rafmacalaba/datause-encoder-data huggingface dataset:
28 are remarkablv similar among AAA and A firns. and both are much closer to 5 (highly significant) than 0 (irrelevant). Managers invarably emphasized that unless the deficit were kept low inflation would go up. making any sort of planning and budgeting very difficult-.

But the _negative_ penalties associated with the reform - the PPWW. the mninimum asset tax (dividend). the compression of marmins - were not all that motivated SOEs and managers _to_ respond. Our survev indicates that there wvas a _positive_ side too. captured bv the expectation of rewards at the time of privatization. Managers by and large expect privanzation. and soon Only 8 out of 59 responding, managers felt they would remain SOEs in the near futire: 43 expected prvatization soon. 5 expected managerial contracts for restructuring and 3 were already privatized. WVhen asked why thev had defied the ne2ative stereotypes associated with state companies and focused on the long-run solvency of the companies. managers would initiallv mention emotional reasons. patriotism, and the like: but when pressed. they all indicated that they expected financial rewards at the time of privatization. in the form of stock options as well as retaining their jobs as the new CEOs. When directly asked whether they were concerned about establishinge their reputations as managers capable of operating in a market environment. the answer was invariably: "obviously".

Thus there seem to have been both carrots and sticks involved in getting managers to improve the performance of their enterprises. On the _stick_ side were the hard budget constraits imposed by the Government (elimination of subsidies for manufacturing) and the banks (especially after the reforms of 1991.4); the fierce import competition: and the PPWW tax system- On the _carrot_ side were admittedly implicit but apparently still effective positive inducements such as the potenial for future cashing in on imanaeerial reputation and the associated financial rewards. This suggests that even the anticipation of future privatization already had positive influence on the performance of state enterprises. This is in striking contradiction to the assumptions made in for example Aghion and Blanchard (1993), where SOE managers consider privatization as the end of their career.

# _5_ Conclusions

The Polish state sector has belied the dire predictions of most outsiders. Privatization was looming ahead and effective enterprise control eliminated after the collapse of the communist central authorities in 1990; in those circumstances. most (present authors included) expecrted the state sector to be headed for destructive control fights and decapitalization Instead, state losses have been curbed. SOEs are behind Poland's spectacular export performance in Westem markers. and SOEs have made a major contribution to making Poland Europe's fastest growing economy in 1993.
# **4. THE PROJECT THE RESPONDENT IS TAKING PART IN**

# **4.1. COMMON  DATA ON THE PROJECT**

# **4.1.1. Title of the project**

# **4.1.2. Project director (Name, position)**

# **4.1.3. Sources of finance (Customer)**

# **4.1.4. Internal project participants**

|Name|Subdivision|Position|Field of<br>specialization|
|---|---|---|---|

# **4.1.5. External project participants**

|Organization|Name|Position|Field of<br>specialization|
|---|---|---|---|

# **4.2. DESCRIPTION OF THE PROJECT**

# **4.2.1Annotation (A brief description of the project. Please, indicate the persons interested in the projects’ results and results specifically) 4.2.2. Field of project specialization**

(e.g.: petrochemistry, catalysts, etc.)

# **4.2.3. Objectives of the project**

# **<u>4.2.4. Project stage</u>**

|**Description of stage**|Invoked project<br>stage|Performed stages|
|---|---|---|
|Fundamental theoretical<br>investigations|||
|Fundamentalprediscovery|||
|Appliedresearches|||
|Production of<br>laboratory/experimentalspecimen|||
|Production of development type/ pre-<br>production model|||
|**Working documentation**|||
|Organizationofshort-runproduction|||

83
we also check whether there are systematic differences in employment spells according to the duration of stay abroad of return migrants. As shown in Figure A3, we do not find any systematic association between duration of stay overseas and duration of unemployment after return. These results do not seem compatible with the hypothesis that return migrants who stayed overseas longer are more likely to opt for self-employment because they cannot access wage employment back home.

Columns (2) and (5) of Table 2 display our estimation results using this instrumentalvariable approach. The first-stage coefficient of the oil-price instrument reported in columns (3) and (6) has the expected sign: A positive growth rate in oil prices during the migrant’s stay abroad increases the total value of earnings overseas in local currency. The coefficient is statistically significant at the one percent level, and the F-statistic for the excluded instrument suggests a strong first stage. The estimated effect of cumulative earnings overseas on the likelihood to become self-employed after return is sizable, with a semi-elasticity of around 0.1. As shown in Table A2, these findings are virtually unchanged when we exclude return migrants who are employed in the transportation sector, which uses a higher fraction of oil-derived inputs than other sectors.

These stylized facts jointly suggest that a key driver of temporary migration from Bangladesh is the desire to work overseas to accumulate sufficient financial savings to fund self-employment upon return. In other contexts, human capital accumulation abroad has been pointed out as an important factor for temporary migration and post-return decisions (McCormick and Wahba 2001). Consistent with this, a wage premium for returning migrants has been estimated, for instance, for migrants from Hungary (Gang and Yun 2000), Albania (De Coulon and Piracha 2005), West-Africa (De Vreyer, Gubert, and Robilliard 2010), Mexico (Lacuesta 2010; Reinhold and Thom 2013), Romania (Ambrosini, Mayr, Peri, and Radu 2015) and Egypt (Marchetta 2012; Wahba 2015; El-Mallakh and Wahba 2017; El-Mallakh and Wahba 2021).

In the context of migration from Bangladesh, however, human capital accumulation is unlikely to be the main motivation. The vast majority of migrants from Bangladesh are employed in low-skilled jobs in the destination countries. These occupations generally rely on physical strength, providing limited opportunity for learning and human capital accumulation. In addition, the occupational patterns of individuals before, during, and after migration do not seem to be compatible with the human-capital accumulation hypothesis (Table A3). For example, while two-thirds of temporary migrants in the sample were employed in the construction sector at their destination abroad, but only 10 percent after returning home (Panel A). In the HIES data, this ratio is only slightly lower (7 percent) for those who did not migrate. In addition, 24 percent of the returning migrants were employed in construction 29
_13_ _Ways Out of Poverty - DiffiLsing Best Practices and Creating Capabilities_ <!-- Start of picture text --> Figure 7: Location of new product innovation<br>in the United States (percent)<br>Other<br>4%<br>New York,<br>LA, Othe r<br>Boston, A large<br>San cities<br>Francisco -- 51%<br>45%<br><!-- End of picture text -->

**Source: Glaeser (1998).**

Clusters of firms as in Silicon Valley (information and communication technology) and in Northern Italy (textiles and garments) provide benefits similar to those in cities. ' As executives in Silicon Valley expressed it: ". . .People change jobs without changing carpools.. ." and "...the network in Silicon Valley transcends company loyalties. We treat people fairly and they are loyal to us, but there is an even higher level of loyalty - to their<sup>7</sup> network..."<sup>3</sup> Cities are forms of clusters, the best known way to incubate small enterprises.

**Firms and the diffusion of best practice.** Then there are firms.<sup>3 8</sup> Large, multi-plant firms-which exhibit some features similar to those of clusters, such as flexible internal labor markets, loyalty to the larger firm, flexible internal contracting-are more productive than smaller ones. In many developing countries foreign firms are more productive than local ones are, suggesting a way to import some types of capability. Most important, firms spread best practices and thus wealth through channels that may cross national boundaries. They equally function within a particular country. De facto, studies of cross-border links between firms have attracted more attention. Hence, some of the following discussion has a flavor of international learning channels. However, the same or similar mechanisms are at work within countries.

**Productive, new entrants from other areas.** One way of improving firm-level performance is for more productive firms to move into new markets through new investments or through mergers or acquisitions. Across borders we call this foreign direct investment. Foreign firms and joint ventures with foreign firms typically exhibit higher productivity than local firms in developing countries.<sup>3 9</sup> Foreign firms employ mostly local workers and managers, training them on the job in new technical and organizational

36 Porter, 1998.

**37** Saxenian, 1996.

> 38 Sutton, 2000.

> 39 Barberis et al., 1996; Blomstrom and Sjoholm, 1998; Djankov and Murrell, 2002; Earle and Estrm, 1998; Havrylyshyn and McGettigan, 1999.
which focused on rapid global growth in biofuel production under conventional conversion technologies, the price increase for major crops ranges between 30% and 76% by 2020. There is significant increase in malnutrition in many developing country regions with Sub-Saharan Africa being the hardest hit. Using the AGLINK and COSIMO models, the OECD predicts the impact of achieving the stated policy targets (as of 2006) for biofuels in several countries [42]. It finds that compared to a situation with unchanged biofuel quantities at their 2004 levels, crop prices could increase by between 2% in the case of oilseeds and almost 60% in the case of sugar by the year 2014.

Partial models have several limitations, such as lack of acknowledgement of the finiteness of resources such as land, labor, and capital; no explicit budget constraint on households; and no check on conceptual and computation consistency of the model [30]. These limitations can be overcome by using a general equilibrium approach. Computable general equilibrium (CGE) modeling is a numerical technique that combines the theoretical framework of Walrasian general equilibrium formalized by Arrow and Debreu [6] with real world economic data to determine the levels of supply, demand, and price that support equilibrium across a specified set of markets [69]. These models, which were initially developed to analyze the impact of changes in trade policies and public finance, have subsequently found wide application in the analysis of relationship between energy and the macro economy, the impact of greenhouse gas policies, and most recently in the context of biofuel policies [10, 13, 31]. GTAP, LINKAGE, and USAGE are some prominent general equilibrium models that were used to analyze biofuels.<sup>4</sup> Dixon, Osborne, and Rimmer [18] use a dynamic CGE model called USAGE to quantify the economy wide effects of partial replacement of crude petroleum with biofuels in the United States. They forecast the impact of the current biofuel policies on the U.S. economy in 2020 [18]. Although there is no direct discussion of the impact of these policies on the global price of food, the model predicts a reduction in agricultural exports and an increase in the export prices. Gohin and Moschini assess the impacts of the European indicative biofuel policy on the EU farm sector with a farm-detailed CGE model and predict positive income effects on farmers in the EU [26]. Birur, Hertel, and Tyner use the GTAP-E model to study the impact of six drivers of the biofuel boom, namely, the hike in crude oil prices, replacement of methyl tertiary butyl ether (MTBE) by ethanol as a gasoline additive in the United States, and subsidies for ethanol

> _4 GTAP – model developed at Purdue University; LINKAGE – The World Bank’s model, and USAGE – model developed at Monash University, Australia._

5
# **4.1 Model setup and baseline results**

We begin with the theoretical gravity equation (2), and model bilateral imports as a function of importer and exporter incomes, distance and other factors affecting trading costs, and importer and exporter aggregate price indices. Following standard practice in existing studies (such as Rose and van Wincoop, 2001; Adams et al., 2003), our trade costs function includes controls for similarity in preferences, geography, cultural linkages, and membership in a PTA. We follow the logic of Feenstra (2004) (see section 2.2) in deciding to model multilateral price indices indirectly using importer and exporter fixed effects. The equation that we estimate is therefore where _xijt_ is the log of total or preferential imports from country _j_ to _i_ in time period _t α, γi, θj, λt_ are the constant, importer, exporter, and time fixed effects _Yit, Yjt_ are the logs of GDPs of countries _i_ and _j_ in _t_<sup>29</sup> _dGDPpcijt_ is the log of the absolute difference in per capita GDPs of _i_ and _j_ in _t GDPsimijt_ is the index of similarity between the GDPs of _i_ and _j_ in time period _t_<sup>30</sup> _distij_ is the log of the geographical distance between the largest cities of _i_ and _j comlangij_ equals 1 if _i_ and _j_ share a common official language _borderij_ equals 1 if _i_ and _j_ share a land border _colonyij_ equals 1 if _i_ and _j_ have colonial linkages _PTAijt_ equals 1 if _i_ and _j_ are members of the same PTA at time _t ε_ is the error term _ijt_

> 29Note that the theoretical gravity equation implies a unity constraint on the coefficients of both income terms: _β_ 1 = _β_ 2 = 1. Some gravity studies, such as Anderson and van Wincoop (2003), impose this constraint _a priori_ by dividing the dependent variable by the product of exporter and importer incomes (or subtracting the latter from the former after taking logs). Other studies, such as McCallum (1995), estimate non-unitary income elasticities, and Anderson (1979) provides a theoretical framework for income elasticities different than one by including non-traded goods in the analysis. Anderson and van Wincoop (2003) show that even in cases when the income elasticities are significantly different from one, imposing the unity constraint has little effect on the estimates of other model

29
# Figure 7: Backyarders as a function of CBD distance-SR1, SR2 parcels

<!-- Start of picture text --> 1<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>0 5 10 15 20 25 30 35 40 45<br>Distance to CBD<br>Number of Bacakyarders<br><!-- End of picture text --> 29
# **INSURANCE AND INCLUSIVE GROWTH**

# **Rodney Lester**<sup>*****</sup>

### **JEL Classification:**

- <mark>G22: Financial Economics / Insurance Companies. Actuarial Studies</mark>

- <mark>G28: Financial Economics / Financial Institutions and Services. Government Policy and Regulation</mark>

- <mark>I32: Health, Education and Welfare/ Measurement and Analysis of Poverty</mark>

- <mark>O16: Economic Development, Technological Change, and Growth/ Financial Markets. Saving and Capital Investment. Corporate Finance and Governance</mark>

- <mark>Q54: Agricultural and Natural Resource Economics, Environmental and Ecological Studies/ Climate Change. Natural Disasters. Global Warming</mark>

## **_Sector Board: Financial Sector (FSE)_**

## ________________________

*This literature review was written by Rodney Lester, Expert Consultant, for the Global Capital Markets and Non-Bank Financial Institutions Group, Financial and Private Sector Development Vice Presidency at the World Bank.   Michel Noel provided support and encouragement and Martin Buehler, Daniel Clarke, Serap Gonulal, John Pollner and Craig Thorburn offered pertinent comments that added greatly to the content and presentation.
# ideologies; and (iii) electoral competition.<sup>12</sup>

We classify the ideology of mayoral and presidential candidates as left, center, or right, following the methodology proposed by Fergusson et al. (2018) and Rozo and Vargas (2021), both published in leading political science and economic journals. The credibility of this classification is supported by Fergusson et al. (2020), who demonstrate that electoral victories of left-wing parties are associated with increased right-wing paramilitary violence, a pattern consistent with theoretical expectations from Acemoglu and Robinson (2008). Similarly, Rozo and Vargas (2021) apply this methodology to classify candidates in Colombian local elections, further validating its relevance.<sup>13</sup> Building on these prior efforts, we extend and refine the classification by manually coding an additional 1,343 candidates from the 2019 local elections. This expansion ensures consistency and comparability with previous work while addressing the complexities posed by Colombia’s highly fragmented party system. Unlike in Europe, Colombia’s electoral framework facilitates the of coalitions and new move- proliferation temporary political ments, complicating ideological classification.<sup>14</sup> Our classification process relies on electoral data from Colombia’s National Civil Registry and follows a structured three-step approach. First, for mayoral elections, we identify the political party or movement that endorsed each candidate. The initial classification considers party names, mottos, and slogans for ideological markers (e.g., terms like “socialist” suggesting a left-wing orientation or “conservative” indicating right-wing alignment). When this step does not yield a definitive classification, we analyze party statutes

> 12Calculated following Chac ́on et al. (2006) as: 1 _−_ (%1st _Candidate −_ %2nd _Candidate_ ). When the margin of victory among candidates is close to zero, the elections were competitive and the variable takes a value close to one.

> 13As part of their classification, they manually assigned ideological labels to 354 independent candidates who were unclassified due to one of the following reasons: (i) they ran unopposed, (ii) they did not represent any formal party, or (iii) they participated only in the 2015 elections.

> 14In 2019 alone, 1,287 political parties competed across 1,122 municipalities, with some municipalities hosting over 20 different parties. Many candidates ran under personalist movements rather than established political parties, further complicating ideology classification based on party affiliation.

17
**_Results._** The CFA devaluation has had excellent intermediate-term effects on growth. For the 12 CFA countries in Devarajan's sample, real GDP growth from 1990 to 1993 averaged almost negative 0.3 percent annually weighted by GDP, according to World Bank data (World Bank, 1999). However, from 1994 to 1997, growth in these same countries averaged 5.1 percent annually, according to the same data source.<sup>23</sup> Cameroon, the largest country in the CFA zone, grew at an annual rate of-3.4 percent in the first period, but 4.5 percent in the second period (World Bankc, 1999). Devarajan found that a year after the devaluation, the average undervaluation was two percent for the group, but with significant variance.

# **Lessons from Africa**

The countries of Sub-Saharan Africa have made significant strides in correctly valuing their exchange rates. In 1980, 45 African countries had pegged exchange rates, four had managed floats, and none had independent floats. By 1996, 25 had pegged exchange rates, six had managed floats, and fully 19 had independent floats, according to Sekkat and Varoudakis. In both years, 14 were pegged to the French franc. This move towards a flexible exchange rate allowed most non-CFA zone countries to experience a real depreciation, starting in 1984-5. The CFA zone countries had their own depreciation in 1994.

By the late 1990s, much of Africa had returned to growth. Herndndez-Catd (1999) reported that real GDP grew 4.25 percent annually from 1995 to 1998, up from less than 1.5 percent from 1990 to 1994, with per capita output up one percent in the later period, against a decline of 2.25 percent in the earlier period. Inflation peaked at 47 percent in 1994, and dropped to 10 percent 1998. He credited the improved perfornance to better policy rather than external developments, which were adverse in 1998.

A great deal of policy reform remains for Africa. Though policy makers have taken large steps, there has been backsliding and failure to implement some important measures. Aggregate growth has returned, but per capita growth is only slightly positive and investment and savings both remain lower than in other developing regions. However, the continent has seen significant successes, and a realistically valued exchange rate has proved essential to these.

# V. **Diverse versus Uniform Tariffs and Incentives**

In defense of the exchange rate, countries often apply trade protection that is very high or prohibitive on selected products or countries. The experience in Kazakhstan in early 1999 is a case in point. We question the efficacy of these policies. Even with a limited objective of reducing the demand for foreign exchange, depending on how porous the borders are, we will observe an increase in imports through informal channels. And with diverse protection, while some sectors will be protected, the burden of the costs of

> 23 The unweighted averages are 0.1 percent for 1990 to 1993 and 4.7 percent for 1994 to 1995.

20
Table 3: Impact on Financial Knowledge

||(1)|(2)|(3)|
|---|---|---|---|
||Financial<br>Awareness|Applied<br>Financial<br>Knowledge|Financial<br>NumeracySkills|
|Migrant-Only Training (A)|0.039|0.009|0.016|
||(0.038)|(0.018)|(0.026)|
|Family-Only Training (B)|0.123***|0.026|0.037|
||(0.037)|(0.018)|(0.025)|
|Migrant and Family Training (C)|0.192***|0.049**|0.042|
||(0.038)|(0.020)|(0.027)|
|R-squared|0.054|0.192|0.018|
|Sample Size|1132|1133|1133|
|Number of Clusters|394|394|394|
|Mean of Dependent Variable in Control Group|0.397|0.318|0.391|
|Test: A-B = 0 (p-value)|0.032|0.332|0.448|
|**Test: A** **C = 0 (p** **value)**<br>-<br>-|**0** **000**<br>.|**0** **041**<br>.|**0** **382**<br>.|
|Test: B-C = 0 (p-value)|0.075|0.247|0.867|
|Test: A = B = C = 0(p-value)|0.000|0.071|0.353|

This table presents pooled regression analysis across three follow-up survey rounds for aggregated measures of Financial Awareness, Applied Financial Knowledge, and Financial Numeracy Skills in Columns (1), (2), and (3), respectively. Individual components of these aggregate measures are presented separately in Appendix Tables A2, A3, and A4. Separate regression analysis by follow-up survey round is presented in Appendix Table A5. All regressions include survey round dummies. Standard errors in parentheses, clustered at the individual level. The bottom half of the table presents p-values for equality of coefficient tests. Statistically significant coefficients are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% significance level).