rafmacalaba/datause-encoder-data huggingface dataset:
# the subregion).

The fi rst column of the table shows that stricter enforcement of labor regulation strongly affects the demand for informal labor by fi rms.<sup>31</sup> Therefore, enforcement is a tool that can potentially be used by governments to effectively fi ght noncompliance with the law. We estimate that, on average, when the number of fi nes per 1000 fi rms in the subregion increases by 1, the share of informal workers falls by 3.5 percentage points. In our sample, the average number of fi nes per 1000 fi rms in the subregion is 0.77 and the average proportion of informal workers in a fi rm is 0.23. Based on these numbers we compute that an elasticity of illegal employment with respect to enforcement of -0.12. This number is comparable to those obtained in the crime deterrence literature using US data: for example, Levitt (1998) fi nds that the elasticity of crime with respect to the arrest rate is about -0.2, and Bar-Ilan and Sacerdote (2004) fi nd that the elasticity of driving violations with respect to fi nes is also about -0.2. However, average enforcement of labor regulation is low and the average amount of violations is relatively high in our sample, so that an elasticity of -0.12 corresponds to a large absolute effect of fi nes on illegal behavior.

Given the strength of the effect of enforcement on informality, we are likely to also fi nd effects on productivity as well. Columns (2) to (5) of table 2 show that weaker enforcement of labor regulation is associated with higher fi rm productivity as measured by value added per worker, average wages, sales per worker and pro fi ts per worker. Furthermore, it is interesting that weaker enforcement is also associated with higher investment in capital and technology. This suggests that labor productivity increases because the fi rm operates more efficiently and the capital stock is higher. We estimate that an increase in 1 in the number of fi nes per

> 31Due to the unusual nature of our dependent variable and to the way the question is asked in the survey we may be concerned with measurement error. Measurement error in the dependent variable is not usually a problem, unless it is correlated with the independent variable of interest. In particular, fi rms may underreport informal behavior when they are faced with strict enforcement, in which case the coefficient of interest captures the effect of enforcement on both informal behavior and miss reporting and therefore is an overestimate of the true deterrent effect of enforcement. Even though we cannot address this concern in our paper we have replicated our results using a transformation of our dependent variable: a dummy which takes the value 0 if the fi rm reports no illegal employment and the value 1 if the fi rm reports any informal employment, although there is no way to say whether this transformation exarcebates or dampens the potential measurement error problem. Our results are essentially the same but some of the standard errors in our instrumental variables speci fi cations in the next section are larger. These results are available on request.

18
_a._ Taking into account both 2020 and 2050 objective <!-- Start of picture text --> b. Taking into account 2020 objective only<br><!-- End of picture text --> Figure 8: Comparison of optimal abatement strategies to reach the same target for 2020, taking into account or disregarding the longer-term 2050 objective. With an ambitious long-term target, the short-term strategy is based on the more expensive option with higher potential, not on the cheapest option.

achieve. As a result, the social planner starts abating latter (in 2010 vs 2006) and uses cheaper and lower-potential options, namely cheap and deep instead of deep only. The discounted expenditures in abatement measures amounts to 28 G$, against 112 G$ when the 2050 objective is taken into account: the optimal short-term nancial eort is much higher if the long-term target is taken into account (even though the abatment in MtCO 2 is the same).

If the 2050 target is not taken into account in the 2000-2020 period, then the 2050 target may appear in 2020 extremely costly, or even impossible, to achieve. In this illustrative example, the fourfold reduction in emissions becomes indeed impossible to achieve in 2050 if the long-term objective is not taken into account from the beginning, in 2000.

In other words, despite aggregate emissions being abated at the same pace in SO as in SL, the SO pathway produces a lock-in in a carbon intensive pathway that cannot be reversed in the second period from 2020 to 2050. In plain language, the optimal strategy to reach the 2020 target is dierent (and more expensive) if the 2050 objective is included in the optimization. With an ambitious longterm objective, the short-term target needs to be achieved through the implementation of the options with the largest potentials and the largest inertia, not with the cheapest solutions, as the MAC curve could suggest (see Fig 3).

# 6 Conclusion

This article investigates the use of expert-based MACCs to design abatement strategies. It shows that introducing inertia, in the form of a maximum amount of abatement that can be achieved over a given period of time, changes signicantly the message from MACCs. This cost in time, which complements the nancial cost, has a large inuence on the rank order of various strategies. In 16
**Primary commodity exporter:** A dummy variable indicating that the country is a net exporter of either oil, natural gas, or coal, or not (i.e., union of countries listed in Appendix 2).

**Resource rents:** Total natural resource contributions to GDP, which is the total rents (including oil, natural gas, coal, mineral, and forest rents) in percent of GDP.

**High state fragility** : A binary measure indicating the country is in high political fragility state.  This variable is built from the quantile of the fragility index (in the range from 0 (no fragility) to 4 (extreme fragility; from Marshall and Cole (2009)), which is one if the fragility index is above 2 (moderate fragility).

**Natural disaster** : This is a dummy variable generated from different disaster variables from EM-DAT database.  It is equal to one if a country had any of the following natural disasters in a given year; extreme temperature, floods, tsunamis or tidal waves, and landslides or avalanches. It is zero otherwise.

**Aid/GNI** : This variable is constructed by dividing nominal value of total amount of aid divided by nominal GNI (following Atlas Method).

**Log income pc in 2000** : Log Income per capita in 2000

**Gini index:** It measures the extent to which the distribution of income or consumption expenditure among individuals or household within an economy deviates from a perfectly equal distribution.  This paper uses the most recent index available in World Bank’s PovCal net and it can be either income or consumption-based depending on the data availability of national surveys in each country.

47
Few in academic or policy circles would dispute the view that infrastructure development fosters growth, but there is little consensus on the actual size of the effect and the factors that shape it. The empirical literature concerned with this issue took off following Aschauer (1989), who found that the stock of public infrastructure capital is a significant determinant of U.S. aggregate TFP. However, his estimates of the elasticity of output with respect to infrastructure were implausibly large (around 0.40), owing to a ‘spurious regression’ problem (see Gramlich 1994).

The massive empirical literature that followed focused on the impact of infrastructure on the level and growth rate of aggregate output or productivity, with numerous papers employing a large variety of data and empirical methodologies. Many authors estimated the elasticity of GDP with respect to infrastructure in an aggregate production function setting, using national or subnational data and time-series or panel techniques suitable for dealing with nonstationary variables and spillover effects. Early applications to panel data on U.S. states found much smaller elasticities than those estimated by Aschauer (e.g., Holtz-Eakin 1994; Baltagi and Pinnoi 1995). Drawing from a large number of subsequent empirical studies using aggregate data, primarily from industrial countries, a meta-regression analysis of the elasticity of output with respect to public capital yields an average estimate around .10, although the individual estimates from the underlying studies vary widely, from -1.73 to +2.04 (Bom and Ligthart 2014).

These studies use monetary measures of public capital, constructed by accumulating investment flows. Alternatively, others employ physical measures of infrastructure assets encompassing multiple infrastructure sectors — sometimes aggregated into a synthetic indicator. Empirical studies using the latter approach on cross-country panel data typically report a significant GDP (or productivity) contribution of infrastructure; see Canning (1999), Calderón and Servén (2003) and Calderón, Moral-Benito and Servén (2014).<sup>5</sup> In the Cobb-Douglas framework used by many of these papers, it is not possible to assess the extent to which the effects of infrastructure reflect its TFP-augmenting role. This is the focus of relatively few studies. Hulten and Schwab (2000) use a growth decomposition approach to examine the contribution of public capital to manufacturing TFP growth across U.S. states. They fail to find significant effects. Hulten, Bennathan and Srinivasan (2006) apply a similar approach

> 5 Regarding individual sectors, Roller and Waverman (2001) find a large output impact of telecommunications infrastructure in industrial countries, while Fernald (1999) reports similar results for roads using industry-level data for the U.S.

7
conclusion was that it would take 24% of the existing crop land in the US to grow oil palm that is considered as a high-yield oil crop. If growing soybean, it would take more than 3 times of the current cropland in the US. In addition, growing oil crops for biodiesel production would compete for arable land against food and feed production. To sustainably produce biodiesel without using a huge portion of the cropland that is critically needed to produce food and feed, it is necessary to explore alternative feedstocks for biodiesel production.

# **3.3. Second-generation biodiesel production from microalgae**

# 3.3.1. Feedstock production systems

Microalgae have a great potential as a future feedstock for biodiesel production because of their high growth rates and high oil content of some species. Microalgae generally double their biomass within 24 hours under normal growing conditions. The doubling time during the exponential growth phase for microalgae can be as short as 3.5 hours. The oil content of microalgae ranges from 15 to 75% (dry weight). Other major components of microalgae include carbohydrates and proteins. Some high-oil microalgal species are listed in Table 4.

**Table 4.** The oil content of some high-oil microalgae

|Microalgae|Oil Content (% dry wt)|
|---|---|
|_Botryococcus braunii_|25–75|
|_Cylindrotheca sp._|16–37|
|_Isochrysis sp._|25–33|
|_Nannochloris sp._|20–35|
|_Nannochloropsis sp._|31–68|
|_Neochloris oleoabundans_|35–54|
|_Nitzschia sp._|45–47|
|_Schizochytrium sp._|50–77|

Source: Source: Chisti, 2007.

Annual oil production from high-oil microalgae can be in the range of 58,700 to 136,900 liters per hectare (Chisti, 2007). If this microalgal oil is used for biodiesel production, it would take 28
**TaMe** 4

# Enrollment and Percent of Female Students In Unlvenltles and Equivalent Instituidons In Arab Countries

||_1_|_965_|_197_|_5_|_19_|_80's_|_Year_|
|---|---|---|---|---|---|---|---|
||_Total_|_%Fem_|_Total_|_%Fem_|_Total_|_%eFem_||
|Algeria|8,177|20.1|41,847|-|108,181|-|1984|
|Egypt|64,06|199|411,097|30.1|613,570|33.6|1983_a_|
|M6loooo|7,979|113|35,081|19.2|481|32.5|1964|
|iby|1,646|72|1 1<sup>,</sup>9 9<sup>7 b</sup>|15.8|27,535|_255_|1982 C|
|Tunisia|_529_|18.1|20,505|25.7|38,829|36.0|1984**c**|
|Jordan|1,169|402|4,805b|292|25,929|38.8|1984|
|Bahrain|--|-|703|52.6|2,085|733|1984|
|Iraq|28,337|26.9|71,456|293|84,751|33.5|1983|
|Kuwait|-|-|6,26|56.0|16,831|53.4|1985|
|Lebanon|20,338|18.1|-|-|70,510|39.0|1984|
|(iar|-|-|779|57.4|3,894|62.7|1984|
|Saudi Arabia|3,625|33|26,437|20.1|<br>79,356|35.8|1983|
|Syria|32,653|<br>16.8|65,348|22A|123,735|<br>29.7|1983|
|u.4.|-|-|-|-|6,326|549|1984|
|Yemaen,<br>AR.|_-_|_-_|2,408|102|4,519|<br>112|1980|
|Yemen, vmD.R|<br>-|_|934b|182|3,645|523|1983**c**|

**a** Excuding Al-Azhar Univerity. b Figures for 1974.

**C** Ibrd set of fips for anl leIels of higher education.

Souw" UNESCO **_Statii YeVbooks_** 1977, 1986 _&8 cULmS Sws and Reearch in Satiaics_ (CSR-B-54, Januaiy, 1987).

Cairo, Ain Shams, Alexandria and Assiut universities in Egypt grew from 109,700 in 1963-64 (Qubain, p. 71) to about 405,000 around 1984. Adding to this the enrollment in the seven other Egyptian universities established in the 1970s and the figures increase appreciably.

The past two decades witnessed heavy demand for entry to universities, coupled with socio-political pressure to meet the demand. There has been strong parental motivation to educate children at higher levels, especially sons. Table 4 provides enrollment figures in universities and equivalent institutions (excluding the non-degree colleges and institutes, unless otherwise specified) of sixteen countries during the period 1965-1985. It reflects the magnitude of the increase in enrollment since 1965 and of the percentage of females. As expected, the rate of the increase is greatest in countries where higher education has been introduced only recently.

It can be found from Table 4 that enrollment of university students grew from about 274,000 in ten countries in 1965 to about 1,336,000 in sixteen countries in the 1980s -- approximately a fivefold increase. Excluding countries which had no students in 1965, the _9_
Policy Research Working Paper 9468

# **Abstract**

Regulatory compliance is key in the fight against climate change and other environmental challenges. But regulatory agencies, especially in developing countries, are often hampered by their capacity to monitor and enforce standards and regulations against recalcitrant firms. There is now a big push toward self-reporting whereby the firms monitor and report on their compliance levels vis-à-vis the standards. This is seen as a way around the costs that agencies must incur if they were to scale up their inspections. In this paper, extensive firm-level data from India are used to compare the compliance level of firms when they are inspected by agencies versus the times when they self-report. Other factors that may determine regulatory compliance, such as age, size, sector, location, and so forth, are also examined. The results indicate that compliance rates are higher in the case of self-reporting than in the case of inspection, suggesting that there is a need to reform the self-report mechanism. Newer and privately owned firms are more compliant. There are also differences between complying with air and water pollution. Finally, the paper examines whether environmental monitoring through inspections leads to improvement in compliance levels, to assess the effectiveness of the regulations and inspections. The findings suggest that the increase in compliance is limited to a few industries.

This paper is a product of the Office of the Chief Economist, South Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at sfranco2@worldbank.org and mmani@worldbank.org.

_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 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._ Produced by the Research Support Team
<mark>Following Abadie et al. (2010) and Cavallo et al. (2013), to arrive at the significance of the estimated average effect of sovereign default, we apply permutation tests, to compare the observed and aggregated normalized treatment effect with the aggregated, normalized effect observed when randomly selecting placebo treatment units and years. The proportion of these placebo-standardized-effects that are at least as large as the observed standardized effect for each post-treatment period can be read as the probability of the observed effects occurring by chance and can hence be understood as time-period-specific quasi p-values, p</mark> t <mark>:</mark> <mark>To further account for the smoothing effect from aggregation across many individual case studies, we run N = 100 rounds of</mark> _<mark>k</mark>_ <mark>pseudo-default event studies, leveraging randomly selected country-year doublets. For each aggregated normalized study, we match the number of pseudo defaults per permutation, k, to the number of defaults considered in the analysis of the actual defaults.</mark>

# **<mark>A note on anticipation</mark>**

<mark>As is the case in any event study leveraging time variation in a variable of interest to measure treatment effects, anticipation can lead to bias or even reverse causality. If sovereign default was caused by the perfect anticipation of future economic downturns, there could be reverse causality at play. It is important to note however that neither the theoretical (see the introduction for an excerpt), nor the empirical literature support that being the typical path of events. Instead, there is both ample amount of evidence of the “This-time-is-different”-syndrome (Reinhart and Rogoff, 2009) leading to governments and agents in an economy turning a blind eye to even the most obvious warning signs; and economic downturns beginning just before the default (LevyYeyati and Panizza, 2011). The latter finding, in fact, hints at a different type of anticipation – the anticipation of sovereign financial distress and eventually default by investors and businesses, that could slow down GDP because of disinvestment: As far as this type of anticipation exists, it of course should be included in the cost of sovereign default (Costalli et al., 2016). To account for the possibility of anticipation of default, an important adjustment of the SCM is to backdate the matching algorithm pre-intervention to a moment before the default</mark> 15
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