rafmacalaba/datause-encoder-data huggingface dataset:
These control variables are needed to make sure that comparisons are made across similar workers. The standard practice in this respect is to estimate Mincerian equations, where the explained variable is the log of labor earnings and the explanatory variables are individual characteristics like those listed above. The analysis is completed by splitting the sample between the individuals who do work in the formal sector, and are therefore covered by labor market policies and institutions, and those who do not.<sup>2</sup> The criteria used to identify the formnal sector vary across studies. In some cases, the sample is split according to the size of the establishments these individuals work in. But more elaborate criteria are applied too. Consider, for instance, the study on C6te d'Ivoire by Vijverberg and van der Gaag (1990). This study applies factor analysis, involving 15 job attributes, to data from the 1985 Living Standards Survey (CILSS). The first component of these data is then used as an indicator of the formality of jobs. This component turns out to be highly correlated with six of the job attributes. It is higher when the wage is subject to minimum wage legislation, when there is a signed contract, when the worker receives paid holidays, when he or she is eligible for paid sick leave, when there is a retirement plan, and when the worker receives social security benefits.

A different approach is adopted by Miller and Vall&e (1995) in the case of Cameroon. Using a plant-level based survey of workers produced by the Regional Program on Enterprise Development (RPED), these authors consider three sectors: formal regulated, formal unregulated and informal. Firms in the formal regulated sector are characterized by multiple ownership, or by some form of public ownership, or by having complained about being constrained by labor regulations. At the other end, firms in the informal sector are characterized by individual ownership, and by either having apprentices or carrying no

- 2 Some of the studies also correct for the self-selection of workers into the different sectors. They take advantage of the fact that workers in the formal and informal sectors have different observable characteristics to make inferences about other, unobservable characteristics that may affect their earnings. Ignoring these other, unobservable characteristics could bias the coefficients associated to the observable ones.

15
this description.

We identi...ed 1,205 sex workers in Busia Town from this "snowball" technique.<sup>8</sup> According to the 2003 Kenyan Demographic and Health Survey, women aged 15-49 make up approximately 21.9% of the rural population, which implies that about 12.5% of Busia women aged 15-49 earn some income from sex work. From this, we argue that the women involved in this project are not particularly atypical of the average woman in Busia Town, and that the results of this study are generalizable to a signi...cant proportion of the female population in Kenya.

# 3.3 Data Collection

Of the 1,205 women that were identi...ed, a random sample of 248 women were selected for project participation, strati...ed by peer group. The data collection took place in two rounds: Round 1 occurred between October and December, 2005, and Round 2 occurred between July and October, 2006. We asked each woman to keep a daily diary for the duration of each round in which she self-reported her income, expenditures, the transfers she had given and received, and the shocks she had encountered that day.

In the diaries, women were also asked to record detailed information on each encounter with a client, including the activities performed, whether a condom was used, and the price that was paid (both in cash and in goods or services).<sup>9</sup> After a preliminary analysis of the Round 1 data, some additional questions were added to the Round 2 diaries. The additions which are relevant to this paper included questions on client characteristics, somewhat more detailed questions on the transfers received from regular clients, and, most importantly, separate measures of unprotected

> 8 Working with a sample identi...ed by women in SHCP-organized peer groups has advantages and disadvantages. One advantage is that women in the peer groups know other single women in their community with multiple concurrent sexual partners, which should have increased the size of the sample, improved its representativeness, and included more informal sex workers. Furthermore, the structure of the peer groups allowed peer educators and peers to better locate the sex workers whom they identi...ed. Finally, SHCP had a long, stable relationship with sex workers in Western Province, so that sex workers trusted the organization, which tended to limit nonparticipation and attrition among sampled sex workers. The major disadvantage is that the women identi...ed in this way may not be fully representative of the sample of sex workers in Busia Town. As in any snowball sampling technique, the sample includes fewer women right at the margin of participation in transactional sex.

> 9 The diaries included questions about a maximum of 3 clients per day.

8
SADC’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

# **Table 2.2 Road conditions along major transit corridors in the SADC**

|||**Con**|**dition(%**|**)**||**Type (%)**||**Pe**|**rcentage in tr**|**affic band**|**s(AADT)**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Corridors**|Good|Fair|Poor|Unknown|Paved|Unpaved|Unknown|<300|300–1,000|>1,000|Unknown|
|**_Gaborone to Durban*_**|**_97.1_**|**_0.5_**|**_0_**|**_2_**|**_99.5_**|**_0_**|**_0.5_**|**_0_**|**_0_**|**_96.5_**|**_3.5_**|
|Botswana|90.5|0|0|10|100|0|0|0|0|100|0|
|South Africa|97.4|0.5|0|2|99.5|0|0.5|0|0|96.3|3.7|
|**_Harare to Durban *_**|**_72.9_**|**_25.3_**|**_0.5_**|**_1_**|**_100_**|**_0_**|**_0_**|0.8|**_3.3_**|**_94.7_**|**_1.2_**|
|Zimbabwe|0|100|0|0|100|0|0|3.3|13.9|82.8|0|
|South Africa|95.8|2|0.7|2|100|0|0|0|0|98.4|1.6|
|**_Lusaka to Durban*_**|**_62_**|**_34.6_**|**_2.4_**|**_1_**|**_100_**|**_0_**|**_0_**|**1.3**|**_5.5_**|**_92.1_**|**_1_**|
|Zambia|26.1|31.3|42.5|0|100|0|0|0|59|41|0|
|Zimbabwe|0|100|0|0|100|0|0|4.2|8.7|87.1|0|
|South Africa|95.8|2|0.7|2|100|0|0|0|0|98.4|1.6|
|**_Lubumbashi to Durban_**|**_59_**|**_35.3_**|**_4.9_**|**_1_**|**_100_**|**_0_**|**_0_**|**1.1**|**_6.4_**|**_89_**|**_3.4_**|
|Congo DR|0|100|0|0|100|0|0|0|0|0|100|
|Zambia|46.2|28.4|25.4|0|100|0|0|0|23|77|0|
|Zimbabwe|0|100|0|0|100|0|0|4.2|8.7|87.1|0|
|South Africa|95.8|2|0.7|2|100|0|0|0|0|98.4|1.6|
|**_Lilongwe to Nacala_**|**_27.2_**|**_60.2_**|**_12.5_**|**_0_**|**_61_**|**_39_**|**_0_**|**0**|**_0_**|**_34.7_**|**_65.3_**|
|Malawi|78.4|18.5|3|0|100|0|0|0|0|100|0|
|Mozambique|0|82.4|17.6|0|40.2|59.8|0|0|0|0|100|
|**_Harare to Beira*_**|**_0_**|**_72.4_**|**_0_**|**_28_**|**_100_**|**_0_**|**_0_**|**4.2**|**_0_**|**_44.3_**|**_51.5_**|
|Zimbabwe|0|100|0|0|100|0|0|8.7|0|91.3|0|
|Mozambique|0|46.4|0|54|100|0|0|0|0|0|100|
|**_Gaborone to Walvis Bay_**|**_59.2_**|**_17.3_**|**_0.1_**|**_23_**|**_100_**|**_0_**|**_0_**|**11.5**|**_44.3_**|**_44.2_**|**_0_**|
|Botswana|50.7|5.1|0|44|100|0|0|8.2|65.4|26.4|0|
|Namibia|68.8|31|0.2|0|100|0|0|15.3|20.6|64.1|0|
|**_Lusaka to Dar Es Salaam*_**|**_68.9_**|**_19.1_**|**_9.8_**|**_2_**|**_100_**|**_0_**|**_0_**|**34.2**|**_23.6_**|**_42.2_**|**_0_**|
|Zambia|70.1|19.3|10.6|0|100|0|0|63.7|26.1|10.2|0|
|Tanzania|67.5|19|8.9|5|100|0|0|0|20.7|79.3|0|

_Source:_ AICD calculations. _Note:_ AADT = annual average daily traffic. * Denotes portions of the TransAfrica Highway in the SADC.

Traffic patterns for the SADC region are heavily concentrated along the north-south corridor, with much lower volumes on the east-west branches. For example, the route connecting Harare and Durban is used by over a 1,000 vehicles a day along 94 percent of its length, whereas the route connecting Harare to Beira reaches this traffic level along only 44 percent of its length. The same observation can be made for traffic between Gaborone to Durban versus Walvis Bay, even though Walvis Bay is slightly closer to Gaborone than Durban. Another route that records low traffic is that between Malawi and Nacala.

13
where, _Ht_ is the stock of housing in the economy, _Lt_ is land area in sq. mt., _Bt_ represents building structure. The parameter ψ _t_ represents the technological parameter and is assumed to be 1. With price per house in period _t_ , pht, the developer’s profit maximization problem can be written as max Π _t_ = [ _pht L_ α _t Bt_ 1 −α − 1( + _rt_ ) _Rt_ ] (9) _t s_ .. _t Rt_ = _Lt pl_ , _t_ + _Bt pb_ , _t_ (10) where, _Rt_ is the total demand for finance by the developer to pay for the plot as well as materials. Based on the developer’s maximization condition in the long run, supply of land and building structure **_per unit of housing_** are Notice that the developer’s supply decision is inversely related to the loan rate, rt. Therefore, any distortion in the credit market that affects the loan rate is expected to influence the land and building supply decision of the developer.

# **_2.3. Financial Intermediary_**

We assume banks to be financial intermediaries that maximize profit in a perfectly competitive environment. Banks convert their entire deposits from households into loans towards the developer without any friction. In return, they charge a loan rate _rt_ . To make our model more general and compatible with our objective, we consider imperfections in the credit market. Such imperfection originates from the default vulnerability of the borrowers and expected recovery costs. This assumption is relevant for Brazil as well as most developing countries. According to a report by the World Bank (World Bank 2002), 10
WPS3653

# **Regime-Switching in Exchange Rate Policy and Balance Sheet Effects**

# **© Norbert Fiess**<sup>*****</sup> **and Rashmi Shankar**<sup>**#**</sup>

# **Abstract**

It is well accepted that exchange rate policy in many emerging markets has been characterized by shifts between a stronger and weaker commitment to peg. This raises the following questions, which we address in our paper: Does intervention policy exhibit clearly defined and periodic shifts? What drives this policy variability? We identify clearly defined switches between high and low intervention in all the countries in our sample. We also find strong evidence that balance sheet effects, proxied by the stock ratio of external liabilities to assets, and economic performance, as measured by GDP and stock market indices, determine the likelihood of the regime shift. Specifically, an increase in reserve currency debt raises potential capital losses from devaluation and reduces the probability of switching to a low intervention regime. We use a panel of quarterly data starting 1985 through 2004 for a sample of 15 countries, mostly from East Asia and Latin America. We adopt a novel two-step empirical strategy in this paper. First, we measure the policy response of the central bank in two ways, both derived from the monetarist model: a standard exchange market pressure index and a model-based volatility ratio that is endogenized relative to Japan, our “benchmark” floater. We apply regime-switching methods to these “policy response indices.” This generates a time-series of unconditional probabilities of switching between high and low intervention. In the second step, we establish a set of variables that explains changes in these probabilities.

**_Key words_** : Markov regime switching, balance sheet effects, exchange market intervention, panel estimation, emerging markets **_JEL Code_** : F310, F340

# World Bank Policy Research Working Paper 3653, July 2005

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

#Shankar (Corresponding author): Department of Economics and International Business School, Brandeis University MS 021, 415 South Street, Waltham, MA 02454; Telephone: 781-736-2265; Fax: 781-7362269; Email: rashmi@brandeis.edu;<sup>*</sup> Fiess: Office of the Chief Economist, Latin America and the Caribbean, the World Bank, 1818 H Street NW, Washington DC 20433; telephone: 202-473-5768; Email: <u>nfiess@worldbank.org.</u>
- 35 -

The final table (Table 17) combines the data on road<sup>user costs and road user charges,</sup> presenting both the lower and higher estimate of the costs<sup>and charges. The results underline</sup> our previous conclusions:

- (a) The annual license fee constitutes only a minuscule<sup>fraction of road user charges.</sup>

- (b) Current fuel taxes (if they continue to be<sup>adjusted to world energy prices and</sup> changes in the rate of exchange) are adequate<sup>to cover road user costs for most</sup> modes, and specifically private cars.

- (c) The only mode where the charges do not<sup>cover costs is the heavy trucks, where the</sup> charge is only 1/4 to 1/2 of the estimated costs.

Table 17. Road-User Costs and Road-User Charges, 1988<sup>(C/km)</sup>

||Road us|er costs||Road-|user cha|rges||
|---|---|---|---|---|---|---|---|
||||Annual<br>fee|Fuel t|axes<br>|Tot|al<br>|
||a|b||a|b|a|b|
||(1)|(2)|(3)|(4)|_(5)_|(6)|(7)|
|Car|0.29|0.32|0.06|0.37|0.44|0.43|0.50|
|Buses:||||||||
|Light|0.40|0.40|0.02|0.40|0.59|0.42|0.61|
|<br>Heavy|0.76|1.22|0.02|0.68|0.93|0.70|0.95|
|Trucks:||||||||
|Light|0.79|0.35|0.02|0.48|0.81|0.50|0.83|
|<br>Medium|1.06|2.03|0.03|0.74|1.82|0.77|1.85|
|Heavy|3.05|8.73|0.11|1.37|1.90|1.48|2.01|

Sources: Col. (1) - Table 1^, panel A; Col. (2) - Table 12, panel B; Col. (3) - Table 16; Col. (4) - Table _15,_ estimate (a); Col. _(5)_ - Table 15, estimate (b); Col. (6) = (3) + (4); Col. (7) = (3) + _(5)._
analysis, by incorporating a fuller accounting of all the different components of compensation (including in-kind allowances, savings from per diem payments, and in-kind benefits such as the use of a government motorcycle) demonstrated that most managers in the Lao civil service are in fact compensated in line with their demographic and private sector peers. Moreover, when additional incentives (such as job security, benefits, and status) are factored in, the motivations for seeking and retaining civil servant status are plentiful. In addition, we were able to build a clearer picture of how civil servants, including education and health cadres, supplement their civil service incomes—enabling a better understanding of how both compensation and broader HRM practices would have be to reformed in order to modernize and enhance the productivity of the Lao civil service.

# **_The Lao Civil Service in Perspective_**

A number of salary and establishment reforms have taken place in the Lao civil service over the past 25 years. Today, in per capita terms—with 1.8 civil servants per 100 population (Figure 1)—the Lao civil service is not too large, yet significant growth in the size of the establishment has occurred over the past decade (Figure 2)—particularly in the key professional grades. Growth in the number of employees is due to a combination of actively increasing the size of the civil service together with weak establishment control, particularly at the provincial level. Sectoral composition has remained roughly constant, with Health and Education sector employees together making up three-quarters of the civil service.

**Figure 1: Civil servants per hundred population, Lao PDR and regional comparators**

**Figure 2: Total number of civil servants, 2002-2008**

<!-- Start of picture text --> Cambodia 1.2 115,000<br>China 2.8<br>110,000<br>Indonesia 1.7<br>Lao PDR 1.8<br>105,000<br>Malaysia 3.7<br>Mongolia 4.4 100,000<br>PNG 1.3<br>95,000<br>Philippines 2.9<br>Singapore 1.4<br>90,000<br>South Korea 2.0<br>Thailand 1.9 85,000<br>Timor-Leste 2.6<br>80,000<br>Vietnam 2.1 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08<br><!-- End of picture text --> _Source: World Bank analysis_ <!-- Start of picture text --> Source: PACSA<br><!-- End of picture text --> The civil service wage bill has exhibited a steadily growing trend over the past decade, with most recent increases ringing a note of caution. From the fiscal perspective, the wage bill has risen significantly over the past decade from 12.5 percent of total expenditure in FY2000/01 to 28.5 percent in FY2008/09; and 33.5 percent to 44.3 percent of recurrent expenditure over the same period (Figure 3).

19
The World Bank study of agricultural protection over the 1955 to 2005 period led by Kym Anderson (2010) finds that the situation regarding agricultural protection has changed dramatically within the space of a generation. As shown in Figure 2, agricultural protection in the developing countries was, indeed, generally negative in the early years of the sample. By contrast, in the industrial countries, agricultural protection was close to 30 percent and rising.

**Figure 2. Average agricultural protection in developed and developing countries, %**

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>1955-59 1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04<br>-10<br>-20<br>DCs incl. K/T HICs incl. ECA<br><!-- End of picture text --> Source: Anderson (2009).

However, the evolution of agricultural protection in developed and developing countries has been quite different. Agricultural protection in developing countries has continued to trend upwards, albeit with some variations over time. But the end of the sample period, agricultural 9
sustainability, the projected future trajectory of the debt ratios and the vulnerability of debt ratios to shocks, as indicated in stress tests.

|**Table 4**<br>**Summary of Publ**|**ic Debt Risk Assessments for 34 SIDS**|
|---|---|
|**Risk Assessment**|**Country**|
|Unsustainable|Suriname, Antigua and Barbuda|
|In Debt Distress|São Tomé and Príncipe, Grenada|
|High Risk of Debt Distress|Cabo Verde, Comoros, Dominica, Haiti,<br>Maldives,<br>Marshall<br>Islands,<br>Mauritius,<br>Micronesia, Papua New Guinea, Samoa, St<br>Vincent and the Grenadines, Tonga, Tuvalu|
|Moderate Risk of Debt Distress|Barbados, Belize, Guyana, Jamaica, St Kitts<br>and Nevis, Solomon Islands, Timor Leste,<br>Trinidad and Tobago, Vanuatu|
|Other risk assessment||
|Substantial Risks|St Lucia|
|Elevated Risks|Bahamas|
|Significant Risks|Fiji|
|Increased Risks|Palau|
|Decreased Risks|Dominican Republic|
|With Risks|Seychelles|
|Sustainable<br>_Source: Debt Sustainability Analysis Assess_|Nauru<br>_ments published with Article IV and other country reports_|

_<u>Source: Debt Sustainability Analysis Assessments published with Article IV and other country reports</u> on the IMF website._ 17
**Exchange Rate Overvaluation and Trade Protection:**

# **Lessons from Experience**

# **Howard J. Shatz and David G. Tarr**

# **Harvard University and The World Bank**

# **I. Introduction**

Despite a general trend in the world toward more flexible exchange rate regimes, over half the countries in the world maintain fixed or managed exchange rates. ' Moreover, based on a fundamental measure of overvaluation, the black market premium,<sup>2</sup> about 25 percent of the countries in the world have exchange rates that are overvalued by at least 10 percent, and 19 countries have premiums of more than 25 percent. In 10 countries, the black-market premium exceeds 100 percent, which implies that the exchange rate fails to serve its role in allocating foreign exchange.<sup>3</sup> There are many reasons why countries maintain fixed exchange rate regimes: Their currencies may be linked to the currency of a partner or partners, e.g., the currencies of the CFA zone in Africa. The exchange rate may be used as a nominal anchor, as in the Southern Cone of Latin America in the early 1980s. Or the exchange rate regime may be employed as a means of regulating foreign trade, as in many of the former communist countries.

There are clearly advantages and disadvantages of both fixed and flexible exchange rate systems and their variants, and we do not discuss their relative merits in this paper. Rather, we note that, as a practical matter, exchange rate management in many countries in the world has resulted in overvaluation of the real exchange rate. Overvaluation can arise from several factors. Among them: It is the nature of cycles of international economic activity that from time to time countries are hit hard by external' shocks. For developing countries, terms of trade shocks are often quite important, deriving typically from significant swings in energy or commodity prices. Financial 1 As of the beginning of 1999 (the latest available data), the IMF (1999, Appendix I) reported arrangements for 185 countries. The exchange rate regimes can be divided into pegged (84 countries), floating (75 countries) and limited flexibility (26 countries). Of the 84 with pegged exchange rates, 37 have no separate legal tender, 8 use a currency board arrangement, 24 peg to another currency, and 15 to a composite of currencies. Of those floating, 27 maintain a managed float and 48 an independent float.

2 The black market exchange rate is likely to be overly depreciated relative to an equilibrium long run real exchange rate since an actual real depreciation would increase the supply and reduce the demand for foreign exchange. See Ghei and Kamin (1999) for a detailed explanation and econometric evidence.

3See Global Currency Report (1999). 38 out of 160 countries had black market premiums of more than 10 percent at the end of 1998. Of these: 19 had premiums of more than 25 percent; 13 had premiums of more than 50 percent; and 10 countries had premiums of more than 100 percent. These latter 10 included Afghanistan, Algeria, Angola, Iraq, North Korea, Liberia, Libya, Myanmar, Sao Tome and Principe, and Somalia.

1
# **5.1  Fish Species Change Scenarios for** **_Upazila_ s in the Sundarbans Region**

For each of the 83 species identified in Appendix Table A1, we build a digital map for 2012 that assigns 1 to pixels that satisfy the species’ stable habitat criterion (pixel salinity range falls within the species’ tolerance range) and 0 otherwise. We add across the 83 maps to determine total species with stable habitat in each of 101,600 pixels. Then we perform the same operations for all 27 salinity scenarios in 2050 and calculate percent changes (2012-2050) in total species for each pixel. Overlaying an administrative map shapefile provided by the Government of Bangladesh, we compute mean percent changes in the 27 scenarios for 110 _upazila_ s in the Sundarbans region.

# **5.2  Poverty Incidence in the Sundarbans Region**

We assess collective risk using estimated total poverty populations for _upazila_ s in 2011. These are the product of 2010 poverty incidence estimates provided by the World Bank (2014b) and 2011 population estimates from the Bangladesh Bureau of Statistics. Following World Bank (2014a), we use two standards to determine poverty incidence: the upper poverty line, for households whose food expenditures are at or below the food poverty line established by the Bangladesh Bureau of Statistics;<sup>15</sup> and the lower poverty line, for extremely poor households whose total expenditures are at or below the food poverty line.

# **5.3  Risk Assessment for** **_Upazila_ s in the Sundarbans Region**

To illustrate the range of results produced by this exercise, we employ the two bounding scenarios for 2050 that are mapped at the pixel level in Figure 1: least change (Scenario B1, GCM MIROC-3.2, SLR 27 cm, land subsidence 2 mm/year); and most change (Scenario A2, GCM

> few coastal fish species may emerge gradually in inland water but their harvesting technology is costly and not affordable to the poor.

> 15 See Report of the Household Income and Expenditure Survey/HIES 2010.  Bangladesh Bureau of Statistics, Government of Bangladesh.

17