages, and the poverty rate) to test whether the initial cyclical position of the economy, and the size of the initial drop in the output gap in a downturn, matter in assessing the extent to which output shocks affect poverty. He applies the technique to Brazil, using annual data for 1981-99. The results indicate that poverty responds asymmetrically to output shocks, showing less sensitivity when the economy is initially in a downturn.

This paper-a product of the Economic Policy and Poverty Reduction Division, World Bank Institute-is part of a larger effort in the institute to analyze the impact of macroeconomic adjustment on poverty. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Maria Gosiengfiao, room J4-282, telephone 202-473-3363, fax 202-676-9810, email address mgosiengfiao@worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at pagenor @Cworldbank.org. October 2001. (29 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
# **3.3 Opportunities to incorporate prediction into insurance**

While the weather derivatives market has received substantial attention with regards to the growing role of climate predictions (Jewson and Brix, 2005), little has been done to formally study the implications of seasonal forecasts on index-based weather insurance schemes like the Malawi pilot. Cabrera et al. (2006) and Mjelde and Hill (1999) explored the farm value of ENSO-based forecasts in the context of common crop insurance contracts. Skees et al. (1999) refer to the possibility that improved skill in seasonal climate forecasting may negatively affect certain index-based insurance schemes. Adverse selection resulting from asymmetric information can create problems for the financial viability of such schemes (Luo et al., 1994). Yet in southern Africa, asymmetric information poses a different kind of problem. Acquiring potentially useful seasonal forecasts may prove too expensive for some subsistence farmers (e.g. even the cost of batteries for listening to the forecast by radio may be prohibitive), and insurers may take advantage of this asymmetry at the expense of the farmers that are supposed to be the main beneficiaries of the Malawi pilot scheme.

Figure 1 (based on Hess and Syroka, 2005, page 29) shows simulated payouts during the period 1962-2004, based on a slightly different contract design and set of assumptions. Without considering the ENSO state, the probability of a payout on any given year was 19% (8 payouts in 42 years). If only El Niño years are considered, the probability of a payout rises to 40% (4 payouts in 10 years). The absence of simulated payouts in La Niña years in the study period, indicating a low probability of payout, although the small sample size prevents estimation of the probability of a payout in future La Niña years.

9
(TRNB and TRNBES) are reported in Table 3 with marginal effects for the ZINB and TRNBES models listed in Table 4.

From the empirical demand relationship in equation (8), we model the participation and trip quantity decisions using travel cost and several individual-specific variables that may co-vary with each decision - income, age, household size, education, and a Yerevan city dummy.<sup>8</sup> Travel costs included: (1) transport costs; (2) on-site costs (per day); and (3) the value of time traveling _to_ and _at_ Lake Sevan. The value of time was elicited from the respondent by asking them how much they would have earned had they not traveled to Lake Sevan. This amount was then divided by the number of days they were at the lake to arrive at a trip-per-day cost. Note that for the household model, each equation (logit and mean) contain the same explanatory variables as they may contribute to either of the participation or quantity decisions.

Beginning with the household survey results in the second and third columns of Table 3, we note that the likelihood ratio (LR) test of α = 0 is rejected indicating the significance of over-dispersion and thus the selection of the negative binomial specification over the Poisson. A further formal specification test between the NB and ZINB is possible (Vuong, 1989). The test statistic is directional and distributed standard normal and for values | _V_ | > 1.96, the zero-inflated version is supported. With a value of 4.86, the ZINB specification is favored over the NB.

Parameter estimates of the household ZINB model reveal that income, age and education, along with respondents who reside in Yerevan significantly determine the household participation decision to recreate at Lake Sevan (see _logit inflation model_ The coefficients are interpreted relative to observing a zero count, thus the positive coefficient on age implies that older respondents are more likely to record zero participation, whereas individuals with higher income or education are less likely to report zero trips to Lake Sevan. Those who reside in Yerevan city are also more likely to

> 8 A dummy variable to capture previous visitation to the lake was also initially considered for each model, however, over 94% of respondents in the household survey and over 95% in the tourist survey visited Lake Sevan at least once in the past three years (and thus insufficient statistical variation).

11
but has had relatively little to say about whether, how and to whom they have diffused across countries, industries, and firms. An important exception is a recent paper by Bastos, Greenspon, Stapleton, and Taglioni (2024), who draw on textual analysis of online job postings to document the diffusion of low carbon technologies across 35 countries, and examine econometrically the role of the 2022 global energy crisis in accelerating their diffusion across countries and firms. The current paper provides complementary stylized facts on both the emergence and diffusion of green technologies by drawing on the textual analysis of patents and ECTs in a wide range of countries; examining the link between green technology adoption and subsequent changes in emissions at the firm-level; and documenting the role of buyer-supplier links in the diffusion of green technologies. Finally, our paper is also related to the broader literature on technology diffusion, including Comin and Hobijn (2004), Comin and Hobijn (2010), Comin and Mestieri (2018), Bloom, Hassan, Kalyani, Lerner, and Tahoun (2021) and Bastos, Stapleton, Taglioni, and Wei (2024). Our approach for examining the role of firm-to-firm linkages in the diffusion of low carbon technologies is related to that adopted in Bastos, Stapleton, Taglioni, and Wei (2024), who use data on online job postings and information on firm-to-firm linkages to examine the spread of new technologies, but do not focus on green technologies.

The remainder of the paper is organized as follows. Section 2 describes the various sources of data, and our approach to identify green technologies and associated keywords. Section 3 presents stylized facts on innovation and dissemination of green technologies based on patent data from the WIPO green database, which allows us to assess the dissemination of the most novel and influential green technologies. Section 4 presents evidence about the diffusion of green technologies among firms, sectors, and countries using textual analysis of earnings calls transcripts. Section 5 reports evidence of patterns of diffusion of green technologies through firm-to-firm relationships. Section 6 concludes the paper with the main takeaways.

# **2 Data sources and textual analysis methodology**

We identify a set of business and technical-relevant keywords for technologies aimed at protecting the environment and making a positive impact on climate change mentioned in the World Intellectual Property Organization (WIPO) green patents database. Using these keywords and textual analysis of patents and earnings conference calls, we build a multi-country firm-year panel data set of exposure to green construction technologies spanning the period 2012-2022 and covering 10,000 firms in 79 countries. This section describes the data sources and our approach in more detail.

3