rafmacalaba/datause-encoder-data huggingface dataset:
_Table 2 (continued)._

# **ii) Performance criteria for the longer term (mostly for 2030):**

||**_Short name_**|_Explanation_|_Related SDGs_|
|---|---|---|---|
||**Energy**|Energysavings(ktoe) per million euros invested|7|
|**act**|**CO2**|CO2emission savings(tn) per million euros invested|13|
|**vironmental imp**|**Low-carbon technologies /**<br>**strategies**|Does the intervention provide the technical means to better integrate or<br>employ low-carbon technologies or strategies (for instance, through<br>improvements to transmission and distribution infrastructure, public transit<br>infrastructure, sidewalks or bike lanes, or by promoting denser urban<br>development) that may yield benefits**beyond the year 2030**? Does it contribute<br>to a deepdecarbonization objective**by 2050**?|13, 15|
|**En**|**Other Environmental Impact**|Other short-term environmental impact (on air quality, nature, water resources,<br>landproductivity,biodiversity,etc.)|3, 6, 11, 15|
||**Economic multiplier**|Economic outputgeneration(million €) per million euros invested|8|
||**Jobs**|Net employmentgeneration(persons) per million euros invested|8|
||**Energy security**|Does the intervention increase local/national energysecurity?|7|
|**mpact**|**Infrastructure & Productivity**|Will the intervention improve local economic productivity through access to<br>better,more reliable infrastructure services?|9|
|**l i**|**R&D and innovation**|Can the intervention spur R&D or innovation in the specific technologies?|9|
|**/ socia**|**Market Failures**|Will the intervention address market failures, such as market distorting<br>subsidies, pricingthat fails to account for externalities,etc.?|8|
|**Economic**|**Economic / Climate Resilience**|Does the intervention improve socio-economic resilience, that is, the ability of<br>the population to cope with and recover from shocks? Does it improve their<br>adaptive capacity, that is their ability to reduce negative impacts (such as<br>adaptingbuildings to improve resilience to extreme temperature)?|1, 8, 10, 11|
||**Decarbonization / Effect on**<br>**NDC**|Does the measure contribute substantially to decarbonization of the economy<br>by 2030? Does it significantly affect the country’s NDC to be submitted to<br>UNFCCC?|12, 13|

9
cause a 2.8% reduction in annual GDP growth rate. Unfortunately, estimates of precipitation from GCM tend to be less skillful than projections of temperature. The projections of mean changes in precipitation are often characterized by wide ranges and in some cases disagreement on the direction of change. The ability to project variability of rainfall is also less skillful than mean projections of temperature or precipitation. Given our findings, it would be very helpful to have increased ability to project future precipitation variability for estimating future impacts of climate change and identifying adaptation priorities.

While estimating future exposure to drought and flood remains problematic, it is clear from these results that historical levels of precipitation variability already impede economic progress. Managing this hydroclimatic risk in the future is likely to be more difficult because of the changing climate conditions that may alter the historical frequency of floods and drought. Traditional engineering methods for designing and managing water resources infrastructure and institutions use the historical hydrologic record as guidance. Our knowledge of climate variability and change implies that this may not be a valid assumption to base water management on in the future. New methods that are able to respond dynamically to evolving climate conditions will be necessary. This is an open and fairly nascent area of research. The results of this analysis imply that it is a quite important one for future economic growth.

# **_Conclusion_**

The analysis presented in this paper was conducted to identify the influences of climate on economic activities in the countries of the world. Based on previous research and the published literature, precipitation, temperature and statistics of precipitation variability were investigated as explanatory variables in fixed effects regressions of economic activity statistics over several decades. Several model specifications were utilized to increase the robustness of any findings derived from the regression results. The analysis resulted in several significant findings.

23
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36
# 3.4 _Informal competition in the service sector_

Our empirical analysis excludes the service sector (retail and other services) because information asymmetry about the quality of the product and management practices in the service sector are very different from those in the manufacturing sector. However, countries with a high level of informality in manufacturing are also likely to have a high level of informality in the service sector. For the countries in our sample, the correlation between the proportion of formal manufacturing SMEs and formal service sector SMEs in the country that compete against informal firms is 0.71. So, we use the proportion of formal service sector SMEs in a country-year (cell average) that compete against informal firms as a proxy for the informal competition faced by the formal manufacturing SMEs in the country-year. The advantage of using this proxy is that the informal competition faced by service sector SMEs is relatively exogenous to manufacturing firms. Regarding reverse causality, it is unlikely or inconceivable that the certification decision of a manufacturing SME has any impact on the level of informal competition faced by service sector SMEs. Regarding the omitted variable bias problem, it is unlikely that the unobserved or omitted characteristics of a manufacturing SME that affect its certification decision are correlated with the informal competition faced by service sector SMEs.

# **4. Data, main variables, and estimation methodology**

# 4.1 _Data sources_

Our main data source is the World Bank Enterprise Surveys, or WBES, which are firm-level surveys of private firms conducted by the World Bank. The surveys are nationally representative of the private sector excluding agriculture, extractive industries, and some service sectors like finance, education, and health care. The WBES covers only formal or registered firms with five or 16
The first case occurs when the ex-ante profitability of the infrastructure project or _RM_ of the industry is low (i.e. for _K ≥ K_ (+ _∞_ )). When _K_ is large there is room for at most one firm. The optimal industrial policy then is monotone in the opportunity cost of public funds. We distinguish two cases. First when _K_ is larger than _V/_ 2 the project is not privately feasible. Depending on _λ_ being either low or high the optimal industrial policy is public ownership (in wealthy countries) or no production (in poor countries). In figure 2 public, regulated monopolies that are desirable under asymmetric information are depicted by the white area denoted _RM_ , no production corresponds to the area denoted _∅_ . Second when _K_ falls below _V/_ 2, the project becomes privately feasible for one firm. Regulation is preferred to privatization if _λ ≤ λ_<sup>�</sup> and it is the reverse otherwise. When _K_ is close to _V/_ 2 private monopoly is preferred for large _λ_ simply because it is feasible whereas regulated monopoly is not. By continuity private monopoly dominates regulated monopoly for lower values of the fixed cost. This situation is denoted _PM_ and _RM/P M_ is represented by the hatched area above the curve _K_ .

Because developing countries have large opportunity costs of public funds, they may implement industrial policies that strongly differ from those implemented in advanced economies. There are for instance a public good aspect and externalities associated to sunk cost investment such as infrastructure (e.g., a road). As recommended by standard economic theory wealthy nations subsidize the construction of the infrastructure and let people use it at marginal cost (e.g., for free). With a low opportunity cost of public funds this policy maximizes welfare. On the other hand many developing countries plagued by financial problems have started build-operate-and-transfer (BOT) programs. In such programs, a private firm finances the sunk cost of an infrastructure, for instance a highway, in exchange for a 10-30 years licence to exploit it in a monopoly position. Clearly, a privately owned and operated infrastructure, even with the monopoly distortion (e.g., a toll), is a better solution than no infrastructure at all, which is, in the absence of public financing, the alternative to privatization. Water supply, which typically is provided worldwide through public ownership, could be a good candidate for such type of privatization. This is at least what is advocated by Brook Cowen and Cowen (1998). In developing countries tariffs are so low that on average they do not cover half of the total cost (World Bank 28
In a study using survey data in Euro area, Gnan et al. (2011) find that education and income are more strongly correlated with inflation expectations than age and gender, and argue that this is consistent with the findings on financial literacy.

# **_In lower-income countries, there is a relationship between financial literacy and having a bank account_**

One of the key questions that arises in developing countries is whether financial literacy and financial access are causally linked. In fact, in most countries surveyed by FinScope, the primary reason cited for not having a bank account is lack of income or the inability to maintain a minimum balance, rather than lack of knowledge. In Malawi, where only 19 percent of the population has a formal bank account, these reasons account for the overwhelming majority of responses. Less than 10 of percent respondents cite financial literacy-related reason, such as not knowing how to apply for an account. (It is possible, however, that perceptions of minimum required balances, for instance, may be incorrect.) At the same time, almost 80 percent had either never heard of savings accounts or did not know what they were, and the figure is lower for current or checking accounts. Income-related reasons are also predominant in Rwanda, Namibia, and Tanzania, although a higher percentage of adults in Tanzania (21 percent) also said that they did not know how to open an account.

In an attempt to provide a more definitive answer, Honohan and King (2009) use data from FinScope surveys to estimate the impact of various potential determinants of having a bank account. They include a variable for financial sector knowledge (a normalized index of the number of products from a given list that a respondent claims to know), which might be regarded as a weak proxy for general financial literacy. They find this variable to be positive and significant when controlling for country fixed effects, but the sign is reversed without the fixed effects. Hence, no clear correlation can be drawn.

Regardless, while the role of financial literacy in determining bank accounts ownership is somewhat unclear, it could prove to be a greater constraint in other areas such as the take-up of insurance products, as discussed below.

18
by industry in the United States and in India, together with the breakdown of the ICT and NICT shares by industry in the US. Notice that, while agriculture has the highest ICT share in absolute terms, it also has a substantial NICT share. The same is true for industry excluding manufacturing. Put differently, the relative expenditure share of ICT and NICT is much higher in manufacturing and services than it is in agriculture and industry excluding manufacturing. Since India is producing much more intensively in the latter two sectors (compared to the US) this suggests that India will use less ICT in the aggregate, simply based on its industrial composition. More precisely, to calculate _Iind_ , we compute We derive _Ius_ analogously and arrive at approximately _I_<sup>_<u>I</u>_</sup> _ind_<sup>_<u>us</u>_=1</sup><sup>_._4.The benchmark ratio of ICT in</sup> the US relative to ICT in India is then given by BR = 28 _._ 5 _·_ 1 _._ 6 _·_ 1 _._ 4 = 63.

Thus, for the specific example of India, an observed 30-fold difference in broadband subscriptions relative to the US is actually suggestive of ICT utilization rates that are high compared to our benchmark. Given the capital labor ratios, the price differentials and the industrial composition, India appears to be utilizing ICT beyond what would be expected.

It is worth emphasizing that the above benchmarking procedure does not utilize our estimates of ICT and NICT capital stocks. The procedure provides a benchmark for ICT prevalence, that requires as inputs only aggregate capital-labor ratios, value added by industry, and estimates of the relative prices of ICT and NICT capital goods. Since capital-labor ratios are available widely from the PWT and industrial composition is available widely from the WDI, the only input that is not readily available is the relative ICT price. Our ICP-based estimates for relative prices are available for a wide variety of countries and can be utilized in this benchmarking procedure.

# 6. Concluding Remarks

This paper documents a correlation between income per capita and the ICT capital stock. This correlation is not surprising: technology adoption lags would imply that capital that embeds rel30
# **Annex Table 6: Development Drivers of the Non-Life Insurance Industry**

## **Annex Table 6: Development Drivers of the Non-Life Insurance Industry (Source: Feyen, Lester, and Rocha (2010))**

The dependent variable is the logarithm of the volume of non-life insurance premiums as a fraction of GDP. The sample consists of annual data for the period 2000-08. All regressions are estimated using pooled Ordinary Least Squares. Year-fixed effects and a constant were estimated but are not reported. LSCHOOLING, LLIFE_EXP, LINCOMETOP20, and LCONTRIB_RATE are sample period averages. In all regressions, White‟s heteroskedasticity-consistent <u>(clustered) t-values are given in parentheses (brackets). ***, ** and * represent significance at 1, 5 and 10% level respectively.</u>

|VARIABLES|1|2|3|4|5|6|
|---|---|---|---|---|---|---|
|LGDPPC|0.14|0.26|0.30|0.20|-0.056|0.20|
||(3.48)***|(18.4)***|(14.6)***|(10.1)***|(-0.46)|(7.84)***|
||[1.81]*|[6.61]***|[7.26]***|[3.59]***|[-0.38]|[3.91]***|
|LPOP|-0.0017|-0.035|-0.073|-0.034|-0.020|-0.069|
||(-0.081)|(-3.40)***|(-5.38)***|(-2.94)***|(-0.61)|(-5.17)***|
||[-0.041]|[-1.22]|[-2.65]***|[-1.05]|[-0.47]|[-2.61]**|
|LPOP_DENSITY|0.091|0.0042|-0.022|0.0015|0.031|-0.046|
||(3.81)***|(0.32)|(-1.20)|(0.11)|(1.01)|(-2.64)***|
||[1.90]*|[0.13]|[-0.60]|[0.039]|[0.78]|[-1.33]|
|INFLATION|0.059|0.22|0.98|1.23|1.32|1.23|
||(0.14)|(1.41)|(2.19)**|(5.32)***|(2.52)**|(3.11)***|
||[0.098]|[0.85]|[1.22]|[4.03]***|[1.91]*|[1.71]*|
|MUSLIM||-0.56|||-0.57|-0.28|
|||(-11.3)***|||(-4.47)***|(-4.53)***|
|||[-4.02]***|||[-3.57]***|[-2.23]**|
|CONCENTR.|||-0.060||-0.23|-0.11|
||||(-1.15)||(-2.06)**|(-1.92)*|
||||[-0.58]||[-1.59]|[-0.97]|
|PRIVATE|||0.15||-0.091|0.12|
||||(2.64)***||(-0.75)|(1.97)**|
||||[1.33]||[-0.62]|[0.99]|
|LEGAL_RIGHTS|||0.055||0.048|0.041|
||||(5.53)***||(2.17)**|(4.20)***|
||||[2.76]***||[1.50]|[2.09]**|
|LPC||||0.30|0.14|0.16|
|||||(10.2)***|(1.60)|(4.80)***|
|||||[4.02]***|[1.09]|[2.35]**|
|LCARS|0.19||||0.24||
||(4.62)***||||(2.44)**||
||[2.44]**||||[2.04]**||
|LTRADE|0.057||||||
||(1.02)<br>[0.54]||||||

38
Table 7: Impact of Incentives on Birth Outcomes

||(1)|(2)|
|---|---|---|
||Intervention Period|Post‐Intervention Period I<br>(Jan – Dec 2011)|
|**A. Birth Weight**|||
|Treatment|‐37.34<br>(48.61)|25.109<br>(40.67)|
|Large Sample_p_‐value|0.44|0.54|
|Wild Bootstrapped_p_‐value|0.49|0.51|
|Control Group Mean|3,304|3,279|
|Sample Size|555|802|
|**B. Low Birth Weight**|||
|Treatment|0.01<br>(0.02)|‐0.01<br>(0.02)|
|Large Sample_p_‐value|0.63|0.60|
|Wild Bootstrapped_p_‐value|0.61|0.56|
|Control Group Mean|0.05|0.06|
|Sample Size|555|802|
|**B. Premature**|||
|Treatment|0.03|‐0.04|
||(0.03)|(0.02)|
|Large Sample_p_‐value|0.31|0.08|
|Wild Bootstrapped_p_‐value|0.28|0.12|
|Control Group Mean|0.09|0.12|
|Sample Size|414|708|

Notes: This table reports LATE estimates of the treatment effect of the modified fee schedule for on indicators of birth outcomes. The observations include woman for whom we are able to obtain information on birth outcomes provided in public hospital birth records. The differences are estimated from 2SLS regressions of the dependent variable on actual treatment status instrumented with clinic treatment assignment type. The _p_ ‐ values are for 2‐sided hypothesis tests of the null that the difference is equal to zero. We present both the _p_ ‐ value computed for large samples and a Wild bootstrapped _p_ ‐value that is robust in samples with small numbers of clusters (Cameron et al. 2008). Our Wild bootstrap procedure assigns symmetric weights and equal probability after re‐sampling residuals (Davidson and Flachaire 2008) and uses 999 replications. Column (1) reports the results for the sample observed in an 8‐month intervention period (May 2010 – December 2010). Column (2) reports the results for the sample observed in the 12‐month period following the end of the intervention (January 2011 – December 2011). Standard errors are in parentheses. * _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.01.

41
is needed. This is because it relies on voluntary action (i.e., people decide whether to change their behavior). Consequently, as stated by the theory of coinciding interest, this instrument can perform well only when the desired actions are both in the private interest of the economic agents and in the public interest of the regulator (Vedung and van der Doelen, 1998).

# **_2.2 Information Policy and Willingness to Pay for Environmentally Friendly Products_**

Because individuals consider relevant available information in their decision-making process, information is one of the determinants of an individual’s reservation price. There is a vast literature analyzing the effects of information provision on individuals’ willingness to pay for consumption goods in several domains. For instance, Oparinde et al. (2016) estimate the effect of nutrition information campaigns and the nature of planting material delivery institutions on consumer demand for biofortified yellow cassava varieties in two states of Nigeria. Consumer demand was estimated using the BDM mechanism. Results indicate that nutrition information results in a large and significant price premium in both states, whereas the nature of the delivery institution has no significant effect. In the health domain, Rousu et al. (2011) estimate the value of counter-marketing information aimed at countering tobacco company claims about the health benefits of reduced-risk cigarettes. By using data from experimental auctions, the authors find no effect of counter-marketing information on smokers’ purchasing behavior when individuals are not exposed to marketing information. However, for individuals who are presented with both tobacco company information and counter-marketing information, there is a significant average value per smoker of 8.5 cents per pack. Similarly, Monchuk et al. (2003) find that consumers who are presented with even a small amount of information are more likely to prefer low-nicotine cigarettes. This effect is especially important for individuals who want to quit smoking, compared with hard-core smokers.

As for the demand for environmentally friendly products, Bougherara and Combris (2009) investigate whether information about an eco-labeled product (i.e., its limited private benefits relative to its social benefits) generates an effect on the willingness to pay of individuals. Findings indicate that information does not affect buying prices, suggesting that consumers’ willingness to pay for the eco-labeled product is not entirely explained by the attributes generating private benefits, but rather from altruistic motives. Thus, information policy can also be used to incentivize the adoption of or willingness to pay for environmentally friendly technologies. An example is Alem et al. (2017), who investigate the effects of information and social networks on the acquisition of solar lanterns in a non-electrified part of Uttar Pradesh, India. The program consisted of giving solar lanterns to randomly selected “seed” 7