rafmacalaba/datause-encoder-data huggingface dataset:
**Table H7: Relationship between food security measures and weight-for-age z-scores (Tanzania): Expanded Controls Version**

|VARIABLES|(1)|(2)|(3)<br>Weight-for-|(4)<br>age z-scores|(5)|(6)|
|---|---|---|---|---|---|---|
|Per capita kcal|-0.034<br>(0.044)||||||
|1-SSR||0.034<br>(0.039)|||||
|1-SSEXR|||0.033<br>(0.036)||||
|DDS||||0.065|||
|||||(0.041)|||
|PC expenditure|||||0.016<br>(0.047)||
|Food share||||||-0.031|
|||||||(0.037)|
|Child is female|0.063|0.066|0.069|0.066|0.068|0.066|
||(0.068)|(0.068)|(0.068)|(0.068)|(0.068)|(0.068)|
|Age (in months)|-0.034***|-0.034***|-0.034***|-0.034***|-0.034***|-0.034***|
||(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|
|Age squared|0.000***|0.000***|0.000***|0.000***|0.000***|0.000***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Household size|-0.000|0.000|0.001|0.003|0.004|0.000|
||(0.012)|(0.012)|(0.012)|(0.012)|(0.012)|(0.012)|
|Share of kids under 5|-0.070|-0.085|-0.081|-0.075|-0.105|-0.112|
||(0.292)|(0.290)|(0.291)|(0.290)|(0.293)|(0.290)|
|Female-headed HH|-0.154|-0.153|-0.155|-0.152|-0.155|-0.150|
||(0.100)|(0.100)|(0.100)|(0.100)|(0.100)|(0.100)|
|_HH Head's Education_|||||||
|Did not finish Primary School|-0.104|-0.108|-0.108|-0.116|-0.108|-0.105|
||(0.106)|(0.106)|(0.106)|(0.106)|(0.107)|(0.106)|
|Finished Primary School|-0.796**|-0.795**|-0.783**|-0.799**|-0.803**|-0.791**|
||(0.346)|(0.344)|(0.353)|(0.353)|(0.348)|(0.347)|
|Did not finish Secondary|||||||
|<br>School|-0.066|-0.075|-0.072|-0.094|-0.078|-0.072|
||(0.171)|(0.170)|(0.170)|(0.170)|(0.171)|(0.170)|
|Finished Secondary School|-0.276|-0.287|-0.300|-0.316|-0.304|-0.300|
||(0.250)|(0.247)|(0.246)|(0.247)|(0.253)|(0.247)|
|Higher than Secondary School|-0.147|-0.152|-0.161|-0.170|-0.169|-0.174|
||(0.286)|(0.287)|(0.284)|(0.286)|(0.288)|(0.289)|
|_Mother's Education_|||||||
|Did not finish Primary School|-0.046|-0.048|-0.054|-0.058|-0.051|-0.053|

97
||Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|---|---|
|Purchased Bought Assets (dummy)|Dummy variable equal to 1 if the firm<br>purchased fixed assets during the last fiscal<br>year and 0 otherwise.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Time Tax (%)|Percentage of the firms’ senior management’s<br>time that is spent in dealing with requirements<br>of government regulations.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Courts: Obstacle Severity|Severity level of (lack of proper) functioning<br>of courts as an obstacle to firms’ day to day<br>operations. The severity level is defined on a<br>0-4 scale as: no obstacle (0), minor obstacle<br>(1), moderate obstacle (2), major obstacle (3),<br>and a very severe obstacle (4).<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Crime Losses (% of sales)|Losses due to crime, theft, robbery, vandalism<br>and arson during the last fiscal year and<br>expressed as a percentage of the firms’ annual<br>sales.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Taxes: Obstacle Severity|Severity level of (high) tax rates as an obstacle<br>to firms’ day to day operations. The severity<br>level is defined on a 0-4 scale as: no obstacle<br>(0), minor obstacle (1), moderate obstacle (2),<br>major obstacle (3), and a very severe obstacle<br>(4).<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Labor Laws: Obstacle Severity|Severity level of labor laws as an obstacle to<br>firms’ day to day operations. The severity level<br>is defined on a 0-4 scale as: no obstacle (0),<br>minor obstacle (1), moderate obstacle (2),<br>major obstacle (3), and a very severe obstacle<br>(4).<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Industry fixed effects|Dummy variables (one for each industry)<br>indicating the industry to which the firm<br>belongs. There are 7 industries defined at the<br>2-digit ISIC Rev. 3.1 level.|

50
several value‐added components (see Figure 1). Our first measure of interest is domestic value added (DVA). It simply measures the amount of value added by the exporting country contained in its exports, i.e. the sum of the first 4 components in the figure. While this is not a direct measure of GVCs, the comparisons of the results for this variable with our second variable of interest (discussed next) sheds light on the relationship between deep trade agreements and GVCs.

The second variable of interest is value added in intermediates. It includes the value of exports that has been produced domestically, exported as an intermediate good, reprocessed by the importing countries and either directly absorbed there (component (2) in the figure), further exported to third countries (component (3)) or re‐exported to the original country (component 4). We define a third variable from the sub‐set of re‐exported intermediates (components (3) and (4)). The re‐exported intermediates variable represents the most fragmented parts of a production process in which goods and services cross at least two borders before being eventually absorbed. These two variables capture the bilateral forward linkages between two countries. We also use foreign value added in gross exports that can be further decomposed between final and intermediate goods and services (components (5) and (6)). It measures all value that has not been produced domestically and that is contained in gross exports. This variable captures backward linkages. At this stage, the decomposition does not allow us to identify the country of origin of the foreign value and hence it is an imperfect measure of bilateral GVC linkages.<sup>12</sup> Second, for the analysis based on gross trade flows, we use trade in parts and components to proxy for global production sharing. There is no broadly accepted definition of trade in parts and components that we can refer to, so our classification builds on the existing literature in this area (WTO, 2011 and Orefice and Rocha, 2011). Specifically, for our analysis, we define as parts and components all non‐fuel intermediates from the Broad Economic Categories (BEC) classification

> 12 The sum of the six above‐mentioned value‐added components does not match exactly the official trade statistics in gross value terms. The difference is due to double counting (column (7)) that tends to increase when goods and services cross borders multiple times.

9
account for this non-random attrition, we control for each of these variables (as well as other factors) in our later regression analysis.

**Table 1:** Extortion During Migration - Arrivals

||(1)|(2)|(3)|
|---|---|---|---|
|Variable|No Extortion|Extortion|Diference|
|Age|30.98|30.99|0.009|
||(9.476)|(8.962)|(0.734)|
|Indigenous|0.387|0.437|0.050|
||(0.487)|(0.497)|(0.038)|
|Female|0.075|0.111|0.036+|
||(0.263)|(0.314)|(0.021)|
|Years Education in GT|6.244|6.623|0.379|
||(3.646)|(3.708)|(0.286)|
|Married|0.297|0.256|-0.040|
||(0.457)|(0.438)|(0.035)|
|Children in GT|1.198|1.241|0.043|
||(1.589)|(1.655)|(0.125)|
|Children in U.S.|0.406|0.518|0.112|
||(0.942)|(1.226)|(0.078)|
|Detained at Border|0.659|0.615|-0.044|
||(0.474)|(0.488)|(0.038)|
|Years in U.S.|4.398|4.392|-0.006|
||(5.949)|(5.865)|(0.474)|
|Number of Migrations|1.622|1.774|0.152+|
||(1.090)|(1.084)|(0.085)|
|Have Visible Tattoos|0.084|0.076|-0.007|
||(0.277)|(0.266)|(0.022)|
|Observations|924|199||
|+ _p <_0_._10, _∗p <_0_._05, _∗∗_|_p <_0_._01|||

# **5 Results**

First, we use a linear model to explore the direct relationship between extortion and political behavior, with a particular interest in civic engagement. Table 2 shows that extortion positively predicts interest in civic acts of protest, community meeting participation, and volunteering, all significant at a 95% confidence level. On a 5-point scale, individuals who experienced extortion say they are 0.25-0.4 points more likely to take these actions in the coming year compared to those who were not extorted. The relationship is similarly positively signed for joining a political party, although it is not statistically significant. The 16
ues of �: To better understand the type of nonlinearity involved Figure 1 plots the time pro...le of the transition function, F (zt), over the range of zt.

# 3 Structural identi...cation and data

This paper uses a unique quarterly dataset from Malaysia to identify government consumption and government spending shocks. The data runs from 1981:1 till 2010:4. The identi...cation scheme is based on a generalised version of reduced form models in, amongst many others, Fatás and Mihov (2001), Blanchard and Perotti (2002), Perotti (2005), Cimadomo, Hauptmeier and Kirchner (2010), Corsetti, Meier and Müller (2010), Auerbach and Gorodnichenko (2010, 2011) and Bachmann and Sims (2011). The identi...cation approach is based on a recursive identi...cation scheme that aims to identify ...scal policy shocks on their timings within the system.

As in Blanchard and Perotti (2002) the basic model is computed using quarterly data and is composed of Xt = [Gt; Tt; Yt]: The variable Gt represents government spending shocks. Just as output multipliers for government purchases may differ according to the regime in which they occur, they can also di¤er for di¤erent components of government spending.<sup>11</sup> Leeper, Walker and Yang (2009), for example, have shown how the short-run multiplier for government investment shocks are smaller than those for government consumption innovations, but are more e¤ective in the longer-term.<sup>12</sup> Two types of government spending shocks are identi...ed: consumption and investment.<sup>13</sup> The government consumption de...nitions follow Perotti (2005a). Thus, government consumption spending is de...ned as gov-

> 11See Perotti (2005a, 2007).

12See Romp and de Haan (2007) for an in-depth survey on public capital and economic growth. 13Much of the recent literature, to capture the stance of ...scal policy, have utilised government consumption spending as de...ned as current real terms spending on goods and services. This is because such a measure contains little automatic cyclical component, thus helping to identify government spending shifts over and beyond those changes caused as a natural consequence of the business cycle. Furthermore, government purchases have no direct link to productivity, thus, limiting the number of channels through which government spending in‡uences the real economy. This measure of ...scal policy is also less likely to be in‡uenced by the automatic stabilisers channels, which operate primarily through tax revenue and transfer payments. Moreover, in economic downturns, it is politically easier to cut capital expenditure (public investment) than government consumption of goods and services, such as health care. For these reasons, the baseline model in this paper includes government consumption spending.

8
<!-- Start of picture text --> Figure 1. Import Growth Decomposition<br>%<br>30.0<br>25.0<br>20.0<br>15.0<br>10.0<br>5.0<br>0.0<br>Demand Change Mkt Share Change Demand Change Mkt Share Change<br>-5.0 1991/92-2001/02 1991/92-2001/02 2001/02-2007/08 2001/02-2007/08<br>-10.0<br>-15.0<br>Import Substituting Other Developing Industrial<br><!-- End of picture text --> Import substituting countries have much higher import growth rates during the first period with little market share changes and during the second period a faster demand growth rate with reductions in import shares. Other developing countries group has higher demand increases than industrial countries (much higher during the second period) but rather similar market share change rates. Industrial countries have market share changes of 4.8 and 3.8 percent p.a. during the first and second period respectively and the other developing country group has an average market share changes of 3.6 and 2.6 percent p.a. It is possible to argue that for most large countries, ceteris paribus, one can expect 3-5 percent trade expansion even if there are no increases in demand. The role of increases in import shares can be seen more clearly in Figure 2.

11
# **Figure 6: Non-linear pricing structure**

Consumer equilibrium Intra-marginal prices and income effect <!-- Start of picture text --> Good 2 Good 2<br>Electricity (Good 1) Electricity (Good 1)<br><!-- End of picture text --> Taylor (1975) has reviewed a number of studies that estimated the elasticity of demand to changes in prices for electricity. The models presented are estimated longitudinally with time data or cross-section with data on states or provinces. These models share some essential elements that can be summarized in the following equation Where the subscript _i_ =unit of observation; = quantity consumed of electricity; = household income; = electricity expenditure; = Price of electricity where this price can be alternatively the marginal price or the average price; _X_ = vector of control variables; = price elasticity and = income elasticity. These models can control for various factors such as substitutes products, household white goods, climate conditions, household size and other factors specific to the case-study.

In our case, we assumed that the researcher has only one cross-section survey which means that the unit of observation is the household and the number of observations relative to the marginal price will be the number of price blocks present in the electricity system. This may be a problem for the estimation of elasticity if the number of blocks is small. In principle, one could use average price instead of marginal price to overcome such problem. However, the use of average price may cause endogeneity. This is because we use the quantity to compute the average price and this will make the average price correlated with the error term. Alternatively, as reported by Taylor (1975), we can use the per capita expenditure on electricity (up to the block before the last so as to exclude the block of the marginal price) together with the marginal price of the last block of consumption as follows: Where = marginal price and = expenditure on electricity up to block b-1 of household .

31
Social Liberal. Evangelicals and white Brazilians were much more likely to vote for the Partido Social Liberal than other religious and ethnic groups. Although the Gallup World Poll does not contain an ethnicity variable, a further examination of the religion variable using the Gallup World Poll reveals that the decline in SWB is not larger for respondents with strong religious beliefs.<sup>26</sup> Table 7 (column 2) also shows that that the middle aged, evangelicals, and people with a partner were more likely to switch parties and withdraw support from the incumbent party. Overall, these results suggest that the rise of the Partido Social Liberal was associated not only with the voting choices of groups that experienced a larger decline in SWB, but also with the support of groups that espouse certain values.

**Table 7: Socio-Demographic Characteristics Associated with the Bolsonaro Vote – Logistic Regression**

||**Dependent variable:**|**Dependent variable:**|
|---|---|---|
||Voted for Bolsonaro in Second<br>Round of Presidential Elections|Switched from voting Dilma in<br>2014 to Bolsonaro in 2018|
||(1)|(2)|
|Age|||
|<30|Ref.|Ref.|
|30-45|0.96|1.37*|
|45-60|1.30#|1.23|
|60+|1.13|0.55**|
|Female|0.64**|1.06|
|Has a partner|1.34**|1.49**|
|Other persons in household|0.85|0.88|
|Evangelical|2.11*|1.34*|
|White (Branco)|1.28*|0.94|
|Immigrant|0.32|1.62|
|Lives in a rural area|1.01|0.93|
|Region|||
|Northeast|Ref.|Ref.|
|North|2.82**|1.05|
|Central-West|2.57**|0.94|
|Southeast|2.48**|0.99|
|South|3.08**|1.27|
|Education level|||
|Elementary|Ref.|Ref.|
|<br>Secondary|1.38*|0.91|
|Tertiary|1.11|0.78|
|Employment status|||
|Full-time employed for employer|Ref.|Ref.|
|Self-employed/manager|0.89|1.07|
|Unemployed|0.53**|0.80|
|Out of workforce|0.81|0.88|
|Household income per capita|||
|Poorest 20%|0.53**|0.73|
|Second 20%|0.65*|0.94|
|Middle 20%|Ref.|Ref.|
|Fourth 20%|0.98|1.12|
|Richest 20%|0.98|0.92|
|Unknown|0.82|0.66#|
|N|1,734|1,734|

All presented values are odd’s ratios. #p<0.10, *p<0.05, **p<0.01. All models are estimated using robust standard errors. Ref.=Reference category

# _5.2. Factors associated with voting behavior in 2018_

The relationship between change in SWB and voting behavior is complex and indirect because a loss in SWB is more likely to translate into a protest vote when people hold the incumbent party

> 26 These results are available upon request.

31
-21-

**TABLE 2.4**

|Mexican Employment and Prod<br>Industrial"|uctivity<br>in<br>and National|1975 According<br>to<br>Accounts|'Censo|
|---|---|---|---|
|"Censo<br>Industrial"<br>Employment|National<br>Accounts<br>Employment|Census<br>Gross Value<br>Added<br>(Present<br>National<br>Accounts Concept|National<br>Accounts<br>GDP<br>(Present<br>Natio-<br>**nal**Accounts<br>) Concept) per|
|||Per Person<br>Employad<br>(US S)|Person Employed<br>(US S)|
|Food Products<br>309,651|411,899<br>|5,713|9,865|
|Beverages<br>69,392|94,353|9,853|10,713|
|Tobacco<br>8,645|9,442|12,245|11,423|
|Textiles<br>138,421|157,480|6,842|8,124|
|Clothing<br>90,606|112,084|3,896|7,813|
|Footwear & Leather<br>48,101|118,292|4,255|5,108|
|Wood Products<br>30,663<br> <br>|54,237<br>|5,587<br>|4,684|
|Furniture<br>44,452|51,452|2,889|7,181|
|Paper & Allied Products39,164|42,130|11,001|12,542<br>|
|Printing & Publishing50,316|56,603|7,291|7,355|
|Chemicals & Allied<br>**b**||||
|Products<br>1 5 7 <sup>, </sup>1 70|165,571|14,346|14,593|
|Rubber and Plastic||||
|Products<br>53,363|57,138|9,683|9,756|
|Stone, Clay & Glass||||
|Products<br>100,714|129,766|7,536|8,387|
|Primary Metals<br>79,035|75,331|14,940|15,014|
|Metal Products<br>127,474|118,246|6,894|6,732|
|Machinery, except||||
|Electric<br>68,009|70,111|10,021|8,759|
|Electrical Machinery &||||
|Equipment<br>114,382|124,301|7,031|8,066|
|Motor Vehicles &||||
|Equipment<br>94,110|96,375|9,364|9,442|
|Other Transport||||
|<br>Equipmer.t<br>16,559|16,319|7,362|7,390|
|Other Manufacturing<br>34,113|41,380|6,111|10,153|
|Total<br>1 <sup>, </sup>6 7 4 <sup>, </sup>3 4 0 <sup>c</sup>|2,002,510|8,032|9,271|

a) converted at **the** official exchange rate of 12.50 pesos to one US **$.** b) includes 25,989 employees in petroleum refining which is an industry not covered in the industrial census Resumen General, but taken from SPP (1981). c) excludes 69,445 head office and auxiliary personnel.

Sources: As for table 2.3
One issue is the choice of counterfactual. The classic formulation of the evaluation problem assesses mean impacts on those who receive the program, relative to counterfactual outcomes in the absence of the program. However, this may fall well short of addressing the concerns of policy makers. While common practice is to use outcomes in the absence of the program as the counterfactual, the alternative of interest to policy makers is often to spend the same resources on some other program (possibly a different version of the same program), rather than to do nothing. The evaluation problem is formally unchanged if we think of some alternative program as the counterfactual. Or, in principle, we might repeat the analysis relative to the “do nothing counterfactual” for each possible alternative and compare them, though this is rare in practice. A specific program may appear to perform well against the option of doing nothing, but poorly against some feasible alternative.

For example, drawing on their impact evaluation of a workfare program in India, Ravallion and Datt (1995) show that the program substantially reduced poverty amongst the participants relative to the counterfactual of no program. Yet, once the costs of the program were factored in (including the foregone income of workfare participants), the authors found that the alternative counterfactual of a uniform (un-targeted) allocation of the same budget outlay would have had more impact on poverty.<sup>63</sup> A further issue, with greater bearing on the methods used for evaluation, is whether we have identified the most relevant impact parameters from the point of view of the policy question at hand. The classic formulation of the evaluation problem focuses on mean outcomes, such as mean income or consumption. This is hardly appropriate for programs that have as their (moreor-less) explicit objective to reduce poverty, rather than to promote economic growth _per se_ . However, as noted in section 3, there is nothing to stop us re-interpreting the outcome measure such that equation (2) gives the program’s impact on the headcount index of poverty (% below the poverty line). By repeating the impact calculation for multiple “poverty lines” one can then trace out the impact on the cumulative distribution of income. This is feasible with the same tools, though evaluation practice has been rather narrow in its focus.

There is often interest in better understanding the horizontal impacts of program, meaning the differences in impacts at a given level of counterfactual outcomes, as revealed by the joint distribution of _Y T_ and _Y C_ . We cannot know this from a social experiment, which only

> 63 For another example of the same result see Murgai and Ravallion (2005).

56
household sector does not issue securities or equity; but the corporate sector does, and there are correspondingly fewer blanks in the columns relating to the corporate sector.

Once again it emerges that the business sector both borrows from and lends to (places diposits with) the financial sector. But the story is even more complex than with the household sector, as the entities in the business sector acquire claims on other eatities in the business sector. For instance about one-half of trade credit is issued within the business sector.

The relevance of the financial sector becomes evident in this Table 2, as it has entries on both sides of almost all the rows: only those rows which relate by definition to claims of or on other sectors or to gold have blanks under the financial sector. The gross acquisition of financial claims by the financial sector is equivalent to over 27% of GNP - some 66 times its net lending, and this pattern is not untypical.

# 2.2 Financial assets

Continuing our perusal of the Korean data, we turn to Table 3 showing stocks of financial assets and liabilities. The total value of outstanding financial assets recorded comes to 264,500 billion won of which 38,100 are held by the rest of the world. The total if financial assets held by residents is thus 226,400 or over 325% of GNP. But this figure involves an element of double-counting including, as it does, both bank deposits and the claims of banks on their borrowers. For many purposes, it is 9