rafmacalaba/datause-encoder-data huggingface dataset:
# **II. MACROECONOMIC SETTING AND THE FINANCIAL** SECTOR

The macroeconomic setting in Turkey over the last two decades has been dominated by chronic fiscal deficits with high and variable inflation, averaging about 70 percent, and relatively high and volatile GDP growth. The main reason for this has been Turkey's inability to implement the necessary structural reforms which, to date, is still the case. In 1989, Turkey liberalized its capital account, complicating macroeconomic management even more. The open capital account established a strong link between foreign and domestic rates and, given the unstable situation in Turkey, a large risk premium developed. As a result, domestic interest rates have been, and continue to be high which, in turn, negatively affects fiscal dynamics<sup>2</sup> .

The openness of the economy, with persistent fiscal imbalances, put a premium on sound exchange rate management. While Turkey has followed a flexible exchange rate policy since 1980, high inflation and capital inflows caused real exchange rate appreciation during the 1990-1993 period. This situation provided strong incentives for external borrowing given the high domestic interest rates and, as expected, commercial bank borrowing abroad increased rapidly. From being negligible in the 1980s, Turkish banks' liabilities to non-residents reached almost 13 percent of their total liabilities by 1993.

Further deterioration in the fiscal situation in 1993, and major policy mistakes in foreign exchange and interest rate policies, led to a crisis in late 1993. The currency depreciated by almost 100 percent, three small banks failed and left the system. GDP Mcfadden (1994) analyzes Australia's experience with foreign bank entry. Bhattacharya (1993) looks at the cases of Turkey, Korea, and Pakistan. Pigott (1986). Terrell (1977) considers the cases of 14 developed countries, of which 8 allowed foreign bank entry.

5
PTAs, which is the realm of preferential agreements excluding partial scope agreements in force and notified to the WTO up to the end of 2015 (Hofmann, Osnago and Ruta, 2017). The methodology is based on the work of Horn, Mavroidis and Sapir (2010), which was also used in the World Trade Report 2011 (WTO, 2011). The data provide information on two key aspects of the content of PTAs: (i) what policy areas are covered in each agreement, based on a list of 52 policy areas; (ii) whether each provision is legally enforceable or not, based on an analysis of the legal language of the treaty text and the possibility of recourse to dispute settlement.<sup>7</sup> As a first measure of depth, we use the number of legally enforceable provisions that are included in an agreement from the World Bank database. While an imperfect metric, this is a logical first step to capture the level of depth of PTAs, as the extent of policy commitments depends on the number of areas that are covered by an agreement. Specifically, we define the variable ������������� �∑����� �������� ‐ i.e. the simple count of ‐legally enforceable‐ provisions � 8 ( ������� ) included in the agreement between countries � and � at time � .

An alternative measure of depth can be constructed on a subset of “core” border and behind the border provisions –i.e. those provisions that have a clear economic content, as opposed to other provisions that do not (e.g. cultural cooperation, anti‐terrorism). Core provisions include tariff liberalization for industrial and agriculture goods, technical barriers to trade (TBT) and sanitary and phytosanitary (SPS) measures, export taxes and anti‐dumping and countervailing measures, trade related intellectual property (TRIPs) and trade related investment measures (TRIMs), movement of capital, state owned enterprises, state aid, competition policies, intellectual property rights (IPR), investment, public procurement and services. The 18 “core” provisions are also those most often included in PTAs (Hofmann, Osnago and Ruta, 2017). We define the variable ��������� as the number of core provisions ( �������� ) included in the agreement �� � between country � and � at time � : ������������ �∑��� ������� .

> 7 Annex Table A1 presents the list of provisions. More details on the methodology and the data on deep trade agreements can be found in Hofmann, Osnago and Ruta (2017). The data are freely available at the following website: https://datacatalog.worldbank.org/dataset/content‐deep‐trade‐agreements.

> 8 Unless otherwise stated, all provisions included in measures of PTA depth are legally enforceable.

7
# Table 7: **Regression Results** – Debt Relief and Investment

This table reports the effect of debt relief on investment. Within a panel, each column reports results from a separate regression. The dependent variables are (i) total spending on agricultural inputs and (ii) spending on agricultural inputs per acre of cultivated land, both measured as averages over the first two post-program crop seasons. All additional controls and fixed effects are as defined in Table 5. Robust standard errors, in brackets, clustered by bank and district. + p _<_ 0.10 ** p _<_ 0.05 *** p _<_ 0.01.

||Model 1||Model 2||Model 3||Model 4||
|---|---|---|---|---|---|---|---|---|
||Banks|Informal<br>lenders|Banks|Informal<br>lenders|Banks|Informal<br>lenders|Banks|Informal<br>lenders|
|_Panel A: Full Sample_|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|100% Relief|-0.24***|-0.20***|-0.15***|-0.14**|-0.19***|-0.17***|-0.15***|-0.15**|
||[0.06]|[0.06]|[0.05]|[0.06]|[0.05]|[0.06]|[0.05]|[0.06]|
|Observations|2,519|2,473|2,519|2,473|2,095|2,079|2,519|2,473|
|R-squared|0.15|0.20|0.13|0.18|0.28|0.24|0.13|0.18|
|_Panel B: Restricted Samples_<br>Audited accounts|||||||||
|100% Relief|-0.26***|-0.18**|-0.16***|-0.08|-0.17***|-0.1|-0.16***|-0.09|
||[0.05]|[0.06]|[0.04]|[0.06]|[0.05]|[0.07]|[0.04]|[0.06]|
|Observations|1,610|1,582|1,610|1,582|1,329|1,323|1,610|1,582|
|R-squared|0.17|0.22|0.14|0.19|0.29|0.26|0.14|0.19|
|Reduced bandwidth sample|||||||||
|100% Relief|-0.24***|-0.17**|-0.15***|-0.12**|-0.17***|-0.15***|-0.15***|-0.13**|
||[0.06]|[0.06]|[0.05]|[0.06]|[0.05]|[0.05]|[0.05]|[0.06]|
|Observations|1,826|1,790|1,826|1,790|1,507|1,497|1,826|1,790|
|R-squared|0.14|0.20|0.12|0.17|0.29|0.24|0.12|0.17|
|Interviewer effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank*district effects|No|No|Yes|Yes|Yes|Yes|Yes|Yes|
|Respondent controls|No|No|No|No|Yes|Yes|No|No|
|Local linear control fctn|No|No|Yes|Yes|Yes|Yes|No|No|
|Quadratic control fctn|No|No|No|No|No|No|Yes|Yes|

38
Policy Research Working Paper 9456

# **Abstract**

Based on their objective economic situation and comparing with their peers, individuals form perceptions of their economic position in a society. Data from the three waves of the Life in Transition surveys of European countries show that these perceptions systematically deviate from the rankings obtained using consumption levels. People position themselves in the middle ranks in larger numbers than those who are in the middle ranks according to their consumption levels. Correspondingly, many people who objectively are classified in the top, richest, or bottom, poorest, ranks subjectively feel that they are in the middle class. This puzzling “bunching in the middle” is the focus of this paper. Explanations are tested and discarded that consider subjective perceptions as misperceptions or the result of other mistakes due to data limitations (such as tail bias). The paper concludes that rather than reflecting a subjective assessment of the distribution of welfare, subjective rankings reveal subjective economic well-being. The paper show that monetary consumption is a strong predictor of subjective economic well-being, but that the latter is influenced by many other factors, including economic security, proxied by employment status or other measures of human capital, such as health and education. These findings have policy relevance, since redistribution measures aiming at simply protecting consumption levels may not be sufficient to restore the economic well-being provided by having fulltime secure types of employment.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region. 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 mbussolo@worldbank.org, mlebrand@worldbank.org and itorre@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
coefficient for the productive asset index is ambiguous. More productive assets may imply higher level of capital intensity and labor saving technology and thus fewer hours spent on agriculture. On the other hand, more assets may also point to more intense cultivation and thus more hours spent on agriculture. We estimate the agricultural activity labor allocation models separately for less and more biomass areas.

# **4. RESULTS**

## <u>Biomass and Forests in Malawi</u>

Figure 1 and Table 4 present a picture of biomass and forest cover in Malawi.<sup>18</sup> The total amount of biomass in the country is 388 million cubic meters. The average biomass available is about 40 cubic meters per hectare; however, this number does not present an accurate picture because of the skewed distribution of biomass. As Figure 1 indicates, Malawi’s forests are mainly in the Northern areas.

The northern region with159 million cubic meters of biomass has 41% of the country’s biomass. The central region has 38 cubic meters per hectare and 38 % of the biomass. The southern region is the area with the least amount of biomass (24%).

It should be noted that these estimates are different than reported in the Biomass Assessment for 1990 (Government of Malawi 1993). A preliminary comparison of these two studies would suggest that biomass in Malawi has decreased by about 16% between 1990 and 2004. Specifically, the northern region went from 218 million cubic meters to 159 million cubic meters (27 % decline), the central region from 142 to 137 million cubic meters, a decrease of 14 % and, the southern region from 102 to 92 million cubic meters, a 10 % decrease. In fact it should be clarified that a precise comparison between the two studies is not feasible because of definitional differences between our study and the 1993 report: the two studies are based on different data with different spatial resolution and different methodologies.<sup>19</sup> Nonetheless, it seems safe to sprayer, panga, hoe, axe, and sickle. These assets are mainly used for agricultural and other productive activities and is thus assumed to affect agricultural labor hours but not hours spent on fuelwood collection. 18 Figure 1 shows a map of biomass at the spatial resolution of 463.3 meters.

19 The main reason for the difference between the 1993 report and our results is because of different definitions of forested land. In the 1993 report a large amount of land is classified as agriculture mixed with 17
characteristics. The year effects control for time-varying factors that affect all countries, such as changes in reporting in the DFO archive.<sup>2</sup> We also include in **Z** two proxies for land-use patterns: the percentage of urban population and total forest area. Increased urbanization and the related increase in impervious surfaces are believed to exacerbate flooding. Despite widespread belief that forests can prevent and reduce floods, the effect of forests on the probability and magnitude of flood events remains controversial (FAO and CIFOR 2005). Bradshaw et al. (2007) used global flood data to show that forests are associated with lower flood risk and severity. Using the same data, Van Dijk et al. (2009) offered an alternative explanation: floods are correlated with population exposure, which Bradshaw et al. omitted. We therefore control for both forest area and population.

Equation (1) is our benchmark specification, but we consider variants with additional controls. We add domestic credit to the private sector to capture access to private investments in self-protection against flooding. We include a Gini coefficient to capture the degree of inequality in the distribution of income within a country, which Kahn (2005) and Anbarci et al. (2005) found to be an important factor explaining earthquake fatalities. Gini values range from zero to one, with values closer to one indicating greater inequality. Finally, as in Kellenberg and Mobarak (2008), we allow for potential nonlinear effects of income through a quadratic specification. We also allow for nonlinear effects of governance, which resource economics studies outside the realm of natural disasters have found to be significant (e.g., Ferreira and Vincent 2010).

> 2 An alternative approach for addressing potential changes in data quality is to limit the sample to only recent years, which Wheeler (2010) does in a study based on EM-DAT data. This is a less desirable approach in our analysis, given that we rely on within-country variation to identify the effects of income and governance in our fixed-effects models.

7
households engaged in the service sector (9.5 percent). The collapse of the construction sector during the post-crisis period explains the relatively high share of households being chronic poor for this particular sector (13.5 percent).

Figure 9: Patterns of economic mobility and sector of employment, 2010 – 2015 Panel A: Broad sector of employment Panel B: Chronicity <!-- Start of picture text --> poor-poor poor-nonpoor poor-poor nonpoor - nonpoor<br>nonpoor - poor nonpoor - nonpoor 70%<br>70% 60%<br>50%<br>60%<br>40%<br>50%<br>30%<br>40%<br>20%<br>30%<br>10%<br>20% 0%<br>10%<br>0% agriculture mining manufacturing utilities construction low services public services<br>retail / logistics/... telecommunication...<br><!-- End of picture text --> _Source: Author’s calculations based on ILCS 2010 and 2015._ As poverty dynamics between 2010 and 2015 were largely driven by higher employment rates and increasing wages, Figure 9 provides a more disaggregated analysis into differences between multiple sectors of the economy. The share of chronic poor is highest for sectors with low productivity (including agriculture and construction). On the other side of the welfare ladder, the share of households being non-poor in both periods seems highest for the high productivity service sector including telecommunication, financing and insurance.

# Georgia

## Poverty Transitions and the Vulnerability to Poverty Approach

The analysis of patterns of economic mobility for Georgia focuses on the period between 2009 and 2015 which roughly coincides with a period of rapid poverty decline. As for Armenia, one key finding that emerges quite immediately is that the net reduction in poverty witnessed during this period masks considerable churning in and out of poverty. Close to a half of the households identified as poor in 2011, 2013 and 2015 were not poor in the previous period, which shows 15
TABLE 7 Sensitivity Results for Monetary, Exchange Rate, and Political Variables Regressions on Growth of Per Capita Income 1960-1989

|Beta|Stan.Err.|T-Stat|N|Re|Other Incl. Vars.*|
|---|---|---|---|---|---|
|Srowth<br>of Domestic Cre|dit<br>(GDC)|||||
|High<br>0011|-0061|.18|85|.66|LAAM,MIX,AFRICA|
|Low<br>-.0054|-0065|.83|85|<br>.5?|X,50C|
|Standard Deviation of|the Rate<br>of|Inflation|(STDI)|||
|High<br>-.00027|-0007|-37|101|-49|LAAM,REVCMIX|
|Low<br>-~.00124|0007|1.84|85|59|<br>GDC,CIVL,REVC|
|RevolutionandCoups|(REVC)|||||
|<br>High<br>-6391|-7699|-83|79|-60|GDC,BMP,PI|
|Low<br>-1.4701|<br>-6371|2.31|101|-52|<br>AFRICA|
|CivilLiberties<br>(high|er=lessCIVL)|||||
|<br><br>High<br>1043|<br>1458|.72|79|-63|GDC,BMP,AFRICA|
|Low<br>~.3491|-1268|2.75|101|52|MIX,LAAM|

*INV, RGDP, GPO, and SEC included in all Growth regressions

Sensitivity Results for Monetary, Exchange Rate, and Political Variables Regression on Investment Share 1960-1989

|Beta|Stan.Err.|T-Stat|N|rR?<br>=|Other Incl. Vars.|
|---|---|---|---|---|---|
|Growth of Domestic Cr|edit<br>(GDC)|1960-1989||||
|High<br>-00041|0003|1.54|85|18|CIVL,REVC,SOC|
|Low<br>-00003|0003|-10|86|.03|SOC,MIX|
|Standard Deviation of|the Rate of|Inflation|(STDI)|1960-198|9|
|High<br>.00006|00002|2.357|92|-32|REVC<br>,LAAM,BMP|
|Low<br>-.00001|-00002|355|105|-08|<br>AFRICA,<br>SOC,MIX|
|RevolutionandCoups<br><br>|(REVC)<br>1960-<br>|1989<br>||||
|<br>High<br>~.0347|0228|1.52|88|234|X,CIVL,$TDD*>|
|Low<br>~ .0880|-0241|3.65|85|-17|<br>SOC,MIX,GDC|
|CivilLiberties<br>(high|erzlessCIVL)|1960-198|9|||
|<br><br>High<br>0029|<br>-0061|70|85|27|REVC,AFRICA,<br>GDC|
|Low<br>-.0108|0032|3.38|88|-15|<br><br>SOC,MIX,STDD|

25 Tf the extreme bounds procedure is not allowed to choose CIVL, the coefficient on REVC is robustly negative even when the procedure is allowed to choose continent dummies.

43
# Broadband Internet, Labor Demand,

and Total Factor Productivity in Colombia Carlos Ospino<sup>1</sup> JEL codes: D24, L25, L60, O15 Keywords: Total factor productivity, broadband internet, labor demand, production function estimation.

1This work has benefitted from funding by the World Bank’s Latin America and Caribbean’s Chief Economist Office, under the regional study “Digital technology adoption, skills, productivity and jobs in Latin America”. I appreciate the very helpful comments and suggestions from Joana Silva, Mark Dutz, Rita Almeida, Truman Packard, Giulia Lotti and the participants at the author’s workshop at the World Bank. Daniel Espinosa provided much appreciated research assistance to this project. Please send comments and suggestions to <u>carlos.ospino.h@gmail.com</u>
controls (except for location and time fixed effects) and the second column including controls for age, gender, education, and length of unemployment. As can be seen from this table, employability ratings are an important determinant of access to matching services. Being rated _employable_ (relative to _less employable)_ increases the likelihood of receiving matching by 5-6pp. Similarly, being rated _highly employable_ increases the likelihood of receiving matching 5-7.4pp (though given the small sample size, this effect is not statistically significant). Providing matching services is costly for the private employment service provider. Unsurprisingly, therefore, they are more likely to provide these to jobseekers who, in their assessment, are most likely to be formally employed as a result of these services. As was shown previously, despite a lower likelihood of receiving matching, treatment effects are positive for _less employable_ jobseekers, suggesting that the counselors make use of additional information (for example what they learn during the profiling interviews and demand for the specific skills of the jobseekers) to identify those most likely to benefit from matching.

Given the importance of jobseeker employability rating we analyze correlates with the rating in Table 12, which marginal effects predicted from an ordered logit model (coefficient estimates are presented in Appendix Table A.1). The first set of marginal effects (in the first three columns) are based on a model including basic demographic characteristics from administrative data. The second set (middle three columns) adds household size, income and previous employment status as additional variables, and the third set (last three columns) includes the “Big 5” personality traits (measured in the baseline survey). First, we note that, consistent with random assignment, treatment is uncorrelated with employability assessments. Second, while gender has no impact on employability assessments, younger jobseekers, those with lower levels of education and the long-term unemployed are likely to have lower employability assessments. In terms of marginal effects, relative to younger jobseekers, jobseekers above the age of 30 are 5.6pp less likely to be rated _less employable,_ and 5.1pp more likely to be rated _highly employable_ . Similarly, those with less than secondary school education are 7.6pp more likely to be rated _less employable_ and 6.9pp less likely to be rated _highly employable_ relative to jobseekers with more than a secondary school degree. The long term unemployed are 11.7pp more likely to be rated _less employable_ and 10.6pp less likely to be rated highly employable relative to jobseekers who have been unemployed for less than a year. Household characteristics including size, number of children and income are uncorrelated with employability assessments.

15
THE REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 6. Evolution in the Herfindahl-Hirschman Index between 2000 and 2010**

<!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Herfindahl Index (Base 100)<br><!-- End of picture text --> Source: Wireless Intelligence 2010.

# **Challenges**

Most of the telecommunications network, including the national microwave transmission backbone, was destroyed during the war. All but one telephone exchange at Pointe-Noire were damaged. Since then, Congo Telecom has undertaken a limited infrastructure rehabilitation program. Most of the backbone is using wireless connections through 32 Mbps Microwave links. The Republic of Congo currently has three active mobile companies. As indicated above, GSM population coverage is already approximately 70 percent. Based on consumers’ purchasing power and willingness to pay for mobile services, it is estimated that the private sector could make sufficient investments in the coming year to cover an additional 15 percent of the population on a commercially viable basis (figure 7). To achieve universal access, however, subsidies will be required to provide coverage to the remaining 15 percent of the population.

The cost of international connectivity is high in the Republic of Congo. A mobile call to Washington, DC, costs an estimated $0.96 (Celtel), compared with $0.40 a minute (Celtel and Vodacom) from Democratic Republic of Congo. Broadband and international calls are also more expensive than in the rest of Africa, and Internet access is slower, at 0.3 mpbs compared with 2.7 mbps for the resource-rich benchmark. However, once the country is connected to the submarine cable, these figures should improve. In other African countries that have connected to the submarine cable, prices have been cut in half as long as access to the international gateway is competitively provided.

In contrast with the mobile sector, fixed telephony remains in the hands of the public monopoly, Congo Telecom (formerly Société des Télécommunications du Congo, or SOTELCO). The government has said it intends to privatize Congo Telecom, but this is unlikely given its shaky financial state. In mid2007, the company announced it had suspended payment of salaries to avoid a financial meltdown.

15