rafmacalaba/datause-encoder-data huggingface dataset:
the audit report available to legislature, but also to a history of regular consultations with the private sector, citizens' groups and NGOs.

Finally, _employee morale_ is also identified as a component of organizational performance. This is both a performance goal in its own right, and a contributor to organizational effectiveness and efficiency as identified in Australian and Canadian studies of public employees of the 1980s.<sup>29</sup> Employee morale is raised by job satisfaction and reduced by the orientational and attitudinal disconnect that managers have with their staff. **30** This latter concept of a disconnect between the managers of organizations and other officials working in them has been usefuilly termed 'vertical solitude'. **31** It is the orientational and attitudinal disconnect that managers have with officials below them. As managers rise in the hierarchy, two factors cause them to become "impersonal." First, they become increasingly aware that they are no longer managing front-line staff, but other people who think like them. Therefore, their thought patterns and worldview increasingly diverge from that of their staff Second, as they rise, the units they manage become larger until it becomes impossible to personally know everyone who works for them.<sup>32</sup> As a result, communication between the managers and general officials in an organization deteriorates. The general officials are in more regular contact with the public and client groups, but do not clearly understand the expectations of their ministers and manager who set the tone and direction in their departments.<sup>33</sup> It should be emphasized that these are intermediate measures of performance. Given the heterogeneity of organizational outputs it is only at this intermediate level that measures can be found that contribute to how effectively those heterogeneous outputs are produced in different organizations, but which are homogenous enough to permit measurement and comparability across disparate public agencies. The key feature of these measures of performance is that they refer to perceptions of past events. While the institutional environment shapes expectations of the future, performance refers to past behavior.

The conceptual framework described above proposes that performance is a function of the institutional environment. To test this, indicators were constructed that allow measurement of the degree of results focus, accountability and employee morale. It thus becomes a testable proposition that performance improves as the institutional environment improves."

- 29 See Jans and Frazer-Jans (1989) and Zussman and Jabes (1989). 30 See also Schein (1996).

- **31** See Zussman and Jabes (1989).

- 32 Schein (1996).

- 33 Zussman, and Jabes (1989).

- 34 Leadership as a determinant of organizational performance has been considered in a 1999 study of US Federal Executives (PricewaterhouseCoopers 2000) and a 1987 study of the Canadian federal public service (Zussman and Jabes 1989).

10
Table 9: Additional Sensitivity Tests

|**Panel A: Test for diference in sampling proportion for Shanghai**|
|---|

|||Health Insura|nce||Pension||
|---|---|---|---|---|---|---|
||Original|Include<br>SH*Treat|Excl. SH|Original|Include<br>SH*Treat.|Excl. SH|
|Treated|0.091***|0.089***|0.088***|-0.006|-0.016|-0.018|
||(0.032)|(0.033)|(0.033)|(0.046)|(0.046)|(0.046)|
|Treated*Price|-1.176***|-1.122**|-1.105**|0.110|0.197|0.211|
||(0.426)|(0.466)|(0.465)|(0.263)|(0.275)|(0.275)|
|Treated*Shanghai||-0.015<br>(0.043)|||-0.053<br>(0.061)||
|No. of Observations|4,587|4,587|4,064|2,925|2,925|2,596|
|R-squared|0.524|0.524|0.516|0.536|0.536|0.533|
|**Panel B: Test for spillov**|**er efect**|Health Insura|nce||Pension||
||Firm<br>spillover|Home-cnty<br>spillover|Both|Firm<br>spillover|Home-cnty<br>spillover|Both|
|Treated|0.087***|0.118***|0.113***|-0.010|0.012|0.013|
||(0.033)|(0.035)|(0.037)|(0.046)|(0.048)|(0.048)|
|Treated*Price|-1.170***|-1.035***|-1.029***|0.151|0.193|0.212|
||(0.426)|(0.424)|(0.425)|(0.265)|(0.266)|(0.267)|
|Mixed-frm|-0.016||-0.015|-0.014||-0.013|
|(both treated & control)|(0.019)||(0.019)|(0.023)||(0.023)|
|Treated*Mixed-frm|0.019||0.019|0.010||-0.012|
||(0.027)||(0.027)|(0.036)||(0.035)|
|Mixed-home cnty||-0.015|-0.015||0.001|0.001|
|(both treated & control)||(0.015)|(0.015)||(0.018)|(0.018)|
|Treated*Mixed-home cnty||-0.051*|-0.050*||-0.049|-0.050|
|||(0.027)|(0.027)||(0.032)|(0.032)|
|No. of Observations|4,587|4,587|4,587|2,915|2,915|2,915|
|R-squared|0.524|0.525|0.525|0.536|0.536|0.537|

Source: RUMiC Migrant Survey 2015 and 2016, RUMiC Information Intervention Field Experiment, 2016. Notes: All regressions are using the Model 3 specification (see note to Table 2). Robust standard errors clustered at workplace of household head are in parentheses. Significance codes: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

36
**Table 5 – Crises and Countries by Income Group (in percentage of all results)**

||**Caseload**|**HICs**|**LICs**|**MICs**|**Total**|**FD pop (%)**|**Est.Time***|
|---|---|---|---|---|---|---|---|
||Burundian and Rwandan refugees in|||||||
|1|Tanzania|0.0|14.4|0.0|14.4|53.3|13.0|
|2|Congolese refugees in Rwanda and Uganda|0.0|0.8|0.0|0.8|17.4|16.7|
|3|Cuban refugees in Miami|9.2|0.0|0.0|9.2|8.1|8.2|
||Ethnic Germans from EE and FSU to|||||||
|4|Germany|1.6|0.0|0.0|1.6|3.5|14.0|
|5|Ethnic Greeks from Turkey to Greece|0.0|0.0|0.5|0.5|20.0|58.0|
|6|Escapees from Algeria to France|0.5|0.0|0.0|0.5|3.3|6.0|
||Expellees from East Europe to West|||||||
|7|Germany|2.9|0.0|0.0|2.9|16.5|6.5|
|8|FSU escapees to Israel|10.5|0.0|0.0|10.5|9.4|6.3|
|9|FY refugees to Europe|1.8|0.0|0.0|1.8|0.3|8.3|
|10|IDPs in Colombia|0.0|0.0|11.8|11.8|10.4|5.0|
|11|IDPs in Sudan (Darfur)|0.0|0.8|0.0|0.8|30.0|3.7|
|12|Palestinians from West Bank to Israel|0.0|0.0|0.5|0.5|50.0|4.0|
|13|Refugees in Denmark|5.8|0.0|0.0|5.8|4.7|14.0|
|14|Refugees in Kenya (Turkana)|0.0|1.3|0.0|1.3|10.0|20.0|
|15|Refugees in the USA|1.6|0.0|0.0|1.6|0.1|30.0|
||Returnees from Angola and Mozambique to|||||||
|16|Portugal|0.0|0.0|2.1|2.1|5.1|13.0|
|17|Syrian refugees in Jordan and Turkey|0.0|0.0|33.9|33.9|6.3|3.3|
||**Total**|**33.9**|**17.3**|**48.8**|**100.0**|**14.6**|**13.5**|

**Legenda** . FD (%) indicates the number of forcibly displaced persons (refugees or IDPs) as a percentage of the host population in a given geographical area affected by refugees or IDPs. (*) Est. Time shows the average time gap between the beginning of the influx and the year for which the impacts are measured in each study.

31
# Figure 5 Minimum Wages in Coastal Countries (at constant 1980 prices)

<!-- Start of picture text --> CFA Francs per day<br>250<br>200 - C8te d'Ivoire (-2.83%)<br>-<br>150<br>.-- s ~ ~ " Benin (-0.77%)<br>100<br>50<br>Togo (-1.88%)<br>1961 1966 1971 1976 1981 1986 1991<br><!-- End of picture text --> Annual growth rates over 1977-1992 in parentheses 35
||M|atching sa|mple||Raw Dat|a|
|---|---|---|---|---|---|---|
|Smaller group|D|P-value|Corrected|D|P-value|Corrected|
|Untreated|0.0021|0.985||0.4904|0.000||
|Treated|-0.0012|0.995||0.000|1.000||
|Combined K-S:|0.0021|1|1|0.4904|0.000|0.000|

Table 7: Kolmogorov-Smirnov test for equality of distribution functions compare the pseudo _R_<sup>2</sup> , which should be much lower in the matched data. Table 11 (in section 7.2 of the appendix) shows that both conditions are satisfied. The pseudo _R_<sup>2</sup> decreases from approximately 0.15 to about 0.008. Regarding the covariates used in the matching specification, most of them are not statistically significant at 5%.<sup>57</sup> Also, Caliendo and Kopeinig (2008) suggest checking the t-test for treated and control group means.<sup>58</sup> Table 12 (in section 7.2 of the Appendix) shows that for most of the covariates I do not reject the null hypothesis that the means of the treated and control groups (with respect to the characteristics of the firms in _t-1_ and _t-2_ ) are similar at 5% of significance.<sup>59</sup> In addition to the mean, it is important that the distribution of the covariates is balanced. Figure 3 (in section 7.2 of the appendix) shows the QQ plots to the covariates used in the matching as procedures to assess balance suggested by Ho et al. (2011). It is noticeable that the matching algorithm worked well, not only on the mean values but also on their distribution.

A critical assumption used for identification was the fact that the treated and the control groups would have a similar trend over time if the program had not been available, the socalled parallel trend assumption.<sup>60</sup> Regarding this assumption, an important feature of the identification strategy was the fact that the matching was carried out on firms that had not exported at periods _t-1_ and _t-2_ . It means that the sample had similar trends concerning the outcome (export status) and firms’ covariates for two periods prior to the treatment.

# **5.2 Is Apex’s impact heterogeneous according to firms’ size?**

When confronted with public policy alternatives, policy makers need to decide not only which policy to be implemented, but also who should be the beneficiaries of these policies. Therefore, in addition to understanding an average effect of a program, it is also important to identify

> 57The few exceptions are lag of manager L3 and Import at period t-2. The variables (Manager L3) and Import are used as covariates in the robustness specifications, where I include the time-varying covariates as control. 58Imai et al. (2008) argues that although it is widely used, t-test of the difference in the mean may be misleading.

> 59I tested various specifications for the PSM procedure, including changes in the variables for which exact matching was done and covariates included in the model. Overall the positive effect of Apex’s treatment on the propensity to export for non-exporting firms remains.

> 60See Arcand and Bassole (2007).

21
# _<u>Proof:  See Appendix A</u>_

Proposition 3 shows how the evolution over time of aggregate moments of the income distribution reflects the parameters of the underlying data generating process. In this section we show that, given � time series data on the aggregate moments �� and �� , we can use Proposition 3 to recover estimates of � the autoregressive parameter, � , as well as bounds on the variance of the individual effect, �� . This in turn means that we can recover bounds on the key mobility parameter ���� , and thereby obtain approximate income distribution dynamics using only aggregate data.

We consider situations in which time series data on mean income and a summary statistic on dispersion, such as the Gini coefficient or a group income share, are available for a series of surveys of the same population. We assume that the unobserved movements over time in the common component of income, �� , can be approximated with a linear function of time plus an i.i.d. zero-mean error term, i.e. �� = �� + ���+ �� . Similarly, we assume that we can approximate the unobserved movements over time � in the variance of the idiosyncratic shock to income, ��� with a linear function of time plus an i.i.d. zero� mean error term. Since the variance of the individual effect, �� , is constant over time, this means that ��� � � we can write the second and third terms in Equation (7) as ���� ~~�~~ �� + ��� = �� + ���+ �� .

Inserting these approximations into Equations (6) and (7) results in the following system of two equations: Given time series data on the mean and variance of log income, we can obtain an estimate of � by simply regressing �� on its lag and a time trend (i.e. from Equation (8)), or we can obtain an estimate of �<sup>�</sup> by � regressing �� on its lag and a time trend (i.e. from Equation (9)). In some of our empirical applications, notably the PSID and the PovcalNet data, the available time series are quite short, rarely longer than 20 years, and frequently shorter. This has two implications for our estimation strategy. First, the short time series raises concerns about small-sample bias in the estimation of � . Specifically, Andrews (1993) shows that the OLS estimator of the autoregressive coefficient in a linear AR(1) process around a deterministic 10
## **Savings methods among savers in economies with the highest use of community-based saving**

Adults saving any money in the past year, by savings method used (%) <!-- Start of picture text --> 100<br>NEITHER<br>FORMAL<br>80 ONLY<br>COMMUNITY<br>BASED AND<br>60<br>FORMAL<br>TOTAL<br>40 37 COMMUNITY<br>BASED<br>COMMUNITY<br>BASED ONLY<br>20<br>0<br>SAVERS AS<br>WORLD CHAD SOUTH LIBERIA KENYA BENIN BOTSWANA COMOROS MOZAMBIQUE LESOTHO CAMEROON NIGERIA % OF ALL<br>36 29 AFRICA 31 35 40 32 29 29 43 26 52 64 ADULTS<br>Source: Demirguc-Kunt and Klapper 2012.<br><!-- End of picture text --> use their accounts to receive wages and government payments, so accounts may be opened mainly for this purpose and not specifically for saving.

Many adults, despite having a formal account, save solely using other methods. These people, who might be classified as “underbanked,” make up 12 percent of account holders worldwide and more than 30 percent in several economies, including Mali and Mexico. Those choosing to use an informal savings method rather than their formal account may do so because the costs of actively using their account are prohibitive—as a result of such barriers as balance and withdrawal fees and physical distance. It is also possible that wage accounts set up by employers cannot easily be used to save. New products that target existing account holders could be used to encourage adults to save in formal financial institutions. These could be especially important in economies with aging populations.<sup>1</sup>

# **What are common alternatives to formal saving?**

In some parts of the world a large share of people who save are clearly choosing alternatives to formal accounts to do so. Among adults who report having saved or set aside money in the past year, only about 35 percent did so using a formal account in Europe and Central Asia, Latin America and the Caribbean, and Sub-Saharan Africa. In three Central Asian countries—the Kyrgyz Republic, Turkmenistan, and Uzbekistan—about 35 percent of adults report having saved in the past 12 months, but less than 5 percent of these savers report having done so at a financial institution. What are the main alternatives being used?

In high-income economies savers may choose from a wide variety of complex (and sometimes risky) investment products offered by equity and other traded markets, purchase government securities or commodities such as gold, or simply hold cash.

MEASURING FINANCIAL INCLUSION

**33**
<!-- Start of picture text --> Schoolswith increased<br>productivity through<br>improved teaching practices<br>" and school cultures<br>a<br>o<br>. i ——<br>a) : : a= gs Extended<br>2 : —_ education<br>g : or : production<br>=a : gen : frontier<br>~- :<br>c :<br>5 a: a<br>aASs 2” : : Schoolswith increased<br>o” :: :: resourceefficiencyallocationthrough improved<br>¢ : :<br>oY Improved : :<br>, teaching : : Current<br>y practices : .~_ === +——_ education<br>cultures =“ : frontier<br>// and school : _—er 4 production<br>i ont- ::<br>t or: :<br>f o* :<br>! - :<br>i o” F<br>! oe” Improved 5 : Existing low<br>i ”¢ utilizationresource : : : : efficiency schools<br>‘<br>,<br>if: :<br>/ _ Classrooms/Teachers<br>Differentlevels of inputs<br><!-- End of picture text -->
|Vanuatu Infrastructure Reconstruction and Improvement Project|Artessa Saldivar-Sali|
|---|---|
|Vanuatu Infrastructure Reconstruction and Improvement Project|Artessa Saldivar-Sali|
|GEF China Sustainable Cities Integrated Approach Pilot|Wanli Fang|
|GEF China Sustainable Cities Integrated Approach Pilot|Wanli Fang|
|GEF China Sustainable Cities Integrated Approach Pilot|Wanli Fang|
|GEF China Sustainable Cities Integrated Approach Pilot|Wanli Fang|
|GEF China Sustainable Cities Integrated Approach Pilot|Wanli Fang|
|GEF China Sustainable Cities Integrated Approach Pilot|Wanli Fang|
|GN-Rural Roads EmergencyImprovement Project|Cheick Omar Tidiane Diallo|
|GN-Rural Roads EmergencyImprovement Project|Cheick Omar Tidiane Diallo|
|GN-Rural Roads EmergencyImprovement Project|Cheick Omar Tidiane Diallo|
|MZ - EmergencyResilient RecoveryProject|Michel Matera|
|MZ - EmergencyResilient RecoveryProject|Michel Matera|
|MZ - EmergencyResilient RecoveryProject|Michel Matera|
|MZ - EmergencyResilient RecoveryProject|Michel Matera|
|MZ - EmergencyResilient RecoveryProject|Michel Matera|
|MZ - EmergencyResilient RecoveryProject|Michel Matera|
|MZ - EmergencyResilient RecoveryProject|Michel Matera|
|MZ - EmergencyResilient RecoveryProject|Michel Matera|
|MZ - EmergencyResilient RecoveryProject|Michel Matera|
|Modernization and Upgrade of Transmission Substations|Maksudjon Safarov|
|Modernization and Upgrade of Transmission Substations|Maksudjon Safarov|
|Modernization and Upgrade of Transmission Substations|Maksudjon Safarov|
|Modernization and Upgrade of Transmission Substations|Maksudjon Safarov|
|Modernization and Upgrade of Transmission Substations|Maksudjon Safarov|
|Modernization and Upgrade of Transmission Substations|Maksudjon Safarov|
|Modernization and Upgrade of Transmission Substations|Maksudjon Safarov|
|Ethiopia Trade Logistics Project|Steven Farji Weiss|
|Ethiopia Trade Logistics Project|Steven Farji Weiss|
|Ethiopia Trade Logistics Project|Steven Farji Weiss|
|Ethiopia Trade Logistics Project|Steven Farji Weiss|
|Ethiopia Trade Logistics Project|Steven Farji Weiss|
|Ethiopia Trade Logistics Project|Steven Farji Weiss|
|Ethiopia Trade Logistics Project|Steven Farji Weiss|
|Macroeconomic & Fiscal Management Operation|Ali Zafar|
|Macroeconomic & Fiscal Management Operation|Ali Zafar|
|Water and Sanitation Development Project|Pascaline Wanjiku Ndungu|
|Water and Sanitation Development Project|Pascaline Wanjiku Ndungu|
|Water and Sanitation Development Project|Pascaline Wanjiku Ndungu|
|Water and Sanitation Development Project|Pascaline Wanjiku Ndungu|
|Water and Sanitation Development Project|Pascaline Wanjiku Ndungu|
|Water and Sanitation Development Project|Pascaline Wanjiku Ndungu|
|Indonesia Fiscal Reform DPL|Yue Man Lee|
|Health System Performance Reinforcement Project|Yohana Dukhan|
|Health System Performance Reinforcement Project|Yohana Dukhan|
|Health System Performance Reinforcement Project|Yohana Dukhan|
|Health System Performance Reinforcement Project|Yohana Dukhan|
most other compilations of public spending data, this one includes all levels of govermnent. In this and other respects, considerable care appears to have gone into constructing the data. There is clearly little sign that social spending was protected from cuts. Indeed, there is sizable co-movement, with indications that (if anything) social spending was more volatile than other types of spending. One can see quite large proportionate declines in social spending in every year in which total spending falls. On the other hand, one often sees smaller (and sometimes negative) changes in social spending when total spending rises. Letting Gs denote social spending at date t, and _Gt_ total spending, one can test whether the elasticity to an increase in total spending differs from that for a decrease using the regression: AlnGs = _a + [yl,5, +_ Y2(1- 3,)]A ln _G, + et_ (8) where a is a time trend, yr is the elasticity when total spending increases, y2 is the elasticity when it falls, and **et** **___** I(AInG _G)_ takes the value unity when AlnG, > 0 and zero otherwise.

The data in Figure 1 yield an estimate of 0.14 for y1 . This is not significantly different from zero (the standard error is 0.37). On the other hand, the estimated elasticity to a decrease in total spending (r2) is 2.14, which is significantly greater than one (the standard error is 0.26). Social spending responds elastically to aggregate cuts, but the responsive to fiscal expansions is not statistically significant.<sup>7</sup> The constant term of 0.086, which is significant (t-ratio=2.83), indicating a sizable independent trend increase in the share of social spending. By contrast, non-social spending was well protected; the elasticity when total spending fell was 0.09, and not significantly different from zero (a standard error of 0.22); on the other hand, the elasticity to an increase was 1.68 (standard error of 0.41).

7 The average elasticity (constraining y, and Y2 to be equal) is 1.366, with a standard error of 0.21; however, the restriction that ri =Y2 performs poorly (t=3.16).

10
premium was largest at the top end of the distribution, especially in sectors subject to heavy entry regulation. Section 5 analyses the relationship between competition and the expansion of the Ben Ali business empire. Section 6 concludes.

# **2 Data**

# **2.1 Identifying the Business Interests of the Ben Ali Family**

In the aftermath of the Tunisian revolution, assets of the Ben Ali clan have been confiscated. The confiscation process, which is still ongoing, was ordained by the new government by means of a decree (notably _Décret-loi n° 2011-13_ ). It involves 114 individuals, including Ben Ali himself, his relatives and his in-laws, and concerned the period from 1987 until the outbreak of the revolution. The seized assets included properties, boats and yachts, stock portfolios, bank accounts, and at least 662 enterprises. The confiscation commission estimates that the total value of these assets combined is approximately 13 billion USD, or more than one-quarter of Tunisian GDP in 2011.

We obtained a list of 662 of the confiscated firms from the Tunisian authorities and use this list to identify the Ben Ali family’s business interests. The list covers firms confiscated up until December 2014, including a number of very prominent firms such as Orange Tunisia, Tunisiana, Carthage Cement, ENNAKL auto-industries, and the International School of Carthage, but also an even larger number of little-known enterprises. We merge these data with the Tunisian Business Register, _the Repertoire National des Enterprises (RNE),_ an annual census containing information on the size, age, location and legal form of all private-non-agricultural registered firms in Tunisia, including one-person firms without paid employees. The overwhelming majority of the confiscated firms are very small and are not major economic players. Only 252 of the 662 connected firms reported both positive output and employment in at least one year.

# **2.2 The Firm-level Data**

6