rafmacalaba/datause-encoder-data huggingface dataset:
consumption expenditures. An important question then is: How do households adjust their consumption due to higher energy prices? While there is no evidence of price elasticity of demand for Armenia, as is the case for most transition economies. However, a post gas hike survey of residents of multi-apartment buildings shows that the use of gas-based heating declined in 2010 given the sharp increase of gas tariffs for small consumers (EDRC, 2011). According to this survey, while gas remained the dominant heating source, the share of households relying on it decreased from 72.5% during the 2008-2009 heating season to about 69.8% during the 20102011 heating season. There was increased use of electricity and firewood for heating during the same period. In the following section, the impact of current gas price hike is simulated for different household groups, under a range of assumptions about price elasticity of demand.

# **3. Welfare Impact of the Gas Price Hike**

Gas is an important fuel for heating and cooking, as well as an input into the production of many goods and services (see Table 5). The gas price increase can therefore influence the cost of the basic consumption basket and the price of other alternative energy sources such as electricity and fuelwood. Ideally the welfare impact should be analyzed in a general equilibrium framework that takes into account such linkages. However, general equilibrium modeling is data intensive and sensitive to a number of assumptions. In this paper, the analysis is necessarily limited to the _direct_ impact of the gas price increase on household welfare.

## **_Methodology and data_**

The 2008 ILCS data is used in the simulation of the impact of gasp price hike on April 1, 2010. The ILCS collects data from nearly 8,000 Armenian households surveyed year round. It is based on a representative sample of about 2,000 households surveyed each quarter. It contains detailed information on household composition, energy use, income, public and private transfers, expenditure, saving and debt, and other indicators of welfare.<sup>3</sup> Household consumption

> 3 For detailed description of the 2008 ILCS, please refer to NSS (2009).

8
Table 2. Global inequality, with new 2005-based PPPs and “old” 1993-based PPPs (based on household survey data from WYD dataset)

||1988|1993|1998|2002|
|---|---|---|---|---|
|Gini (new data)|68.4|69.9|69.4|70.8|
||(1.7)|(1.8)|(1.8)|(1.3)|
|Gini (old data)|62.3|65.5|64.4|65.7|
||(2.0)|(1.7)|(1.9)|(1.3)|
|_Change (in Gini points)_|_+6.1_|_+4.4_|_+5.0_|_+5.1_|
|Theil (new data)|87.5|93.7|94.2|100.1|
||(6.3)|(7.1)|(7.3)|(5.7)|
|Theil (old data)|71.5|81.2|79.1|83.4|
||(5.9)|(5.6)|(6.7)|(5.4)|
|_Change (in Theil points)_|_+12.0_|_+12.6_|_+15.1_|_+16.7_|
|Top decile share, in percent<br>(new data)|51.4|53.4|57.0|57.5|
|Population coverage|87|92|92|94|
|(in percent)|||||
|GDP coverage|96|95|96|98|
|(in percent)|||||
|No of household surveys<br>(countries)included|103|122|124|123|

Sources: For 1988-1998 see Milanovic (2005). Data for 2003 calculated by author from WYD (World Income Distribution) dataset available at <u>http://econ.worldbank.org/projects/inequality.</u> Table 3. Decile and top ventile shares of global income (year 2002; “new” PPPs; in %)

|Decile|Percentage shares|
|---|---|
|First|0.61|
|2|0.94|
|3|1.25|
|4|1.62|
|5|2.19|
|6|3.13|
|7|4.99|
|8|8.26|
|9|19.71|
|Tenth|57.52|
|_Total_|_100_|
|Top ventile|31.34|
|Top-to-bottom decile ratio|94.6|

13
Yivt = α+ βRt + γTv + δIvt + εivt where, Yivt is the outcome for individual i , in village v at time t (for the village level outcomes the subscript is vt). Rt is a dummy (=1) for the follow-up survey, Tv is a dummy (=1) for the treatment group and Ivt is the dummy for the post-treatment effect on the treated—thus δ identifies the average treatment effect. The error term, εivt, is clustered at the level of randomization—the GP. In addition to the above specification, we also estimate models with individual fixed effects for household outcomes, and village fixed effects for village outcomes. This specification controls for any unobserved time-invariant heterogeneity that may exist at the individual/village level.

# <u>Qualitative Analysis</u>

The qualitative sample is a 10% subset of the quantitative sample. The qualitative sample was chosen from Raichur and Gulbarga districts because the majority of the quantitative sample was located there, and from these districts we randomly selected three talukas from Gulbarga and two from Raichur. From each sampled taluka we picked a pair of treatment and control GPs that were the best matched in 5 attributes measured in the 2001 census (the last available census at the time) .

While the quantitative data are available for two years, our ethnographic investigation from five treatment and five control GPs covers a four-year period from 2007-2011 to allow us to examine the long-term effects of the intervention. From 2007-2010, each GP was assigned a field investigator, typically someone with an MA degree in a social science or in Social Work, who was from the region and therefore very familiar with the milieu and dialect and easily able to blend into the community and establish rapport. The investigator either resided in the GP or in a location that was a short, easily accessible distance away. In the first round of reports each investigator mapped the village’s social and political structure, outlining the various caste and religious groups residing in the GP, relationships within and between them, structures of social networks and power, major events in the GP’s history including its experience with development projects, etc.

Subsequently, once a month, the investigators sent in a 5-10 page report on important changes that had taken place. S/he was instructed to record important local events, interview important actors in those events, investigate new village constructions and the financing behind them, track electoral activities and

> 5 The matching methodology employed for the qualitative analysis is similar to Barron, Diprose and Woolcock (2011) and Baiocchi, Heller and Silva (2011).

9
While Haltiwanger et al. (2008b) finds similar total job creation and destruction rates in the US, OECD countries, Latin America, and Eastern Europe, both entry and exit play a greater role in both job creation and destruction in Cote d‟Ivoire. This finding may not be unique to Africa as Bedi and Shiferaw‟s (2009) research on Ethiopian manufacturing establishments finds a similar pattern to Cote d‟Ivoire‟s. An important implication therefore, is that new firm creation appears to have the potential to be an important source of job creation in Cote d'Ivoire. This might suggest that expanding opportunities for entrepreneurial potential is important for macroeconomic growth that is not currently achievable. An important caveat, however, is that jobs created by new firms are highly volatile and not necessarily as good as those provided by large firms (Haltiwanger et al., 2009a, 2009b). This suggests that economic and employment growth in Cote d'Ivoire might be hampered by the inability of incumbent firms to grow. This data can be interpreted as quantitative evidence of the impact of barriers in the business environment – such as costly and timely procedures to obtain licenses, register property and movable collateral, resolve disputes, and close a business (World Bank, 2010a) – on private sector growth.

# **_5.2 Survival analysis_**

The previous section highlights barriers in employment creation at existing incumbent firms. To better understand some of the barriers to growth, we begin by addressing the primary question of what determines firm survival.<sup>17</sup> Understanding the factors that appear to most influence firm survival is necessary for the development and implementation of policies that can improve and help firms better adapt and prepare for challenges as well as promote opportunities for growth. We aim to identify firm characteristics, such as sector and size, which predict greater vulnerability to economic conditions and policy interventions. We first examine the relationship between economic growth (measured by annual real GDP growth rates) and firm survival before exploring the impact of specific trade liberalization (1985-1987), deficit reduction and political reform (1989-1990), and exchange rate devaluation (1994) episodes on firm survival.

We acknowledge that our results cannot be fully interpreted as causal since there are many variables and underlying economic conditions that may play a role in determining firm

> 17 We thank the referee for pointing out that survival strategies can be growth constraining.

15
# **Figure 15. Methodological quality of the studies in general population, by country. Andean Region; 2009**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>Limited<br>50%<br>Intermediate<br>40%<br>High<br>30%<br>20%<br>10%<br>0%<br>Peru n= 14 Ecuador n= 8 Bolivia n= 7<br><!-- End of picture text -->

**Source:** Authors’ calculation using information from _Database and References for an Evidence-based Strategy_ in Peru, Ecuador and Bolivia.

**Findings:** The following statement held true for each of the three Andean countries: the proportion of its studies of the general population that had high methodological quality was significantly larger than the proportion of its overall database of studies that had high methodological quality.  This is most likely due to the simple fact that the general population is easier to access than more narrowly-defined groups.

# **Youth and adolescents**

In **Peru** , there were seven studies done on youth and adolescents. Six of them had high methodological quality (86%); and one had intermediate quality (14%). In **Ecuador,** there were 11 studies done on youth and adolescents. Seven of them had high methodological quality (64%); two had intermediate quality (18%); and two had limited quality (18%). In **Bolivia** , there were five studies done on youth and adolescents. All of them had high methodological quality (100%) (Figure 16).

31
<!-- Start of picture text --> Figure 9a. Average R 2 is high in Brazil (2009) Figure 9b. Average R 2 is high in Brazil (2008)<br>0.8 0.9<br>0.7 0.8<br>0.7<br>0.6<br>0.6<br>0.5<br>0.5<br>0.4<br>0.4<br>0.3<br>0.3<br>0.2<br>0.2<br>0.1 0.1<br>0.0 0.0<br>Developed Developing BRAZIL Developed Developing BRAZIL<br><!-- End of picture text --> _Source_ : Authors’ calculations based on BankScope data _Source_ : Authors’ calculations based on BankScope data

# **_5.5 Brazil and the United States_**

We have already mentioned that Brazil’s micro spreads are the dominant determinants of NIM between 1995 and 2009 (Figure 10a) but the opposite is true for the U.S. NIMs. We observe that I&R factors started decreasing in the U.S. at the end of the 1990s, when the Clinton administration allowed the banking sector to consolidate by permitting commercial banks to acquire or develop investment banking arms. We see a steady decline in the U.S. I&R spreads, becoming almost non-existent in 2003-2004 and increasing again from 2005 to 2007 before they decrease dramatically during the crisis in 2008-2009 (Figure 10b). Our results show that macro-factors are driving the spread in the U.S. over the last decade. At the same time the NIM did not change dramatically, even during the two recessions of 2001 and 2007-2009, while micro-factors decrease and macro-factors increase at the same time.

Despite the greater volatility of NIM in Brazil, I&R remain the dominant factors in the determination of the spread and there is no clear trend during recessions. It appears that attempts to reform the banking sector in Brazil are not noticeable in terms of changing the amount of the spread determined by micro-factors. We observe that I&R determine the trend and the strength of the NIM (Figure 10a).

We find that Brazil’s macro spreads are surprisingly lower in absolute terms than in the U.S. in 2009 but also for several years between 1995 and 2009 (Figure 10a). Banks in Brazil place less emphasis on macro-factors when making investment decisions than their counterparts in the U.S. U.S. banks’ macro-factors account for a large percent of the total spread. When making lending and borrowing decisions, U.S. banks take into account mostly macro-factors that affect their borrowers’ and the banks’ ability to repay the loans. We observe that macro-factors account for an average of 75 percent of the total spread of U.S. banks, while for Brazilian banks macro-factors account for 9 percent between 1995 and 2009 (Figure 11b).

23
# Table B.5: **Effects of Inpres Schools on Intergenerational Educational Mobility : Partially Exposed Cohorts Only**

**Panel A: Dependent Variable: Primary Completion**

||**Linea**|**r Model**|**Quadr**|**atic Model**|
|---|---|---|---|---|
||**Sons**|**Daughters**|**Sons**|**Daughters**|
||(1)|(2)|(3)|(4)|
|Born 1963-67_×_Inpres|0.611**|-0.537*|0.569**|-0.491|
||(0.270)|(0.323)|(0.286)|(0.335)|
|Father’s Edu_×_Born 1963-67_×_Inpres|-0.067|0.100*|-0.112|0.027|
||(0.042)|(0.058)|(0.088)|(0.087)|
|Father’s Edu Sq_×_Born 1963-67_×_Inpres|||0.006<br>(0.006)|0.007<br>(0.007)|
|R2|0.317|0.403|0.317|0.403|
|Observations|433272|399906|433272|399906|
|**Panel B: Dependent**|**Variable: Ye**<br>**Linea**|**ars of Schooli**<br>**r Model**|**ng**<br>**Quadr**|**atic Model**|
||**Sons**|**Daughters**|**Sons**|**Daughters**|
||(1)|(2)|(3)|(4)|
|Born 1963-67_×_Inpres|0.094***|0.040|0.082***|0.030|
||(0.028)|(0.025)|(0.030)|(0.030)|
|Father’s Edu_×_Born 1963-67_×_Inpres|-0.013***<br>(0.004)|-0.003<br>(0.003)|-0.020**<br>(0.008)|-0.001<br>(0.009)|
|Father’s Edu Sq_×_Born 1963-67_×_Inpres|||0.001**<br>(0.000)|0.000<br>(0.001)|
|R2|0.128|0.194|0.141|0.214|
|Observations|433272|399906|433272|399906|

Notes: Robust standard errors in parentheses clustered at the district of birth (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). Quadratic model is adopted following Becker et al. (2015). Sample corresponds to children born between 1957 to 1967. Covariates include birth district FE, year of birth×1971 enrollment, year of birth×1971 number of children, year of birth×water sanitation program, year of birth dummies, following Duflo (2001). Primary completion takes the value of 1 if the child has completed primary and 0, otherwise. The variable Inpres measures the number of Inpres schools per 1000 children at the district level divided by the highest number of schools received by one district. Father’s Edu represents father’s years of schooling, which was calculated based on the education level completed. Family background is measured by father’s years of schooling. For the sake of parsimony, only intercept, linear, and quadratic terms are reported in this table; full table, with all coefficients, is available upon request. Data sources: Indonesia’s full count census 2000 and Duflo (2001).

68
for Argentina. It is collected quarterly since 2003, and it is representative of the urban areas. The sample size is around 17,000 households in each quarter. Each household is interviewed in two consecutive quarters and then again in the same two quarters of the following year. We construct the transition matrices by comparing the sector of employment of the individual in the first and third interviews. We derive seven year-on-year transition matrices, the first matrix reflecting transitions between 2004 and 2005 and the most recent between 2010 and 2011.

The simulation exercises require information on key parameters, including labor shares and consumption shares by sector for each country. Complementary data sources were required to compute these parameters. The labor shares were defined as the ratio of the wage bill over total value added for each sector. These parameters were calculated from input-output (IO) tables from Mexico, Brazil and Argentina. We used the IO table of 2003 for Mexico, 2005 for Brazil and 1997 for Argentina. Average formal and informal wages were calculated with data from the Labor Force Surveys (LFS). We use CPI hasket shares along with input-output tables on consumption expenditures to calibrate the utility function parameters. The parameters for the simulations are shown in Table 1.

# **5 Results**

This section presents three sets of results. The first set of results is related to the direct effect of China’s trade on LAC’s exports. This direct effect of China is calculated for three sectors (manufacturing, agriculture and mining) and 29 Latin American countries, using our vulnerability and opportunity indexes. The second set of results are the PPML estimation of the workers’ perceived entry costs. These costs are associated with movements across industries as well as with movements in and out of formality. Finally, the estimated entry costs and the net-exports effects are utilized to simulate the total effect of China’s trade on Argentina, Brazil and Mexico’s labor markets.

24
and 10 percent per year of schooling.<sup>23</sup> But if the average individual now has far fewer years of effective schooling, this would imply that the returns to effective schooling (or learning-adjusted schooling) are much higher—up to 16 to 20 percent per year. Is this plausible?

It isn’t possible to test this directly, because we don’t directly observe the returns to effective years of schooling. But what we can do is flip that around: test whether differences in the implied quality of schooling explain differential returns to schooling within a common labor market. For this purpose, we can draw on Schoellman (2012), who calculates the returns to schooling in the US market for immigrants from many countries. These data have the advantage of holding constant national labor-market conditions, and thus the demand for schooling, so that the returns reflect only the quality of schooling and not demand-side differences. He finds that an additional year of Mexican or Nepalese education raises the wages of Mexican or Nepalese immigrants by less than 2 percent, while an additional year of Swedish or Japanese education raises the wages of Swedish or Japanese immigrants by more than 10 percent.

The left panel of Figure 5.7 compares Schoellman’s estimates of returns to the learning adjustment ratio underlying _LAYS_ , following an example from Figure 1(b) in his paper. It shows a strongly positive relationship between the two measures: countries whose students do better on learning metrics also have emigrants who earn higher returns for each year of schooling in the US labor market. There are a few significant outliers: most notably, the Republic of Korea (represented by the bottom-right point) does much better on learning than on the returns measure, while on the other side, South Africans (left-most point) do much better on labormarket returns than on learning. But on the whole, the figures and the statistical correlations strongly suggest that the relative learning measure is picking up something economically meaningful in terms of skills.

> 23  Psacharopoulos and Patrinos (2018), for example, find that the global average is 9 percent per year of schooling.

35
In our model we take each location’s amenities _Ai_ as exogenously given. Real wages in each and every location are instead endogenously determined in our model.

# _3.1 Determining real wages – the role of transportation infrastructure_

Real wages in each and every location are determined in the exact same way as in Roberts _et al._ (2012). Here we briefly set out the main features of their model, referring to their original paper for the full details. We focus in particular on the way that transport costs determine real wages in the urban and rural sectors of each prefecture. By influencing market access they are the main conduit through which the NEN affects spatial economic outcomes in our model.

The model in Roberts _et al._ (2012) is an elaborated _n_ -region version of the original NEG model of Krugman (1991a, 1991b). Each location consists of an urban and a rural part. The urban part of each prefecture houses the urban sector which is characterized by internal economies of scale and monopolistic competition. Its rural part is instead home to a constant returns rural sector in which perfect competition prevails. Perfectly tying each of the two sectors in Roberts _et al._ (2012) to the rural and urban part of each prefecture allows us to explicitly model migration between the urban and rural part of each prefecture. In effect, in our model, the decision to move from the rural part of a prefecture to its urban part necessarily involves a change of sector.<sup>15</sup> Both sectors face “love of variety” preferences and (iceberg) transport costs to the rural sector, whereas, in the original Krugman model, these are confined to just the urban sector. It implies that the construction of the NEN also has an impact on the prices and wages in the rural sector. Finally, following Südekum (2005), the model allows for variations in labor efficiency both across regions and between the urban and rural sectors.

As set out in Appendix A, equilibrium in the model is characterized, for each prefecture, by a system of five simultaneous non-linear equations. For each prefecture, these equations determine wages and the price indices in both its urban and rural part (sector). Importantly, these depend on two factors – _(i)_ the region’s (exogenous) level of labor efficiency in its urban/rural sector, and _(ii)_ the region’s level of real market access (RMA) in that sector. The RMA in each sector provides the main channel through which transport costs – and, hence, the construction of the NEN – affects the overall spatial equilibrium. In particular, a decline in transport costs associated with e.g. the construction of a new highway network link between two prefectures has two opposing effects on wages working through RMA. To see this, take the example of the urban sector. A reduction in the costs for urban firms in prefecture _i_ of

> 15 We think this is a realistic assumption because migrants in urban areas of China predominantly work in lowskilled manufacturing or service sector jobs. See also World Bank (2013).

9