rafmacalaba/datause-encoder-data huggingface dataset:
size, industry, and country*time fixed effects. The detailed rational and data sources of these variables are discussed in the next section.

We use Rule of Law and Regulatory quality to proxy for institutional quality. Rule of law reflects perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence. Regulatory quality reflects perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development. The data are from the Worldwide Governance Indicators (WGI). It is a research data set summarizing the views on the quality of governance provided by a large number of enterprises, citizen and expert survey respondents in industrial and developing countries (Kaufmann, Kraay and Mastruzzi, 2010). We use the property right index by the Heritage Foundation<sup>6</sup> to proxy for property right protection. Property right protection assesses the extent to which private economic activity is facilitated by an effective legal system and rule-based governance structure in which property and contract rights are reliably respected and enforced. For the patent right protection, we use the patent right index from Park (2008).

# **3. Model**

# **3.1 Model setup**

The baseline weighted regression is the following: where ∆yijt = ln�yijt�−ln (yijt−1) is the dependent variable, and yijt are sales and sales per worker of firm _i_ in country _j_ at time _t_ respectively. Dijtinnov equals 1 if firm _i_ in country _j_ innovates between time _t_ and time _t-1_ . The interactive dummy fj ∙ft captures the macroeconomic conditions for country _j_ at time _t._ The dummy _fs_ captures the sector fixed effects. Xjit are different firm-level control variables.

The extended regression (to interact with various institution variables) is the following: where Insj is the institutional variable for country _j_ . Note that institutional variables here are timeinvariant. Since the surveys are typically very close together, the institutional quality rarely changes.

> 6 http://www.heritage.org/index/property-rights

7
necessarily describe the policies needed to achieve the transformation, their coupling with the macroeconomic models does not make it necessary to assume that all changes are driven by price policies.

These investments, costs, and benefits are then fed into macroeconomic models to explore the feasibility of the illustrative scenario and its implications for growth and other macroeconomic variables as well as household welfare and employment at the sectoral level. The focus of the macroeconomic analyses is to ensure consistency across the various sectoral scenarios, to identify positive or negative spillovers across sectors, and highlight the economic trade-offs that mitigation policies could entail. We analyze these potential trade-offs in our basic framework using two types of models, first a multi-sector computable general equilibrium (CGE) model to highlight the intrasectoral consequences of the transition. We then use a more aggregate framework to discuss the macro-fiscal consequences. As an illustration, we apply this methodology to Türkiye.

Section 2 presents the net zero path used in the macroeconomic simulations. Section 3 discusses the integration of this path into a multi-sector CGE model. Then, Section 4, in the same vein as Section 3, discusses a similar integration but to a more aggregate macroeconomic model. Finally, Section 5 concludes.

# 2 Bottom-up approach to define illustrative sectoral roadmaps to achieve net zero GHG

We illustrate the methodology by applying it to Türkiye, which ratified the Paris Agreement in October 2021 and committed to the 2053 net zero emissions target. This climate target will be used to anchor the analysis. The left side of Figure 1, prior to 2020, shows Türkiye 's GHG emissions history in which the energy sector (i.e., power, transportation, building, and industrial sectors) is the largest contributor to the nation's GHG emissions, accounting for three-quarters of total emissions.

**Figure 1: Historical emissions (left) and RNZP scenario (right). Source: CAIT and CCDR**

4
# _Fig. 6._ Lel Status of Abortion in Sub-Saharan Africa

|||_Me_|_dkc_||_Juridical_|_Social_<br>||
|---|---|---|---|---|---|---|---|
||||||||_Ll_|
||||||_rape,_|_an_|_ega_|
|_Country_|_llegal_|_Nbarow_<br>_(life)_|_Broad_<br>_P"ealth)_|_Euge iic_<br> <br>_(fetal)_|<br>_incest,_<br>_others)_|_social-_<br>_medical_|_(grounds_<br>_not specifeW_|
|Benin||x||||||
|Burkina|x|||||||
|Burundi|x|||||||
|Cameroon|||x||x|||
|Central African||||||||
|Republic|x|||||||
|<br>Chad||x||||||
|Congo|||x|||||
|<br>Cote d'lvoire||x||||||
|Ethiopia|||x|||||
|<br>Ghana|||x|||||
|Guinea|||x|||||
|Kenya|||x|||||
|<br>Liberia|||x|x|x|||
|Madagascar||x||||||
|<br>Malawi||x||||||
|Mali|x|||||||
|Mauritania|x|||||||
|Mozambique||x||||||
|<br>Niger|x|||||||
|<br>Nigeria||x||||||
|<br>Rwanda|x|||||||
|Senegal||x||||||
|<br>Siena Leone|||x|||||
|Somalia|x|||||||
|Sudan||x||||||
|Tanzania|||x|||||
|Togo||x||||||
|<br>U|||x|||||
|Zaire|x|||||||
|Zambia||x||x||x||
|Zimbabwe|||x|x|x|||

_Source:_ Tietze (1983), pp. 16-17.

_Source:_ World Bank (1986). Population Growth and Policies in<sup>Sub-Saharan Africa: p. 47.</sup>
48

**Table A.5. Recent and possible future distribution of Ho Chi Minh City’s Population in districts in this study area.**

||2010 Percent of Total<br>Ho Chi Minh City<br>Population|2050 Percent of Total Ho<br>Chi Minh City Population<br>(World Bank estimate)|
|---|---|---|
|Binh Thanh|6.4%|3.0%|
|District 1|2.5%|1.12%|
|District 10|3.1%|0.83%|
|District 3|2.6%|0.71%|
|Go Vap|7.4%|2.8%|
|Phu Nhua|2.4%|0.7%|
|Tan Binh|5.8%|3.23%|

# **Ho Chi Minh City Poverty Rate**

The most recent official estimate of Ho Chi Minh City’s poverty rate by district is from the Inter ‐ ministerial Poverty Mapping Task Force in 2003 (ADB 2010). This study estimates an overall poverty rate of 5.4%. The literature did not offer estimates of future poverty; therefore, we use estimates of recent poverty to provide a plausible range for this value. Estimates of recent poverty rates vary widely based on methodology and definitions of poverty. The United Nations Development Program conducted an Urban Poverty Survey that estimates the poverty rate of Ho Chi Minh City at 13.9%, using an annual income threshold of 12 million VND per person (2010, p.90).<sup>29</sup> One can also distinguish between the poor and non-poor according to their housing facilities, a differentiator that may be important when considering exposure to flood. The GIS data provided by the Steering Center for Flood Control suggests a poverty rate of 2.4% under the assumption that the area of semi-permanent homes relative to the area of permanent homes indicates poverty levels. Another study cites the percent of housing the government classifies as “slum” or “temporary” as 25% (Coulthart _et al._ 2006). Clearly there is much variation in recent estimates, and little can be said with confidence about future estimates. We therefore use a wide range of values for poverty rate -- 2.4% as a lower bound and 25% as an upper bound. We assume that the relative poverty rates of districts in our study area remain the same as estimated by the 2003 Inter ‐ ministerial Poverty Mapping Task Force.

_________

> 29 The Vietnam Household Living Standards Survey of 2008 estimated the overall poverty rate in Vietnam at 13.4% and estimated a poverty rate of 0.3% in Ho Chi Minh City. The Ho Chi Minh City estimate has been questioned for having methodological shortcomings (UNDP, 2010). The UNDP’s Urban Poverty Survey was aimed at overcoming these shortcomings.
Resultado del Análisis Técnico Económico (Economic Technical Analysis Result) (RATE), which results in one of the following RATE results:

- a) Recommended Favorably (RS);

- b) Missing Information (FI), to specify that records lack required information necessary to secure favorable recommendation;

- c) Technical Objection (OT), reflecting a negative assessment;

- d) Reassessment (RE), wherein the project is recommended for additional analysis; or

- e) Breach of Regulations (IN), when spending is executed without the support of MDS.

Projects must attain a favorable RATE (RS) at each stage to move to the next stage of development.

For typical projects, the information required to pass each stage is summarized in Table 1.

Table 1. SNI Informational Requirements for Investment Project Assessment

|Stage Transition|Submission Requirements|
|---|---|
|Profile to Prefeasibility / Feasibility|Pre‐investment study containing:<br><br>Definition of the problem<br><br>Analysis of supply and demand<br><br>Study of solution alternatives<br><br>Initial cost estimates<br><br>Preliminary strategic and economic evaluation<br><br>Further specification of best solution|
|Prefeasibility / Feasibility to Design|<br>Detailed budget<br><br>Feasibility study<br><br>Detailed line‐item budget|
|Design to Execution|<br>Full engineering design<br><br>Draft bidding proposal|

Sources: Government of Chile (2017). _Standards, Instructions, and Procedures for the Public Investment Process (PIN);_ Presentation: _Public Investment Management Conference: The Chilean Experience_ Once a project moves to the Execution stage, the proponent may seek funding for the project (in the SNI, the designation ‘Execution’ simply reflects authorization to seek funding, but does not necessarily mean that the project is funded or under implementation).

Projects are typically funded from the proponent unit’s annual budget allocated by the Ministerio de Hacienda. Depending on the agency, funding may also come from other sources. For example, projects formulated by municipalities are funded by the regional government through the National Fund for Regional Development (FNDR). Projects funded solely by the national government do not have to necessarily have an RS from SNI, though projects that are regionally funded do require an RS RATE by law.<sup>4</sup> Within the limits of their respective budget allocations, public entities (ministries, government agencies, etc.) then apply their own approaches to prioritize and select projects. In the case of nationally-funded projects, agencies must select from among projects that have been positively recommended by MDS for funding (assigned a RATE of RS). In the transport sector, projects are given ‘high’, ‘medium’, or ‘low’ priority by a largely qualitative consideration of the project’s

> 4 Organic Constitutional Law on Government and Regional Administration, Law 19175, Article 75.

12
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**Table 8: Sensitivity Analysis for Measles, High Income Countries, IV with country fixed effects**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||Measles rate<br>(log)|Measles rate<br>(log)|Measles rate<br>(log)|Measles rate<br>(log)|Measles rate<br>(log)|Measles rate<br>(log)|Measles rate<br>(log)|Measles rate<br>(log)|
|Convention on the Rights of the Child-|0.054|0.067|0.051|0.082|0.088|-0.003|0.049|0.071|
|yes/no (CRC)|(2.61)**|(3.19)**|(2.47)*|(3.04)**|(3.82)**|-0.18|(2.29)*|(3.33)**|
|GDP per capita (in constant 2000 US|-0.095|-0.041|-0.002||-0.007|-0.031|-0.308|-0.043|
|dollars, log)|(2.13)*|(0.9)|(0.08)||(0.18)|(0.93)|(5.95)**|(0.83)|
|ICRG-Bureaucratic Qualit|-0.02|-0.025|-0.015||-0.028|-0.011|-0.019|-0.004|
|y|(1.76)|(2.12)*|(1.37)||(2.42)*|(1.25)|(1.76)|(0.17)|
|polity2x|0.037<br>(2.88)**||||||||
|Plti Dit l|-0.08|-0.509|0.002||-0.3|-0.511|2.482|0.492|
|opuaon ensy (og)|(0.07)|(0.45)|(0.14)||(0.26)|(0.62)|(0.71)|(0.37)|
|Total Population (log)|-0.077<br>(0.07)|0.357<br>(0.32)|-0.002<br>(0.18)||0.153<br>(0.13)|0.475<br>(0.58)|-2.627<br>(0.75)|-0.624<br>(0.47)|
|YEAR|0.007<br>(4.54)**|0.006<br>(3.75)**|0.004<br>(3.37)**|0.007<br>(4.36)**||0.004<br>(3.09)**|0.014<br>(7.54)**|0.004<br>(2.55)*|
|D||-0.027|-0.005||-0.027|-0.015|0.02|-0.005|
|emocracy||(4.05)**|(1.78)||(3.88)**|(2.94)**|(0.93)|(0.98)|
|GDP per capita (in constant 2000 US<br>dollars, log) (Lag)||||-0.05<br>(1.09)|||||
|Log Measles Immunization Rate (Lag)||||||0.46<br>(21.05)**|||
|B-spline on [1989,1995)|||||-0.069<br>(3.28)**||||
|B-spline on [1995,2000)|||||-0.045<br>(2.77)**||||
|B-spline on [2000,2006)|||||-0.016<br>(1.24)||||
|ICRG-Bureaucratic Quality (Lag)||||-0.024<br>(2.10)*|||||
|Democracy (lag)||||-0.026<br>(3.86)**|||||
|Log Population Density (Lag)||||-0.964<br>(0.85)|||||
|Log Total Population (Lag)||||0.756<br>(0.67)|||||
||-8.318|-10.955|-3.666|-16.863|3.597|-10.174|12.302|4.497|
|Constant|(0.59)|(0.77)|(1.56)|(1.2)|(0.27)|(0.97)|(0.29)|(0.27)|
|Observations|633|635|635|606|635|635|720|405|
|Number of group(countryc)|48|48|48|47|48|48|51|29|

24
that PTAs serve as commitment mechanisms to foreign investors regarding the treatment of their assets that is more credible than domestic regulations. (Osnago, Rocha, & Ruta, 2015) find that deep trade agreements facilitate vertical FDI because they reduce the contractual uncertainty associated with the difference between PTA members’ institutions.

In terms of methodology, recent works have grasped the importance of accounting for the variation in content to distinguish the effects of deep versus shallow PTAs. Advances in data availability regarding the content of PTAs have enabled researchers to depart from the use of restrictive dummy variables and incorporate more sophisticated measures of PTA depth. Equipped with these new empirical tools, some papers have focused on the impact of PTA depth on trade flows, while others have addressed the same question for FDI.<sup>9</sup> For example, (Lesher & Miroudot, 2006) construct an index of investment disciplines in PTAs but limit their coverage to 24 North-South agreements. Several papers have attempted to build similar types of indexes based on careful analysis of the information contained in agreements’ treaties. However, none conducted this exercise on the universe of PTAs as well as their overall content, up until the work conducted by the World Bank for which experts accomplished a thorough mapping of all disciplines included in all PTAs notified to the WTO (see (Hofmann, Osnago, & Ruta, 2018) for a presentation of the content of PTAs data set). (Osnago, Rocha, & Ruta, 2015) were the first to use this data set to construct a comprehensive measure of PTA depth and use it to explore the impact of deep integration on vertical investments.

Several papers analyze the impact of specific policy areas on foreign investments

> 9 (Mattoo, Mulabdic and Ruta 2017) find that deep trade agreements increase gross trade and can have a positive spillover effect on trade with third countries if their design and implementation are non-discriminatory. (Orefice and Rocha 2014)  study trade in parts and components, and (Laget, et al. 2018) for trade in value added, both finding that deep PTAs increase countries’ participation in GVCs by enhancing regulatory frameworks and easing cross-border operations.

6
also control for changes in the inflation rate, using the main IBGE consumer price index, the INPC/IPC.

Our fourth data source is the set of published _summary statistics of the 1970 Census_ , published by IBGE (1972), from which we obtained state-level information on infant mortality rates, the distribution of years of schooling on the population, the prevalence of various forms of infrastructure, the rate of labor unionization, and a number of other variables of interest, all disaggregated at the state level. When using these data, the Rio de Janeiro and Guanabara states (in 1970) were combined (into present-day Rio de Janeiro, RJ).

Descriptive statistics for the main variables during the study period are presented in Table 1. These include mean household income per capita from the PNAD survey, GDP per capita (with alternative deflators); the participation of each sector in GDP per capita; the inflation rate; effective rates of protection; and the national poverty and inequality indicators, over the 19852004 period. We use a region-specific poverty line, designed to cover the cost of a “minimum food basket” that would generate 2,288 calories per day, on the basis of the observed consumption bundles for deciles 2-5 in the Living Standards Measurement Survey (PPV) of 1995-6. This standard, which varied spatially to allow for cost of living differences, was interpreted as an “extreme” poverty line, and corresponded to R$65.07 in 1996 prices (in the São Paulo metropolitan area).<sup>23</sup> Unsurprisingly, given the country’s mediocre growth performance, poverty reduction over this twenty-year period was modest. National poverty incidence fell from 33% to 29%. Almost all of this reduction arose in rural areas, where the incidence fell from 59% to 54%. The decline in the urban headcount was less than one percentage point. Inequality, here measured by the Gini coefficient, rose between 1985 and 1989; became volatile until 1993; and then started a gradual decline which lasted to the end of the period.

# **5. Econometric analysis and results**

We exploit the spatial, temporal and sectoral variation in these data to shed light on the determinants of poverty dynamics in Brazil. Since we have a panel of 26 states by 16 surveys, we allow the regression coefficients to vary by state. We control for the mechanical effect of initial poverty rates by including state-level fixed effects. As always, the inclusion of fixed 23 See Ferreira, Lanjouw and Neri (2003) for the construction of the poverty line, and the corresponding poverty profile.

12
Figure 3.4 shows the contributions of various household assets to the composition effect. The figure reveals that the contribution of assets to the composition effect is mostly accounted for by changes in the distribution of years of schooling.

Figure 3.5: Structural Effects <!-- Start of picture text --> .6 1.6<br>.4<br>1.2<br>Demographics<br>.2 Overall Structural Effect<br>.0 0.8<br>Household Assets<br>-.2 0.4<br>-.4<br>0.0 Overall Structural Effect<br>-.6 Sector of Employment Geography<br>-.8 -0.4<br>10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 70 80 90<br>Quantile Quantile<br>Change in L og E xpenditure Change in L og E xpenditure<br><!-- End of picture text --> Finally, figure 3.5 presents results of a decomposition of the structural effect. These results suggest that the overall shape of the structural effect is determined by the sector of employment and, to a certain extent, geography. These are the two characteristics that explain the negative values of the structural effect in some range in the lower end of the distribution. This negative contribution is mitigated to some extent by household demographics.

# **_A Closer Look at the Rural-Urban Differential in Living Standards_**

<!-- Start of picture text --> Figure 3.6 Change in Rural-Urban Differential in Living Standards<br>Total Differential<br>1.2<br>1.1<br>1.0<br>0.9<br>0.8<br>2007<br>0.7<br>0.6 2001<br>0.5<br>0.4<br>0.3<br>10 20 30 40 50 60 70 80 90<br>Quantile<br>Difference in Log Expenditure<br><!-- End of picture text --> 31
120 family planning activity. For instance, an over supply of physicians is reducing vages in some countries and leading to unemployment among doctors. Creating incentives to encourage their participation in family planning delivery (as is occurring already in Mexico under the USAID Enterprise Program project) can be a means of obtaining services at a reasonable charge for the government's target population. Greater reliance on the for-profit private sector for the provision of family planning services in the developing vorld is possible vhere the private sector resources already exist, are dormant, or can be mobilized through proper use of incentives. The potential seems greatest in Latin America and the Middle East where physician unemployment is a rising problem. Hany of the NICs in Asia already have large private sectors and these can also be encouraged to expand and modify short term objectives (e.g., profits).

In Africa, where the monetized economy is smaller and public programs tend to be weak, however, contraceptive prevalence is low even among urban couples employed in the monetary economy. Therefore, getting private firms to add family planning benefits to their health benefits may be a very fruitful approach to raising contraceptive prevalence.

The degree of subsidy necessary to attain high cont.raceptive prevalence is going to depend on the ability of people to pay for contraception and on their desire to contracept. The ability and willingness to pay for contraception often move together. In Africa, the commercial prospects for increasing contraception seem poor because the profit-maximizing prices are beyond the reach of most (potential) users. However, the public sector is also underdeveloped and typically lacks the administrative capacity and resources to expand family planning services that the Thailand and Indonesian governments have. Therefore, donor and government resources should be directed at using the

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