rafmacalaba/datause-encoder-data huggingface dataset:
and state-owned firms, especially SMEs, are rare in the cohesion countries and are almost absent from the cohesion country samples. Table III.2 shows that in terms of sales, stateowned and privatized firms in TEs went from being relatively stagnant in 1996-99 to predominantly expanding 2002-05, and indeed the share of firms with expanding sales in the latter period was very similar for state-owned, privatized and new private firms. In terms of employment, however, state-owned and privatized firms had large shares of downsizing firms during the period spanned by the BEEPS, though declining over time. This is in contrast to the new private firm sector, which was expanding employment. These patterns of employment growth have been previously found in earlier studies based on smaller samples covering one or few transition countries, such as Richter and Schaffer (1996), Earle, Estrin and Leshchenko (1996) and Bilsen and Konings (1998), among others. What is interesting and new in the BEEPS data is that the downsizing of the traditional firms, according to the BEEPS data, continues in all the regions even a decade after the start of transition.

Table III.3 looks at job growth in the more concrete terms of job creation (JC), job destruction (JD), net job growth (JG) and job reallocation (JR) rates, thus focusing on aggregate employment growth. The picture is rather different from what one finds in the balance table, Table III.1, because that table simply counts firms, and the smaller firms are more likely to be expanding but contribute less to the aggregate growth. The table shows that job growth (defined as JCR-JDR) is higher in the richer TEs in the 1996-99 period, but this reverses by 2002-05, when the poorer TEs have faster employment growth. The reversal is driven by both job creation and job destruction. Job creation rates in the EU8 are lower than those in the cohesion countries, and the acceleration in JC takes the poorer TEs ahead of the EU8 by 2002-05. Job destruction rates are persistently higher in the EU8 than in the cohesion countries, and fall markedly in the poorer TEs so that by 2002-05, job destruction is less common than in the EU8. Job reallocation (defined as JC+JD) is, however, fairly constant across time and across groups of TEs. Again, these patterns are broadly consistent with previous literature suggesting that high rates of job destruction are typical of the earlier stage of the transition process and level off over time while job creation rates increase (Haltiwanger et al. 2003).

14
sophisticated mental models and more complete information are able to form more accurate predictions of the consequences of a devaluation, and that their self-reported well-being will therefore respond more strongly to the news of a devaluation. Against this background, we proceed by describing the quasi-experimental design.

# **II. Quasi-Experimental Research Design**

Late in the afternoon on May 29, 2005, the Bank of Botswana—the country’s central bank—and Botswana’s Ministry of Finance and Development Planning issued a press release stating that the national currency—the Pula—would be devaluated by 12 percent against a basket of international currencies, with effect from the following morning, May 30, 2005. The central bank’s decision to devaluate the Pula came as a shock to the general public, the business community, and currency markets in Botswana, as we will show in more detail below. Our research design exploits this sudden and unanticipated intervention to examine the effect of economic shocks on individuals’ subjective well-being. We are able to do so because, incidentally, the devaluation occurred during the period where the _Afrobarometer_ —an independent research project conducting surveys of political and social issues in Africa—was interviewing a representative sample of citizens in Botswana.<sup>2</sup> The chance occurrence of the devaluation two days into the survey demarcates the

> 2 The data are published as part of the third round of the _Afrobarometer_ . Technical details on the sampling of respondents and the methodology of the survey are available on the _Afrobarometer_ website http://afrobarometer.org/. See also Bratton et al. (2005) for descriptions of the _Afrobarometer_ , and Mattes (2007) for a discussion of survey research in developing countries.

8
# **Criss-Crossing Migration**

_Aaditya Mattoo* and Arvind Subramanian**_

# _Keywords_ :  North South migration, immigration policy, insurance portability JEL codes:  F6, F22, J6

*Development Economics Research Group, World Bank. Email: amattoo@worldbank.org.  **Center for Global Development and Peterson Institute for International Economics.  Email:  asubramanian@piie.com. The authors are grateful to C. Fred Bergsten, Nancy Birdsall, Chad Bown, Michael Clemens, Isaac Stone Fish, Dani Rodrik, Caglar Ozden, and participants in seminars at the Center for Global Development and Peterson Institute for International Economics for helpful discussions and useful comments. The views expressed in the paper are those of the authors and should not be attributed to the World Bank, its Executive Directors or the countries they represent.
# **<mark>Figure 6. P-value in the placebo analysis</mark>**

<mark>Panel A. 1-3 months difference: 3.3% with P- value<=0.05</mark> <!-- Start of picture text --> 0 .2 .4 .6 .8 1<br>Pvalue<br>1.5<br>1<br>Density<br>.5<br>0<br><!-- End of picture text --> <mark>Panel C. 2-month difference: 3.0% with P- value<=0.05</mark> <!-- Start of picture text --> 0 .2 .4 .6 .8 1<br>Pvalue<br>2<br>1.5<br>1<br>Density<br>.5<br>0<br><!-- End of picture text --> <!-- Start of picture text --> Panel B. 1-month difference: 1.8% with P-<br>value<=0.05<br><!-- End of picture text --> <!-- Start of picture text --> 0 .2 .4 .6 .8 1<br>Pvalue<br>2<br>1.5<br>1<br>Density<br>.5<br>0<br><!-- End of picture text --> <mark>Panel D. 3-month difference: 5.5% with P- value<=0.05</mark> <!-- Start of picture text --> 0 .2 .4 .6 .8 1<br>Pvalue<br>2.5<br>2<br>1.5<br>Density<br>1<br>.5<br>0<br><!-- End of picture text --> Note: This figure shows the distribution of the p-value of the variable “born earlier” in the reduce-form regressions of maternal employment on “born earlier” (1, 2, or 3 months earlier). Source: Estimation from VHLSS 2010, 2012, 2014 and 2016.

32
misleading<sup>8</sup> given the dramatic transformation of cropping patterns observed over the past two decades in the area under study. We thus take the latter approach.

In fact, in the presence of the volatile and ever-changing conditions that have characterized NTX production and marketing in the highlands of Guatemala over the past quarter century, framing the problem within a multi-decisional context is far more appealing, and likely to produce more relevant results. We analyze farmers’ repeated decisions to adopt and/or withdraw from NTXs within a hazard model framework in which the spell of NTX adoption – as well as the spell of foregone adoption – will be analyzed in relation to a number of time-variant (and invariant) covariates in order to shed light on the dynamics of export crop adoption, its sustainability for smallholders, and the efficacy of policy interventions in sustaining commercialization among smallholders.

Specifically, we first model farmers’ decision to adopt snowpeas at some point after 1979, where the spell is identified as the number of years between the first year of the farmers’ exposure to the “risk of adoption” and the actual adoption.<sup>9</sup> In some cases, households had not yet adopted at the time of the survey even after being “at risk” of adoption for over 20 years. In duration modeling, these cases are treated as rightcensored, meaning that real time at risk continues even though the observation period has ended, as it is plausible that they may end the spell by adopting at some unobserved future date.<sup>10</sup> Similarly, for NTX adopters only, we also model the subsequent decision to withdraw from growing NTXs, where the spell indicates the

> 8 For further evidence, see Mohr (1982).

> 9 The beginning of the spell is either 1979 or the year of household formation, whichever comes later. This is because for households which were not yet formed in 1979, their exposure time begins at household formation. However, as the 2005 survey is part of a panel from a 1985 survey, most households were already formed by 1979.

> 10 Although nothing precludes farmers from adopting NTX at a later stage, it would be reasonable to assume that the probability of doing do after such a long spell is close to zero. As a possible extension of this analysis, a split model could be estimated to account for this fact.

8
- INS (Institut National de la Statistique), BAD (Banque Africaine de Développement), and World Bank. 2012. “Mesure de la pauvreté, des inégalités et de la polarisation en Tunisie 2000– 2010.” Tunis, l’INS.

- IMF (International Monetary Fund). 2014. _Subsidy Reform in the Middle East and North Africa: Recent Progress and Challenges Ahead_ . Middle East and Central Asia Department, IMF, Washington, DC.

- Jomaa, M. 2014. “Personne n'a le droit de bloquer les permis pétroliers.” _Kapitalis_ , September 4, http://www.kapitalis.com/kapital/24453-mehdi-jomaa-personne-n-a-le-droit-de-bloquerles-permis-petroliers.html.

- Societé Tunisienne d’Electricité et du Gaz. 2014. “Tables des Tarifs.” Direction des Etudes et de la Planification.

- World Bank. 2013. “Vers une meilleure equité: Les subventions energetiques, le ciblage, et la protection sociales en Tunisie.” Policy Note, Washington, DC.

- ———. 2014. “The Unfinished Revolution: Bringing Opportunity, Good Jobs and Greater Wealth to All Tunisians.” Development Policy Review, World Bank, Washington, DC.
## WPS3405

# **A Survey of Securities Laws and Enforcement**

Preliminary Draft By

### **Florencio Lopez-de-Silanes**

**YALE University and NBER**

October 2003

*I am indebted to Patricio Amador, Jose Caballero and Manuel Garcia-Huitron for their comments and excellent research assistance. I would also like to thank the Global Corporate Governance Forum and the International Institute of Corporate Governance at Yale University for funding. This paper draws on my previous work with several coauthors, including Simeon Djankov, Simon Johnson, Rafael La Porta, Andrei Shleifer and Robert. W. Vishny.

1
Hirshleifer, Jack (1991), ‘The Technology of Conflict as an Economic Activity’, _The American Economic Review_ , 81(2): 130-134.

International Crisis Group (2007), “Sudan : Breaking the Abyei Deadlock”, 12 October.

International Crisis Group (2008), “Sudan’s Comprehensive Peace Agreement: Beyond the Crisis,” Policy Briefing No: 50, March.

Institute for War & Peace Reporting (IWPR), (2008) “War Threats Sounded” – available at http://iwpr.net Jack, William and Roger Lagunoff (2006), “Dynamic Enfranchisement”, Journal of Public Economics (90), 2006.

Keefer, Philip (2008), “Insurgency and Credible Commitment in Autocracies and Democracies.” World Bank Economic Review 22 (1).

Mansfield, Edward D. and Jack Snyder (2005), _Electing to Fight: Why Emerging Democracies Go to War_ . Cambridge, Mass: MIT Press.

Monty G. Marshall and Keith Jaggers (2002), Polity IV Data Set. [Computer file; version p4v2002] College Park, MD: Center for International Development and Conflict Management, University of Maryland.

Sambanis, Nicholas (2008). “Short- and Long-term Effects of United Nations Peace Operations.” World Bank Economic Review 22 (1).

Skaperdas, Stergios (1996), ‘Contest Success Functions’, _Economic Theory_ 7(2): 283290.

Snyder, Jack (2000). _From Voting to Violence: Democratization and Nationalist Conflict_ . New York: Norton.

Swaroop, Vinaya and Andrew Sunil Rajkumar (2002), “Public Spending and Outcomes: Does Governance Matter?” Policy Research Working Paper 2840, World Bank, Washington,DC.

Winter, Roger and John Prendergast (2007), “An All-Sudan Solution: Linking Darfur and the South”, ENOUGH Strategy Paper #9 – available at <u>www.enoughproject.org</u> World Bank (2004), "The Millennium Development Goals: Rising to the Challenges." World Bank, Washington, DC.

Winter, Roger and John Prendergast (2008), “Abyei: Sudan’s Kashmir”, ENOUGH Strategy Paper #11 – available at <u>www.enoughproject.org</u> 51
1. I sometimes find it difficult to see things from the ”other guy’s” point of view.(PT) (-)

2. I often have tender, concerned feelings for people less fortunate than me. (EC) 3. I try to look at everybody’s side of a disagreement before I make a decision. (PT) 4. Sometimes I don’t feel very sorry for other people when they are having problems. (EC) (-) 5. I sometimes try to understand my friends better by imagining how things look from their perspective. (PT) 6. When I see someone being taken advantage of, I feel kind of protective towards them. (EC) 7. If I’m sure I’m right about something, I don’t waste much time listening to other people’s arguments. (PT) (-) 8. When I see someone being treated unfairly, I sometimes don’t feel very much pity for them. (EC) (-) 9. I believe that there are two sides to every question and try to look at them both. (PT) 10. I would describe myself as a pretty soft-hearted person. (EC) 11. When I’m upset at someone, I usually try to ”put myself in his shoes” for a while. (PT) 12. I am often quite touched by things that I see happen. (EC) 13. Before criticizing somebody, I try to imagine how I would feel if I were in their place. (PT) 14. When I see someone get hurt, I tend to remain calm. (EC) (-) 15. Other people’s misfortunes do not usually disturb me a great deal. (EC) (-) Note: PT = perspective-taking scale; EC = empathic concern scale. (-) denotes item to be scored in reverse fashion. A = 0, B = 1, C = 2, D = 3, E = 4. Except for reversed-scored items, which are scored: A = 4, B = 3, C = 2, D = 1, E = 0

# **VIII. Social desirability bias**<sup>29</sup>

1. It is sometimes hard for me to go on with my work if I am not encouraged. True/False

2. There have been times when I was quite jealous of the good fortune of others. True/False 3. I am always willing to admit when I make a mistake. True/False 4. I am always courteous, even to people who are disagreeable. True/False

# **IX. Emotions (for game and video treatment groups)**

1. Below you will find a scale with different adjectives that characterize different emotional states. On a scale of 1 to 10, where 1 means ”Not at all” and 10 means ”A lot”, please indicate how much you experienced the following emotional states when you watched the video / realized the virtual activity. You can also use any numbers between 1 and 10 to indicate where you fall on the scale, like 1, 2, 3, 4, 5, 6, 7, 8, 9, 10:

- a. Compassion:

- b. Distress:

- c. Happiness:

> 29Questions are adapted from Crowne, Crowne and Marlowe (1964)

69
## **2.3 Distance between locations**

The baseline measure of distance we employ is geodesic distance, that is, the shortest distance between two locations. Table 2 reports the mean, minimum and maximum geodesic distance between origin and destination locations.

Alternatively, following Atkin and Donaldson (2015), we use two additional measures to control for quantity and quality differences in countries’ road networks. The first, which aims at capturing quantity differences, is the distance between two locations along the quickest route, as calculated by Google Maps. Figure 1 shows the road network in each country. The second, which should capture quality differences, is the time it takes to complete a trip between two locations following the quickest route, also from Google Maps.

Table 2: Descriptive statistics

||KE|MG|NG|RW|TZ|GE|
|---|---|---|---|---|---|---|
|Mean origin price|2.306|0.922|0.902|2.328|0.898|2.577|
||(1.833)|(0.451)|(1.223)|(2.993)|(0.840)|(2.775)|
|Mean destination price|2.357|0.918|0.960|2.302|0.910|2.584|
||(1.984)|(0.520)|(1.386)|(2.980)|(1.001)|(2.729)|
|Mean price gap (trading pairs)|0.052|-0.004|0.058|-0.025|0.013|0.007|
||(0.454)|(0.264)|(0.433)|(0.480)|(0.662)|(0.454)|
|Geodesic distance (km)|279|414|597|80|493|170|
||(208)|(224)|(292)|(35)|(276)|(79)|
|Log distance to origin location|5.305|5.858|6.214|4.273|5.943|5.001|
||(0.870)|(0.605)|(0.691)|(0.499)|(0.927)|(0.551)|
|Min. distance (trading pairs, km)|20.6|114.3|34.2|7.9|2.2|50.9|
|Max. distance (trading pairs, km)|915.8|874.9|1,254.8|152.9|1,362.6|318.5|
|Number of trading pairs|134|18|153|30|85|20|
|Observations|9,280|5,997|48,379|26,215|50,583|6,173|

Notes: This table presents descriptive statistics for our sample of countries. The first and second rows report the mean price at the factory location (or location of the port of entry) and the mean price at destinations. The third row reports price gaps using data form trading pairs. All prices are deflated by inflation at the origin location and then converted to U.S. dollars using the exchange rate prevailing during the base period. The fourth row reports mean geodesic distance between trading pairs measured in kilometers. The fifth row reports the average distance to the origin location (in log kilometers). The fifth and sixth rows report the minimum and maximum distances between trading pairs, respectively. The seventh row reports the number of trading pairs. The last row reports the number of observations. Source: Authors’ estimates based on national CPI data and distances calculated using Google.

# **3 Methodological framework**

The theoretical framework underlying our estimation strategy is based on the model developed by Atkin and Donaldson (2015). In this section, we outline the main elements 7
Table 3 Uni-variate Statistics for Variables Averaged over the Period 1978-1990

|Variables|N|Mean|Std Dev|Coe. of Variation|Minimum|Maximum|
|---|---|---|---|---|---|---|
|MCY|19|0.134|0.160|1.194|0.006|0.565|
|MIY|19|0.161|0.109|0.677|0.060|0.466|
|QMY|19|0.244|0.164|0.672|0.031|0.584|
|DCPY|19|0.305|0.162|0.531|0.110|0.617|
|GNPCAP|19|1821|1206|0.662|298|4266|

Table 4 Correlation Analysis for the Period 1978-1990

> Pearson Correlatio Coefficients / Prob > I R I under H0: Rho=0 / N= 19

|MIY|MCY<br>0.513<br>0.0246|
|---|---|
|QMY|0.630<br>0.0039|
|DCPY|0.774<br>0.0001|
|GNPCAP|0.242<br>0.3183|

18
to be raised after the liberalization oi financial markets-is estimated in Section 4.16 We conclude this section with a utiscussion of the factors that might justify the imposition of financial repression as a means of taxation. The first is the presence of costs in the administration of alternative forms of taxation. Gordon and Levinsohn (1989), for example, sugge-' mnodeling the administrative costs associated with the use of different tax bases as fixed costs: in order to set up the necessary bureaucracy to organize, administer, and monitor a given tax, the government has `;o incur an initial "capital" expense. In some countries, and for certain taxes, this initial expense may be very high. Income taxes or value-added taxes, for example, can be costly to raise in developing countries.'<sup>7</sup> Using the models discussed above one could show, trivially, that when the fixed costs required to levy other taxes are high enough, the government uses only financial repression."<sup>8</sup> The second factor justifying the use of financial repression is the application of distributional criteria by the government. Redistribution objectives can be achieved by taxing the return to capital, thereby inducing the production distortions illustrated above, and transferring income away from capitalists to wage earners. Finally, political considerations may favor the less transparent means of taxation that is characteristic of financial repression.

# _2.2 The Financial Repression Tax and the Inflation Tax_

The discussion in the previous section has relied on models where money has no role. In practice, however, financial repression is typically the result of nominal interest rate ceilings that are well below the prevailing rate of . lation and currency depreciation. It

> 1 6 This revenue is irrmplicit, since, as the discussion above has iUustrated, it is really reflected in a fall in budgetary expenditures, rather than in an increase in revenues.

1<sup>7</sup> lndeed, many countries (including industrial countries) estimate income taxes based on tangible wealth holdings of the taxpayers (see OECD (1988)). A more accurate, voluntary system of tax assessment with the backup of government audits might not be viable in many poor countries. Value-added taxes require an efficient system of border adjustments, as well as a sophisticated method of tax assessment based on individual producers' returns. 1<sup>8</sup> Deviations from production efficiency are justified also when domestic production gives rise to pure profits, that are not easily taxable by the government. See Giovannini (1988) for an illustration.

8
TABLE 5. GHANA: BASIC FISCAL INDICATORS (Calendar Years)

||1980|1981|1982|1983|1984|1985|1988|1987|1980-1987<br>Avorage|
|---|---|---|---|---|---|---|---|---|---|
|||||(In per|cont of C|DP)||||
|Central Government||||||||||
|CashDeficit|-8.0|-6.4|-4.7|-2.7|-2.1|-2.7|-0.7|-0.3|-3.5|
|Operational Deficit|0.8|5.5|-0.4|4.6|-4.5|-0.4|0.6|-0.4|0.7|
|Primary Deficit|-6.0|-4.3|-2.1|-1.5|-0.9|-1.2|1.6|1.2|-1.7|
|Monotary Authority<br>1/||||||||||
|______________________||||||||||
|CashDeficit|-8.0|-6.4|-4.7|-2.7|-2.1|-2.7|-0.6|-0.1|-3.4|
|Operational Deficit|0.8|6.2|-0.1|4.6|-4.7|-0.4|0.8|0.1|0.9|
|Primary Deficit|-6.0|-4.3|-2.1|-1.5|-0.9|-1.2|1.5|1.2|-1.7|
|Memorandum Items||||(in|percent)|||||
|CDP Growth Rate<br>AverageNominal|1.1|-3.6|-6.9|-4.6|8.9|5.1|6.2|4.8|1.3|
|Domestic Interest<br>Rate|10.8|14.2|14.0|8.9|4.9|S.4|12.3|13.1|10.5|
|Domestic Inflation<br>Rate|60.2|116.0|22.0|122.0|_40.0_|_10.0_|25.0|40.0|63.2|
|Change in Roal Effective||||||||||
|Exchange Rate2/|30.7|122.4|25.0|-32.8|-61.4|-27.3|-42.4|-22.8-.|-1.1|
|Debt/CDP3/|24.6|22.0|24.9|18.7|26.6|29.8|31.5|36.6|26.8|

- 1/ Consolidated accounts of Central Government and Central Bank in rospoct of holdings of international reservos and positions vis-a-vis the IMF.

2/ A negative sign signifies depreciation. A positive sign signifies appreciLtion.

3/ Not domestic and external debt of the Monetary Authority.
# **_Annex VII: Details of Empirical Procedures:_**

## **_Testing for Integration_**

The unit root tests lay the foundation for the VECM framework outlined above. This paper will adopt the ADF procedure to test for unit roots as indicated above. The test will incorporate both constant and trend to account for trend stationarity. Since the data is in annual frequency, then no seasonal adjustment is required. The lag length for the ADF test is determined based on the Schwert (1989) formula for annual data: where T is the number of observations and INT stands for integer value. Since T = 54, the lag length suggested by this criteria is 4 lags.

All tests are conducted by Oxmetrics 6.1. Tables (3a), (3b), and (3c) provide summary statistics of the ADF test. Following the Dickey and Pantula (1987) sequential approach to confirm the order of integration for each variable, unit roots are tested under the assumption that the order of integration is, at most, two. The second difference for the data is employed for this purpose and as shown in table (3a), data provide strong evidence to reject the null hypothesis of non-stationarity at a 1 percent level.<sup>19</sup> This is replicated for the first difference of the series. The null hypothesis of existence of a unit root is rejected for the change in inflation at 5 percent significance level, and for the change in exchange rate at 1 percent significance level. The critical values for the change in real money balances, change in real GDP, change in the interest rate and discount rate cannot reject the null of a unit root (see table 3b). This might suggest that these variables may follow an I(2) process. However, the numerical values for the coefficients of the tests that minimize the Akaike's Information Criterion (AIC) (see Akaike's (1973) ) lie between 0.55 and 0.02, respectively. This

> 19 While the visual inspection of the series in graphs 1 and 11 confirm the absence of structural breaks; future research would consider testing for structural breaks at unknown dates and conditioning on the most recent break for estimation following El-Shazly (2015).

84