rafmacalaba/datause-encoder-data huggingface dataset:
thus we expect a negative coefficient. As measures of political institutions and business climate we use an index of civil liberties from Freedom House (available from 1972-2004).<sup>17</sup> It ranges from one denoting the most-free countries to seven denoting the least-free countries. A negative coefficient is, therefore, expected. The political risk rating provided by the International Country Risk Guide (ICRG), available for 1984-2006, is also used.<sup>18</sup> A positive coefficient is anticipated since the variable ranges from 0 to 100, where 0 means very high political risk and 100 very low political risk.

In some specifications, we use gross capital formation or gross fixed capital formation as the dependent variable. Both variables come from IMF’s _International Financial Statistics._ In the instrumental variable approach, we use information on aid inflows per capita (in current US dollars) and official development assistance and official aid (in current US dollars), both from the WDI. To test whether the effect of investment promotion depends on the quality of the business environment, we use measures compiled by Kaufmann, Kraay, and Mastruzzi (2006). Unfortunately, these measures are available only for the years 1996, 1998, 2000, 2002-2005.<sup>19</sup> The summary statistics are presented in Tables 5 – 8.

# **_4. Results_**

## **4.1 Sector-level analysis**

We start our investigation of the effectiveness of investment promotion efforts with a sector-level analysis. Our identification strategy relies on the fact that most IPAs focus their efforts on a certain number of priority (target) sectors.<sup>20</sup> Sector targeting is viewed by investment promotion practitioners as best practice as it is believed that more intense efforts concentrated on a few priority sectors are likely to lead to greater FDI inflows than less intense across-the-board attempts to attract FDI. Targeting means engaging in the standard investment promotion activities, such as image building, investment generation, investor servicing and policy advocacy (see subsections 2.1 and 2.2), but applying them to a selection of industries rather than to foreign

> 17 <u>http://www.freedomhouse.org/. The results are also robust to using political rights from Freedom house.</u>

> 18 <u>http://www.icrgonline.com/</u>

> 19 <u>http://info.worldbank.org/governance/kkz2005/pdf/2005kkdata.xls</u>

> 20 86 IPAs, out of the 104 responding to the question on sector targeting, answered that they were using sector targeting or had done so in the past. Of these, 56 gave complete timing of the targeting efforts towards at least one sector and we were able to include these targeted sectors in the sample. We did not include in the sample targeted sectors for which we did not have complete timing.

18
the NHIS, girls spent an average of 8 hours per week doing household chores, while boys spent around 4 hours. Although both boys and girls spent fewer hours on household chores in the post-NHIS sample, the difference across gender remained high after the introduction of the NHIS, with girls spending significantly more time on them.

We note that there was a small change in the age composition of girls in the samples before and after the NHIS was introduced: girls were younger on average in the sample observed after the NHIS. This difference across samples is statistically significant but, at the same time, small in absolute terms (less than four months). Hence, we do not expect that it could drive gender dis-aggregated results.

Table 10: Descriptive Statistics by Gender of Child

|||**Boys**|||**Girl**|**s**|
|---|---|---|---|---|---|---|
||Sample o|f analysis|Test of equal means|Sample|of analysis|Test of equal means|
||Before NHIS<br>(1)|After NHIS<br>(2)|p-value<br>(3)|Before NHI<br>(4)|S After NHIS<br>(5)|p-value<br>(6)|
|Age|11_._52<br>(2_._64)|11_._75<br>(2_._70)|0_._14|11_._90<br>(2_._84)|11_._62<br>(2_._73)|0_._08|
|**Schooling**|||||||
|School enrollment|0_._39|0_._40|0_._62|0_._31|0_._32|0_._75|
||(0_._31)|(0_._32)||(0_._27)|(0_._27)||
|Hours attendance|17_._24|23_._27|_<_0_._01|16_._35|21_._38|_<_0_._01|
||(29_._82)|(33_._76)||(27_._67)|(30_._50)||
|Hours homework|0_._63|0_._57|0_._50|0_._61|0_._52|0_._29|
||(1_._82)|(1_._71)||(1_._88)|(1_._65)||
|School costs (_×_10_,_000)|5_._60|7_._13|0_._08|5_._90|5_._64|0_._73|
|**Labor**|(11_._74)|(19_._32)||(14_._34)|(14_._59)||
|Child work|0_._15|0_._09|_<_0_._01|0_._15|0_._07|_<_0_._01|
||(0_._31)|(0_._27)||(0_._31)|(0_._24)||
|Hours household chores|3_._66<br>|1_._87<br>|0_._03|7_._67<br>|3_._75<br>|_<_0_._01|
||(21_._72)|(14_._04)||(36_._38)|(19_._18)||

_Notes:_ See Notes in Table 1.

Table 11 reports the estimated effects of the NHIS on children outcomes, by gender of the child. On average, class attendance of boys increased by roughly 1% as a result of the introduction of the NHIS. This increase comes from boys in households experiencing more sickness. We find no effects of the NHIS on the incidence of child labor among boys. We estimate a modest and statistically significant decrease in the amount of hours that boys living in households with more sickness spent doing household chores (last row of Column 3).

Girls school enrollment and class attendance significantly increased for the full sample. We do not find any statistically significant effects for the more sickness sub-sample. This suggests that the results are driven by household that do not experience serious health shocks and is indication that induced change in economic uncertainty also has effects ex ante, i.e., before a serious shock actually takes place. Alternatively, this result may simply be due 27
Assumption 1 is crucial for imputation and ensures that the sampled data in survey 1 and survey 2 are representative of the population in each respective time period. Put differently, this assumption implies that, for two contemporaneous (i.e., implemented in the same time period) surveys, these estimates are identical since they equal the population values; and for two noncontemporaneous surveys, estimates based on the same characteristics _x_ in these two surveys are consistent and comparable over time. While surveys of the same design (and sample frame) are more likely to be comparable and can thus satisfy Assumption 1, there is no _a priori_ guarantee that these surveys can provide comparable estimate across two different time periods, or even the same estimates in the same time periods. Examples where Assumption 1 may be violated include the cases where national statistical agencies change the questionnaire for the same survey over time as with the NSS for India discussed earlier, or where one considers different surveys that focus on different population groups (e.g., the average household size may differ between a household survey and a labor force survey depending on the specific definition that is used). Violation of Assumption 1 rules out the straightforward application of survey-to-survey imputation technique and would require that additional assumptions be made on the relevance of the estimated parameters from one survey to the other. To make notation less cluttered, we will suppress the subscript _t_ for time in subsequent expressions.

**Assumption 2:** _Let_ ∆ _P and_ ∆ _x respectively represent the changes in poverty rates and the explanatory variables x over time, and_ Θ _j the set of parameters (_ β _j_ , μ _j_ , ε _j_ **)** _that map the variables x into the household consumption space in period j where the consumption data are available. Then_ ∆ _P_ = _P_ ( ∆ _x_ | Θ _j_ ) _, where P(.) is the given poverty function._

Assumption 2 implies that, given the estimated consumption parameters from survey 1, the changes in the distributions of the explanatory variables x between the two periods can capture the change in poverty rate in the next period. Given the commonly observed variables in the two surveys, this assumption allows the imputation of the missing household consumption for survey 14
The situation is better for power and transport. There is fairly abundant data on two widely-used (albeit far from perfect) measures of quality – the percentage of power losses and the share of paved in total roads, respectively.

It is worth noting that all these infrastructure quality indicators show a high correlation with their corresponding quantity indicators reviewed above. In a large panel data set, Calderón and Servén (2004b) find sector-wise correlation coefficients (e.g., between power generation capacity and power losses, or between road density and road quality) around 0.5 and significantly different from zero at any reasonable significance level. The implication is that more abundant infrastructure typically comes along with better infrastructure.

In view of the limitations of these indicators of infrastructure quality, increasing effort has been devoted in recent years to the compilation of survey-based assessments of infrastructure performance. Two leading sources of such kind of data are (a) international surveys of business conditions that reflect the views of international experts, and (b) firmlevel surveys that capture the perceptions of infrastructure users. At present, however, the time-series dimension is in both cases very limited (virtually nil, in the case of firm surveys), and comparability over time and across countries is sometimes hampered by changes in the relevant survey questions.

# **2.2.1 Officially-recorded statistics of infrastructure quality**

_Telecommunications._ Table 1 reports the evolution of an indicator based on the waiting time (in years) for the installation of main telephone lines. This indicator has been rescaled such that it takes values between 0 and 1, with higher values indicating shorter wait times and, in this interpretation, higher quality of telecommunication services.

Along this dimension, LAC’s progress over the last two decades was spectacular, as the region’s median waiting time was reduced from six months in 1981-5 to a few days by 2001-5, well below the norm of non-LAC middle income countries. In rich countries waiting time fell to zero in the early 1980s, while in East Asia the same happened by 1991-5 (see Table 1). The fast improvement in the reduction of waiting time for main telephone installation extended to all LAC sub-regions, with Central America and the Southern Cone showing the fastest pace of improvement in the quality of service.

7
|**_Government_**|**_WP_**|**_Memorandum_**|**_Tariff Offers_**|**_Service_**|_|_**_Draft_**<br>**_Working_**|
|---|---|---|---|---|---|
||**_Establishment_**|||**_Offers_**|I**_Pan, Report_**<br>_I_|
|**_Other Countries in Tran_**|**_sition(continuaton)_**|||||
|**_Croatia_**|**_10/93_**|**_06/94_**|**_04197, 04/98_**|**_05/97, 11/98_**|**_08/98_**|
|**_FYR Macedonia_**|**_12/94_**|**_04/99_**|**-**|**-**|**-**|
|**_Laos_**|**_02/98_**|**-**||||
|**_Vietnam_**|**_01/95_**|**_09/96_**||||
|**_Others_**||||||
|**_Algeeia_**|**_06/87_**|**_07/96_**|||l|
|**_Andorra_**|**_10/97_**|**_03/99_**|**-**|**-**||
|**_Jordan_**|**_01/94_**|**_10/94_**|**_07/98, 10/99_**|**_01/99_**||
|**_Lebanon_**|**_01/99_**|**-**|**-**|**-**||
|**_Nepal_**|**_06/89_**|**_02/90_**|**-**||l|
|**_Oman_**|**_06/96_**|**_10/96_**|**_10/97_**|**_10/97, 03/98_**||
|**_Samoa_**|**_07/98_**|**-**||**-**||
|**_Saudi Arabia_**|**_07/93_**|**_07/94_**|**_09/97_**|**_09/97_**||
|**_Seychelles_**|**_07/95_**|**_08/96_**|**_06/97_**|**_05/97_**||
|**_Sudan_**|**_10/94_**|**_01/99_**|**-**|**-**||
|**_Chinese Taipei_**|**_09/92_**|**_10/92_**|**_02/96_**|**_09/94,10/98_**|**_03/98_**|
|**_Tonga_**|**_11/95_**|**_05/98_**||**-**||
|**_Vanuatu_**|**_07/95_**|**_11/95_**|**_06/97_**|**_09/97_**||

*Accession completed; awaiting ratification

**_Source:_** _WTO,_ **_1999b_**

Nonetheless, delays and problems have arisen for all countries and in all phases of the process. Some are due to the inherent complexities of enacting legislation and regulations that bring into conformnity the regimes of transition economies with WTO rules. These extend far beyond the obvious such as the Law on Customs, the Tariff schedule and related regulations on imports and exports. They include such items as the laws on joint stock companies, the Central Bank and credit institutions, licensing of economic activity, domestic taxation, regulations on food and alcoholic beverages, veterinary medicine and pests subject to quarantine, patent and copyright protection, consumer protection etc. Design and enactment of all this legislation and regulations are quite demanding on the institutions of the acceding countries. But the WTO accession process provides 27
Weitzman (1998) argues in favor of using a zero discount rate for half century time horizons and, as mentioned, the Stern Review relies on a near-zero discount rate. Portney and Weyant (1999) provide a good review of the related literature. Dasgupta (2005, 2007) looks at the discounting issue. The ethical and conceptual bases for the discount assumptions of the Stern report are discussed by Beckerman and Hepburn (2007), Dasgupta (2007) and Nordhaus (2007).

# **Technology development and transfer as a policy instrument**

As discussed, modeling outcomes point to the importance of new technologies in affecting the cost of meeting emission reduction goals. However, several studies indicate that past investments in related research and development has been insufficient, suggesting that markets for new technologies will need non-market support. For example, Margolis and Kammen (1999) show, using data from the US, that there has been a long-term pattern of underinvestment in R&D in the energy sector, compared to other sectors and they conclude that a deployment effort for increased research in the energy sector is needed. Subsequent authors conclude that, while additional research in energy technologies is necessary to improve energy efficiency, a broader approach is needed. For example, Sagar (2000) argues that development and deployment efforts should focus on additional sectors as well as energy. Sagar and van der Zwaan (2006), using data from OECD countries, demonstrate a lack of correlation between energy efficiency level and energy R&D. They conclude that energy R&D is sufficient but not a necessary condition for improved energy efficiency. They identify roles for institutions, deployment and learning as necessary conditions for transfer of energy R&D innovations to the market.

Modeling results indicating a strong role for technology and that carbon prices will be lower than expected, following the US decision not to ratify the Kyoto Protocol have also worked to focus attention on policy instruments that support technology development and technology transfer. This interest is reflected in the climate change literature and in actions taken by the UNFCCC delegates. Institutionally, a framework was developed during the Marrakesh Conference of the Parties to enhance the implementation of Article 4.5 of the Convention, which recognizes the importance of technology development and transfer in battling climate change and allowing steady growth in developing countries. The framework includes 5 activities/requirements, namely: technology needs and needs assessments; available technology information; enabling environments in developed and developing countries; capacity building in developing countries; mechanisms for technology transfer. To date, there are three special funding mechanisms under the UNFCC: the Least Developed Countries Fund, the Special Climate Change Fund and the already mentioned Adaptation Fund.<sup>51</sup> To

> 51 In addition, funding for mitigation and adaptation efforts is available under the Global Environment Facility.

30
_of schools_ ;<sup>18</sup> and (iii) an index of _coverage and implementation of the School Operation Assistance Program_ (BOS or “Bantuan Operasional Sekolah”) – a school grant program administered by the central authority introducing school-based management and targeting non-salary operational expenditures of schools. Variables (i) and (ii) are analogous to those used to proxy the governance and institutional environment of health facilities. Variable (iii) is introduced because implementation of the BOS program can potentially bring about a significant change in the governance environment of the school, by affecting how non-salary expenditure decisions are made.

See Table 3 for the detailed results from the estimation of models (1) and (2) for all three samples and Appendix B for the full description of variables. Like in the case of satisfaction with health facilities, we find that after conditioning for expectation, satisfaction (the binary variable _S_ ) with public schools is correlated with certain indicators of objective quality and governance. There are key differences between rich and poor districts in terms of the factors that influence satisfaction with public schools.

For the full sample of households, _none_ of the objective indicators of quality available from the facility survey are significantly correlated with satisfaction. Interesting patterns however emerge from models estimated separately for rich and poor districts. Better infrastructure facilities in schools (e.g. condition of classrooms, library, sports hall, computer rooms and availability of books) and higher coverage of students by schools (level of enrollments and attendance) are associated with significantly higher level of satisfaction in poor districts, but has no effect for rich districts. Conversely, higher teacher quality (in terms of

> 18 The index of active participation indicates whether decisions about school’s mission and vision were made together by the principal, teachers and community.

22
**Table 35. Governance drivers of political risk: Administrative complexity**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.0889*|0.0810|||0.205*|0.200*|||
||(0.0527)|(0.0521)|||(0.108)|(0.111)|||
|Export % of sales|-0.000297|0.000538|-0.000580|0.000287|-0.000647|0.00124|-0.00137|0.000647|
||(0.000695)|(0.000720)|(0.000724)|(0.000737)|(0.00142)|(0.00153)|(0.00153)|(0.00162)|
|Share of sourcing via imports|0.00260***|0.00166**|0.00252***|0.00154**|0.00511***|0.00331**|0.00507***|0.00314*|
||(0.000735)|(0.000740)|(0.000778)|(0.000780)|(0.00149)|(0.00157)|(0.00164)|(0.00172)|
|High-income source country|-0.00299|0.0587|-0.0156|0.0590|-0.00351|0.135|-0.0362|0.137|
||(0.0780)|(0.0793)|(0.0813)|(0.0817)|(0.162)|(0.173)|(0.172)|(0.183)|
|>250 employees|-0.0476|-0.0525|-0.0644|-0.0675|-0.120|-0.130|-0.166|-0.167|
||(0.0647)|(0.0648)|(0.0653)|(0.0653)|(0.132)|(0.138)|(0.136)|(0.141)|
|Cumulative inv. >US$10M|-0.0615|-0.127*|-0.0664|-0.130**|-0.105|-0.264*|-0.106|-0.263*|
||(0.0656)|(0.0647)|(0.0655)|(0.0649)|(0.136)|(0.139)|(0.138)|(0.141)|
|Years in country|0.00189|0.00238|0.00220|0.00256|0.00266|0.00339|0.00310|0.00348|
||(0.00176)|(0.00170)|(0.00175)|(0.00171)|(0.00376)|(0.00375)|(0.00380)|(0.00385)|
|Level of foreign ownership (%)|-0.00152|-0.000720|-0.00169*|-0.000555|-0.00334|-0.00146|-0.00375*|-0.00126|
||(0.00105)|(0.00116)|(0.00101)|(0.00112)|(0.00220)|(0.00251)|(0.00220)|(0.00252)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.016|0.074|0.035|0.092|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "In general, how much of an obstacle were the following factors in this country? [Complexity of administrative procedures]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

52
food line in the HBS 2007 is obtained by dividing the HBS 2011/12 line by 2.15. The NPS survey report indicates that food prices increased by 22 percent between the NPS 2008/09 and the NPS 2010/11, and by 34 percent between the NPS 2010/11 and the NPS 2012/13.<sup>13</sup> The food line in the NPS 2008/09 and in the NPS 2012/13 is obtained by dividing the adjusted NPS 2010/11 food line by 1.22 and by multiplying it by 1.34 respectively. These adjustments show an increase of extreme poverty rates across the different NPS rounds, while the HBS shows a decline in extreme poverty. When the food line is adjusted for inter‐year differences in the cost of living using the CPI, extreme poverty initially increases and then stagnates in 2012/13.

**Table 11: Food line comparisons in Tanzania Mainland**

||**H**|**BS**||**NPS**||
|---|---|---|---|---|---|
||**2007**|**2011/12**|**2008/09**|**2010/11**|**2012/13**|
|(a) Survey‐specific food line in survey year<br>prices|13,502|26,086|17,028|22,482|30,747|
|%individuals below the foodpovertyline|16.1|9.7|7.6|11.8|13.8|
|_Adjusting for inter‐year variatio_|_n in cost of li_|_ving using s_|_urvey‐based_|_deflators_||
|(b)Baseyear food line in survey yearprices|12,133|26,086|18,428|22,482|30,126|
|%individuals below the foodpovertyline|11.7|9.7|9.3|11.8|13.0|
|Foodprice index|2.15|1|1.22|1|0.75|
|_Adjusting for inter‐year v_<br>|_ariation in_<br>|_cost of living_<br>|_using the CP_<br>|_I_<br>||
|(c)Baseyear food line in survey yearprices|15,737|26,086|18,783|22,482|28,271|
|%individuals below the foodpovertyline|23.3|9.7|9.6|11.8|10.7|
|CPI ratio|1.66|1|1.20|1|0.79|

# **iii. Basic needs poverty line**

In both surveys, the non‐food component of the basic needs poverty line is based on average non‐ food consumption of households whose total consumption is close to the food poverty line.

In the HBS 2011/12, the households in the reference group are those whose total consumption lies within the interval between the food line and 1.2 times the food line. These households devoted approximately 71.5 percent of their total consumption to food. Scaling up the food poverty line by this ratio delivers the basic needs poverty line of TZS 36,482 per adult per month and a basic needs poverty headcount of 28.2 percent. In the NPS, the original basic needs poverty line was measured by dividing the food line by the average proportion of total expenditures devoted to food for the bottom 25 percent of the population in the total distribution of consumption per adult equivalent.

> 13 See Table 5 in the National Panel Survey 2012/13, pg. 10.

37