rafmacalaba/datause-encoder-data huggingface dataset:
and 2.85 years. For the money demand equation, heteroskedasticity, autocorrelation and normality statistics are satisfactory at conventional critical levels. The estimated equations for log real GDP and inflation have satisfactory heteroskedasticity and normality statistics; but they nevertheless appear to exhibit residual (higher-order) autocorrelation. Goodness of fit measures indicate a generally good fit, including that there is an improvement with respect to alternative random walk with drift models.

**Venezuela: Structural Time Series Model for Log Real Money Balances, Log Real GDP and Inflation** **_Key Summary Statistics (T=35)_**

|**Statistic**|**LogReal**<br>**Ml**|**LogReal**<br>**GDP**|**Inflation**|
|---|---|---|---|
|Normality(Bowman-Shenton):n<br>2|2.28|0.71|3.69|
|Skewness:_s_X2|0.18|0.07|2.65|
|Kurtosis:k_X42|2.10|0.64|1.04|
|Heteroskedasticity:_H(l_1)_F,_|0.31|0.13|0.21|
|Autocorrelation(upto14torder)|8.85|31.18|15.13|
|(Box-Ljung):Q(14,6)<br>6||||
|Autocorrelation(first order)|1.98|1.66|1.47|
|**_4_**<br>Durbin-Watson:DW_- N(2,-)_<br>**_T_**||||
|GoodnessofFit(improvementoverrandom<br>walkplusdriftmodel)<br>_<br>_(T-d&_<br>_2_|0.82|0.74|0.30|
|GoodnessofFit (ordinaryR<sup>2</sup>)|0.85|0.76|0.75|
|Akaike InformationCriterion|-3.57|-5.29|-4.76|
|BayesInformation Criterion|-1.83|-3.55|-3.03|

We note, however, that the estimated variance of the slope disturbances is non-zero, suggesting that the estimated trends are I(2), instead of 1(1) as we would have expected based on the results of conventional unit root tests. Moreover, the periods of the 15 Money Demand in Venezuela, Mario A. Cuevas
**Figure 24: Per Capita Income and FDI Flows by Federal Region**

<!-- Start of picture text --> 20<br>18 y = 3.3976x ‐ 5.162<br>R2 = 0.4362<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>2 3 4 5 6 7 8<br>ln per capita income,2003 rubles<br>millions, 2011<br>ln FDI inflows/capita, US<br><!-- End of picture text --> _Source:_ Rosstat .

0
The model, which draws on some of my earlier work (Basu 2006, 2010), is presented in a threadbare fashion, to show how shifting targets, from overall growth, through growth of the poorest people, to an exclusive focus on inequality mitigation, leads to different policies on the ground and also to raise some interesting questions pertaining to the challenges of policy making in a globalized world.

Consider a society in which half the population is skilled and half unskilled. This is a static exercise and so what led to this predicament is unimportant for our current investigation. It will be assumed that the skilled people will be the rich ones and the unskilled the poor (I am aware that this is often not the case in reality). Hence, the World Bank’s goal of shared prosperity, focusing on the bottom 40% of the population, will, in this model, translate to focusing on the income growth of the unskilled.

Suppose, if the tax rates are zero, each skilled person earns $A and each unskilled person earns 0. Now I introduce a simple system of proportional income tax, t, which is used by the government as a mechanism for redistribution. Remember this is no more than an illustrative exercise and, as such, this is an economy with no public goods, no defense expenditure, no security challenges, no political conflict. I will assume that as the government increases the tax rate, the skilled people choose to work less. In particular, if the income tax rate is t ε [0, I], the skilled person, it will be assumed here, earns an income of A(I – t)<sup>2</sup> . Hence, this person’s post-tax income is A(I - t)<sup>2</sup> (I – t) = A(I - t)<sup>3</sup> . I shall denote this, in brief, as: Since the unskilled people earn their income from the transfer via the tax collection and the number of unskilled people is the same as the number of skilled people, if the income tax rate is t, the income earned by each unskilled person, y(t), will be given by: 18
I POLICY RESEARICH WORKING PAPER 1363

# Summary findings

The Brazilian sugar and ethanol story goes like this: Direct market intervention overrides market forces. Markers undergo dramatic change. Intervention establishes vested interests. Rent-seeking blocks adjustment to market change. Economic obicctives become blurred behind political objectives. Opportunities go begging. Industry profitability suffers. And national income is forgone.

Borrell, Bianco, and Bale use a simple economic model of the Brazilian sugarcane sector and policy interventions to measure the costs of existing policies and to develop better policies.

Brazil is an efficient produccr of sugar, but policy intervention has caused:

- Underproduction of sugarcane.

- The wrong mix of sugar and ethanol from cane (too much ethanol, not cnough sugar). Missed opportunities to market ethanol in highvalue uses (as an octane enhancer and clean fuel). * Missed opportunities to make the world sugar market more competitive.

Adopting more market-based policies could be worth billions of additional dollars annually to Brazil.

- This paper a product of the Latin America and the Caribbean, Country Department I, Natural Resources and Rural - Poverty Division is part of a larger effort in the region to analyze economic and sectoral policies in Brazil. Copies of the paper arm available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Malcolm Bale, room Q7-167, extension 31913 (22 pages). October 1994.

_lhc Policy ewarich ' Paper Scries Pordibg dismimts the rgs of zrc k iiss pmina_ to _encotage_ r _hc exbange of s bot damlopment i &An oeiuof the series is toget tkefmdingsosstquidly,even ifbe prc-sationsarc Zest*an f[iypdsished.The papers airy the aninss of tb authors and sboldd be used and cied acodn*h. The s,bg ixterpretarions, and conduo are the autbor' own and should not be atcbwd to the World_ Bank, _;ItS five Boar of Dirctrors, or ay of its msember countri_ Produced by the Policy Research Dissmination Center
Comparable Area (MCA) concept as implemented by IPEA researchers. MCAs group municípios in each of the four census years so that their boundaries do not change during the study period. All data have then been aggregated to match these MCAs. The resulting data set represents 123 urban agglomerations that consist of a total of 447 MCAs.

The sources for the majority of data employed in this paper are the Brazilian Bureau of Statistics (IBGE) Population and Housing Censuses of 1970, 1980, 1991 and 2000. We used the full Brazilian census counts to get information about total population and housing conditions (urbanization rate). Other data were collected only for a sample of households. We used this census sample information for income, industrial composition, education, piped water provision, and electricity availability. The sample sizes varied across census years (1970: _25_ percent; 1980: _25_ ; 1991: _12.5_ ; 2000: _5_ )., but all are representative at the município level, and thus are also reliable at the MCA level employed in this study. Table 15 reports the main variables, their source and the years available.

**Table 15: Variable definitions and data sources**

|**Data**|**Source**|**Years**|
|---|---|---|
|Population|Population Censuses|1970, 1980, 1991, 2000|
|Urbanization rate|Population Censuses|1970, 1980, 1991, 2000|
|Income per capita (monthly deflated to 2000<br>values)|Population Censuses (sample)|1970, 1980, 1991, 2000|
|Industrial composition (percentage of labor|Population Censuses (sample)|1970, 1980, 1991, 2000|
|force employed in an economic activity sector)|||
|Years of schooling (average)|Population Censuses (sample)|1970, 1980, 1991, 2000|
|Percent of households with access to piped<br>water provision|Population Censuses (sample)|1970, 1980, 1991, 2000|
|Percent of households with access to electricity|Population Censuses(sample)|1970, 1980, 1991, 2000|

# _Income vs. wages_

As discussed in the text, per capita income is not the preferred proxy for productivity growth as it includes not only real wage income, but also transfer payments and dividends or capital gains that were not necessarily generated locally. However, we used income because consistent wage data 37
<!-- Start of picture text --> TPP12 Output, billion $ TPP12 Output, %<br>Wearing apparel and leather 2.<br>Textiles DT<br>Food, beverages, tobacco a<br>Machinery Other agriculture Hh “7<br> and equipment nec [haus Lk<br>Electronic equipment [ikeas _<br>Transportequipment [ilies LL<br>MetaBTM 5 5<br>Natural resources/ mining ilies L<br>Chemical, rubber, plastic products [INN | =<br>Other manufacturing [hems le<br>Social services [ccc L<br>Communication and business services nec [XE x<br>Finance and other business services [UUcs =<br>Trade and transport Sg =<br>Utilities Mies =<br>Construction [Ts =<br>20 0 20 40 60 80 100 120 140 160 180 1 4 8 4 19 24<br>mStandard m Productivity kick mStandard m Productivity kick<br><!-- End of picture text -->

<!-- Start of picture text --> TPP12 Exports, billion $, Exports, %<br>Wearing apparel and leather EE. cs<br>Textiles DI. PrrC“(‘;‘SC”TM<br>Food, beverages, tobacco TT. es<br>Other agriculture (.. |<br>Machinery and equipment nec SE... | 5<br>Electronicequipment —... L<br>Transport equipment ST. L<br>Metals ies 4<br>Natural resources/ mining ET... \<br>Chemical, rubber, plastic products I... | 4<br>Other manufacturing SL 4<br>Sodal services SN 4<br>Communication and business services nec =z<br>Finance and other business services [NIL Li<br>Trade and transport =<br>Utilities | 1<br>Construction ff \<br>0 10 20 30 40 50 60 70 80 90 ©6100 5 15 35 55 75 95<br>@Standard TM Productivity kick mStandard m Productivity kick<br><!-- End of picture text -->

<!-- Start of picture text --> TPP12 Imports, billion $ TPP12 Imports, %<br>Wearing apparel and leather ST... eeeecrcrcrrrCU<br>Textiles ts as<br>Food, beverages, tobacco i. POeeeeeereeCSS<br>Other agriculture LT a<br>Machinery and equipment nec [IIe ;keltc<br>Electronicequipment [ills ll.<br>Transport equipment SL ; ss ke<br>Metals [ills =z<br>Natural resources / mining [EL a.<br>Chemical, rubber, plastic products —'. a.<br>Other manufacturing SE a.<br>Social services SE Fl<br>Communication and business services nec SE, pee“SS<br>Finance and other business services ST. a.<br>Trade and transport S' Ft C(t<br>Utilities ,<br>Construction [ies Le<br>0) 5 10 15 20 25 30 0 5 10 15 20 25 30<br>mStandard TM Productivity kick mStandard _TM Productivity kick<br><!-- End of picture text -->
limitations, it allows us to capture to the extent that is possible the impact of services trade liberalization on cross-country trade flows while minimizing concerns for endogeneity often present in country-level indicators of services liberalization. One should however be cautious in interpreting the results of bilateral (or preferential) services liberalization.

# _Bilateral measure of GVC participation_

The outcome variable is a bilateral measure of GVC-trade in manufacturing.<sup>23</sup> The focus on manufacturing GVC-trade is conceptually because the objective of this paper is to identify the role of services liberalization in the fragmentation of the production process, going beyond facilitating services trade. In addition, this solves potential problems arising from the fact that services trade data in the inter-country IO tables are often imputed using a gravity model, which can hence cast doubt to the validity of using the gravity model to analyze the data.

The most basic measure of GVC-trade would be _gross trade in intermediate goods_ , as trade within a GVC entails parts and components crossing borders multiple times. More sophisticated indicators of GVC participation can be calculated based on either backward or forward linkages. At the unilateral country level, backward and forward linkages are defined as the foreign value added in domestic exports, and the domestic value added in foreign exports, respectively. It is worthwhile looking at GVC indicators based on both backward and forward linkages as they bring out different aspects of GVC participation, depending on the country’s specialization pattern. For instance, exports of countries that are specialized in high value-added tasks are better captured by forward linkages, while exports of countries specialized in simpler tasks will have stronger backward linkages. This implies that backward linkages may empirically be a better measure for the GVC participation of lower income countries, while forward linkages are more appropriate for wealthier countries or primary commodity exporters (Kummritz, 2016).

enhancing effect of services agreements.

> 23Developing novel indicators of GVC participation is still an ongoing process. One of the earliest attempts to measure international fragmentation was the Feenstra and Hanson (1996) foreign content of domestic production, using information from inputoutput (I-O) tables. Hummels et al. (2001) further narrowed the Vertical Specialization (VS) measure to capture the import content of exports. Recent efforts to build international I-O matrices has led to more advanced measures such as the VAX ratio which is the share of value-added to gross exports as an inverse measure of GVC (Johnson and Noguera, 2012). Koopman et al. (2014) summarize the relationship among these different measures by deriving a comprehensive decomposition of gross exports, and Wang et al. (2013) extend the decomposition to a bilateral and sector level. See Amador and Cabral (2016) and Johnson (2017) for a recent review of different GVC measures.

13
# Figure 2. Transfers and Social Costs or Trade Barriers in Turkey

<!-- Start of picture text --> Price<br><!-- End of picture text --> <!-- Start of picture text --> STurk<br>- -- -<br>_ '79 FTurk,<br>STurk '89<br>T 79<br>- --<br>189f- . T '89{ - - - - - ~~~~~~~~~~~~ Turk '89<br>Pwsrld<br>'~DTurk '89<br>Quantity<br>.5~ ~~~~~Ipot 7<br>Imports 8<br>V!,~~~~~~~~~~~~~~~~~~~~~~8<br>c~~~~~~~~<br>m Transfer<br>Z LA/ Social Loss<br>T = tariff or equivalent trade barrier<br><!-- End of picture text -->
**Figure 6.** RIF results: Differences in per capita expenditure between Female and Male PA households Wave 2013-2014 Wave 2017-2018

<!-- Start of picture text --> 17.1 p90 p90-22.0<br>Unexplained<br>8.6 p80 p80 -18.1<br>Explained:<br>4.0 p70 p70 -16.2<br>Demographic<br>characteristics<br>2.6 p60 p60 -15.0<br>Explained:<br>Family type<br>-0.4 p50 p50 -13.9<br>-2.2 p40 p40 -13.4<br>-5.4 p30 p30 -13.0<br>-9.1 p20 p20 -14.1<br>-11.2 p10 p10 -16.4<br>0.8 Mean Mean -16.2<br>-20.0 -10.0 0.0 10.0 20.0 -25 -5 15<br><!-- End of picture text --> Note: Both graphs show the results coming from RIF decompositions. These decompositions use two groups of variables: demographic characteristics and family types. Each bar is an estimated gap between female and male-headed households. A negative sign means that female-headed household spending is smaller than that of male-headed households. Complete tables can be found in Annex, Tables A5 and A6.

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The literature mentioned above suggests the importance of interviewing both spouses in the household and documents a positive relationship between spousal agreement and well-being outcomes. Presenting new empirical evidence from Sub-Saharan Africa, we focus on studying spousal disagreement over decision-making roles from a measurement perspective, comparing modes of spousal response discrepancy and examining what they reveal about intra-household decision-making and their potential for measuring women’s agency.

# **Spouses’ views and patters of disagreement over decision-making roles: Evidence from the DHS**

As mentioned above, the DHS is a nationally representative population-based household survey that has been conducted since 1984. Below, we explore DHS data from 20 countries in Sub-Saharan Africa.<sup>20</sup> We study the dynamics of and patterns over decision-making, focusing on the analysis of the following two key decision-making questions: who in their household “usually makes decisions” over (1) large purchases, one of the most common decision-making questions typically asked in household surveys; and (2) how the husband’s earnings will be used, as these are the only two decision-making domains asked to both spouses in the DHS. As in the studies above, this analysis presents challenges on how to reconcile potentially conflicting responses, but also provides opportunities to better understand intra-household decision-making and why that very conflict may shed light on power dynamics.

## _Relationship between women’s decision-making power and empowerment proxies_

In this section, we first want to understand how women’s reports about their own decision-making power relate to their overall empowerment.<sup>21</sup> Before turning to analyzing spousal disagreement, it is useful to establish whether there is a link between measures of empowerment and simple reports from just one spouse. To do this, we identify a number of DHS questions that capture information on women’s status and are commonly used as proxies of empowerment in the literature (e.g. Thomas, 1994; Doss, 2013). In our analysis, we consider variables such as education, employment, reported earnings relative to their husband and land ownership, polygamy, as well as women’s perceptions on whether domestic violence is acceptable, along the same lines as Hanmer and Klugman (2016). Although, as outlined in Section 1, we do not believe such proxies to be complete measures of empowerment, our primary motivation for including

> 20 Note that our two decision-making variables are contained in 24 Sub-Saharan African countries. We exclude Ghana, Kenya, Lesotho and Madagascar from our analysis as they do not contain information on land ownership. Results for the full set of 24 countries are very similar, and available upon request.

> 21 As defined in Section 2, agency is closely related to empowerment, although the latter is a broader concept which includes components such as resources (pre-conditions) and achievement (outcomes) across health, education, and economic opportunities.

34
market-based relative to our estimated financial structure for each economy. Tables 1, 2, and 3 provide information on the **actual Financial structure ratio / Optimal financial structure ratio** , where we discuss the construction of the Optimal financial structure ratio below. This variable indicates that the average financial structure of countries such as Guatemala, Namibia, and United Arab Emirates are highly distorted relative to their estimated optimum in that they are too bank-based (>4.91; 90<sup>th</sup> percentile), while the financial structure of countries such as Denmark, Japan, and United Kingdom, are highly distorted relative to their estimated optimum in that they are too market-based (<0.03; 10<sup>th</sup> percentile). Appendix 1 contains a list of countries and provides an overview of the period medians for GDP per capita, Private credit, Stock Value Traded, the Financial structure ratio, and the Financial structure ratio / Expected-Optimal financial structure.

# **1.2 Other data**

As a measure of economic activity, we use **Log Real GDP per capita** , which equals the logarithm of GDP per capita in constant 2000 U.S. dollars. And, to assess the independent link between finance and economic development, we control for many other country characteristics that have been employed in the development literature. In some specifications, we use “standard controls” to evaluate the independent relationship between finance and economic activity. These standard controls include: years of schooling, openness to trade, inflation, government size, the initial GDP per capita of the economy in 1980, and dummy variables for the 5-year periods of analysis. Table 1 provides the specific definitions. In other specifications, we use “exogenous controls,” which include dummy variables for the legal origin of the country along with the country’s distance from the equator. Table 1 gives the detailed definitions and sources of these data and Table 2 provides descriptive statistics

# **1.3 Correlations**

The correlations in Table 3 highlight key features about the financial system and economic development. First, bank and securities market development are positively correlated with economic development. Second, bank and securities market development are positively correlated, suggesting that financial development involves both bigger banks and bigger markets. Third, the Financial structure gap is negatively correlated with bank and securities development, 8
potential supply of technology of a parent as it invests in R&D and grows in size.<sup>31</sup> The interaction term of the reform dummy with the high patent use dummy remains positive and significant here.

The specifications presented in columns (4)-(6) of Table III control for the scale of affiliate activity more directly by scaling royalty payments by sales. The results of these specifications reveal whether affiliates use technology provided by the parent more intensively after reforms occur. The positive and significant coefficient on the post reform dummy in column (4) indicates that this is the case. The samples used in the last two specifications again include only affiliates of firms that can be linked to the NBER patent database. The coefficients on the post reform dummy interacted with the high patent use dummy are positive and significant in these specifications, indicating larger increases in the value of parent provided intangibles, relative to sales, for affiliates that have parents that make extensive use of the U.S. patent system.

To allow for a more direct comparison with the effects reported in columns (1)(3), it is helpful to calculate the elasticity of the royalty to sales ratio with respect to reform. To calculate this elasticity, we differentiate log(1 + royalties/sales) with respect to reform. Defining ratio = royalies/sales, this gives us:

> 31   As already noted, we recognize the potential endogeneity of these variables in this context.  However, the purpose here is not to derive structural estimates of, for instance, affiliate size on technology transfers. Rather, we only include these variables to control for parent-firm specific changes in technology supply and affiliate-specific changes in technology demand.

18