rafmacalaba/datause-encoder-data huggingface dataset:
housing expenditures for three African cities (Table 4) suggest that housing expenditure is less responsive in these cities than in other developing countries (see Malpezzi and Mayo 1987).

Malpezzi and Mayo (1987) review previous housing demand studies in over a dozen developing country cities and find that income elasticities are fairly consistent across countries, and in most cases fall between 0.5 and 1, with renters generally have slightly lower income elasticities than owners.<sup>31</sup> These estimates however, were mostly for Latin American and East Asian countries/cities, with little to no representation of low-income or SSA countries. Using the SHIP data to estimate income elasticity of housing expenditures in Kigali, Abidjan, and Maputo yields coefficients on log income ranging from 0.15 and 0.37 (Table 4). Interestingly, early estimates of the income elasticity for low income housing in the US are in a similar range: 0.19 for renters and 0.45 for owners (Mulford 1979). The estimated elasticities suggest that a 10 percent increase in income will only lead to increases in housing expenditures ranging between 1.5 and 3.6 percent. Cash transfers to households would not lead to increased expenditures in housing unless strongly earmarked.

**<u>Table 4. Income Elasticity (dependent variable: log actual rent)</u>**

||**Kigali**||**Abidjan**||**Maputo**||
|---|---|---|---|---|---|---|
|Log Income|0.162<br>(.016)***|0.159<br>(.016)***|0.150<br>(.015)***|0.150<br>(.015)***|0.364<br>(.065)***|0.367<br>(.065)***|
|Household<br>size|0.105<br>(.052)**|0.085 (.053)|0.046<br>(.022)**|0.052<br>(.023)**|-0.140 (.120)|-0.127<br>(.120)|
|Household<br>size<br>Squared|-0.002 (.005)|-0.003 (.005)|-0.001 (.002)|-0.001 (.002)|-0.009 (.009)|-0.008<br>(.009)|
|Log Age of<br>household<br>head||0.496<br>(.169)***||-0.089 (.089)||0.338 (.103)*|
|Constant|9.927<br>(.225)***|8.332<br>(.588)***|10.075<br>(.201)***|10.373<br>(.360)***|5.804<br>(.717)***|8.271<br>(1.475)***|
|R squared|0.218|0.230|0.107|0.105|0.190|0.204|
|Sample size|551|551|1251|1251|226|226|

Significant at the *10%, **5%, and *1% levels.

Rather than emphasizing policies that purport to increase expenditures on housing at this stage of development, policymakers in SSA should focus on extending access to basic services and strengthening coordination between land use planning and service provision. As incomes increase, this would allow households the opportunity to access houses that are equipped with basic infrastructure and help countries move towards better overall quality of housing. Also, as incomes increase, the income elasticity of

> 31 Likewise, Struyk, et al. (1990) find a similar difference between renters and owners in Indonesia and also note that income elasticities decrease with city size.

24
exhibit an even larger income gap (Figure 2). In order to further highlight absolute divergence and illustrate this last point, Figure 2 in the second panel contrasts long-term trend of per capita income in Oman and the two Congo (Democratic Republic of Congo and Congo Republic).

**Figure 1: Long-term Trend of GDP per capita Income in Oman and Botswana (in constant 2000 US$)**

<!-- Start of picture text --> 12000<br>10000<br>8000<br>6000<br>4000<br>2000<br>0<br>Oman Botswana<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006<br><!-- End of picture text --> Source: World Bank World Development Indicators.

**Figure 2: Long-term Trend of GDP per capita Income in Oman, DRC and Congo (in constant 2000 US$)**

<!-- Start of picture text --> 12000<br>10000<br>8000<br>6000<br>4000<br>2000<br>0<br>Congo, Dem. Rep. Congo, Rep. Oman<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006<br><!-- End of picture text --> Source: World Bank World Development Indicators.

6
licences were issued (H1 Telekom in December 2004 and OT- Optima Telekom in November 2004), we identify 2005 as year when competition started. Currently, there are 10 operators that technically and legally can perform at fixed telecom market.

The same criterion is applied in mobile market. According to the 1994 Telecommunication Act (Art. 12.2), entry into mobile market is possible provided that company has concession issued by the Telecommunication Council . Vipnet eceived concession in September 1998 and started operation in 1999, as a second GSM operator in Croatia (the first being HT). The third licence was issued in December 2004 (TELE 2).

As technical preconditions for participation in international mobile market existed, the liberalization of international telephone market started in 2003, the second operator being Vipnet (i.e. mobile operator)

# State ownership

Act on Privatisation of Croatian Telecommunications (NN 65/99,68/01) defined privatization procedure.

Market structure For market structure we used data from research on telecom market conducted by Competition Agency . We consider HT as incumbent operator and all the other operators as new entrants. However, it should be noted that Vipnet (2nd mobile operator) is considered as operator with significant market share (OG 134/07). Market share of third mobile operator Tele 2 (that entered the market in 2005) was less than 5% in 2005 and 2006 (according to Competition Agency) and roughly 8 % in 2007 (according to Tele 2 data).

# 2. POST

- 2.1.  Selected Legal Acts – Postal Services

- Postal Act (Official Gazette 172/03, 15/04)

- Act on Amendments to the Postal Act (OG 92/05)

- Establishment of a Croatian National Postal and Telecommunications Company Act (OG

- 42/90, 61/91, 109/93)

Note: the Constitutional Court of the Republic of Croatia has annulled Article 16 of this Act

- Postal Ordinances (OG 37/95, 89/95, 5/96)

- Ordinances on the Postal System of the Public Operator (OG 5/05)

- Division of Croatian Post and Telecommunications into Croatian Post and Croatian

- Telecommunications Act (OG 101/98)

- Ordinances on General Conditions for the Provision of Postal Services (OG

- 37/95;151/04)

- Ordinances on Amendments to Ordinances on General  Conditions for the Provision of

- Postal Services (OG  122/05)

- Ordinances on Licence Fee for the Provision of General Postal Services (OG 20/04)

# 2.2. Notes

84
focus on different firm groups. The first column of the first term, uA<sup>G</sup> HE<sup>G</sup> , represents the direct value added exports by large SOEs (SL) in all 42 sectors. Notice the direct value-added exports based on the forward-linkage and backward-linkage approaches are identical (i.e. `a&<sup>I</sup> <]<sup>^</sup> b<sup>T</sup> in eq. (13) = &<sup>I</sup> <]<sup>^</sup> Z in eq. (11)).

However, the indirect value-added exports measures can be very different for each firm group-sector pair. The two measures are only equal to each other at the country level (see WWZ, 2013 for details). In the second term, a&<sup>I</sup> <(9 −\)]<sup>^</sup> , the first column is the sum of 6 multiples as follows: Where u_ is a 1x42 row vector. u_A<sup>G</sup> ��H (B��,�� −I)EG�� is LSOEs’ indirect VAX via large LSOEs; u_A<sup>G</sup> ��H B��,��EG��, u_AG��H B��,��EG��, u_AG��H B��,��EG��, u_AG��H B.�,��EG��, and u_AG��H B.�,��EG�� represent SSOEs, LFIE, SFIE, LP, and SME’s value-added embodied in LSOE’s gross exports, or these firm groups’ indirect value-added exports via LSOE, respectively. Other columns of a&<sup>I</sup> <(9 −\)]<sup>^</sup> in eq. (13) can be interpreted similarly for other firm groups. Therefore, eq. (14) thus provides detailed information about the value-added sources in exports produced by each firm group. If we consider the 42 sectors within each firm-group-pair, we can analyze the value-added composition for each firm group by sector. The full decomposition of each firm type’s exports by value-added sourced from the 6 firm groups and 42 sectors are reported in Table A7 in the appendix.

# **2.2 Estimation Method**

Eqs. (9)-(14) allow us to study the indirect value added by firm type at the aggregate and sector levels, decompose each firm group’s sectoral exports into its various value-added sources, as well as shed light on the effects of exports on the distribution of operating surplus (an empirical measure of firm profit) across sectors and firm types. However, since statistical agencies in most countries normally provide only a conventional IO matrix, A, and not the disaggregated block matrices by firm groups, such as A<sup>��,��</sup> or A<sup>�,��</sup> , we need to develop a method to construct those subaccounts from the original IO tables using information available from official statistics. IO tables already include data on industry-level total output, value added, imports, and exports as well as aggregate inter-industry transactions. To estimate our extended model with 6 sub-accounts, we need to complement these aggregate data with firm-level data, which are from the 2008 National Bureau of Statistics of China (NBS hereafter) economic census. See 15
Based on the current status of worldwide research, the risk ratios, or concentration-response coefficients from Pope et al (2002) were considered likely to be the best available evidence of the mortality effects of ambient particulate pollution (PM 2.5).

Damages due to anthropogenic factors are measured from a baseline PM2.5 concentration, which we set equal to 7.5 ug/m3 (as in WHO (2002). This is considered to be the level one would find in the natural environment. A log-linear function for estimating cardiopulmonary mortality associated with outdoor air pollution was applied.

The morbidity effects assessed in most worldwide studies are based on PM10. Concentrationresponse coefficients from Ostro (1994, 1998) and Abbey et al (1995) have been applied to estimate these effects. Ostro (1994) reviews worldwide studies and based on that Ostro (1998) estimates concentration-response coefficient for restricted activity days, and Abbey et al (1995) provides estimates of chronic bronchitis associated with particulates (PM10). A linear function for estimating morbidity end-points associated with outdoor air pollution was applied. The mortality and morbidity coefficients are presented in Table 3.1 based on these estimates.

|**Table 3.1Urban Air Pollution Concentration-Response Coefficients**|
|---|

|Annual Health Effect|Concentration-<br>response<br>Coefficient|Per 1 ug/m<sup>~~3~~</sup><br>annual average<br>ambient<br>concentration of:|
|---|---|---|
|Long term mortality (% change in cardiopulmonary and lung<br>cancer mortality)|0.8% *|PM 2.5|
|Acute mortality children under five (% change in ARI<br>deaths)|0.166%|PM10|
|Chronic bronchitis(% change in annual incidence)|0.9%|PM10|
|Respiratoryhospital admissions(per 100,000population)|1.2|PM10|
|Emergencyroom visits(per 100,000population)|24|PM10|
|Restricted activitydays(% change in annual incidence)|0.475%|PM10|
|Lower respiratoryillness in children(per 100,000 children)|169|PM10|
|Respiratory symptoms (per 100,000 adults)|18,300|PM10|

*Mid-range coefficient from Pope et al (2002) reflecting a linear function of relative risk.  In the analysis however, we used a log-linear

_Source:_ Pope et al (2002), Ostro (2004) for the mortality coefficients. Ostro (1994, 1998) and Abbey et al (1995) for the morbidity coefficients.

The health effects of air pollution can be converted to disability adjusted life years (DALYs) to facilitate a comparison with health effects from other environmental risk factors. DALYs per 10 thousand cases of various health end-points are presented in Table 3.2.

12
week for males, and a 0.3 hours per week for females. We can not reject that the change in hours is the same for males and females, and neither effect is significant.<sup>8</sup> To isolate the impact of capital, we subtract the implicit wage earned by the firm owner. We estimate the marginal return to own labor using the baseline data to regress profits on capital stock, owner characteristics, and hours of work. We allow the hours effect to vary by three education levels and by gender. The value of an additional hour worked varies from zero to 9.2 rupees per hour.

Profit data are noisy. Measurement error can cause extreme changes in profits from one period to the next, reducing our power to detect changes in profits resulting from the treatments. We verified with the survey firm all observations with very large changes in either direction from one period to the next. Many of these were keypunch errors, which we corrected. Others may be errors of enumerators made in the field, but we have no way to verify this. For several observations with a large fall in profits, the survey firm told us that the business was closed for a period because of illness of the owner, or because of a lack of demand. Because these occurrences represent real risks of operating a business, we choose to trim only the top 1% of the sample ranked by changes in profits (in both percentages and levels). In practice, this trims observations for which profits increase by more than 608 percent or 15,000 LKR from one quarter to the next.<sup>9</sup> Table 2 reports the results of estimating equations (1) and (2). Columns 1 and 2 show the mean treatment effect for real profits and real profits adjusted for hours worked by the owner. Columns 3 through 4 display the corresponding returns to capital regressions. Column 1 shows that a 10,000 LKR treatment increases real profits for treated males by 804 LKR per month, or 8.0 percent of the treatment amount. However, there is a large negative and significant gender interaction. The overall female effect is the sum of the female interaction and overall effect, and is -20 rupees or negative 0.20 percent per month, which is not statistically significant from zero. That is, we can not

> 8 There is a significant increase in hours worked in the periods immediately following treatment, but this dissipates with time. In the period immediately following treatment, hours worked increases by 4.4 hours per week for males and 4.6 hours per week for females, both significant at the .05 level. In the second or later rounds after treatment, however, the measured effect on male hours returns very close to zero while the effect on female hours worked is always negative but insignificant.

> 9 Note that if the increase represents a permanent change in the operation of the business, then we will lose only one observation, since in subsequent quarters growth rates will be modest again. The observationspecific trimming thus eliminate coding errors without excluding firms which are growing rapidly.

11

-  -
adaptation. The plan identifies eight core “national missions” running through 2017 and directs ministries to submit detailed implementation plans to the Prime Minister’s Council on Climate Change by December 2008.<sup>61</sup> Emphasizing the overriding priority of maintaining high economic growth rates to raise living standards, the plan “identifies measures that promote our development objectives while also yielding co-benefits for addressing climate change effectively.” The missions include: tripling renewables to 10% of installed capacity by 2012; 500% increase in nuclear power (to 20GW) by 2020; decreasing 7% of coal plants by 2012 and another 10,000MW by 2017, and increasing energy efficiency in order to save 10,000 MW by 2012.

**South Africa** -  In July 2008 the government of South Africa approved a progressive policy on climate change that puts the country on a low carbon economic development path.62 The policy calls for emissions to peak at 546 megatons of carbon by 2025 and decline in absolute terms by 2030-35.  One of the measures being considered is a carbon tax, introduced by the Minister of Finance in his Budget Speech in February 2008. The Cabinet has mandated the National Treasury to study a further carbon tax as a potential option. Other measures being considered are stringent vehicle fuel efficiency standards, the development of 10,000 gigawatt hours of energy from renewable energy sources by 2012, mandatory use of carbon capture and storage (CCS) for all new coal-fired power stations, and the increase in nuclear generation.

**South Korea** -  Korea has not formalized its post 2012 intent in written form.  However, in August 2008 Amb. Rae-Kwon Chung, chief climate negotiator for the country, announced that South Korea would adopt a national carbon reduction target next year.  A few months later he called for the establishment of an international registry for developing countries to record their domestic emission reduction policies. Registering would be voluntary, but laying out a domestic policy would translate into an international commitment that could be monitored and verified.

**Brazil** - in October 2008 the Minister of the Environment of Brazil announced that Brazil could achieve a 10-20% reduction of emissions from 2004 during the period 2012-2020, presumably by reducing illegal deforestation rates. However, the government warned that these reductions are conditional to certain international prerequisites, which the Brazilian government will announce at a later date.

> 61 http://www.pewclimate.org/international/country-policies/india-climate-plan-summary/06-2008

> 62 Scenario Building Team 2007. Long Term Mitigation Scenarios: Strategic Options for South Africa, Department of Environment Affairs and Tourism, Pretoria, October 2007.

36
implemented a positive reform during the year on a given indicator and 0 otherwise. A positive reform, as defined in Doing Business, is one that makes it faster, cheaper or administratively easier for local businesses to start and run operations; or a reform that defines and increases the protection of property rights. An example is reducing the number of days to get an industry license, eliminating the minimum capital requirement for start-ups, or increasing the legal rights of creditors and minority shareholders.

Using this dataset, we define the main dependent variable, _Reform_ , as a dummy variable equal to 1 if a positive reform occurred in one or more of the ten indicators in a given year and 0 otherwise. The mean value of the variable equals 0.55 and the standard deviation is 0.50 (Table 2). For robustness, we also report our main results using an alternative measure that equals (log of 1 plus) the number of indicators on which a positive reform occurred during a given year ( _Number of Reforms_ ). For example, in 2005, Turkey implemented reforms in paying taxes and international trade implying a score of 1.099 (log of 1 plus 2) for the dependent variable. The two dependent variables are highly correlated (correlation of 0.877).

Information on changes in the quality of the business environment is also available from alternative sources such as Heritage Foundation’s _Freedom of the World Index_ or Fraser Institute’s _Economic Freedom of the World_ . One could use annual changes in these data to construct a measure of reform similar to the ones described above. However, the Doing Business data offer two advantages. First, unlike other data sources that are based in part on experts’ perceptions, the Doing Business data are based on actual reforms. Second, and more important, since the Doing Business data cover a specific set of policy reforms, reverse causality from our dependent variable to natural 5
is no consensus among economists on how an RCT should be designed and reported to avoid these problems (Miguel et al. 2014).

In the first part of the paper, we aim to help fill this gap. We draw upon the medical literature described above and the economics literature on RCTs and program evaluation to identify potential sources of bias in economics RCT estimates for which there is applicable evidence from medicine.

Having made the case for the importance of these issues, the second part of the paper addresses our main research question: have economists running RCTs taken the necessary steps to avoid the relevant bias-inducing pitfalls identified by the medical literature? To answer this question, we attempt to collect the universe of RCTs in economics published between 2001 and 2011 in a set of well-regarded journals. For each study, we then generate two assessments: first, whether the article provides the reader with enough information to evaluate the risk of bias in its estimates, and second, whether the study falls into any of the traps that have been associated with biased treatment effect estimates in medicine.

We find that most studies in our sample do not report several pieces of important information necessary for the reader to determine the risk of bias in the presented results. It is important to note that even in medicine, while bad reporting is often associated with poor practice, it may also be the case that bad reporting masks good practice and may not necessarily imply bad methods (Soares et al. 2004). Several of the studies in our sample report quite well in most regards and, as economics lacks standards for reporting, it is not surprising that reporting of RCTs in economics is uneven. Still, this reporting gap begs 4
**<u>Table 8. Probit estimation results of correlates of contraceptive use</u>**

||Model 1|Model 2|Model 3|
|---|---|---|---|
||(Program areas)|(Poorest 20%)|(Richest 20%)|
||Coef(t-ratio)|Coef(t-ratio)|Coef(t-ratio)|
|Wealth|0.000***<br>(4.29)|-|-|
||0.585***|0.245**|0.421***|
|Year 2005|(10.30)|( 2.02)|( 4.86)|
||0.970***|0.793***|0.659***|
|Year 2011|(16.86)|( 7.14)|( 7.45)|
||1.331***|1.311***|0.863***|
|Year 2014|(18.13)|( 10.74)|( 6.29)|
|Interaction: Time tend X wealth_index|-0.000***<br>( -3.09)|-|-|
|l|<br>-0.582***|-0.116|-0.510***|
|Rura|( -8.63)|( -0.32)|(-6.64)|
|A|-0.026***|-0.005|-0.064***|
|ge|(-3.05)|( -0.29)|(-3.21)|
|b f hild d d|-0.031**|-0.095***|0.090***|
|Numer o cren 5 an uner|( -1.98)|( -2.67)|(2.66)|
|Edi|0.281***|0.316***|0.295***|
|ucaton|( 7.52)|( 3.38)|(4.40)|
|Region dummies (historically<br>disadvantaged)|-|-|-|
|_Af_|-0.576***|-1.673***|-0.180*|
|_ar_|( -5.95)|(-8.03)|(-1.76)|
||-1.345***|-1.766***|-0.899***|
|_Somali_|(-7.18)|( -8.25)|( -3.28)|
||-0.008|-0.116|0.138|
|_Benshangul-Gumuz_|(-0.15)|(-0.86)|(1.24)|
||0.069|-0.491***|0.206**|
|_Gambella_|( 0.79)|(-3.37)|(2.25)|
|C|<br>-1.032***|<br>-1.680***|<br>-0.633***|
|onstant|( -12.93)|( -4.31)|( -4.93)|
|Number of observations|29,971|7,480|6,010|

Note: *** p<0.01, ** p<0.05, * p<0.1 35