rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table 8:  Means of evaluations of external constraints</u>**

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|**(9)**|**(10)**|**(11)**|**(12)**|**(13)**|**(14)**|**(15)**|**(16)**|**(17)**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Afghanistan|2.69|1.58|1.41|1.37|1.43|2.12|1.32|1.17|2.95|2.25|1.09|1.98|0.38|0.99|na|na|0.24|
|Bangladesh|2.93|1.37|1.26|1.56|1.33|1.96|1.99|1.32|2.80|2.46|1.21|1.91|0.83|1.52|1.84|1.83|0.22|
|Bhutan|0.84|0.53|1.30|1.09|0.58|0.86|1.29|1.20|0.20|0.74|0.50|1.67|1.31|1.35|na|na|0.36|
|India|1.75|0.52|0.85|1.01|0.87|0.71|1.55|0.89|na|1.70|0.44|1.16|1.13|1.02|1.03|0.92|0.21|
|Maldives|1.13|0.81|1.25|1.12|1.07|2.27|0.59|0.47|na|1.87|1.69|2.59|1.23|1.93|1.52|0.87|0.01|
|Nepal|2.74|0.57|1.58|0.98|0.97|0.29|0.90|0.33|3.41|1.32|0.11|1.11|1.02|0.54|na|na|0.21|
|Pakistan|2.33|0.75|0.94|1.02|1.28|1.45|1.84|1.05|2.15|2.21|2.00|1.64|0.88|0.95|1.96|2.12|0.45|
|Sri Lanka|2.04|0.95|1.16|0.88|1.07|0.50|0.80|0.50|na|1.04|na|1.14|1.37|1.32|1.66|1.60|0.35|
|Total|2.24|0.80|1.08|1.17|1.10|1.23|1.61|1.00|2.55|1.95|0.91|1.51|0.97|1.13|1.43|1.44|0.26|

Notes: (1) Electricity; (2) Telecoms; (3) Transport; (4) Customs; (5) Competition; (6) Access to land; (7) Tax admin.; (8) Bus. licensing; (9) Political instability; (10) Corruption; (11) Courts; (12) Access to financing; (13) Labour regulations; (14) Inadequately trained labour; (15) Government policy uncertainty; (16) Macro instability; (17) Insufficient water supply.

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measured by its impact on the economy’s overall innovative capacity, rather than the successful growth of a select group of industries.<sup>42</sup> Thus, while it may be necessary to set targets for the growth of the creative industries,<sup>43</sup> the benefits of investing in the necessary infrastructure for creative industries should go beyond such direct contributions.

Secondly, innovation infrastructure should not be viewed merely as an investment good. It is also an important consumption good that helps improve the quality of life for all residents, regardless of whether they are part of the innovative sector. This aspect of innovation infrastructure differentiates it from other efficiency and industry-specific infrastructure and greatly reduces the potential of “over capacity”. The Esplanade is a good example in this regard. Including consumption as part of the objectives of infrastructure investment will raise the level of investment that is considered “socially optimal”, if social optimality for this category of goods can ever be quantified.

> 42   See Stern et al, 2000, for a summary of the various determinants of a country’s innovative capacity.

> 43   In the report on creative industries (which include arts and culture, design and media), for example, the ERC set the target of having the industries contribute 6% of GDP by 2012 (from about 3% in 2000), and provide employment for 5-7% of the national workforce.

36
find a positive causal link between formality – as proxied by tax compliance – and firm performance: they find that more formal firms have higher levels of profits and that their owners are less likely to go out of business. Kenyon (2006) uses data from the Brazil IC survey to study the link between tax compliance, equity issuance and foreign licensing. He finds a positive association between compliance and both of these variables. Using the same data for Brazil, Almeida and Carneiro (2006) investigate the impact of enforcement of labor regulation on firmlevel use of informal labor and productivity. Using firm-level, cross-country data, Almeida and Fernandes (2006) investigate the extent to which majority and minority foreign ownership affect innovation; and Ayyagari et al. (2007) analyze the determinants of firm innovation in emerging economies. They identify the characteristics of innovative firms and then focus on the role of access to finance, competition, and governance in facilitating firms’ ability to innovate.

# **III. Data description**

We use a rich database from the World Bank Investment Climate surveys project. These are firmlevel surveys conducted in a large number of developing countries between 1999 and 2005. The surveys share a similar sampling design involving the selection of a stratified random sample of firms from each country based on location and industry. Once we exclude countries for which variables of interest are missing, we are left with a sample of 49 countries and about 11,000 firms.<sup>7</sup> The IC surveys contain detailed information on firms’ characteristics such as size, firm age, industry, ownership structure, firms’ performance, innovation, as well as perception-based and objective measures of the investment climate, including access to finance, the efficiency of infrastructure services, labor market relations, and corruption. Moreover, in the productivity section, firms are also asked to report accounting variables for the current year and up to two years before.

The number of firms surveyed in each country varies from the largest samples in Egypt (1,973 observations) to the smallest sample (47 observations) in Cape Verde. In order to take into account this asymmetry, we weight our regressions according to the country sample size. All countries cover the manufacturing sector; only some countries include firms operating in services and construction. We focus our analysis only on the manufacturing sector to limit heterogeneity and to maximize the number of countries in our sample. Similarly, we limit the analysis to fully

> 7 Although in some countries more than one survey has been conducted, we limit our analysis to the crosssectional dimension of the data as longitudinal information is still limited.

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international commodity prices and strong internal demand and supply restrictions for certain goods, led to an increase in the average CPI inflation from 4.7 percent in 2005, the lowest inflation rate after the crisis, to 8.1 percent in 2012, considerably over the target range set by the monetary authority (4 to 6 percent). Since the beginning of 2011, the Central Bank therefore repeatedly increased the monetary policy rate up to 9.3 percent in December 2012 to curb inflation and to help realign inflation expectations with the official inflation target range.

# **3.2 Poverty, shared prosperity, labor market and demographics**

The economic crisis of the early 2000s had negative impacts on the welfare of the population that lasted several years. Uruguay had traditionally been among the LAC countries with the lowest poverty and inequality incidence<sup>3</sup> (Lopez-Calva and Lustig, 2010; Gasparini et al., 2011), but the effects of the crisis significantly and negatively affected most social indicators, including poverty, inequality, unemployment, labor informality and economic mobility. Moderate poverty almost doubled in one year – from 18.8 in 2001 to 35.2 percent in 2002 – and reached 40 percent of the population in 2004 (Figure 5). At the same time, the size of the middle class shrunk from over half of the population to less than three fifth (World Bank, 2013). Most of these indicators began to recover only after 2004, once the economic recovery was well under way and new social policies had been implemented.<sup>4</sup>

**Figure 5: Poverty developments, 2000-2012**

<!-- Start of picture text --> 45<br>39.4 39.9<br>40 35.2 36.6 34.4<br>35 30.8<br>30<br>25 22.7 21.4<br>17.8 18.8 18.7<br>20<br>14.3 13.1<br>15<br>10<br>5 1.5 1.3 2.3 3.1 4.7 3.9 2.6 3.2 1.9 1.6 1.2 0.5 0.6<br>0<br>Moderate Poverty Extreme Poverty<br>% population<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br><!-- End of picture text --> Source: INE; _Note:_ For comparability across time, all numbers in the figure are representative of urban areas (more than 5,000 inhabitants), since ECH became representative at the national level only after 2006.

Poverty and extreme poverty incidence showed a fast growth trend in the early 2000s, and a decline that started after 2004 (Figure 5). The upward trend in poverty rates observed in the early 2000s reverted between 2004 and 2005, first at a slow pace and, in recent years, more rapidly. By 2012 only 12.4 percent of the population was living with a per capita income below the poverty line, about

> 3 Estimates of poverty in Latin America using SEDLAC (CEDLAS and The World Bank).

> 4 See Annex 1 for more detail on the data and sources of information.

5
is employed, all measures of her empowerment would be higher, with an increase in empowerment index ranging from 1.5 percentage points in reproductive freedom to 9.5 percentage points in financial autonomy. The second most important explanatory variable is women’s education. Each additional year of education achieved is associated with a 1.8 percentage point increase in mobility, 1.1 percentage point increase in financial autonomy, 0.6 percentage point increase in social participation, and 0.9 percentage point increase in overall empowerment. Regular listening to radio is found to slightly increase reproductive and financial autonomy, while regular TV watching is associated with an increase in financial autonomy and social participation. Being sick during the last 30 days is negatively correlated with mobility but does not have any statistically significant impact on other empowerment indexes, possibly because it may be an imperfect approximation of a woman’s general health.

# **VI. Conclusion**

This paper investigates the causal link between access to electricity and women’s empowerment using a large gender-disaggregated data set from the India Human Development Survey. To measure the multidimensional aspects of empowerment, we use factor analysis to combine an array of information on women’s intrahousehold decision-making and resource allocation into five indicators of empowerment: decision-making ability, mobility, financial autonomy, reproductive freedom, and social participation. We also construct an overall empowerment index based on the five factors.

The analysis shows that getting access to electricity enhances women’s positions on all five dimensions of empowerment and the overall empowerment measure. However, the magnitude of improvement is small for women’s decision-making ability and reproductive freedom. Gaining access to electricity is associated with a 4.6 percentage point increase in women’s decision-making ability on intra-household resource allocation and 2.7 percentage point increase in their reproductive freedom. Women’s bargaining power, primarily involving her own well-being (such as traveling alone, having a bank account, and participating in social groups), increases by 6.9–10 percentage points because of electrification.

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drought conditions or increasing the use of chemical fertilizers can boost productivity, probably to a larger extent compared to regenerative agriculture. However, these strategies may not be sustainable in the long run when groundwater depletion and soil degradation become an issue. As a result, farmers may face a trade-off between more effective short-run adaptation to climate shocks and more sustainable long-run adaptation to climate change. What adaptation strategies are more optimal in the short-run versus in the long-run and how to weigh these trade-offs remains an open question.

# **<mark>3.3 On Mitigation</mark>**

The global soils contain a significant reservoir of organic carbon, storing approximately 1,500 gigatons of carbon (equivalent to 5,500 gigatons of CO2) within the first meter of depth. There is an additional 900 gigatons of carbon stored in the subsequent meter. The organic carbon present in the top meter alone is three times greater than the amount of carbon found in atmospheric CO2 (Powlson, et al. 2014). Consequently, the focus on carbon sequestration in soil has attracted much attention as a possible solution to address and mitigate global carbon emissions.

A meta-analysis of 417 peer-reviewed articles has shown some positive evidence on soil organic carbon (SOC) sequestration using regenerative agricultural practices (Bai, et al. 2019). Their findings indicate that, on average, the most effective method for increasing SOC content was the application of biochar, showing a 39% increase. This was followed using cover crops (6%) and conservation tillage (5%). Additional analysis suggests that these practices were more prominent in regions with warmer climates or lower nitrogen fertilizer inputs.

Nonetheless, it is still premature to claim that regenerative agriculture can increase carbon sequestration and mitigate carbon emissions in global agriculture. The impact of regenerative agriculture on SOC sequestration is highly context-specific and encounters numerous challenges and trade-offs.

First, regenerative agricultural practices may decrease, rather than increase SOC stocks under certain climate conditions. For instance, the adoption of no-till on existing cropland can increase the SOC stocks in the surface topsoil at depths less than 20 centimeters while decreasing SOC at depths exceeding 20 centimeters. The reduced levels of SOC in deeper soil layers may offset the increased SOC near the soil surface under no-till management, which leads to an overall reduction in SOC stocks. The SOC stocks in the topsoil should be sufficiently high to generate a net increase in SOC stocks. Certain soil conditions, such as sandy soils of tropical moist climates, are more likely to generate a net increase in SOC stocks (Ogle, et al. 2019). As a result, identifying the right practices at the local level is necessary to evaluate the carbon mitigation benefits of regenerative agriculture.

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_i i_ where _sk_  _xk_ / _xk_ is the share of origin country _i_ in OECD imports of product _k_ and<sup>_n_</sup> _k_<sup>is the total number of countries exporting good</sup><sup>_k_(we will discuss in more</sup> detail below alternative definitions of the set of exporting countries).

Theil’s entropy index (Theil 1972) is given by

> These indices are dependent on the definition of<sup>_n_</sup> _k_<sup>, the number of “potential</sup> exporters”. Our baseline definition of the set of potential exporters is the simplest one: it is the set of all countries having exported good _k_ to _some_ destination in the world (not necessarily OECD countries) at least two years in a row over the sample period. We impose the requirement of two consecutive years of exports instead of just one in order to ensure that the exporter is a successful one (Besedes and Prusa 2006a, 2006b show that two years is the median duration of export spells; only one year might signal failure rather than the capacity to export). This definition has the advantage of being time- and importer-invariant (the latter matters for the part of our analysis where we disaggregate OECD imports by importing country). In order to explore action at the extensive margin, we also consider the simple number of exporters of good _k_ to OECD countries.

Our data are COMTRADE import data for OECD countries (either taken as a bloc or disaggregated by importer) at the product level. Our preferred product classification is SITC4. The alternative, HS6, is more disaggregated (with 4’990 to 5’016 lines depending on the year against 1’158 to 1’300 for SITC4), but the sample period is longer with SITC4, which also underwent fewer revisions. In terms of country coverage, SITC4 data cover 210 countries between 1962 and 2006 (44 years); HS6 coverage is nominally available starting 1988, but with only 12 countries (9 of which are OECD members) expanding gradually to 116 16
<!-- Start of picture text -->
-3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3<br>.1<br>.6<br>.05<br>.4<br>0<br>-.05 .2<br>-.1 0<br>mean of youth unemployment gap mean of youth unemployment gap<br>-.15 -.2<br><!-- End of picture text -->

Source: NIS, NBR calculations Note: The time of the minimum wage hike is marked “0” on the x-axis Figures 15-16 seem to suggest that a minimum wage hike could be associated with a reduction in inflationary pressures during the positive output gap period (i.e. negative unemployment rate gap shrinks or becomes positive), while having a reflationary effect during the negative output gap episode (i.e. positive unemployment rate gap shrinks or becomes negative). Similar nonlinearities can be observed in the case of the growth rate in the number of employees. In the positive output gap phase of the cycle, a minimum wage hike corresponds to an increase in the growth rate of the number of employees (see Figure 17, left). Conversely, in the negative output gap phase of the cycle, an increase in the minimum wage is associated with a temporary slow-down in the growth rate of the number of employees (see Figure 17, right).

_Figure 17. Mean of the rate of growth in the number of employees in 2004 Q1-2008 Q3 (left) and 2011 Q1-2016 Q4 (right) within a period of three months before/after a minimum wage hike_ <!-- Start of picture text -->
-3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3<br>.25<br>.25<br>.2<br>.2<br>.15 .15<br>.1 .1<br>mean growth in no. of employees, p.p. .05 mean growth in no. of employees, p.p. .05<br>0 0<br><!-- End of picture text -->

Source: NIS, NBR calculations Note: The time of the minimum wage hike is marked “0” on the x-axis We continue our employment impact assessment with a sector level analysis. Based on averages during the post-crisis period, we construct two groups of economic sectors. The one with above21
3 <!-- Start of picture text --> Figure 1: Global Privatization Proceeds<br>(US$ billon)<br>150<br>-<br>50 I I :<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999<br>*MNon-OECD Countiies *OECD Countries OTotal<br>Source: Mahboobl, 2000.<br><!-- End of picture text --> 7. Privatization activity in developing countries<sup>hasbeen growing. While noaccurate</sup> account is available, it is safe to say that tens of<sup>thousandsof enterpriseshave beensold. A rough</sup> estimate is that $250 billion in revenues were<sup>raisedbetween1990-1999.Proceedsincreased</sup> four-fold since 1990, reaching $44 billion<sup>in1999after apeakof$66billionin1997.The</sup> revenues are largely accounted for by infrastructure<sup>privatization,mainlytelecommunications</sup> and power, followed by the primary sector,<sup>includingpetroleum,mining,agriculture,and</sup> forestry. Recent large sales have<sup>beenconcentratedintheoilandgassectors</sup> in Argentina, Brazil, India, Poland and Russia. Manufacturing<sup>privatizations raised about16percentof total</sup> developing country proceeds between<sup>1990-99,mainly fromsalesin Eastemand Central Europe</sup> and Latin America (Figure 2A).

<!-- Start of picture text --> Figure 2A: Privatization Proceeds by Figure 2B: Privatization Proceeds by<br>Sector, 1990-99 Region, 1990.99<br>(USS billions) (US$ billions)<br>50 so1s9 5 _ I IIl1S9<br>kVma,~. mwnaacuAt PRVs." 80dm FbMW, Ot,. Ea As. SoM Asia Europd Leti Mis Eastd SubSawa<br>mad PacBc Cw&i Asia Amaca and nd Nooit aM=c<br>Iii Attic<br>Source: Global Development Finance 2001 Source: Global Development Finance 2001<br><!-- End of picture text --> 8. In regional terms, Latin America accounted<sup>for a large shareofnon-OECDprivatization</sup> activity, particularly in terms of revenues<sup>(Figure2B).CountriessuchasArgentina,Brazil,</sup> Bolivia, Chile, and Mexico sold small and medium<sup>sizedfirmsat first, butrapidly expandedtheir</sup>
# Table 4.  Panel fixed-effects regression of government spending on commodity prices

|||**Dependent**|**variable: Real**|**Government**<br>|**Spending**<br>||
|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|**Explanatory variables**|**EMDE**|**EMDE**|**EMDE**|**Full**|**Full**|**Full**|
|**Commodity Export Price Index (EPI)**|**0.061 ***|**0.080 ***|**0.061 ***|**-0.123 *****|**-0.072**|**-0.121 *****|
|**Terms of Trade**||**-0.060**|||**-0.072**||
|**GDP (-1)**|||**0.688 *****|||**0.625 *****|
|**Commodity Export Price Index x EMDE**||||**0.184 *****|**0.158 *****|**0.183 *****|
|**Observations**|**276**|**269**|**276**|**415**|**341**|**413**|
|**Countries**|**11**|**11**|**11**|**15**|**15**|**15**|
|**F-Test joint Commodity EPI and EMDE inter**|**action**|||*******|******|*******|

Notes: Panel least squares with country fixed effects. All variables are in log-differences. Full refers to the sample with AE and EMDE. *,**, and *** indicate statistical significance at 0.10, 0.05, and 0.01 percent, respectively.
# **Baseline dynamics**

In the baseline scenario the model is calibrated to project a real GDP growth rate that fluctuates between 5 and 6 percent per year – which basically replicates the official short-term projections. Hence, GDP grows at 4.5 percent in 2007, increasing to 6 percent in 2010 and then fluctuating between 5 and 6 percent until year 2015 – the end of the simulation period. The projected fiscal deficit, investment levels and the current account balance are also consistent with official projections (see Table A2).

The results show that despite a positive outlook in terms of economic growth unemployment rates in urban areas would go down only very gradually. Hence, the model projects that the urban unemployment rate would first increase and remain constant at around 13 percent.<sup>15</sup> Only in year 2010 it would start to gradually decline converging to 9.3 percent in year 2015. These results are consistent with the aggregate projections that one would obtain assuming that the aggregate employment-output elasticity remains unchanged (see World Bank 2007, Chapter 2).

The dynamics of unemployment rates are very different between skills categories; prospects are especially worrisome for Specialized Technicians. For unskilled workers, unemployment rates could rapidly converge to “natural levels.” In part this occurs as their share among new entrants in the labor force diminishes. For low-skilled workers unemployment rates are also on a downward trend although it would take them longer (around year 2015) to reach natural levels. For highly skilled workers initial unemployment rates are higher and could increase over the short term, but would start declining after year 2010. Still, by year 2015, 26 percent of university graduates in non-technical fields and 9.3 percent of graduates in technical fields would remain unemployed. The situation is more critical, however, for Specialized Technicians. They start with the highest unemployment rates (41.4 percent) but because of a rapid increase in their number, unemployment rates could continue to climb stabilizing at around 47.2 percent in year 2015. This result is driven of course by the

> 15 For consistency reasons, labor data in the model are in full-time equivalents, which means that the baseline unemployment rate is higher than the official one.  Indeed, employment and unemployment figures are presented in number of people mixing full time and partial time workers. In models we always transform the data in full-time equivalents because we cannot add someone who works one month and someone who works 9 months. This means that our unemployment figures are also in full-time equivalents and thus higher than official ones.

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