rafmacalaba/datause-encoder-data huggingface dataset:
combined share of 59 percent. The largest exposures of CGSs in these deciles are wholesale and retail trade, construction of buildings, specialized construction activities, and accommodation.

**By combining exposures with GHG emission data we find that 72 percent of the transition risk for CGSs is driven by five sectors.** Figure 3 shows the 15 sectors at a 2-digit sectoral level that have the highest value for the indicator. The top five sectors are crop and animal production, land transport, wholesale trade, retail trade, and electricity, gas, steam and air-conditioning supply. For crop and animal production the transition risk is highly concentrated, with two CGSs having more than 40 percent of their total guarantee portfolios allocated to this sector. For wholesale and retail trade the GHG emission intensity is more limited than for the other three top-5 industries, however their exposure share is much higher (about three times).

Table 2 – Emissions, exposure, and value-added shares per decile (NACE 2 sample, 2020)

|**GHG emissions**<br>**decile**|**GHG emissions**<br>**share**|**CO2 emissions**<br>**share**|**CGS exposure**<br>**share**|**Value added**<br>**share**|
|---|---|---|---|---|
|1|53%|67%|2%|4%|
|2|27%|14%|6%|5%|
|3|6%|4%|4%|7%|
|4|3%|4%|16%|15%|
|5|3%|4%|21%|9%|
|6|3%|3%|22%|13%|
|7|2%|2%|3%|10%|
|8|1%|1%|7%|7%|
|9|2%|2%|11%|16%|
|10|1%|1%|8%|15%|

Source: Authors based on AECM survey; Eurostat.

Figure 3 –Sectoral transition risk scores for highest ranking 15 sectors, based on GHG emissions and total CGS exposures (NACE 2 sample, 2020) 12
ESTIMATES OF COSTS AND BENEFITS 73 n A - Z RE/(l+r+p)t t-l where A - advance payment

- n - number of periods in advance

- r - real discount rate

- p - rate of price increase t - tivte period.

Figure 5.2: Advance by Length of Tenure Some 105 out of 754 sample households made advance payments in lieu of rent for a specified period (14 percent). A nominal discount rate of 20 percent was used to calculate the rental equivalent. While it might be expected that households with higher incomes would be willing to pay higher advances to secure a rental unit of their choice, advances were .iot highly correlated with e total consumption.

<!-- Start of picture text --> 60 000 Ced.s N (OvtoisRenters)100<br>_ u8w,mo<br>Aso A- X +eow. ao N<br>40 am 1.6|Wb k.m b<br>6t e<br>e 3<br>m 40<br>0 20<br>n<br>' t<4/ \ 20<br>'<br>teRgth ot Tenure (Years)<br><!-- End of picture text -->

Figure 5.2 shows that, according to the sample of 105, at least some advances have increased markedly in recent years. Since 1980 the median advance -- of those wno report advance -- is on the order of 2-12,000 cedis, and up to maxima of 15-50,000. The latter represent 50-167 months typical rent. The relative frequencv oI households paying advance has also increased.

# Anecdotal Rrecent Evidence on I-.crease in Advances

The 1986 survey was carried out at a time wher., with the recent decree raising controlled rents to a level above that to which rents had crept informally, landlords were satisfied. Since that time, however, prices have risen very rapidly while rents have not and landlords have begun to demand advances from all-comers. By early 1988, anecdotal evidence pointed to all new tenancies being subject to between two ard five years' rent in advance and even some existing tenants being asked for their rent to be paid yearly in advance. At that time, the general belief appeared to be that an amount of, say. twenty times C300 would last twenty months unless controlled renots were increased. However, this appears to have been over-optimistic on the tenant's part.

in February, 1989, it was universal practice to ask for advances from all renters, except where landlords had -trong personal relationships (an advantage for particularly long-standing tenants). Renters were reportirng that their advances were being used up at a rate of about C1,OO0 (US$4) per month per room and the advance demanded was C50,000 (US$190) or more for a single room or C250,000 (US$960) for a self-contained apartment.
# **Fiscal Spending and Economic Performance: Some Stylized Facts**<sup>♣</sup>

Céline Carrère* and Jaime de Melo**

> ♣ We thank Jean-François Brun, Antonio Estache, Eduardo Ley and Peter Heller for comments on an earlier draft.

*    CERDI- CNRS, Université d’Auvergne, FRANCE, <u>Celine.Carrere@u-clermont1.fr</u>

** University of Geneva, SWITZERLAND, <u>demelo@ecopo.unige.ch, corresponding author.</u>
|Table10:Mem<br>_(Italicized_item<br>organizationsa<br>percentlevel).|bership<br>sareco<br>ndposit<br>|insocialor<br>nsistentwith<br>ivecommun<br> <br>|ganizations<br> thehypothe<br>ityeffects,i<br>_<br>______tag<br>|andtenproxi<br>sisofpositiv<br>temsbolded <br>Margi<br>__<br>|es forgovern<br>e privateeff<br>arestatistical<br>__Pe_r_entage<br>|ance.<br>ectsofsocia<br>lysignifican<br>|l<br>tatthe10<br>1<br>|
|---|---|---|---|---|---|---|---|
||Pred.<br>prob.|Marginal<br>effects|Percentage<br>change|Marginal<br>effects|Percentage<br>change|Sumof<br>marginal|<br>Percentage<br>change<br>|
|||(p-level)|<br>I<br>|(p-level)||<br>effects|I|
|||<br>_Pr_<br>(Hou|__<br>_ivate_<br> <br>old)|<br>_Com_<br>(Villageles|_munity_<br> Household)|(Sumo|_Net_<br>fthetwo)|
|HHinformed||<br>_0099_||<br>_015_||||
|<br>about3types|.327|_._<br>_(495)_|_5.3%_|_._<br>(-.651)|8.1%|.-.005|-1.6%|
|HHreportsall3<br>"moreopen"|.185|.021|**_11.4%_**|_(031)_|_6.5%_|_.033_|_17.8%_|
|Someparticipation<br>inplanning_desa_<br>programs|0.344|0.050<br>(.001)|_14.4%_|-0.008<br>(.832)|-2.2%|.04|12.2%|
|Someparticipation<br>indetermining<br>sanctions|0.138|0.026<br>(.011)<br>(01)(08|_18.6%_|-0.042<br>(.088)|-30.1%|-.016|-11.5%|
|HHinvolvedin<br>protest|0.089|_0.012_<br>_(104)_|1<br>_13.5%_|_0.028_<br>_(127)_|_31.5%_|_0.04_|_44.9%_|
|No expressionin<br>spiteofproblem<br>(positiveisless<br>voice)|0.174|0.0054<br>(.643)|3.1%|-0.056<br>(.037)|-32.2%|_-0.0506_|_-29.1%_|
|Mosteffective<br>expression<br>(problem,voice,<br>solution)|0236|-0.003<br>(.763)|-1.3%<br>.|_0.0143_<br>_(629)_|_6.1%_|_0.011_|_4.8%_|
|PErcoblemsc|.076|_.012_|_15.1%_|_.0171_|_15.8%_|_.024_|_30.9%_|
|SocialProblems|.389|.047|_12.1%_|-.057|-14.7%|-.010|-2.6%|
|Environmnental<br>||-.006<br>||-.034<br>||||
|Problems|.523|.006|-1.1%|(.456)|-6.5%|-.040|-7.6%|
|Notes:Boldeditems<br>a)seeAnnex2forad|arebased <br>escription|onprobit coeffici<br>ofthereportingo|entsstatistically<br>ftheprobitresul|significantat ap<br>ts.|-levelof10%lev|elorlower.||

_Net impact._ Looking across the ten indicators, the _net_ effect of social

> organizations stands in sharp contrast to that of the _desa_ government groups. The sum of

> the private and community impacts indicates that increased activity in social

organizations is usually associated with improved governance outcomes. However, for some of the indicators (such as participation in determining sanctions), a negative community impact outweighs the positive private effect. In spite of the mixed results (both in terms of statistical significance and direction of signs), it is worth noting the generally beneficial effects of higher engagement in social organizations. Although they 31
- 14-

# **2.2. Investment Dynamics: An Error Correction Approach**

**The** process by which firms move from actual to desired levels of capital stock is hypothesized to follow an error correction process. There are a number of advantages to such an approach. Firstly, error correction models have proven to **be** usefvl for explaining a variety of **long** run macroeconomic **relationships.24 Secondly,** unlike **the** more common partial adjustment model, the error correction approach implies that firms incur no costs for changes that are planned.25 Thirdly, the recent literature on cointegration provides a theoretical rationale for the empirical success of error correction models.26 Specifically, the error correction levels term captures the long run equilibrium relationship between variables while the differenced terms capture the dynamics. Granger establishes that error correction models produce cointegrating sets of variables and that cointegrating series can be represented by an error correction process.27 Series are said to cointegrate if some linear combination produces a stationary, or "white noise," error. Fourthly, the error correction framework is intuitively appealing because it provides a realistic representation of how rational, but fallible, agents make decisions. In the context of a developing country in which information may be far from perfect, decisions about the long run capital stock desired are likely to be characterized by gradualism and revision.

> 24 Davidson, Hendry, Srba, and Yeo, 1978; Bean, '981; Currie, 1981; Salmon, 1982; Henry and Minford, 1988.

> **25** Nickell, 198.5.

**26** Engle and Granger, 1987; Jenkinson, 1986; Dolado and Jenkinson, 1987; Giovanetti, 1987; Henry and Minford, 1988.

**27** Granger, 1983 and 1986.
<!-- Start of picture text --> a 1990 b 1995<br>ro po oi HE ie . = os GO ee e.<br>Ts a F g . = Sees i A<br>‘ @. a. ><br>> ia<br>c 2000 d 2005<br>- 7 — ms + 2 ale Rae<br>( @ (<br>oo . #<br>e 2010 f 2015<br>a. - ( @.*<br>GDP (PPP) in constant 2011 int. USD<br>Slt A<br>0 25x10° USD 50x10" USD 75x10°USD — 100x10® USD<br><!-- End of picture text -->
|Traditional|-0.035**|-0.019|-0.011|-0.073|-0.054|-0.026|-0.056|-0.036|0.041|0.076*|0.028|-0.067|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(-2.42)|(-1.27)|(-0.73)|(-1.30)|(-0.87)|(-0.51)|(-1.31)|(-0.58)|(0.55)|(1.74)|(0.58)|(-0.58)|
|Age||0.150**|0.171**|0.147**|0.155**|0.151**|0.152**|0.151**|0.147**|0.156**|0.157**|0.158**|
|||(2.13)|(2.30)|(2.00)|(2.13)|(2.12)|(2.13)|(2.14)|(2.10)|(2.25)|(2.23)|(2.25)|
|Age_sq||-0.002**|-0.002**|-0.002**|-0.002**|-0.002**|-0.002**|-0.002**|-0.002**|-0.002**|-0.002**|-0.002**|
|||(-2.34)|(-2.43)|(-2.23)|(-2.30)|(-2.33)|(-2.33)|(-2.34)|(-2.30)|(-2.44)|(-2.41)|(-2.45)|
|Married||0.242|0.185|0.235|0.176|0.222|0.236|0.252|0.215|0.179|0.238|0.225|
|||(0.62)|(0.47)|(0.59)|(0.44)|(0.55)|(0.60)|(0.64)|(0.54)|(0.46)|(0.62)|(0.57)|
|At least secondaryschool||0.105|0.270|0.096|0.106|0.109|0.117|0.115|0.127|0.151|0.271|0.087|
|||(0.29)|(0.75)|(0.26)|(0.29)|(0.30)|(0.32)|(0.31)|(0.35)|(0.41)|(0.74)|(0.24)|
|Gender of head(male=1)||-0.052|0.448|-0.023|0.159|-0.028|-0.066|-0.011|-0.038|0.096|-0.012|0.162|
|||(-0.07)|(0.62)|(-0.03)|(0.21)|(-0.04)|(-0.09)|(-0.01)|(-0.05)|(0.13)|(-0.02)|(0.20)|
|Muslim(Christian is base)||-0.010|0.065|0.007|-0.036|0.006|-0.004|-0.012|0.025|0.138|0.037|0.018|
|||(-0.02)|(0.15)|(0.01)|(-0.08)|(0.01)|(-0.01)|(-0.03)|(0.05)|(0.29)|(0.08)|(0.04)|
|Spousepresent||-0.244|-0.798|-0.272|-0.444|-0.258|-0.225|-0.317|-0.234|-0.304|-0.238|-0.427|
|||(-0.35)|(-1.19)|(-0.39)|(-0.65)|(-0.37)|(-0.32)|(-0.47)|(-0.33)|(-0.42)|(-0.33)|(-0.59)|
|Household size||-0.035|-0.040|-0.034|-0.035|-0.035|-0.036|-0.034|-0.036|-0.048|-0.043|-0.040|
|||(-0.53)|(-0.59)|(-0.49)|(-0.52)|(-0.53)|(-0.54)|(-0.52)|(-0.55)|(-0.75)|(-0.68)|(-0.61)|
|Poor||0.085|0.122|0.099|0.058|0.085|0.079|0.083|0.085|0.181|0.183|0.025|
|||(0.22)|(0.32)|(0.25)|(0.15)|(0.22)|(0.21)|(0.22)|(0.22)|(0.47)|(0.48)|(0.06)|
|Urban||1.496***|1.536***|1.509***|1.474***|1.498***|1.514***|1.484***|1.493***|1.624***|1.502***|1.501***|
|||(2.79)|(2.89)|(2.82)|(2.74)|(2.80)|(2.80)|(2.77)|(2.81)|(3.01)|(2.79)|(2.82)|
|Ashanti(base: Northern)||0.579|0.420|0.608|0.513|0.580|0.586|0.585|0.592|0.546|0.542|0.611|
|||(1.06)|(0.79)|(1.09)|(0.96)|(1.03)|(1.07)|(1.08)|(1.02)|(0.96)|(1.00)|(1.12)|
|Impulsive|||-0.111|-0.897|||||||||
||||(-0.45)|(-1.08)|||||||||
|Work centrality|||-0.216||-0.628||||||||
||||(-1.10)||(-0.90)||||||||
|Tenacity|||0.081|||-0.133|||||||
||||(0.38)|||(-0.19)|||||||
|Polychronicity|||-0.019||||-0.555||||||
||||(-0.09)||||(-0.78)||||||
|High locus of control|||-0.313*|||||-0.317|||||
||||(-1.89)|||||(-0.36)|||||
|Achievement|||0.297||||||0.484||||
||||(1.02)||||||(0.53)||||

37
<!-- Start of picture text --> Global<br>re<br>a<br>0 30 vena : Indirect Tax<br>*<br>~~ Payroll Tax<br>o<br>35 o0-- . | Personal Income Tax<br>><br>Ma ery<br>to4- : a Corporate Income Tax<br>0 ;<br>1965 1975 1985 1995 2005 2015<br>High-Income Countries Low & Middle-Income Countries<br>_— _<br>oO a<br>= =<br>ES &a)<br>oo<br>3<br>na ia3<br>s 10 : 4 105° :<br>oF: ' o4:ae .<br>1965 1975 1985 1995 2005 2015 1965 1975 1985 1995 2005 2015<br><!-- End of picture text -->
# **Appendix Table A3c: Impact on Individual Stockup Practices (ITT)**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Top Selling Products<br>Always in Stock|Adjusted Stock Based<br>on Product<br>Profitability|Negotiated Lower<br>Prices with a Supplier|<br>Compared Product Prices<br>and Quality Across<br>Suppliers|
|Assigned Handbook|0.031|-0.001|-0.008|-0.044|
||(0.029)|(0.033)|(0.028)|(0.034)|
|Assigned Handbook & Movie|0.066**|0.039|0.018|0.037|
||(0.028)|(0.034)|(0.029)|(0.035)|
|Assigned Handbook & Assistance|0.026|-0.010|0.041|0.007|
||(0.028)|(0.034)|(0.029)|(0.034)|
|Assigned All Three|0.041|0.091***|0.055*|0.038|
||(0.029)|(0.034)|(0.029)|(0.034)|
|R-squared|0.054|0.111|0.108|0.153|
|N|2205|2205|2204|2204|
|Dependent Variable Mean in Control Group|0.770|0.420|0.195|0.500|
|F-test (p-value): Book = Book & Movie|0.198|0.226|0.350|0.018|
|F-test (p-value): Book = Book & Assistance|0.862|0.785|0.084|0.125|
|F-test (p-value): Book = All Three|0.720|0.007|0.025|0.013|
|F-test (p-value): Book & Movie = Book & Assistance|0.130|0.144|0.446|0.372|
|F-test(p-value): Book & Move = All Three|0.348|0.130|0.210|0.965|

**Notes:** This table presents impact analysis (Intent-to-Treat) for individual stock up practices across midline and endline survey rounds. Regressions use an ANCOVA specification, where a midline survey round dummy, baseline value of the outcome variable as well as stratification controls are included on RHSs and standard errors are clustered at retailer level. Statistically significant p-values are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% significance level). Details on business practices can be found in Appendix Table A10.
# **-** **<u>Table 7: Comparison of Means Household Outcomes (2007)</u>**

|||**Baseline**||||
|---|---|---|---|---|---|
||**Comparison**|**Treatment**|**Diff**|**Stdev**|**Pval**|
|**Respondent Information**||||||
|Correctly Names Ward Member/President|58%|64%|5.2%|0.034|0.126|
|Heard of MNREGS|24%|24%|-0.2%|0.026|0.938|
|Heard of Gram/Ward Sabha|17%|23%|6.5%|0.022|0.003|
|Heard of Jamma Bandhi|1%|3%|1.3%|0.005|0.018|
|**Participation**||||||
|Attended Gram/Ward Sabha|9%|12%|3.2%|0.014|0.023|
|Attended any meeting in last year|4%|6%|2.5%|0.008|0.004|
|Feels President is responsive to needs|26%|27%|0.7%|0.029|0.816|
|**Government Transfers**||||||
|House provided under government|15%|16%|1.3%|0.019|0.492|
|Government transfer for housing|19%|19%|0.3%|0.020|0.882|
|Eligible and received BPL card|57%|48%|-8.8%|0.033|0.009|
|**Contribution and Payments**||||||
|Paid tax last year|62%|58%|-4.1%|0.043|0.350|
|Made contributions last year|74%|67%|-7.0%|0.042|0.095|
|Proportion of infrastructure contributed|0.09|0.09|0.2%|0.007|0.761|

***Note:** The standard errors for the difference in means are clustered at the level of the Gram Panchayat.

# **Table** **<u>8: Difference-in-differences – Household Outcomes</u>**

||w/o<br>|FE<br>|w/ indivi<br>|dual FE<br>|
|---|---|---|---|---|
||treateff|pval|treateff|pval|
|**Respondent Information**<br>|||||
|Correctly names Ward Member/President|-0.0561|[0.159]|-0.0545|[0.178]|
|Heard of MNREGS<br>|0.0411|[0.236]<br>|0.0346|[0.327]<br>|
|Heard of Gram/Ward Sabha<br>|0.0300|[0.444]<br>|0.0269|[0.497]<br>|
|Heard of Jamma Bandhi|-0.00700|[0.154]|-0.00707|[0.150]|
|**Participation**<br>|||||
|Attended Gram/Ward Sabha<br>|0.0326|[0.138]<br>|0.0292|[0.167]<br>|
|Attended any meeting in last year<br>|0.0185|[0.436]<br>|0.0180<br>|[0.456]<br>|
|Feels President is responsive to needs<br>|0.0484|[0.150]|0.0706*|[0.0792]|
|**Government Transfers**|||||
|House provided under government scheme|-0.0110|[0.446]|-0.0194|[0.195]|
|Government transfer for housing|-0.00300|[0.858]|-0.0128|[0.431]|
|Eligible and received BPL card|0.0471|[0.211]|0.0537|[0.135]|
|**Contribution and Payments**|||||
|Paid tax last year|0.0616|[0.167]|0.0639|[0.162]|
|Made contributions last year|0.124|[0.104]|0.124|[0.105]|
|Proportion of infrastructure contributed to|0.00120|[0.919]|0.000696|[0.954]|

***Note:** The standard errors for the difference in means are clustered at the level of the Gram Panchayat.

33
||**wetland by 0.16 m**<br>**of SLR**||**wetland by 0.16 m**<br>**of SLR**|
|---|---|---|---|
|West Africa|-0.07|Australasia|-0.12|
|Central Africa|-0.13|North Africa|-0.21|
|East Africa|-0.12|West Asia|-0.22|
|South Africa|-0.17|West Europe|-0.17|
|South Asia|-0.1|Central Europe|-0.2|
|South-East Asia|-0.12|East Europe|-0.19|
|East Asia|-0.22|Canada|-0.06|
|Central Asia|0|USA|-0.24|
|Meso-America|-0.18|South America|-0.19|
|Brazil|-0.09|-|-|

Each of the 140 GTAP9 database regions has been associated to one macro-region of Table 1. The percentage loss in coastal wetland (Table 1) has been multiplied by the percentage of erodible coast and applied to the whole coast. For the European regions, the shares of erodible coast have been obtained from the Eurosion project (www.eurosion.org), while for the remaining countries we have adopted the 70% value suggested by Bird (1987, 2010). Considering which fraction of total coast is suitable for agricultural and other productive activities we have estimated the fraction of agricultural land which is lost when SLR equals 0.16 meters. Scaling up, we got the share of productive land which is lost for one meter of SLR, labelled _LR_ . Data on coastline length are provided by the CIA database (www.cia.gov); data on the fraction of coast suitable for agricultural activities have been obtained from UNEP (2005).

_LRT_ The percentage change in the land stock by year and country, , is computed by multiplying the percentage of effective land change by meter of SLR, _LR_ , and the predicted adjusted SLR, as follows: Notice that the impact function (3) has four parameters. Two parameters (α _,_ β) are common across all regions, two other parameters ( _LR_ and _VR_ ) are country/region specific.

Table A1 in the Appendix shows, for each GTAP9 region, the percentage loss of land by meter of SLR, corresponding to the parameter _LR_ in (3), and the vertical land motion (VLM), corresponding to the parameter _VR_ .

Table A2 in the Appendix illustrates the percentage losses of productive land endowments for +1, +2, +3, +4 and +5 °C increases in average temperature, at the years 2050 and 2100, for all 140 countries and regions. As one can see, relevant physical effects of SLR are concentrated in a few countries, in particular: small island states of Oceania, Central America and Asia, Hong Kong SAR, China, Japan, Singapore, Jamaica, Puerto Rico, Trinidad and Tobago, Cyprus, Croatia, Bahrain, Kuwait, Qatar, United Arab Emirates and Mauritius.

# **3. Climate change impact #2: Variation in crop yields (agricultural productivity)**

Climate change is expected to bring about higher temperature, higher concentration of carbon dioxide in the atmosphere, and a different regional pattern of precipitation. These are all factors affecting crop yields and agricultural productivity. Not surprisingly, effects of climate change on agricultural production volumes are perhaps the most studied area of sectoral impacts.

5
connection; titling non-SOE, titling non-SOE * political connection. Here “political connection” is either _bureaucrats on board_ or _the party member ratio_ . The results are consistent. For SOEs, the titling effect does not hinge on further political connections—that is, state ownership is enough. For non-SOEs, the titling effect highly hinges on the existence of political connections.

What are the _channels_ through which political connections help connected firms to take advantage of the titling program while effectively prevent the non-connected ones from doing so? We search for evidence on the roots of the lack of responses of non-SOEs directly from the annual reports of those titling firms. We checked their annual reports for information on their progress in the land titling program. When non-SOEs explain the slow progress of their application for titles, they do not mention financial constraints and investment opportunities. In contrast, they frequently cite dealing with legal and administrative barriers. Firms usually state that the slow progress of their application for land title is due either to their disputes with the government (or the court) or to the disputes with other firms. This is consistent with international evidence that government discretion in enforcing rules is substantial, and that the government’s treatments of regulatory rules differ greatly within countries among firms (Hallward-Driemeier and Pritchett 2015).

We therefore explain whether the titling firm has a dispute with the government or other firms over its land. We estimate the linear probability model with the dependent variable being the dummy variable of the firm having a dispute with government or other firms over the land, using the sample of titling firms. The result is in Table 11. In Column (1), the probability of having disputes with either the government or other firms are presented. The negative and large coefficient of the SOE dummy suggests that those SOEs are much (i.e., 37 percent) less likely to have a dispute with either government or other firms, rendering them a significant advantage to implement the titling program quickly. The results remain robust when we consider only the disputes with the government (column (2)) or the disputes with other firms (column (3)). Thus, 33