rafmacalaba/datause-encoder-data huggingface dataset:
- 43 -

through roadbed maintenance or efforts to abandon light-density lines are likely to have a rather large impact on costs, while the abandonment of general track per se will lead to relatively few economies.

Econometric estimates from the United States reveal quite clearly that the low-density lines are a significant drain on railroad profitability, and constitute a serious impediment to the attainment of static and dynamic efficiency in the industry. The avoidable losses recoverable by abandonment appear to be quite significant. In addition, the burden of excess capacity seems to have a dynamic impact on efficiency. The abandonment of low-density lines stimulates the formation of new capital on the highdensity portions of the rail network. First, since abandonment reduces the need for cross-subsidization, rates on the high-density lines are permitted to fall toward marginal cost. The lower rates attract additional traffic, and thus raise the level of desired capital. Second, abandonment of low-density lines lowers the cost of capital to rail firms by improving their long-run profitability and reducing the risk of bankruptcy.
|**Table 11: Heterogeneity in Treatment Impacts f**|**or New Fir**<br>Round 1<br>Op|**m Applica**<br>Round 2<br>erates a F|**nts**<br>Round 3<br>irm|Round 1<br>Tot|Round 2<br>al Employ|Round 3<br>ment|Round 1<br>Profi|Round 2<br>ts and Sale|Round 3<br>s Index|
|---|---|---|---|---|---|---|---|---|---|
|**Panel A: Heterogeneity by Gender**||||||||||
|Assigned to Treatment|0.185***|0.341***|0.354***|1.411|6.119***|4.976***|‐0.002|0.282***|0.159***|
||(0.032)|(0.025)|(0.026)|(0.860)|(0.471)|(0.508)|(0.054)|(0.041)|(0.048)|
|Assigned to Treat*Female|0.189**|0.104*|0.120*|0.100|‐0.638|1.557|0.119|0.093|0.055|
||(0.078)|(0.063)|(0.067)|(1.021)|(0.895)|(1.321)|(0.084)|(0.077)|(0.099)|
|Sample Size|1021|1181|1085|987|1159|1044|995|1152|1063|
|Control Mean Females|0.420|0.481|0.422|1.674|2.165|2.883|‐0.233|‐0.239|‐0.144|
|Control Mean Males|0.574|0.586|0.562|3.964|3.539|3.937|0.035|‐0.067|‐0.032|
|**Panel B: Heterogeneity by Business Plan Score**||||||||||
|Assigned to Treatment|0.677***|0.484***|0.371***|‐0.305|3.462|2.456|0.451|0.723***|0.211|
||(0.173)|(0.141)|(0.141)|(3.642)|(2.274)|(2.686)|(0.319)|(0.210)|(0.241)|
|Assigned to Treatment*Business Plan Score|‐0.009***|‐0.002|0.000|0.032|0.047|0.051|‐0.008|‐0.008**|‐0.001|
||(0.003)|(0.003)|(0.003)|(0.075)|(0.043)|(0.051)|(0.006)|(0.004)|(0.004)|
|Business Plan Score|0.004|0.001|0.001|0.053|0.054*|0.071*|0.012**|0.007**|0.005|
||(0.003)|(0.003)|(0.003)|(0.037)|(0.032)|(0.039)|(0.005)|(0.003)|(0.004)|
|Sample Size|1021|1181|1085|987|1159|1044|995|1152|1063|
|Control Mean Bottom Quartile|0.513|0.548|0.563|3.497|3.263|4.372|‐0.064|‐0.131|‐0.065|
|Control Mean Top Quartile<br>Notes:<br>Robust standard errors in parentheses, *, **, *<br>Experimental estimates are ITT estimates and c|0.592<br>** indicate<br>ontrol for r|0.604<br>significan<br>andomiza|0.567<br>ce at the 1<br>tion strata|3.542<br>0, 5, and 1<br>.|3.556<br>percent le|4.353<br>vels.|0.081|‐0.034|‐0.062|

48
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32
Table 19. Indonesian Exports, ICT use, and Log of Skills Measures. Low-educated workers. 2011-2015

||||||N|on-routine s<br>|kill measure<br>|s|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Dependent variable:||Anal|ytical|||Interp|ersonal|||Ma|nual||
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
|IDN exports per worker|0.00023||0.000229|0.000122|0.000148||0.000149|0.0000613|-0.000104||-0.000107|-0.0000309|
||(0.000252)||(0.000251)|(0.000254)|(0.000236)||(0.000236)|(0.000238)|(0.000234)||(0.000235)|(0.000237)|
|Share of hhlds with computer||-0.0364|-0.0352|-0.048||-0.0097|-0.00842|-0.0188||-0.0174|-0.0188|-0.00982|
|||(0.0416)|(0.0424)|(0.0426)||(0.0322)|(0.0327)|(0.0325)||(0.0317)|(0.0315)|(0.0311)|
|IDN exports per worker *||||0.00253||||0.00207||||-0.00179|
|Share of hhlds with computer||||(0.00104)**||||(0.000836)**||||(0.000719)**|
|Observations|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|
|R-squared|0.866|0.866|0.866|0.866|0.421|0.421|0.421|0.423|0.801|0.801|0.801|0.802|

|||Ma|nual|Routine ski|ll measures|Cog|nitive||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
|IDN exports per worker|-0.000604||-0.000609|-0.000496|-0.000036||-0.0000378|0.00002|
||(0.000364)*||(0.000365)*|(0.000381)|(0.000283)||(0.000284)|(0.000284)|
|Share of hhlds with computer||-0.0454|-0.0458|-0.0322||-0.00662|-0.0077|-0.000524|
|||(0.0560)|(0.0553)|(0.0538)||(0.0460)|(0.0460)|(0.0459)|
|IDN exports per worker *||||-0.00269||||-0.00142|
|Share of hhlds with computer||||(0.00140)*||||(0.000970)|
|Observations|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|
|R-squared|0.979|0.979|0.979|0.979|0.834|0.834|0.834|0.834|

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010 and 2011-2015, 2015-2016 Philippines STEP survey, and UN Comtrade database 2005-2010 and 2011-2015.

Notes: IV regressions. IV: IV: Global demand for foreign products excluding Indonesian products. Low-educated workers: workers with uncomplete secondary education or less. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province level controls include the age structure of the population, the share of urban population, real GDP per capita, and the minimum wage level. Industry level controls include the gender and educational composition of employment. All models include province, industry, year fixed effects, and province linear time trends. Robust standard errors between parentheses clustered by province-industry. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively.

38
respondents. Suffering households on the other hand are likely to be lacking in basic food and shelter and report to be under physical pain, stress, worry and sadness. They carry more than double the disease burden as “thriving” respondents within each country.

Using data from Gallup for Nepal, we find that 90 percent of Nepalis fall in the suffering or struggling categories. In contrast, only 10 percent of the population actually falls in the thriving category (Figure 10). Juxtaposing this with the earlier result on the size of the middle class, it appears that even though there is a nascent middle class building up in Nepal, roughly half of this group is fundamentally insecure about the prospects of realizing continued improvements in its living standards.

**Figure 10: The Proportion of Nepalis Struggling, Suffering and Thriving**

<!-- Start of picture text --> 0.8<br>0.7<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.2<br>0.1<br>0.1<br>0.0<br>Suffering Struggling Thriving<br><!-- End of picture text --> Source: World Bank staff estimates based on data from Gallup World Survey. The numbers presented are country averages for the 2007-2014 period.

In the qualitative survey carried out for this analysis, respondents were asked about what they saw as key characteristics of the middle class in Nepal. In both in-depth interviews and focus group discussions, an overwhelming majority of respondents/participants associated being in the middle class with being engaged in relatively secure professions such as government jobs, teaching, medical and engineering professions, and other forms of private sector salaried employment. This association was reinforced by widespread recognition among respondents that economic prospects of those in this group hinge more critically on professional skills and labor as opposed to things like inherited capital and other forms of financial assets. This view is consistent with what has been noted in the literature on middle class. The primary driver of Nepal’s recent growth in living standards has been either workers’ remittances or increased employment and wages in low to semi-skilled professions largely in informal service sectors. As a result, it appears that even though a majority of Nepalese today may not be in poverty directly, they are still struggling to attain the level of economic security required to be comfortably ensconced in the middle class.

17
<!-- Start of picture text --> 0.6 0.6<br>Pes ’.<br>Pesae<br>I<br>I<br>0.4 0.4<br>0.2 Po Jody IW.<br>0.04 anTe ReTSPRR : serena- oN SYne1 verre, 0.04 --re--ee. a ere?:es‘ *-<br>Cc<br>S Measure<br>1990 2000 2010 1990 2000 2010 _<br>o — Labor productivity<br>£ EMDE -- Technology<br>6 0.6 0.6 -- TFP<br>0.4 0.4<br>0.2 0.2: oy<br>0.0 DNN eoENT,DON ee eres,fo77 0.0; Wh+vf°* =!/ Nagwosv VWAaies IBAS<br>1990 2000 2010 1990 2000 2010<br>Year<br><!-- End of picture text -->
**Table 1:** Median Sample Periods

|Regions|**Labor Prod.**|**TFP**|**Spectral**|
|---|---|---|---|
|AEs|1962-2018|1951-2018|1973-2018|
|EMDEs|1972-2018|1971-2018|1981-2018|
|LICs|1981-2018|1981-2018|1981-2018|

(TED). Data on capital services and human capital are taken from the Penn World Table 9.1, and they are used to estimate TFP (Dieppe et al., 2020b). This results in an unbalanced panel of 30 advanced economies and 96 EMDEs. The average sample length is just under 40 years for EMDEs and 45 years for advanced economies (Table 1).

Both individual and panel VAR estimations are performed. The panel VAR estimation is performed separately for both advanced economies and EMDEs to illustrate the typical effects of technology and primary business-cycle shocks on representative economies in both groups.

In the standard specification, two lags of the endogenous variables are included in the VAR estimations. This is the minimum number of lags required to account for cyclical processes (which can be described as an AR(2) process). Results are robust to including four lags (accounting for four years of data).

# **5 Technology Shocks: Dominant Long-Run Drivers of Productivity**

For our sample of countries and over the time period studied, the impulse responses to technology shocks exhibit many similarities but invariably differ in magnitude and persistence across advanced economies and EMDEs.

Despite our methodology placing no restriction on the direction of the impact of technology shocks on the variables contained in the VAR, the impulse response functions (IRFs) for both advanced economies and EMDEs are generally consistent with theory and previous findings for technology shocks in advanced economies (Ramey, 2016). A technology shock raises the level of labor productivity persistently in advanced economies and EMDEs (Figure 3). We find it prudent to scale IRFs by the effect of the technology shock on labor productivity given the greater volatility of EMDEs over our sample period. The greater impact of technology shocks on EMDE labor productivity is likely to reflect the higher volatility of productivity growth and the high vulnerability of EMDEs to shocks with long-lasting effects on output (Aguiar and Gopinath, 2007).

TFP responds immediately to a technology shock in both advanced economies and EMDEs, explaining the majority of the improvement in labor productivity (in contrast to a capital-deepening driven improvement). As investment responds to higher production efficiency, some of the TFP boost in EMDEs fades and the increase in labor productivity is increasingly driven by capital deepening.

The remaining IRFs are also scaled to the response of labor productivity to an improvement in 9
# **1. Introduction**

Poverty reduction is one of the key objectives of socioeconomic development. The first World Development Report (WDR) argued that development efforts should be aimed at the twin objectives of rapid growth and poverty reduction<sup>1</sup> (World Bank 1978). This vision of development has been reiterated in one form or another in subsequent reports culminating in a conception of development as _opportunity equalization_ presented in WDR 2006 (World Bank 2005). In this context, equity is defined in terms of a level playing field where individuals have equal opportunities to pursue freely chosen life plans and are spared from extreme deprivation in outcomes. In this sense, the pursuit of equity also entails that of poverty reduction.

A recent review of poverty trends across the world has shown that poverty had been on a steady decline for a wide variety of countries from the late 1990s up until 2009 (when the financial crisis hit the world economy). The evidence on inequality reduction, a key determinant of poverty outcomes, is however mixed. From the policymaking perspective, it is important to understand the factors driving these observed outcomes.

Focusing on the fact that distributional statistics are computed on the basis of a distribution of the living standards which is fully characterized by its mean and the degree of inequality, several authors have proposed counterfactual decomposition methods to identify the contribution of changes in the _mean_ and in _inequality_ to variations in overall poverty. These decompositions include the Datt-Ravallion (1992) method, which splits the change in poverty into distribution-neutral growth effect, a redistributive effect and a residual interpreted as an interaction term. The Shapley method proposed by Shorrocks (1999) is analogous to that of Datt and Ravallion, but does not involve a residual. Kakwani (2000) has proposed an equivalent approach. Ravallion and Huppi (1991) offer a way of decomposing change in poverty over time into intrasectoral effects, a component due to population shifts and an interaction term between sectoral changes and population shifts. We present a detailed review of all these macro methods in the appendix.

> 1 This recommendation is consistent with the theme underlying the study of redistribution with growth by Chenery et al. (1974).  This study advocates the use of explicit social objectives as a basis for choosing development policies and programs.  In particular, any development intervention must be evaluated in terms of the benefits it provides to different socio-economic groups.

2
The second pillar would require a contribution rate of 10% to 15%, depending on what assumptions are made about the future combination of interest rates and wage growth. The net pension rate of 40% that might be targeted in the second pillar would be easy to achieve with the suggested contribution rates as can be verified from Tables 2a and 2b. Thus, the total contribution rate could vary from 15% in the case of young countries to 25% in the case of old ones.

Before concluding this paper it is worth repeating the point made in footnote 9 above. While most of the analysis is based on constant contribution and pension rates, both rates could in practice be variable. In fact, allowing workers to set both their own contribution and pension rates, within a specified range, might be preferable and more consistent with greater reliance on personal choice.

34
diverse set of manufacturing products like Mexico.

After calculating the size of positive and negative shocks that can be attributed to China via the indices, we estimate and simulate a neoclassical small open economy model similar to Arias _et al_ (2013). The model is also related to Artu ̧c, Chaudhuri and McLaren’s (2010) labor mobility model but with a special emphasis on informal employment (which is especially important for Latin American economies). The model is empirically tractable, requiring only worker employment transitions across industries and employment status from labor-force surveys with panel data, which are available for Argentina, Brazil and Mexico. With these estimates of labor mobility costs, we simulate the effect of changes in labor demand across sectors to study labor-market adjustments caused by the growth of Chinese exports in global markets.

The estimation of labor mobility costs, as in Arias _et al_ (2013), highlights three common features in the labor markets of Argentina, Brazil and Mexico. First, it is less costly to become formal if the worker stays in the same industry. Second, the highest entry costs involve moving from informal to formal and changing the sector of employment. Third, the lowest entry costs are associated with movements from formal to informal within sectors. Moreover, in the three countries the highest mobility cost is found for workers moving out of informality into formality while changing sector towards agriculture or mining. This will limit the movement of workers to these sectors which were precisely the sectors that were positively affected by China. These high switching costs thus limited the reduction of informal employment of the positive shocks emanating from China.

From the simulations we conclude that, for Argentina and Brazil, the positive shocks in agriculture and mining offset the negative shock in manufacturing leaving the total level of employment and real wages almost at the same level. In these two countries, a larger positive shock in mining and agriculture is needed to offset a smaller shock in manufacturing because this sector employs a larger proportion of workers. In Mexico, the larger shock in manufacturing provokes a reduction of employment in the long run.

The export vulnerability index we introduce herein is closely related to the studies by 4
Mortality is significantly lower the more years leaders are in office, suggesting that longerserving leaders are more likely to internalize the future political costs of neglecting earthquake preparedness. Results reported in column 5 of table 2 also demonstrate that the results in models 1-3 are robust to the post-1975 sample. In fact, in this sample, which is restricted to the post-1975 period since the variable measuring the tenure length of the chief executive is only available from 1975 onwards, not only is the magnitude of the democratic response to increased propensity greater than that of the autocratic response, as in model 3, but the difference becomes statistically significant if the sample is restricted to the post-1975 period.

The exact death toll of earthquakes is typically unobserved. Therefore, no one actually _knows_ the exact number of quake victims, which is instead estimated. There can thus be no doubt that the estimation results we have reported in the previous section are based on the analysis of noisy data. We therefore conducted a Monte Carlo study, similar to what Plümper and Neumayer (2009) do for mortality from famines, which aims at exploring the effect of measurement error on the estimates. Specifically, we re-estimated all models 100 times. In each re-estimation, we multiplied the value of the dependent variable of approximately 15 percent of observations by a uniform random number of the interval [0.5..1.5], which mirrors measurement errors of up to 50 percent.<sup>16</sup> By drawing the measurement error from a uniform distribution, it is _on average_ unlikely to be correlated with the explanatory variables. However, the actually drawn measurement error _in each iteration_ may well be correlated with some of the regressors even if the average correlation over infinite iterations is zero. By

> 16 To determine the ‘subsample with measurement error’ we drew a second continuous uniform random variable of the interval (0..1) and changed only those observations for which the randomly drawn parameter exceeded 0.85. Thus, on average, we changed the dependent variable of about 15 percent of the ‘nonzeros’ in each iteration of the MC.

30
(SOCWAGE). The high government expenditures are also linked with a low share of investment spending in the total (GINVEXP).

The low investment of high spenders may be a cause of a slower growth of income per capita in the long run. We have not been able to test this growth effect, especially given the limited significance of any recent growth figures for Central Europe. Nevertheless referring to the literature quoted in the section 2, it can be expected that low investment and large tax distortions are harmful for growth. Social security can be efficiency enhancing in the few cases where it raises the productivity of the labour force by selecting the most productive workers but its financing on a pay-as-you-go basis can have negative effects on investment and hence on growth.

**Table 4. The Size of Public Redistribution**

|3|GDPHS<br>(*OE-3)|INEQUAL|GLABOUR|GINVEXP|1TAXREV|SOCWAGE|R<sup>2</sup><br>F<br>Obs.|Missing/<br>Residuals|
|---|---|---|---|---|---|---|---|---|
|EXPGDP|I|-0.99<br>(-2 26*)|0.54<br>(2.69*)|-0.91<br>(-2.62*)||0.12<br>(1.90+)|82.4<br>16.4|Cz.RJ<br>Hu. -5.3|

## Note:

Regressors DUMMYI, DDO, ADRDDO, ADR were not significant. DEBTGDP was marginally significant in a regression with INEQUAL and GLABOUR.

See Appendix III for variable description and rules of presentation. Source: Fakin and de Crombrugghe (1996), p.<sup>12</sup> .

The regression analysis of total and social expenditure thus shows that **the CEC's and many of**

**the other European countries** are off the income line and **have opted for generous and/or**

# **easy accessible social transfers, large government payrolls and egalitarian distribution of**

**income.** They face nevertheless high unemployment and low investment rates. We leave the

18