rafmacalaba/datause-encoder-data huggingface dataset:
# **5.2.3 Intra-household power and incentives**

Finally, we turn to examining changes to decision-making power, economic control and other household dynamics. Recall that we administered a gender awareness training to both the individual and couples’ group to net out any unintended signaling effects on gender equality not operating though the skills or management channel. However, these channels—in particular, the effective management channel—may themselves result in changes in women’s productive incentives. Table 14 shows the planter’s and wife’s declared decision-making power at endline. Couples’ training spouses claim around 0.32 more of a decision in agriculture than the control group’s spouses at endline (a 10% increase), while the planters symmetrically claim 0.33 fewer decisions (or 4.2% less power).

Table 14: Household decision-making

||Of 10 important<br>decisions on agri: you<br>would decide how<br>many?(planter)|<br>Of 10 important<br>decisions on agri: you<br>would decide how<br>many?(spouse)|<br>Of 10 important<br>decisions on rubber:<br>you would decide<br>how many?(planter)|Of 10 important<br>decisions on rubber:<br>you would decide<br>how many?(spouse)|
|---|---|---|---|---|
|Seedling Subsidy|-0.16|0.24|-0.09|-0.04|
||(0.13)|(0.15)|(0.14)|(0.20)|
|Couples' Training|-0.05|0.08|-0.24|0.19|
||(0.16)|(0.20)|(0.16)|(0.21)|
|Seedling Subsidy + Couples'<br>Training (beta)|-0.20|0.32*|-0.33**|0.15|
|H0: Seedling Subsidy +<br>Couples' Training = 0 (p-val)|0.14|0.10|0.03|0.40|
|Observations|1393|1038|1392|1037|
|Mean for control group|7.45|3.27|7.93|2.74|
|Mean for individual training|7.22|3.48|7.79|2.75|

Robust and clustered standard errors in parentheses. * p<0.1 ** p<0.05 *** p<0.01 Note: Baseline control variables in all regressions: Age of household head, Household size, Number of adults, Number of children, Dependency ratio, Married planter, Polygamous relationship, Matrilineal ethnicity, Planter has IGA, Asset index, Credit constrained, Share of plots owned, Average distance to plots (minutes), Number of plots, Average plot size.

This higher decision-making power claimed by spouses translates into slightly more economic control. Table 15 shows a significant increase in the amount of rubber income for which the spouse partakes in control of, as well as in the proportion of rubber plots for which spouse partakes in income decisions, compared to the control group. While women partake in income decisions for 0.1% of rubber plots in the control group, they do so in 1.8% of rubber plots in the couples’ training group. It must be stressed that the absolute value of these indicators of income controlled remains low: these are large changes relative to a low baseline, but by no means transformative. However, they do provide evidence that the couples’ training may have slightly increased women’s incentives to invest time in agricultural production via strengthening their claims over the proceeds.

2
contains 784 poverty rates at national poverty lines. Of these, 623 observations from 110 countries can be matched to surveys available in PovcalNet. Using this, we derive 623 thresholds which reflect the national poverty rate when applied to the PovcalNet per capita welfare measure – more than an eightfold increase over the number of poverty lines used by Ravallion, Chen, and Sangraula.<sup>24</sup> Plotting these national poverty lines on the log of per capita consumption reveals a positive gradient over the entire range of mean consumption, suggesting that national poverty lines are increasing in consumption for even the poorest of countries.<sup>25</sup> We are therefore not able to replicate the methods used by Ravallion, Chen and Sangraula to identify the threshold below which national poverty lines do not increase with mean consumption and use this as a reference group for the international poverty line. Given that we have no data-driven basis on which to select national poverty lines from our sample of 623 lines, we propose to form an international poverty line reflective of national poverty thresholds among countries classified as “low-income” by the World Bank. While the World Bank’s income classification is admittedly arbitrary, it is nonetheless well established and has stayed constant over time.<sup>26</sup> subgroup estimates from household surveys. The source for this data is the World Bank’s Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines. The database is available at http://databank.worldbank.org/data/views/variableselection/selectvariables.aspx?source=Poverty-andInequality-Database.

24 In addition to having much greater coverage of the population of poor people, and having more recent lines; we believe this approach produces a set of lines that are more comparable across countries than those used in Chen, Ravallion and Sangraula (2009).

25 This finding is in contrast to Ravallion, Chen and Sangraula (2009) who find that for the poorest of countries, national poverty lines are not increasing in consumption.

26 As of 1 July 2014, low-income economies are defined as those with a GNI per capita, calculated using the World Bank Atlas method, of $1,045 or less in 2013; middle-income economies are those with a GNI per capita of more than $1,045 but less than $12,746; high-income economies are those with a GNI per capita of $12,746 or more. Lower-middle-income and upper-middle-income economies are separated at a GNI per capita of $4,125.

18
- Elbers, C., and R. van der Weide (2014) ‘Estimation of normal mixtures in a nested error model with an application to small area estimation of poverty and inequality.’ World Bank Policy Research Working Paper 6962, The World Bank

- Elbers, C., J. Lanjouw, and P. Lanjouw (2003) ‘Micro-level estimation of poverty and inequality.’ _Econometrica_ 71, 355–364

- (2005) ‘Imputed welfare estimates in regression analysis.’ _Journal of Economic Geography_ 5, 101–118

- Fujii, T (2006) ‘Community-level estimation of poverty measures and its application in cambodia.’ In ‘Spatial Disparities in Human Development’ (United Nations University Press) pp. 289–314

- Fujii, T. (2010) ‘Micro-level estimation of child undernutrition indicators in Cambodia.’ _World Bank Economic Review_ 24(3), 520–553

- Fujii, T., and R. van der Weide (2013) ‘Cost-effective estimation of the population mean using prediction estimators.’ World Bank Policy Research Working Paper 6509, The World Bank

- Grosh, M.E., and Mu ̃noz (1996) ‘A manual for planning and implementing the living standards measurement study survey.’ Living Standards Measurement Study Working Paper 126, The World Bank

- Hansen, M.H., and B.J. Tepping (1990) ‘Regression estimates in federal welfare quality control programs.’ _Journal of the American Statistical Association_ 85(411), 856–864

- Hidiroglou, M. (2001) ‘Double sampling.’ _Survey Methodology_ 27(2), 143–154

- Humphreys, C.P. (1979) ‘The cost of sample survey designs.’ In ‘Proceedings of the Survey Research Methods Section’ American Statistical Association pp. 395–400

- Kijima, Y., and P. Lanjouw (2005) ‘Economic diversification and poverty in rural India.’ _Indian Journal of Labour Economics_ 48(2), 349–374

- Kim, J., A. Navarro, and W. Fuller (2006) ‘Replication variance estimation for two-phase stratified sampling.’ _Journal of the American Statistical Association_ 101(473), 312–320

33
23 empirical work has focused on the investigation of the _overall_ effect of non-tariff measures on trade. We review this literature and the empirical methodology behind constructing aggregate measures in the second subsection (2.3.3).

# **2.3.1 Methodology**

There are two main methodologies used in estimating the trade impact of NTMs. The first and most common, which Chen and Novy (2012) refer to as the “direct approach”, involves collecting observable data on the incidence of NTMs and then estimating their impact on the market. Such data can be the frequency or coverage data referred to in the previous section (which simply measures the presence of an NTM) or, in the case of case studies on particular policies, more detailed data which provides information on the stringency of the regulation as well. Thus, in this case the researcher uses information on the incidence of NTMs to econometrically estimate their effect on either price-gaps or trade flows across countries.

The second approach, which Chen and Novy (2012) refer to as the “indirect approach” involves estimating the existence of NTMs from market anomalies (e.g., unexplained price gaps or smaller than expected trade flows). Thus, the indirect approach typically involves estimating hypothetical trade flows (or prices) in the absence of NTMs and then inferring the size of NTM barriers from the deviation between the hypothetical and actual trade.

It should be apparent that both approaches have their advantages and disadvantages. While the direct approach allows for a direct calculation of the trade effects of known policies, observable data on NTMs often suffers from transparency problems –and, more broadly, the precision problem discussed earlier. Thus, dummy or count variables which measure the presence of an NTM, often contain little information about stringency. On the other hand, while the indirect approach does not require data on NTM incidence (and may uncover the existence of NTMs that are otherwise hidden), it is often difficult to disentangle the effects of such NTMs from other forces.

# **2.3.2 NTM trade restrictiveness: Specific measures**

Notwithstanding their benefits, aggregate measures of NTMs have the obvious limitation that the trade impact of non-tariff measures will largely depend on their specific details. In this
**Table 6 (Continued)**

|||IHPS|**School P**|**articipa**|**tion Rate**|FIAS|
|---|---|---|---|---|---|---|
||1||2||1|2|
|**Weather Shocks†**|||||||
|Weather Shock|-0.042|*|||-0.002||
||(.024)||||(.047)||
|Current Shock|||-0.042|*||0.021|
||||(.024)|||(.049)|
|Sequential Shock|||-0.011|||0.101|
||||(.050)|||(.074)|
|**Aid * Shock**|||||||
|Aid|-0.035||-0.021||0.135|0.177<br>*|
||(.076)||(.077)||(.102)|(.107)|
|Aid * Weather Shock|0.195|***|||0.035||
||(.075)||||(.114)||
|Aid * Current Shock|||0.090|||-0.131|
||||(.086)|||(.134)|
|Aid * Sequential Shock|||0.293|***||0.107|
||||(.112)|||(.135)|
|**Explicit HH Targeting Characteristics**|||||||
|Dummy, Female Head|0.053|**|0.050|**|0.054|0.046|
||(.025)||(.025)||(.035)|(.035)|
|Dummy, Children<3|-0.009||-0.009||0.015|0.018|
||(.016)||(.016)||(.024)|(.025)|
|Dummy, All Members>59|||||||
|Dummy, HH Member Disabled|0.012||0.012||0.053|*<br>0.058<br>**|
||(.018)||(.018)||(.029)|(.029)|
|**Wealth**|||||||
|Wealth Index|0.102||0.108||0.058|0.075|
||(.071)||(.071)||(.123)|(.125)|
|# Mobile Phones|-0.001||-0.002||0.010|0.006|
||(.008)||(.008)||(.013)|(.013)|
|**Constant**|0.293||0.357||-0.096|0.007|
||(.239)||(.240)||(.401)|(.409)|
|# of Obs.|3257||3257||1539|1539|
|R2 (within)|0.086||0.085||0.109|0.099|
|R2 (between)|0.195||0.197||0.239|0.241|
|R2 (overall)<br>**Notes:*****/**/* denote statistical sig<br>**Weather Shock**is a dichotomous varia|0.152<br>nificance at<br>ble that is eq|1/5/10 per<br>ual to 1 if t|0.153<br>cent resp<br>he househ|<br>ectively.<br>old has|0.172<br>Standard Err<br>been exposed|0.168<br>ors in parentheses.**†**<br>to a weather shock in|

**Notes:** ***/**/* denote statistical significance at 1/5/10 percent respectively. Standard Errors in parentheses. **† Weather Shock** is a dichotomous variable that is equal to 1 if the household has been exposed to a weather shock in the current period (irrespective of potential exposure to past weather shocks), and 0 otherwise. **Current Shock** is a dichotomous variable that is equal to 1 if the household has been exposed to a weather shock _only_ in the current period, and 0 otherwise. **Sequential Shock** is a dichotomous variable that is equal to 1 if the household has been exposed to a weather shock in consecutive periods, and 0 otherwise.

36
Uncertainty and ambiguity brought about by new products and business processes can undermine essential considerations for cost-effectiveness and allow for higher extra profits for the corrupt. When pricing is more uncertain, corrupt buyers can collude with firms to push prices unreasonably high, yet claim that no corrupt overpricing took place (Lundberg and Marklund, 2013; Halonen, 2021). Applying these arguments to Bulgaria where corruption is endemic and hence established corrupt networks are likely to take advantage of opportunities presented by GPP, we hypothesize: _H2 Green public procurement is expected to increase corruption risks, through enabling more restrictive tendering conditions._ Third, green considerations in public procurement are expected to impact firm productivity in the short term through at least two channels: selection and investment. Less competitive and higher corruption risk in procurement has been shown to lead to less allocative efficiency and thus lower productivity growth in the economy (Brugués, Brugués and Giambra 2024). Thus, if GPP presents a market entry barrier and increases corruption risks, it can contribute to the selection of less productive firms as suppliers of green products. By contrast, if GPP follows more competitive procurement procedures and practices, it can thus enhance the economy’s allocative efficiency by channeling more resources to more productive firms. Moreover, the compliance with green product and process criteria in public procurement contracts may require more sophisticated technology, which may attract more productive firms to compete for green contracts. The relationship between GPP and firm productivity is thus ambiguous, unless the allocation of GPP contracts follows more competitive practices that are less prone to corruption risk. Building on the previous two hypotheses, we hypothesize: _H3: If green public procurement is associated with less competition and high corruption risks, it is expected to allocate public resources to less productive firms._

# 3. Methodology

## 3.1 Data

Two public procurement data sources were used for the analysis, following Fazekas et al. (2024). First, all tenders and contracts were collected from the historic national e- procurement portal, AOP (http://www.aop.bg//). Second, all publications from the new national e-procurement portal, EOP (https://app.eop.bg) were collected too. The collection and processing were performed using automated web-scrapers and data cleaning algorithms, in line with the opentender.eu data collection infrastructure (Fazekas 8
since 1992 while that in the East Asia and the Pacific region increased. There have also allegedly been a significant amount of portfolio rebalancing by investors by "switching" their portfolio allocations from one emerging market to anotlher. Table 2 highlights the marked increase in portfolio investment flows, especially in equities, to Asia as compared to the emerging markets in Latin America in 1992. This has been accompanied by an increase in the number of regional closed-end country funds that endeavor to tap these switchable portfolio investments in emerging markets. It is this portfolio "switching" behavior by investors between different emerging markets that this paper sets out to examine.

Table 2: Gross Portfolio Flows to Asia nad Latin America, 1989-93

|_CUSS_<br>_billions)_|_1989_|_1990_|_1991_|_1992_|_1993e_|
|---|---|---|---|---|---|
|EastAsia & Pacific|2.9|**3.1**|4.0|10.2|15.9|
|Equity Portfolio Investment|2.6|2.2|**1.0**|**4.5**|**4.2**|
|Debt Portfolio Investment|0.3|0.9|3.0|5.7|11.7|
|South Asia|0.7|0.4|0.2|0.3|0.6|
|Equity Portfolio Investment|0.2|0.1|0.0|0.3|0.3|
|Debt Portfolio Investment|0.5|0.3|0.2|0.0|0.3|
|Latin**America**& Caribbean|1.4|3.8|15.0|20.5|27.2|
|Equity Portfolio Investment|0.4|1.1|6.2|7.9|6.0|
|Debt Portfolio Investment|1.0|2.7|8.7|12.6|21.2|

_Source: Computed by authorfrom data in WYorld Bank World Debt Tables. 1 993-94_ _NVote: a = estimated_ Figures I and 2 show the behavior of gross portfolio flows in the sample of countries included in this study. It can be gleaned from these charts that although total portfolio flows have increased between 1991-93 in both Latin America and Asia, equity portfolio flows to the countries in Latin America are declining since 1992 while those to Asia were stagnant during the same period. Figures 3 and 4 show the diversity in the composition of private capital flows to the eight countries being examined here.<sup>6</sup> Figure 5 shows that about 90 percent of the gross portfolio flows to Latin America (bonds and

> **6** Private long-term debt includes intemational bond issucs and portrolio investments through Certificates of Dcposits and Commcrcial Paper.

9
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**Figure 2:** Total Individual Income Distribution for Colombia, Tax Records. Source: author’s calculations using income tax data (DIAN), 2010. Note: The analysis considers only positive total individual incomes and the population above the age of 20. The red bar indicates the legal threshold for mandatory tax filing, _b_ .

in Section 2, setting the threshold _b_ to various levels. The bold row corresponds to the optimal _b_ (the minimum income that mandates filing of the tax record). The first column reports the level of _b_ , the second column the estimated fraction of the population with incomes below _b_ , or _F_ ( _b_ ). Columns 3 and 4 report the Gini coefficient estimated using the conditional distribution of incomes below _b_ and above _b_ , respectively. Finally, column 5 reports the estimate of the Gini coefficient that combines the two numbers as described in Proposition 2. The resulting preferred estimate of the Gini coefficient is 0.5978. The estimate varies minimally with the choice of _b_ around the optimal threshold of 81 million – pesos. However, choosing a higher _b_ for example focusing on the top 1 percent of the income distribution ( _b_ = 106 million pesos) – does have a nontrivial impact on the Gini coefficient, by about 0.2 points (see last row of Table 1). Considering that the average annual reduction of the Gini coefficient in Latin American countries over the last 10 years was 0.51 percentage points, the differences in the estimates are not trivial.

14
11 the small rise in the Gini index in these two countries could have two alternative explanations: that everyone at a given income experienced similar proportionate income increases but those who were better off initially experienced somewhat larger proportionate increases; or that people who were initially _worse off_ experienced larger proportionate increases but this pro-poor vertical redistribution was more than offset by the inequality-increasing redistribution associated with people with similar initial incomes experiencing different income increases (horizontal redistribution) and reranking. Section IV finds evidence in favor of the latter explanation. Without horizontal redistribution and reranking, the Gini coefficient for income in China might have fallen between 1989 and 1997 by as much as 21.6 points. In the event, the horizontal redistribution and reranking involved in the move from the 1989 to 1997 income distributions was so large that the Gini _increased_ —by 2.4 points. A similar picture—albeit less dramatic— emerged from the Vietnam panel for 1993-98.

Do these results matter? If vertical redistribution ( _V_ ), horizontal redistribution ( _H_ ) and reranking ( _R_ ) matter only insofar as they contribute to the change in the Gini ( _∆ G_ ), knowing the relative magnitudes of _V_ , _H_ and _R_ is immaterial. If the policymaker is simply interested in _∆ G_ , she would not care whether a small value of _∆ G_ derives from small values of _V_ , _H_ and _R_ , or from a large negative value of _V_ being offset by a large positive value of _H_ + _R_ . Nonzero values of _H_ and _R_ would be considered bad by an inequality-averse policymaker because— _and only because_ —they reduce _∆ G_ for a given value of _V_ .

There is, in fact, one strand of literature that would regard nonzero values of _H_ and _R_ as a _good_ thing, namely the literature on economic mobility. Mobility is often defined and measured in terms of reranking across income distributions at two points in time (cf. e.g. Schiller 1977, King 1983). Where _w_ in section II can be reduced to zero because there are exact equals in the
dummy variable equal to 1 if the top manager of the firm is a female and 0 otherwise; exports as a proportion of firms’ annual sales; and a dummy variable equal to 1 if 10 percent or more of the firm is owned by foreign individuals and companies and 0 otherwise. The motivation for country fixed effects, firm-size, age of the firm and industry has been discussed above. For the remaining variables, several studies have found a link between firm productivity and gender of the manager/owner of the firm for reasons such as difficulty that women face in obtaining finance, public services, etc. (Coleman 2000; Du Rietz and Henrekson 2000; Sabarwal and Terrell 2008). Exporting activity and foreign ownership have also been linked to productivity in several studies, although the direction of causality remains a debatable issue (Melitz 2003; Bernard and Jensen 1999; Wagner 2007). Thus, if the gender of the top manager, exporting activity and the foreign ownership of the firms happen to vary systematically with informal competition, our main results for the relationship between labor productivity and informal competition could suffer from the omitted variable bias problem. To guard against this possibility, we control for a dummy variable equal to 1 if the top manager of the firm is a female and 0 otherwise, proportion of firm’s annual sales that is exported, and a dummy variable equal to 1 if foreigners own 10 percent or more of the firm and 0 otherwise.

The second part of the empirical analysis involves exploring the heterogeneity in the labor productivity and informal competition relationship. To this end, we replace country fixed effects in equation (2) with controls for some macro-level variables and their interaction terms with the dummy for informal competition. The choice of the macro-level variables is based on the existing literature on the determinants of productivity and includes the following: (log of) GDP per capita taken from World Development Indicators, World Bank; Control of Corruption measure from Worldwide Governance Indicators, World Bank (Corruption); Distance to the Frontier (DTF) 17