rafmacalaba/datause-encoder-data huggingface dataset:
**Table 13. Benchmarking ICT indicators**

|**Access**||2000|2008|2009|Low-income countries|
|---|---|---|---|---|---|
|GSMcoverage|% populationundersignal|16|100|100|56|
|International bandwidth|Bits/person|0.08|16|61|24|
|Internet|Users/100 people|0.2|7.8|8.4|4.6|
|Landline|Subscribers per 100<br>people|0.3|0.5|0.6|4.6|
|Mobile phone|Subscribers per 100<br>people|0.6|26.7|28.3|28.5|
|**Prices**||||||
|Price of monthlymobile basket|US$||9.7|8.3|10|
|Price of monthly fixed-line<br>basket|US$||13.3|11.3|9.8|
|Price of monthly fixed<br>broadband|US$||300|300|102|
|Price of a call to the United<br>States per minute|US$||0.26|0.22||
|Price of an inter-Africa call per<br>minute|US$||0.63|0.31||

_Source_ : Adapted from UCC, UTL, MTN, and World Bank ICT At-a-Glance.

_Uganda was an early telecommunication sector reformer._ It introduced a private cellular operator in 1994, created the industry regulator UCC in 1997, licensed a second national operator in 1998, and privatized the incumbent Uganda Telecommunications Limited (UTL) in 2000 (ITU 2001). It has progressively introduced competition in the mobile sector with five operators providing service by 2009. It has also created a technology neutral streamlined licensing process.

_Inflow of foreign investment in Uganda, particularly from companies that are involved in more than one country, has contributed to connectivity and service enhancement_ . Collaboration over mobile roaming and fiber optic connectivity between Rwandatel and Uganda Telecom has been facilitated by common ownership. These multi-country networks have provided beneficial regional roaming arrangements (box 2)

# **Box 2. Cooperative private sector development of fiber-optic infrastructure in Uganda**

As of 2009, Uganda’s telecommunications market had five mobile operators and several ISPs. MTN and UTL are the two national fixed line operators and are also significant mobile operators. Both companies also have operations in Rwanda and thus have a common interest in establishing a communications link across the border from Uganda to Rwanda. In 2007 MTN constructed a fiber-optic cable from Kigali, Rwanda, to the border with Uganda. The company also recently announced a deal with its competitor UTL to jointly develop the fiber-optic network on the Uganda side—a good example of competing operators forming a cooperative arrangement to lower the costs of developing fiber-optic networks outside major urban areas. But such an arrangement raises concerns for the market in Rwanda since the only fiber optic connection to the country will be jointly controlled by the only two network operators in Rwanda. Such concerns may ease as more licenses are issued in Rwanda and competition grows.

_<u>Source:</u>_ World Bank 2009a.

39
# **Table A.4b: Difference in Difference Results for Retail-World Margin** **<u>(Margins Based on 2 Week Lagged World Price of Crude Palm Oil)</u>**

||||Retail-World|Price Margins|||
|---|---|---|---|---|---|---|
||Full S|ample|Restricted|Sample|Matched|Sample|
|**Effect of Announcement**|||||||
|Announcement Dummy|-4.828***|-4.208***|-5.761***|-4.053***|-4.283***|-3.994***|
||(-7.865)|(-5.593)|(-10.24)|(-7.377)|(-5.854)|(-5.410)|
|**Announcement*Palm**|**17.60**|**11.51**|**20.76**|**12.51**|**13.24**|**11.46**|
||(0.751)|(0.502)|(0.898)|(0.549)|(0.595)|(0.475)|
|**Announcement*Palm world Price**|**-0.0967**|**-0.0534**|**-0.114**|**-0.0643**|**-0.0718**|**-0.0489**|
||(-0.329)|(-0.186)|(-0.393)|(-0.226)|(-0.257)|(-0.161)|
|**Effect of Implementation**|||||||
|Policy Implement. Dummy|-2.464***|-2.144***|-4.539***|-2.380***|-0.390|-1.378*|
||(-3.969)|(-2.896)|(-7.370)|(-3.620)|(-0.543)|(-1.729)|
|**Policy Implement.*Palm**|**27.06*****|**19.82*****|**27.02*****|**19.68*****|**28.02*****|**27.55*****|
||(8.138)|(5.388)|(8.314)|(5.346)|(8.315)|(6.106)|
|**Policy Implement.*Palm world**<br>**price**|**-0.231*****|**-0.166*****|**-0.208*****|**-0.168*****|**-0.272*****|**-0.261*****|
||(-5.289)|(-3.602)|(-4.947)|(-3.721)|(-6.071)|(-4.444)|
|World price*palm|0.170***|-0.210***|0.199***|0.0672|0.160***|-0.0115|
||(3.955)|(-3.595)|(3.735)|(0.991)|(3.461)|(-0.149)|
|World Price|-0.487***|-0.131**|-0.498***|-0.410***|-0.496***|-0.341***|
||(-9.937)|(-2.254)|(-8.265)|(-5.421)|(-9.584)|(-4.813)|
|Palm|18.59***|31.68***|15.10***|24.95***|22.30***|27.68***|
||(15.55)|(14.38)|(13.53)|(12.24)|(14.25)|(7.810)|
|Intercept|22.75***|18.27***|20.47***|24.51***|22.27***|16.35***|
||(20.87)|(6.747)|(13.89)|(4.477)|(19.42)|(5.765)|
|Observations|1,912|1,912|1,374|1,374|1,292|1,292|
|Year and Quarter dummies|Yes|Yes|Yes|Yes|Yes|Yes|
|Ramadan Dummies|No|Yes|No|Yes|No|Yes|
|Relative Prices|No|Yes|No|Yes|No|Yes|
|Fuel Price|No|Yes|No|Yes|No|Yes|
|Dummyif World Price rising|No|Yes|No|Yes|No|Yes|
10 Taxes", Economic Letters, Vol.13, pp.55-57.

- Rodrik, D. (1990), "Trade Policies and Development: Some New Issues", mimeo, Kennedy School of Government, Harvard University.

- Selden, L. (1978), "A New Representation of Preferences over "Certain X Uncertain" Consumption Pairs: The "Ordinal Certainty Equivalence" Hypothesis", Econometrica, Vol. 46, no. 5, pp 1025-45.

- Weil, Ph. (1990), "Non-Expected Utility in Macroeconomics", Ouarterly Journal of Economics, Vol.CV, pp 29-43.

- van Wijnbergen, S. (1985), "Trade Reform, Capital Flight and the Value of Information", Economic Letters, Vol.19, pp.369-72.
common short-run dynamics then give some idea of the average effect on spreads across the set of emerging market countries.

Of particular interest to us is to see whether the forecasted stance of US monetary policy, and not just current interest rate variables, helps explain the evolution of emerging market spreads. Therefore, we first attempt to relate our US interest rate variables—the T Bill rate, the 10-year Treasury rate, and the interest rate spread between high and low risk corporate borrowers—to indicators of inflationary pressures and the strength of US economic activity. These are presented in Table 2, where 3 lags of each of the explanatory variables are included in each case. Changes in the latter variables are often cited by “Fed watchers” as leading indicators of changes in Fed policy.

The three interest rate variables are affected differently by movements in these indicators. The T Bill rate seems to respond significantly, and positively, to upticks in retail sales and capacity utilization, while evidence of a significant effect of producer prices and M2 is weaker. Indeed, the latter variable is negative, so that a liquidity effect of monetary expansion may operate. Long-term bonds also respond positively to retail sales, while the first lag of M2 expansion is significantly positive, perhaps reflecting fears of future inflation from an easier monetary policy. Finally, the corporate bond spread does not seem to respond systematically to our set of indicators.

We then proceed to use the predicted values of the forecasting equations of Table 2—and other explanatory variables—in dynamic equations for the emerging market spread. These are reported in Table 3, where all the coefficients, except for the intercepts, are identical across countries.

19
We use additional controls for robustness purposes. Some of these controls are not included in the baseline controls above because they involve a noticeable decline in sample size due to missing data (see table A3 in the Appendix). Firm-level controls all taken from ES include: dummy variable equal to 1 if the firm competes against unregistered or informal firms and 0 otherwise; firm’s growth proxied by the average annual growth rate of employment over the last 3 years prior the year ES was conducted; a dummy variable equal to 1 if the firm spent on R&D activity last year and 0 otherwise; dummy variable equal to 1 if the firm introduced a new product or product in the last three years and 0 otherwise; and severity level (on 0-4 scale) of labor regulations as an obstacle to firm’s current operations. Macro-level controls include the square of (log of) GDP per capita for any non-liner effects of higher incomes; ease of entry proxied by Fraser Insitutues sub-index of Freedom to Start a business; and social norms proxied by the percentage of country’s population that is Muslim, Catholic, Protestant (residual category is all other religions) taken from Maoz and Henderson (2013).

# 3.8 _Interaction term controls_

As stated above, we focus on three heterogenous effects of maternity leave. These relate to how the impact of maternity leave depends on who pays for maternity leave (government or employer), share of females in 20-29 years age group in the country’s total population, and the skill level of workers proxied by the wage rate and labor productivity of the firm. To ensure that these heterogeneities do not proxy for other heterogenous effects, we control for several interaction terms as well as non-linear effects of maternity leave, etc. These controls are discussed in section 5 and 6.

25
# Table 17 Explaining Changes In Welfare In Terms of Expenditure Components

|Income Components|Due to Chanos In<br> <br>Exp Comp<br>Aver_se_|Due to Chanose<br>In<br>Exp Comp<br>Distribution|Total Chanoe<br>due to Exp<br> <br>Wm|
|---|---|---|---|
||Change In I|nequality between 1989|to 1991|
|Food|11.5|2.2|13.7|
|Non food|6.6|1.9|8.5|
|Alcohol|-0.2|0.2|0|
|Social Expenditure|-2.4|0.3|-2.1|
|Taxes|1.7|-0.4|1.3|
|Other Expenditures|-0.7|0.8|0.1|
|Savings|-0.4|0.1|-0.3|
|Total Expenditure|12.7|5.9|18-6|
||Change in I|nequality between 1991|to 1992|
|Food|-12.4|-0.6|-13|
|Non food|-9.9|-0.4|-10.3|
|Alcohol|-0.6|0|-0.6|
|Social Expenditure|-4.4|-0.1|-4.5|
|Taxes|1.2|0.5|1.7|
|Other Expenditures|-1.5|-0.3|-1.8|
|Savings|-0.7|0.5|-0.2|
|Total Expenditure|-30.7|-1.4|-32.1|

35
revenue deficit, particularly one driven by current consumption, is a concern because it does not augment the productive capacity of the economy.

Expenditure composition also worsened with stagnation of maintenance and capital spending and a big increase in expenditure on wages, pensions and subsidies (Figure 5). Since the 1990s Tamil Nadu’s capital outlay to GSDP ratio has been on average 1.6 percent. This ratio has been much lower than that of comparable states where annual capital outlays to GSDP varied between 2 and 3 percent. It has been lower compared to other countries too as India in general has had lower capital outlay than, for instance, China where infrastructure investment has stayed around 8-10 percent of GDP per annum for the last 15 years.

# **Figure 5. Expenditure Composition**

<!-- Start of picture text --> 5.0%<br>4.5%<br>4.0%<br>3.5%<br>3.0%<br>2.5%<br>2.0%<br>1.5%<br>1.0%<br>0.5%<br>0.0%<br>Non Wage O&M Capital Outlay Fiscal Deficit<br>Percent of GSDP<br>1990/91 1991/92 1992/93 1993/94 1994/95 1995/96 1996/97 1997/98 1998/99 1999/2000 2000/01 2001/02 2002/03 2003/04(RE) 2003/04<br><!-- End of picture text --> Source: Government of Tamil Nadu’s Budget documents. Note: The deficits in 2000/01 and 2001/02 underestimate the actual levels because they do not account for non-pension arrears equaling 1.7 percent of GSDP in 2000/01 and 2 percent of GSDP in 2001/02. The deficit in 2002/03 and 2003/04 includes partial clearance of accumulated pension liabilities.

The traditional measure of the fiscal deficit does not capture the quasi-fiscal activities of state-owned public enterprises (SOEs). The budget does capture grants, capital outlays and loans extended by the government to SOEs, but it does not capture their financing gaps, which are covered by market borrowing and direct government subsidies. Since most SOEs in Tamil Nadu, and other Indian states,<sup>13</sup> incur losses,<sup>14</sup> and hence, are not 13 There are three types of SOEs in Indian states. The first type is public sector undertakings (PSUs) covering agriculture, housing construction, finance, manufacturing, mining, trading, passenger transport and drug procurement, among others. The second type is Statutory Boards formed under independent legislative acts. These are mostly in utilities and infrastructure, from electricity to maritime transport. The third type is cooperatives in which the government owns equity. All three types of SOEs have suffered financial losses. 14 The accumulated losses of PSUs in Tamil Nadu were Rs.28.24 billion as of end 2001/02. The outstanding guarantees issued to PSUs amounted to Rs.120.04 billion as of March 31, 2002. The 18 State Transport Units, with 120,000 employees and an accumulated loss of Rs.20.9 billion as of March 2002, accounted for about 70 percent of employment and 73 percent of accumulated losses of all PSUs in 2001/02. Tamil Nadu’s Electricity Board (TNEB), with a total employment of 94,000, accounted for 80 percent of total employment, 10
-8-

biases are potentially exacerbated by systematic differences across groups in unobserved (to the analyst) characteristics, such as motivation, ability, or tastes. A second, related issue arises because trainees are overrepresented in our data relative to their true weight in the population; they represent 74 percent of our data sample versus 10 percent in the total unemployed population in 1990. The oversampling of trainees, who have very different attributes from other unemployed individuals, gives rise to estimates of program effects that are not representative of the underlying unemployed population.

We address these methodological issues in several ways. Two approaches are used to control for selectivity bias. One uses PROBECAT's own selection criteria to define comparable samples of controls for the trainees. This involves first estimating a probit model relating the likelihood of program participation to the PROBECAT selection criteria for which we have data, such as marital status, number of children, dependents, schooling and time spent unemployed prior to entering PROBECAT, and then limiting the control sample to individuals with high predicted probability of program particip--ion.12 1 This approach is adopted in the analysis of training effects o. employment. A second approach, which we will use in the analyses of monthly salary, hours of work, and hourly wage, is based on the two-stage selectivity carrection procedure developed by Heckman (1979).D1' This involves estimating a model of selection into PROBECAT, calculating a variable to capture the individual's likelihood of program selection, and including this variable as a regressor in the outcome models.

The second issue arises when program effects are estimated without taking into account the overrepresentation of trainees in the data. The estimates are biased because these models give undue weight to the attributes of trainees that differ, often substantially, from those of the control group. This issue may be resolved by constructing choice-based weights to reflect the true relative proportions of both trainees and controls in the unemployed 1i/ A similar approach is followed in Westat, "Continuous Longitudinal Manpower Survey Net Impact Report No.1", Report prepared for the US DOL, 1981. Westat, "Summary of Net Impact Results", Report prepared for the US DOL, 1984. Laurie Bassi, "The Effect of CETA on the Postprogram Earnings of Participants", Journal of Human Resources. Vol. 18, No. 4, 1983, pp. 539-56. And Vincent Geraci, "Short-Term Indicators of Job Training Program Effects on Long-Term Participant Earnings", Report for U.S. DOL, 1984.

JU/ James Heckman, "Sample Selection Bias as a Specification Error", Econometrica, Vol. 47, No. 1, 1979, pp. 154-61.
liability (=max(output VAT - input VAT, 0)). We use annual data for our main analysis and later show robustness of our results using monthly data. This is because firms outside the large – – taxpayer unit report taxable sales a key outcome variable only annually, and they report output VAT and net liability monthly but retrospectively at the end of each year.<sup>21</sup> In our preferred specifications, we winsorize the outcome variables at the 99th percentile within each treatment group _×_ year, and we confirm robustness of the results to alternative top-coding approaches.

# **5.2 Results**

Our main DiD results are shown in Figure 3. Each column pertains to a different outcome variable. In the top row, we show the normalized trends over time in the treatment and control group, and the DiD point estimate _β_<sup>ˆ</sup> on the _Retaileri · PostReformt_ interaction from equation 2. In the bottom row, we plot the period-specific _βk_ estimates from Equation 3 to confirm that we cannot reject the parallel trends assumption.

If the expansion of electronic transactions triggered an improvement in tax compliance, it should first manifest through an increase in reported taxable sales. However, we observe parallel trends in this outcome and hardly any divergence between the treatment and the control group. We estimate that taxable sales in the treatment group increased only by an additional 3.2 percent after the reform, compared to the control group, an effect which is statistically indistinguishable from zero (Figure 3, column A). The fact that reported sales do not change differentially in the treatment group after the reform, and that the statutory VAT rates did not change, would imply that the output VAT remitted should also be unchanged. Indeed, we find that the DiD point estimate on reported output VAT is also close to zero (-2.6 percent), which is again statistically indistinguishable from zero (column B). The fact that the consumer response to the VAT rebates is immediate already suggests that any tax compliance response should also emerge relatively quickly. The empirical results also contradict the possibility of a gradually emerging effect, as the event-study estimates for 2015 are smaller than those for 2014 (bottom row). Consistent with the absence of an impact on reported sales and output VAT, the effect on the reported net tax liability is also close to zero and statistically insignificant (column C). The reform thus had no impact on treated firms’ reporting behavior or tax remittance. Our findings starkly contrast with the findings in Naritomi (2019), who shows that the roll-out of e-receipts in Brazil increased reported sales of retail firms by at least 21 percent.

> 21For the annual specification, _PostReformt_ indicates the years 2014 and beyond, taking into account that the year 2014 is partially treated as the VAT rebates enter into effect in August.

17
**Table 5. The Growth-Accounting Results of Chinese SOEs: 1980-89** a

|**CHANGE**OF ln(AVERAGE VALUE**ADDED)**||.26|
|---|---|---|
|**Total Contributions to change in ln(average value added) from:**<br>**Based**|**on Mo**|**del (2)**|
|**Conventional Factors:**||-.107|
|Capital-labor ratio|.006||
|Number of employees|-.028||
|Mix of jobs as engineers, management personnel, and production workers<br>Input price index|-.013<br>-.072||
|**Change of Management:**||.097|
|The firm was governed by the central gov't, and had new manager|.005||
|Yearx(a firm was governed by the central gov't, and had new manager)|-.002||
|The firm was governed by the provincial gov't, and had new manager|-.001||
|Yearx(a firm was governed by the provincial gov't, and had new manager|) .023||
|The firm was governed by the prefecture government, and had new|-.017||
|manager|||
|Yearx(a firm was governed by the prefecture gov't, and had new manager)|.071||
|The firm was governed by the county gov't, and had new manager|-.009||
|Yearx(a firm was governed by the county gov't, and had new manager)|.027||
|**Marginal Profit Retention Rate**||.008|
|**Wage Control Rights**||.109|
|W_DISCRETION|.026||
|W_DISCRETIONx(year<br>since the manager had W_DISCRETION)|.039||
|W_ELASTICITY|.044||
|CRSb||-.033|
|Share of firms under Management Responsibility System (CRS)|.016||
|CRSx(year since the firm adopted CRS)|-.049||
|**Decentralization of production decision rights:**||.132|
|<br>Share of output under the government's mandatory planc|.006||
|Share of firms whose managers had autonomy|.036||
|Autonomyx(year since the manager had autonomy)|.090||
|**Changes of Institutional Arrangements in total:**||**>**0.109|

**a** Based on FE estimates of the growth model.

b The effects of CRS are the total effects of adopting CRS net of the incentive effects associated with firm-level pay sensitivity, part of the CRS package. If we include the effects of pay sensitivity, then the total effects of CRS is 0.011.

' This variable is observed only until 1984, showing almost no change from 1984 to 89. Its small positive magnitude reflects a small change of this variable between 1984 and 1989.

27
income deciles and energy consumption, finding those households below the 5<sup>th</sup> deciles as energy poor. Pachauri et al (2004) also determined energy poverty according to calculations for the required energy use of households in India, and found a general reduction in the numbers of energy poor over time. Others, such as Pereira et al (2010), analyzed the effects at the local level of rural electrification on the decrease in energy poverty in Brazil.

Gertler et al (2009) focused on how cash transfers to poor households in rural Mexico may increase their demand for energy. These authors, using data only for the extremely poor households receiving Oportunidades anti-poverty cash transfers, find that cash transfers increase the assets that demand more energy, increasing consumption.

In general, research has focused on relating different energy consumption to income and other characteristics, either at household or at aggregate levels. This is the approach we follow for Mexico, since this issue has not been addressed and it may be relevant to know how to target possible subsidies in a highly unequal country. On the basis of the previous literature, our hypothesis is that there is an increasing relation between income and energy consumption, which may differ in urban and rural areas and according to the energy source. We standardize the measures of energy so we can compare where the effects from income or other characteristics could have a larger incidence. We also differentiate between the energy sources that a household can consume. In doing so, we outline some possible directions for better targeting of public subsidies.

# **3. Data, methodology, and preliminary relations**

# **_3.1 Data_**

We use microdata from the National Income Expenditure Surveys (ENIGH) 2010. A survey is carried out every two years, and includes detailed questions regarding 7