rafmacalaba/datause-encoder-data huggingface dataset:
# **<u>Table A5:</u> World diffusion and diffusion by income category using the number of subscribers**

||Low and Lower M|iddle Income|Upper Middle a|nd High Income|Wo|rld|
|---|---|---|---|---|---|---|
||[1]|[2]|[3]|[4]|[5]|[6]|
|Log real GDP per capita|2.61|0.035|3.459**|2.000|5.818***|0.612|
||[1.605]|[1.137]|[1.717]|[1.703]|[1.201]|[1.021]|
|Log real cost|0.128|-0.016|-0.168|-0.032|0.06|-0.002|
||[0.148]|[0.047]|[0.192]|[0.112]|[0.130]|[0.049]|
|Log lines per capita|2.725***|0.662*|1.417|1.059*|1.409***|0.877**|
||[0.459]|[0.346]|[0.993]|[0.569]|[0.377]|[0.383]|
|Log computers per capita|0.792***|0.036|2.601***|0.118|1.476***|0.026|
||[0.260]|[0.332]|[0.336]|[0.228]|[0.285]|[0.277]|
|Lag Internet subscribers||0.605***||0.410**||0.516***|
|||[0.108]||[0.167]||[0.135]|
|Constant|-12.872|0.007|-31.469*|0.081|-46.825***|0.05|
||[13.080]|[0.052]|[17.072]|[0.069]|[10.845]|[0.062]|
|Method of estimation|GLS|IV|GLS|IV|GLS|IV|
|Fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Number Observations|287|168|250|153|537|321|
|R<sup>2</sup>|0.91|-|0.91|-|0.95|-|

Notes: The dependent variable is the log of Internet subscribers per capita. Robust standard errors in brackets. * , ** and *** indicate significance at the 10 percent, 5 percent and 1 percent levels respectively.

The regression uses countries with GDP per capita growth in the interval [-4.9 , 9.2] and controls for country fixed effects.

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experiment with trade openness (as measured by imports plus exports divided by GDP) as a measure of real integration. We include a variable measuring real integration because, in the end, any redistribution of endowments due to (implicit or explicit) risk sharing agreements should be materialized through ‡ows of goods. Lastly, because domestic ...nancial depth can also contribute to risk sharing among domestic agents (perhaps lessening the need for, or the value of, international risk sharing) we also use data on domestic ...nancial depth, as captured by the ratio of domestic credit to GDP. Like the trade openness indicator, the credit depth measure is drawn from the World Development Indicators. To avoid simultaneity concerns, the empirical exercises assessing the covariates of risk sharing employ these variables measured at the beginning of the sample –that is, in 1972 (or the ...rst year in which data is available).

# 4 Empirical results

We proceed in two stages. First, we estimate the income process given by (15) and construct the innovations to permanent income (17); then we use the latter to estimate the risk sharing model (10).

## 4.1 Estimation of the income process

To estimate (15), we construct the country shares of global GDP s using averages over the entire sample period.<sup>12</sup> We estimate three di¤erent speci...cations of (15). The ...rst one ignores common shocks by setting b = c = 0, and therefore simpli...es to a system of N AR1 equations, similar to that employed by Crucini (1999). The second speci...cation allows for contemporaneous e¤ects of common factors by leaving b unrestricted while still imposing c = 0: The third speci...cation allows for both current and lagged common shocks by leaving both b and c unrestricted.

Table 1 summarizes the estimation results. The ...rst column reports the results of the AR1

> 12The correlation between the growth rate of global GDP per capita and the sum of individual-country per capita GDP growth rates weighted by these shares equals 0.95.

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stable, affecting on average 43 percent of the workforce. There is, however, an important regime shift underway in Brazil, and this change is related to the price index to which nominal wages are indexed. During the first years of the sample, up to 1999, the price index to which wages are indexed is the change in the minimum wage. Subsequently, and coincident with the introduction of inflation targeting in 1999, the price index that represents the focal point of wage negotiations moves away from the minimum wage and becomes closely related to the evolution inflation. Interestingly, the variance of this price index across agents also increases after 1999 -a feature that is consistent with the index being an indicator of inflation expectations.

The results in this paper are related to two recent macro studies. In the context of price setting, Benati (2008) shows that the rate of inflation persistence in different countries and time periods varies with the monetary policy regime, while Hofmann et al. (2010) show similar time variation in US wage dynamics. These two papers offer compelling time series evidence that challenges the notion that the persistence of inflation, or wage indexation to past inflation, are intrinsic to the deep structure of the economy, and invariant to changes in the monetary regime. Our work is a first attempt to examine the stability of downward nominal and real wage rigidities in an environment subject to policy changes and rapid macroeconomic stabilization. To this end, relying on matched establishment-worker data constitutes a great advantage. A general problem with the aggregation in macro series is that it assumes that the evolution of wage rigidities is the same for all establishments or groups of individuals. However, agents with different characteristics are likely to react differently to macro and policy changes. Some of these are subtle changes. For instance, our results for Brazil show that after the introduction of inflation targeting some agents appear to start negotiating wage changes as a function of expected inflation, while others remain pegged to changes in the minimum wage. Understanding the evolution of the focal point of wage negotiations is important for the design of monetary policy, and to guide the need for possible structural reforms in the labor market. However, these changes may be blurred by aggregation at the macro level. The use of micro data instead allows us to examine time-varying worker and establishment heterogeneity in wage setting.

5
Policy Research Working Paper 7638

# **Abstract**

This paper uses a unique firm-level data set for Mexico, with information never used for research before, to assess how use of information technology (IT henceforth) influences firm performance. Further, the paper explores if, in the context of increasing competition from China, this effect is different for firms more strongly affected by competition where incentives for upgrading and innovation may be more intense. In this perspective, the paper analyzes the complementarity between IT and other changes spurred by competition, taking advantage of the exogenous shock generated by Chinese competition. The results indicate that IT use has higher effects over productivity in the case of firms facing higher competition from China, in the domestic market and in the U.S. market. Furthermore, the paper shows how these changes appear to be driven by complementary investments in innovation and organizational changes.

This paper is a product of the Trade and Competitiveness Global Practice Group. The paper has been prepared as a background research paper for the World Development Report 2016 “Digital Dividends.” 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://econ.worldbank.org. The authors may be contacted at liacovone@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
Table 1 reports summary statistics on casual wages for the week prior to the survey. In R1 (2009), median PW wages were nearly 30% higher than the casual wage. Wages are higher for men than for women in both segments of the labor market, but for women, PW pays much better than the private sector. Between 2009 and 2010, average PW wages maintained value in real terms (increasing by 14.6% in nominal terms, compared to a 12% rate of inflation),<sup>21</sup> but the gap between public works and labor market wages narrowed as mean casual wages rose by 21%, and median casual wages rose by 43%. Women, however, still earned significantly higher wages under BREGS in R2 than in the casual labor market (t=3.91 in R1 and t=4.85 in R2).

Figure 2 and Table 2 take a closer look at wages for casual labor in relation to the stipulated BREGS wage, and their evolution over time. It can be seen that wages owed, as reported by participants on the scheme, are lower than the stipulated BREGS wage rate (top panels of Figure 2). Summary statistics and tests reported in Table 2 show that on average, workers received Rs. 10 less per day than the stipulated wage, for much of the recall period. Note that this gap is not due to payment delays, as the wages summarized are total wages _owed_ to the individual, not the amount actually received by the time of the survey interview.

Turning to the second source of data on wages on BREGS from our survey, Figure 3 plots the mean wage rate by month for both men and women based on Block 1. The figure also gives total days of work and identifies the survey periods. There is a marked seasonality in days of employment. As before, we see a persistent gap between the stipulated BREGS wage rate and the wage actually reported. The absolute gap is roughly unchanged over time. There is some sign of convergence in male and female wages, but this is possibly deceptive, given that there were very few observations in the early months when the female wage was lower. The longer recall periods required by this source of wage data also raise doubts about the early data points.

A third source of wage data is the NSS for 2009/10. Table 3 reports mean and median wage rates, spanning the period between R1 and R2 of the BREGS survey. Here too we see an increase in agricultural wages, notably between sub-rounds 2 and 3, corresponding to the last quarter of 2009 and first quarter of 2010. BREGS activity picks up in the first quarter of the year, so this agricultural wage increase does coincide with BREGS. However, also note that there was an even steeper increase in manual non-agricultural wages over the year.

> 21 The inflation rate is based on the consumer price index for agricultural laborers in the state.

14
legal12 and governance'<sup>3</sup> variables. Other variables include dummies<sup>forfinancialcrises</sup> (Caprio and Klingebiel 1999), likely to have a negative effect on<sup>mutualfundgrowth and</sup> indicators of financial system development and structure<sup>(Becketal2000),especially</sup> distinguishing between market-based and bank-based financial<sup>systems.Ofparticular</sup> relevance are restrictions on the payment of interest on checking<sup>accountsandother</sup> short-term bank deposits, which would tend to stimulate directly<sup>thedevelopmentof</sup> money market mutual funds and indirectly other types of mutual funds.

Cross-country panel estimations were used to help determine whether<sup>economic</sup> and financial variables play significant roles in the development<sup>of themutualfund</sup> industry. Several alternative model specifications were<sup>triedinordertotestthe</sup> robustness of different variables. All regressions were estimated<sup>usingrandomand fixed</sup> effects models. The fixed effects model included country dummies<sup>inadditiontoother</sup> independent variables discussed in previous sections. The<sup>modelhelpedtocontrolfor</sup> omitted variables assuming they remained constant over the<sup>estimationperiod.In</sup> addition, fixed effect modeling might control for differences<sup>inmutualfundindustry</sup> definitions used across countries. We used first lags of all variables<sup>to correctfor the</sup> possibility of reverse causality.

The regression results are summarized in the tables in the Annex.<sup>Forthe</sup> regressions covering all mutual funds in all countries we find a<sup>strongcorrelationwith</sup> capital market development (both value of traded equities and<sup>bond market development</sup> are highly significant), while the accountability index<sup>andrestrictionsonthe</sup> remuneration of retail deposits also have a positive impact. Although<sup>they havetheright</sup> sign, the level of real interest rates and equity market returns<sup>areinsignificant. The</sup> volatility of equity market returns and financial system crises<sup>haveanegativeand</sup> significant impact as expected. In contrast, per capita income<sup>andopennesstotrade have</sup> positive correlation between this variable and the growth of mutual funds,<sup>especially</sup> in<sup>high-income</sup> countries.

> 12 Common law countries tend to have more transparent and more reliable<sup>accounting systems and to</sup> provide stronger protection of the rights of outside investors<sup>(Dermiguc-Kuntand Levine1999,Beck et al</sup> 2001). They are associated with better-developed capital markets.<sup>Legalorigin is expectedto havea</sup> positive effect on the,growth of mutual funds.

Two governance indicators from Kaufman et al (1999) are used:<sup>Voice and Accountability;and</sup> Regulatory Burden. They measure the consistency and accountability<sup>of governmentpolicy, including the</sup> 31
Inklaar, R. and D.S.P. Rao (2015), “ _Cross-country income levels over time: did the developing country suddenly become richer_ ?”, Groningen Growth and Development Centre Research Memorandum 151 (revised June, 2015). Copy of this paper is available at <u>http://www.ggdc.net/publications/memorandum/gd151.pdf.</u> Jolliffe, D. and E. Prydz (2015), “ _Global Poverty Goals and Prices: How Purchasing Power Parity Matters_ ”, Policy Research Working Paper 7256, Development Research Group, World Bank.

Majumder, A., Ray, R. and Sinha, K. (2012 _)_ , “The calculation of rural urban food price differentials from unit values in household expenditure surveys: a new procedure and comparison with existing methods”, _American Journal of Agricultural Economics_ , 94(5), pp. 1218 -1235.

Majumder, A., Ray, R. and Sinha, K. (2015a), “Spatial comparisons of prices and expenditure in a heterogeneous country: methodology with application to India”, _Macroeconomic Dynamics,_ 19 (5), 931-989.

Majumder, A., Ray, R. and Sinha, K. (2015b), “Estimating Purchasing Power Parities from Household Expenditure Data Using Complete Demand Systems with Application to Living Standards Comparison: India and Vietnam”, _Review of Income and Wealth,_ 61(2), 302, 328.

McCarthy, P. (2013), “Extrapolating PPPs and Comparing ICP Benchmark Results”, in World Bank (ed.), _Measuring the Real Size of the World Economy_ , Ch. 18, World Bank, Washington DC.

Pelagatti, M. (2010), “Price Indexes across Space and Time and the Stochastic Properties of Prices”, in L. Biggeri and G. Ferrari (eds), _Price Indexes in Time and Space: Methods and Practice,_ Springer-Verlag, Berlin, 97-114.

Piketty, T. (2015), “Putting Distribution Back at the Center of Economics: Reflections on ‘Capital in the Twenty-First Century”, _Journal of Economic Perspectives_ , 29 (1), 67-88.

34
generation mix. The policy not only placed the currency risk with the off-taker but also placed the onus of fuel supply for these IPPs with the government as well. The policy guaranteed a fixed return over the life of the project regardless of the efficiency and performance of the plants. Because fuel cost was a pass-through in the tariff, and the policy favored developers who were willing to build plants that could be brought online fast, most of the plants brought online were oil-based. The shortage of supply, and the government’s urgent need to address the crisis, led to the adoption of a policy with far-reaching consequences.

**Box 2: The meaning and causes of the circular debt in Pakistan**

“The term ‘circular debt’ is used somewhat differently by various entities in Pakistan. In essence the term is understood to be the amount of cash shortfall within the CPPA, which it cannot pay to power supply companies. This short fall is the result of (a) the difference between the actual cost of providing electricity and the revenue realized by the DISCOs from sales to customers, plus subsidies; and (b) insufficient payments made by DISCOs to CPPA out of the revenue realized since they give priority to their own cash flow needs.” (Planning Commission of Pakistan and USAID, 2013).

“Circular debt is created in the power sector when end-customers (both public and private) do not pay their electricity bills to distribution companies, and the GoP is not able to fully furnish its commitment to fund subsidies. As a result, the distribution companies are unable to pay their purchase cost to the CPPA/single buyer, who is in return unable to fill its obligation to power generation companies. And the power generation companies...fail to pay fuel suppliers. The fuel suppliers in turn default on their payments to refineries, gas producers, and international fuel suppliers. The term ‘circular debt’ is based on the fact that two gas utilities...are also in government ownership.” (C. Trimble, N. Yoshida, and M. Saqib, 2011).

World Bank and IMF reports use the term “intercorporate debt” or “intercompany arrears.” The Brent oil price was $16 a barrel in 1994 but by 2004 it had risen to $38 a barrel (without inflation adjustment). As a result of the heavy dependence on oil and the drastic depreciation of the rupee in the wake of Pakistan’s nuclear test in 1998, Pakistan’s generation mix was the most expensive in the region. Later, during the 2010-2013 circular debt crisis marked by fuel supply shortages, the 1994 IPPs, despite being some of the most expensive generation, were supplied fuel on a priority basis.

In 1995 the government issued another policy: “Policy Framework and Package of Incentives for Private Sector Hydel<sup>11</sup> Power Generation Projects in Pakistan”. This policy was aimed at attracting private investment into small hydropower projects (under 300 MW) to offset the large number

> 11 Hydel means hydroelectric.

13
<!-- Start of picture text --> Figure S1.2d: Residuals with Time Trend, NO2 at Siri Fort<br>Blue Line (second extension)<br>Source: Based on data described in the text<br>Figure S1.2e: Residuals with Time Trend, CO at ITO<br>Yellow Line (first extension)<br>Source: Based on data described in the text<br>1<br>.5<br>0<br>-.5<br>-1<br>8Oct2006 12Oct2006 16Oct2006 20Oct2006 24Oct2006 28Oct2006 1Nov2006 5Nov2006 9Nov2006 13Nov2006 17Nov2006 21Nov2006 25Nov2006 29Nov2006 3Dec2006 7Dec2006 11Dec2006 15Dec2006<br>1<br>.5<br>0<br>-.5<br>-1<br>-1.5<br>1Jun2005 5Jun2005 9Jun2005 13Jun2005 17Jun2005 21Jun2005 25Jun2005 29Jun2005 3Jul2005 7Jul2005 11Jul2005 15Jul2005 19Jul2005 23Jul2005 27Jul2005 31Jul2005 4Aug2005 8Aug2005<br><!-- End of picture text --> 53
alcohol, tobacco, coffee, and tea.<sup>16</sup> It appears that generally migrant and nonmigrant households devote similar shares of household income to the various commodities, with the exception of adult goods, whose share is significantly larger in nonmigrant households. This is somehow suggestive of the fact that both parents consume alcohol, tobacco, coffee, and tea, so that when there is only one of them in the household, the share of income spent on these items falls. Yet this drop is larger in the case in which the husband migrates, because men generally have stronger preferences for these goods. Indeed, the comparison between households with migrant mothers and households with migrant fathers reveals that the latter devote a significantly larger share of income to food expenditure and less to adult goods, reflecting differences in tastes between men and women.

**Table 8.** Household Income Levels and Shares.

||Nonmigrant<br>households|Migrant<br>households|Migrant<br>father|Migrant<br>mother|Difference<br>(1)-(2)|<br>Difference<br>(3)-(4)|
|---|---|---|---|---|---|---|
|Household income<br>(log)|16.56<br>(0.711)|16.26<br>(0.704)|16.25<br>(0.717)|16.26<br>(0.621)|0.3***|-0.01|
|Predicted household<br>income (log)|16.47<br>(0.711)|16.36<br>(0.644)|16.42<br>(0.687)|16.25<br>(0.500)|0.11***|0.17**|
|Shares of income spent|on:||||||
|Food|0.634|0.711|0.764|0.582|-0.077|0.182**|
||(4.717)|(0.800)|(0.858)|(0.668)|||
|Education|0.153|0.161|0.171|0.137|-0.008|0.034|
||(1.460)|(0.255)|(0.304)|(0.158)|||
|Health|0.0513|0.0481|0.058|0.027|0.003|0.031|
||(0.586)|(0.228)|(0.292)|(0.094)|||
|Durables|0.0139|0.130|0.0202|0.001|-0.116|0.019|
||(0.528)|(2.188)|(0.377)|(0.007)|||
|Adult goods|0.117|0.080|0.060|0.108|0.037***|-0.048***|
||(0.826)|(0.135)|(0.133)|(0.133)|||

**~~Notes:~~** ~~Standard deviations reported~~ <u>in</u> ~~parenthesis.  Differences computed~~ <u>with</u> ~~standard errors clustered at~~ <u>village</u> level. *** p<0.01, ** p<0.05, * p<0.1

Equation (14) is first estimated through OLS; the results are reported in table 9, controlling only for household size in the first panel, and adding an indicator for rural households, the number of years of education of the mother and the (log of) total household expenditure in the second panel. To correct for correlation of errors within villages, standard errors are heteroskedastic robust and clustered at village level in all specifications. The OLS results confirm that households in which mothers have migrated spend significantly more on adult goods, the shift being about 6 percentage points, and less on common goods,

> 16 Note that these shares do not add up to one for two reasons: the first is that the denominator is a measure of predicted income and not total household expenditure; this is done to better replicate the theoretical predictions. Second, there is a residual, unobserved category of expenditure, which is that for private consumption of the migrant.

19