rafmacalaba/datause-encoder-data huggingface dataset:
# **Appendix C. Historical Ownership Data Cleaning Procedure**

To develop the historical ownership module, we apply the following sequence of cleaning steps, following the methodology and SQL code developed by Cusolito and Vranic (2020).

1. _Merge all Links files_ : create a unique file that compiles the individual annual Links files.

2. _Remove duplicates and keep the most updated information_ : eliminate duplicate observations and keep the most updated information.

3. _Harmonize the time frame of the information_ : use the information-date variable to identify the latest month where the ownership data was collected. If the latest month is June or after, then assign to the firm the Links file of the same year. Otherwise, assign the Links file of the previous year. We apply this rule to keep consistency with the timing rule applied when cleaning the financial information.

4. _Replace BvD ownership codes with numeric values_ : in some specific cases, BvD has missing information about the ownership stakes of a particular shareholder. However, using secondary sources of information, BvD collects imprecise, though valuable, information that can be used to fill the data gaps. The following table presents the codes that BvD uses, their meaning, and the numeric value the company assigns to each link (Bureau van Dijk, 2018).

Table C.8: BvD Ownership Codes

|BvD code|Meaning|Defnition|Numeric Value Assigned|
|---|---|---|---|
|WO|Wholly Owned|The shareholder has at least 98% of the company|98%|
|MO|Majority Owned|The shareholder has at least 50.01% of the company|50.01%|
|JO|Jointly Owned|The shareholder has 50% of the company|50%|
|CQP1|General Partner|The shareholder has 50% of the company plus 1 share|50.01%|
|NG|Negligible|The shareholder has 0.01% of the shares or less than that|.|

35
**Table 3. Relative discrepancy (** ∆ **_ij_ ) for sawnwood imported by Romania during 1982-97, aggregated across all exporting countries.** Positive discrepancies indicate that reported imports were greater than reported exports; negative discrepancies indicate the reverse.

|_Product_|_Number of_<br>_observations_|_Mean_|_Standard_<br>_deviation_|_Minimum_|_Maximum_|
|---|---|---|---|---|---|
|NC sawnwood|32|0.199<sup>**</sup>|0.427|–0.690|+0.953|
|C sawnwood|11|–0.090|0.524|–0.263|+0.442|

Notes: a. Romanian trade flows in the EFI Database are mainly for the 1990s.

b. Sample includes only trade flows with reported imports and exports both ≥ 100 m<sup>3</sup> .

c.<sup>***, **, *</sup> : mean is significantly different from zero at 1%, 5%, 10% level.

**Table 4. Relative discrepancy (** ∆ **_ij_ ) for sawnwood imported by Romania during 1982-97, disaggregated by exporting country.** Positive discrepancies indicate that reported imports were greater than reported exports; negative discrepancies indicate the reverse.

|_Country_|_NC sa_<br>_Number of_<br>_observations_|_wnwood_<br>_Mean discrepancy_<br>|_C sa_<br>_Number of_<br>_observations_|_wnwood_<br>_Mean discrepancy_|
|---|---|---|---|---|
|Germany|6|0.656<sup>***</sup>|5|0.272|

Notes: a. Romanian trade flows in the EFI Database are mainly for the 1990s.

b. Sample includes only trade flows with reported imports and exports both ≥ 100 m<sup>3</sup> .

c.<sup>***, **, *</sup> : mean is significantly different from zero at 1%, 5%, 10% level.

# **3.3 Romanian discrepancies compared to those for other European countries**

Table 5 shows the mean discrepancies for exports of sawnwood by all European countries with at least 25 observations in the EFI Database. It includes the mean values for Romania from Table 1. In addition to the significance levels for tests of equality to zero, the table shows the significance levels for pairwise tests of the equality of each country’s mean to the mean for Romania.<sup>9</sup> Results from these tests are indicated by daggers (<sup>†</sup> ).

Unlike in Romania, the mean discrepancies for exports of NC sawnwood were significantly different from zero in many of the other European countries. In these cases, the mean discrepancies were mostly negative: imports reported by trading partners were less than European countries’ reported exports. With the exception of Latvia, western European countries accounted for the largest discrepancies: Belgium-Luxembourg, Italy, Netherlands, Portugal, and Spain. Imports reported by the trading partners of these countries were, on average, only about two-thirds of the countries’ reported exports. The mean discrepancies for these countries and several others were significantly different from the mean discrepancy for Romania.

Like Romania, many other European countries had discrepancies for exports of C sawnwood that were negative and statistically significant. Among these countries, only Slovenia had a mean discrepancy that was significantly larger than Romania’s.

> 9 Variances were allowed to differ between countries in these tests.

7
- The **EIU** , consistent with NIT and CPIA, shows deterioration for Poland. The four countries with improving ratings overlap little if at all, however, with the countries showing improvement in ICRG, NIT and CPIA.

These changes appear to be inconsistent, perhaps sufficiently so to cast doubt on the argument that expert-based assessments consult each other’s ratings, or are otherwise based on very similar information. A partial answer to this puzzle is that ratings changes in expert-based systems do not always reflect a belief that actual conditions have changed, but often are intended to correct a previous year’s rating that in retrospect appears too high or too low.

“Regression to the mean” is commonplace among these corruption indicators. When ratings changes from 2002-2005 are regressed on initial (2002) levels, the coefficient on initial levels is negative and highly significant. This pattern holds for the ICRG and CPIA, and for most BEEPS and WEF measures. It does not hold for NIT or EIU; in those cases the coefficient on initial values is near 0 and does not approach statistical significance. The declines in NIT are concentrated in the former Soviet republics, which were already rated in 2002 as more corrupt than the European countries in ECA.

Regression-to-the-mean is exemplified by the cases of Czech Republic and Poland. The decrease for Czech Republic by ICRG appears inconsistent with its improvement in NIT and CPIA. But the ICRG in 2002 ranked it higher among ECA countries than the other sources did. Ratings changes for Czech Republic reflect a convergence in assessments among those three sources, and with the WEF and BEEPS data, which also tend to place Czech in the upper half of the ECA rankings, but not among the top 5 or 6.

35
risk of masking the enormous variation experienced by different countries over time. This kind of analysis should be complemented by studies with more detailed country and location-specific analysis. In a growing number of countries, studies based on long series of cross-sectional household surveys have uncovered important roles for agricultural intensification, urbanization, rural non-farm diversification, land reforms, human capital, migration, social protection and redistributive policies, and political and governance reforms. With increased availability of appropriate data the number of countries in which such studies are undertaken has expanded over time. Further research of this sort is needed to better understand structural transformation and its growth and distributional impacts.

**Second, we need to better understand how access to economic opportunities can be broadened to ensure rapid poverty reduction and promote human development.** We have learned that inequalities of opportunity lie at the heart of inequitable growth processes in developing countries.  Lack of access to opportunities, particularly for investing in education, health, and other forms of human capital, stifles growth and makes the growth that does occur less effective in reducing poverty.  The large differences in the rate of progress against poverty at a given rate of growth that one sees across countries can be linked both theoretically and empirically to inequality.  On the data side, advances in the measurement of inequality of opportunity can help us see how societies have expanded opportunities to their populations.<sup>12</sup>

We have learned that ensuring better services for poor people is one key element. However, across countries, social outcomes are only weakly related to development spending in the social sectors and, in many countries, additional spending on social services is subordinated to other priorities. Understanding why similar programs work better in some countries than others and identifying the binding constraints to better service delivery are research priorities. We know that governance plays a role in explaining the inadequacy of services to the poor. Past research has documented large scale absenteeism and poor on-the-job performance by front-line service providers. Reforms to improve provider incentives frequently face substantial political opposition, because improving the quality of service delivery is not as politically salient as the provision of protected jobs or price subsidies. Too often front-line service providers and political

> 12 _<u>World Development Report 2006: Equity and Development</u>_ (World Bank. Washington, DC) broke important new ground on this topic.

19
not very informative about the impact of these interventions. For example, the 95 percent confidence interval for the impact of coaching on whether or not the client pays more than the minimum payment ranges from -25 percentage points to +18 percentage points. The control mean is 54 percent, so this is equivalent to almost halving the percent paying on time, or increasing it by one-third.

This is where standard analysis using experimental methods would stop. We would conclude that there is no significant impact of either intervention, but that we have insufficient power to rule out a wide range of positive and negative impacts. We therefore turn to combining non-experimental methods with the experiment to obtain more informative results.

# **4. Combining Experimental and Non-Experimental Methods to Measure Impact for Those Who Actually Take Up Treatment**

# **4.1 Empirical Approach**

The basic challenge for identifying the impact of training and coaching for those who actually took part in these interventions is that take-up is not random. Section 2 showed that those who attended the workshops or received coaching differ in current and past financial behaviors from those who did not. As a result, simply comparing those who took-up the interventions to the full set of individuals in the control group would yield biased results.

Our solution is to use the richness of the financial data available on credit card clients to combine experimental and non-experimental methods. We use propensity score matching to match individuals in the treatment group who took up the treatment to similar individuals in the control group, and then difference-in-differences on this matched sample to estimate the impact of attending the workshop or receiving coaching. That is, we estimate the following equation for the matched sample: Where _ReceivedTreatmenti,t_ takes on value one for individuals in the treatment group in postintervention periods, and zero otherwise; _EverTreatedi_ is an indicator of whether individual _i_ is in the treatment group and ever took-up the treatment; and the time fixed effects are now included for up to 18 months pre-treatment, as well as 9 months post-treatment. The standard errors are again clustered at the client level.

There are several concerns that typically apply when applying propensity score matching. The first is a concern of omitted variables: individuals who look similar in terms of baseline observable variables might differ in terms of unobserved characteristics that also matter for client outcomes. A particular concern here is that of dynamic selection, similar to the problem of an Ashenfelter dip in labor economics experiments. For example, people might be more willing to engage in financial education if they suddenly find themselves struggling with their credit card, whereas those who have been experiencing problems for a while may be less likely to participate. Matching on current behavior only would not be able to distinguish between these two types. Secondly, a critique 12
Table 3: Robustness Checks

||(1)<br>Baseline<br>Model|(2)<br>Unweighted|(3)<br>Scaled by<br>Utilization|(4)<br>Spatial and<br>Autocorrelation SE|(5)<br>Net<br>Imported|
|---|---|---|---|---|---|
|Imported Incidence|1.094***|1.563***|1.294***|1.230***|1.217***|
||(0.357)|(0.509)|(0.449)|(0.326)|(0.384)|
|Lag Incidence|0.563***|0.563***|0.567***|0.330***|0.561***|
||(0.129)|(0.129)|(0.128)|(0.0543)|(0.130)|
|Rain in cm|-0.000654|-0.000654|-0.00119|0.00640|-0.000852|
||(0.00790)|(0.00790)|(0.0206)|(0.00863)|(0.00788)|
|Lag Rain in cm|0.0269|0.0269|0.0703|0.0292*|0.0258|
||(0.0181)|(0.0181)|(0.0466)|(0.0167)|(0.0180)|
|Lag 2 Rain in cm|0.0319**|0.0319**|0.0812**|0.0380**|0.0318**|
||(0.0156)|(0.0156)|(0.0398)|(0.0150)|(0.0156)|
|Constant|-0.0688**|-0.0688**|-0.181**||-0.0663**|
||(0.0290)|(0.0290)|(0.0756)||(0.0288)|
|Health Post x Month Obs|432|432|432|432|432|
||Robust s<br>*** p|tandard errors<br>_<_0.01, ** p_<_0|in parenthes<br>.05, * p_<_0.1|es||

Notes: Augmented version of the Arellano-Bond Generalized Method of Moments estimator used in all specications except for Column 4. In Column 4, I use Conley standard errors, which account for spatial autocorrelation and serial autocorrelation over time, but do not cluster at the health post catchment area level. In Column 2, I do not weight movement observations to scale up to the full population of Senegal. In Column 3, I scale the expected imported incidence and total incidence by the utilization of the region based on DHS data. In Column 5, I calculate net imported incidence by subtracting out the expected cases leaving a health facility area divided by the population of the area.
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Policy Research Working Paper 10194

# **Abstract**

This paper examines the influence of gender inequality on poverty among Syrian refugees in Jordan between 2013 and 2018. Two waves of Home-Visit surveys, collected by the United Nations High Commissioner for Refugees, are analyzed to track the evolution of poverty among Syrian refugees in Jordan. To compare changes in poverty between female- and male-headed households, the paper uses relative comparisons of deciles in the expenditure distribution and quantile regressions. The analysis adjusts the poverty measure for economies of scale as the cost per person of maintaining a given standard of living may fall as household size rises. The findings show that the spending distribution has shifted over time, negatively affecting female-headed households. In 2013, female-headed households below the median had lower expenditure than male-headed households. In 2018, this pattern occurs in all deciles. The findings also show small differences between poverty rates of female- and male-headed households whether the poverty measure is adjusted for economies of scale or not. Regardless of the poverty measure, the poverty gender gap has increased over time, with female-headed households experiencing poverty more intensely. Female single caregivers remain at the most risk of falling into poverty when compared with other types of households and over time. This approach can help policy makers design more effective programs of assistance that respond to gender-based differences in vulnerability to poverty and find durable solutions for displaced populations.

This paper is a product of the Gender Global Theme. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at lhanmer@worldbank.org and erubiano@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table 4: Regression results: the correlation between individuals’ demographic factors on their tobacco choices with manufactured cigarettes as the baseline comparison group

|Variables|Hand-|Non-|
|---|---|---|
||rolled<br>|processed<br>|
||Cigarettes|Cigarettes|
|Wealth Quartile: quartile 1 (poorest) as the baseline<br>comparison group|||
|Wealth Quartile 2|-0.09|-0.33***|
||[0.168]|[0.111]|
|Wealth Quartile 3|-0.31*|-0.35***|
||[0.175]|[0.112]|
|Wealth Quartile 4|-0.24|-0.81***|
||[0.180]|[0.125]|
|Region: Southern as the baseline comparison group|||
|Highlands|-0.22|0.97***|
||[0.171]|[0.114]|
|Momase|0.91***|2.40***|
||[0.139]|[0.108]|
|Islands|-0.31|1.01***|
||[0.191]|[0.123]|
|Age: age between 18-30 as the baseline comparison group|||
|Age between 30 to 50|-0.03|0.18*|
||[0.134]|[0.092]|
|Age above 50|0.19|0.43***|
||[0.184]|[0.123]|
|Education: read without difficulty as baseline comparison|||
|group|||
|Read with difficulty|-0.06|0.37***|
||[0.178]|[0.116]|
|Can not read|-0.12|0.86***|
||[0.176]|[0.106]|
|Married|-0.06|-0.15|
||[0.131]|[0.090]|
|Urban|-1.21***|-1.73***|
||[0.133]|[0.092]|
|Male|-0.13|0.20**|
||[0.131]|[0.090]|
|Constant|-0.74***|-0.40**|
||[0.222]|[0.159]|
|Observations|4,455|4,455|

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

16
estimates of β1 in columns 1, 3 and 5 are negative but two out of the three are statistically significant. The size of the impacts also indicates that the negative effects are quantitatively meaningful. Daily food consumption per capita fell by 1.85 meticais (corresponding to 24.8% of the median value at baseline) among households exposed to a one standard deviation of the shock intensity caused by the 2008 Cyclone Jokwe during the cropping cycle. Similarly, on average, food consumption per capita was 2.3 meticais (31.8%) lower for households hit by the 2005 drought.

Roughly speaking, the decline in food consumption attributed to the shocks translates into a decline of 150 to 200 calories per person per day,<sup>7</sup> enough to deteriorate the nutritional status of individuals at the margin of falling into malnutrition. Related to this, we explore whether food security worsened among households affected by the natural disasters. The outcome variable for this part is a proxy indicator that measures the average number of meals eaten per person per day. The results are in line with the observed decline in food consumption (Table 1, columns 2, 4 and 6). On average, the number of meals fell by 0.72 (2007 floods), 0.21 (Cyclone Jokwe) and 0.13 (2005 drought) by an increase of one standard deviation in the intensity of the shock. With an average baseline value of 3.35 meals per person per day, these effects amount to a reduction in the number of meals that ranges between 3.8% and 21.4%.

The negative effects of extreme weather on total (food and non-food) household consumption per capita are also evident in the data. The results are summarized in Table 2, columns 1, 3 and 5. Cyclone Jokwe and the drought reduced the total level of consumption among affected households by over half (54.8%) (-7.31/13.26) and 21% (-2.83/13.26), respectively, relative to the median consumption at baseline. The decline in consumption brought about by the shocks pushed some households below the poverty line, a threshold that defines the minimum consumption deemed enough to meet the basic food needs and other non-food essential expenditures of the average household in Mozambique.<sup>8</sup> The negative effects are not trivial (columns 2, 4 and 6). The floods led to a large increase in poverty in affected areas, 36 percentage points (66.6%) relative to baseline poverty. Similarly, the drought and the cyclone increased the poverty headcount by 12 and 17.5 percentage points (32% and 22%), respectively.

> 7 Using calorie-income elasticities in the 0.2-0.5 range as reported by Strauss & Thomas (1995) and Subramanian & Deaton (1996). 8 The poverty lines used in the analysis are 9.3 meticais in 2002/03 and 18.8 meticais in 2008/09 (World Bank 2018b).

12
Table 3: Regression results: the correlation between individuals’ demographic factors on their smoking status

|Variables|Smoking|
|---|---|
||Status|
|Wealth Quartile: quartile 1 (poorest) as the baseline||
|comparison group||
|Wealth Quartile 2|-0.04|
||[0.036]|
|Wealth Quartile 3|-0.01|
||[0.037]|
|Wealth Quartile 4|-0.06<br>[0.040]|
|Region: Southern as the baseline comparison group||
|Highlands|0.01|
||[0.036]|
|Momase|0.23***|
||[0.032]|
|Islands|0.10**<br>[0.040]|
|Age: age between 18-30 as the baseline comparison group<br>Age between 30 to 50|-0.07**|
||[0.030]|
|Age above 50|-0.37***<br>[0.038]|
|Education: read without difficulty as baseline comparison group<br>Read with difficulty|<br>0.22***<br>[0.039]|
|Can not read|0.39***|
||[0.035]|
|Married|0.10***|
||[0.029]|
|Urban|-0.11***|
||[0.030]|
|Male|1.00***<br>[0.026]|
|Constant|-0.97***|
||[0.047]|
|Observations|12,181|

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

15