rafmacalaba/datause-encoder-data huggingface dataset:
|||PRE Working Paper Series|||
|---|---|---|---|---|
||IILQ|Author<br>Iate|a<br>pe|Contact<br>for a<br>r|
|WPS416|World Bank Etforis at Poverty<br>Measurement in the Third World: Th<br>Living Standards Measurement Stud|Paul Glewwe<br>e<br>y|||
|WPS417|Modeling the Macroeconomic<br>Requirerrents of Policy Reform|William Easterly<br>E. C. Hwa<br>Piyabha Kongsamut<br>Jan Zizek|May 1990|R. Luz<br>34303|
|WPS418|Does Devaluation Hurt Private<br>Investment? The Indonesian Case|Ajay Chhibber<br>Nemat Shafik|May 1990|M. Colinet<br>33490|
|WPS419|The Design and Sequencing of<br>Trade and Investment Policy Reform<br>An Institutional Analysis|Brian Levy<br>:|||
|WPS420|Making Bank Irrigation Investments<br>More Sustainable|Gerald T. OMara|May 1990|C. Spooner<br>30464|
|WPS421|Taxation of Financial Intermediation<br>Measurement Principles and<br>Application to Five African Countries|: Christophe Chamley<br>Patrick Honohan<br>|May 1990|W. Pitayato-<br>nakarn<br>37666|
|WPS422|Civil Service Reform and the World<br>Bank|Barbara Nunberg<br>John Nellis|||
|WPS423|Relative Price Changes and the<br>Growth of the Public Sector|M. Shahbaz Khan|May 1990|WDR Office<br>31393|
|WPS424|Mexico's External Debt<br>fe stIr u to,<br>IG-<br>i.:A<br>Eo.com.c Ar--vs s|Sweder van Wijnbergen|||
|WPSA25|**TheFrmarVki**<br>**f (-,eovrnmont**|WilliamA. McCleary|||
|WPS426|GrowTh-Oriented<br>Adjustmient<br>Programs: A Statistical Analysis|Ric^<br>Faini<br>_Jd me de_Me!o|||
|WPS427|Exchange Reform. Parallel Markets<br>and Inflation in Africe: The Case<br>of Ghana|Ajay Chhiober<br>Nernat Shafik|||
|WPS428|Perestroyka and Its Implications<br>for European Sociaiist Countries|Bela Baiassa|May 1990|N. Campbell<br>33769|
# Figure A2.4. Global policies

<!-- Start of picture text --> A. Government expenditure<br><!-- End of picture text --> <!-- Start of picture text --> A. Government expenditure B. Policy interest rate<br>Percent Percent<br>30 15<br>20<br>10<br>10<br>5<br>0<br>-10 0<br>1950 1960 1970 1980 1990 2000 2010 2020 1950 1960 1970 1980 1990 2000 2010 2020<br>Sources: Bank for International Settlements; Haver Analytics; International Monetary Fund; Mauro et al. (2015);<br>Organisation for Economic Co-operation and Development; World Bank.<br>Note: Variables are weighted by GDP in U.S. dollars. Shaded areas indicate global recessions in 1975, 1982, 1991,<br>and 2009, and global downturns in 1958, 1998, 2001, and 2012. World aggregates are not computed if data are<br>available in less than 10 countries (Panel A) and 4 countries (Panel B).<br><!-- End of picture text --> 69
assessments at each round of data collection. The rules used to score each assessment, and to aggregate subscales into indices are also described in great detail in the Appendix.

Anthropometric measurements were made at baseline to (i) assess balance across groups, (ii) control for any direct or indirect influences growth faltering (specifically stunting or chronic malnutrition) might have on child assessments, and (iii) assess heterogeneity of treatment effects. Child height and weight were measured according to the nearest 0.1 cm and 0.1 kg, respectively, following established guidelines (Cogill, 2003). Height-for-age (HAZ), weight-for-height (WHZ), and weight-for-age Z-scores (WAZ) were then calculated using the 2006 World Health Organization (WHO) growth standards (WHO Multicentre Growth Reference Study Group, 2006).

# **Primary Caregiver Measures**

In addition to gathering data on household characteristics, we collected data on the primary caregiver’s health and the home environment. At baseline and the 18-month follow-up (Round 2), the primary caregiver’s mental health status was assessed. At all rounds, the provision of household stimulation for the child’s learning and development, as well as the use of positive disciplinary techniques was measured. A standardized parenting quality index combining these three scales was created for Rounds 1 and 2. The scales administered to each caregiver are described in detail in the Appendix; scales that were child specific (e.g. the Parenting Stress Index) were administered once for each child.

# **CBCC Measures**

Extensive information on the characteristics of the CBCC, staff, and quality of staff-child interactions was gathered at baseline and both follow-up rounds. The CBCC questionnaire and observation measure were adapted from the _La Escala de Evaluación de la Calidad Educativa de Centros de Educación Preescolares_ (ECCP) (Martínez et al., 2004) from Mexico and a preschool quality tool used in Cambodia (Rao et al., 2012). Key information from the questionnaire included teacher characteristics, such as education, experience, and training.

Classroom observations were conducted while the CBCC was operating to provide an objective account of classroom organization, activities and teacher-child interactions. To complete the observations, 14
# **III.** The Data

The data set for this study is compiled from diverse sources for 14 major states of India over the period 1960-1994.11 The political data on elections is taken from Butler, Lahiri and Roy [1995]. The public finance data on taxes and expenditure is available from the 1960-1994 volumes of the Reserve Bank of India Bulletin, a quarterly publication of the central bank of India with annual issues on the finances of state governments<sup>12</sup> . Highways and roads data is compiled from the 1961-1995 volumes of the Basic Roads Statistics, an annual publication of the Ministry of Surface ransport of India and from various state Statistical Abstracts<sup>13</sup> . State demographic and economic characteristics, and a state-level price index to convert all variables into real terms, are available from an Indian data set put together at the Poverty and Human Resources Division, Development Research Group of the World Bank. A detailed description of these variables is available in Ozler et al. [1996].

" l The States Reorganization Act of 1956 divided the Indian federation into 14 states and 5 union territories that were administered by the Central government. In 1960, the state of Bombay was divided into Gujarat and Maharashtra. In 1966, the PEPSU (Patiala and E. Punjab States Union) was divided into its two main constituents, Haryana and Punjab. This study includes 13 states that were already established in 1960, namely Andhra Pradesh, Assam, Bihar, Gujarat, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Orissa, Rajasthan, Tamil Nadu, Uttar Pradesh and West Bengal. The fourteenth state in 1960, Jammu and Kashmir, has been excluded because of the political uncertainties in the region that continue to this day. The state of Punjab is included after 1966, when it attained separate statehood. Haryana is not included because data for this state is not available across many explanatory variables.

Currently, India has 25 states because several union territories have attained statehood over the years, the most recent converts occurring as recently as 1991. Therefore, to maintain consistency in our analysis over a reasonable time period, we only include those states that existed since 1960 and 1966.

121 am grateful to Tim Besley of the London School of Economics for providing me with some of this data that had already been compiled in his research group.

13Data on state roads was taken from a district-level data set put together by Robert Evenson at Yale University using official Government of India sources. Evenson, Pray and Rosegrant [1994| provide a detailed description of this data set.

14
# **Creating and Using Fiscal Space for Accelerated Development in Liberia**

**Hans Lofgren**<sup>*****</sup> **World Bank**

JEL classification: C68, E62, O15, O21 Keywords: Computable General Equilibrium, MAMS, Development Strategy, Liberia. Sector board: Economic Policy

* Hans Lofgren is in the Development Prospects Group of the World Bank. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author. 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.
<!-- Start of picture text --> A Relative IC block contribution depending on the number of questions *<br>90%<br>100% FTW ea a<br>60%<br>50%<br>40%<br>30%<br>N N N N N N N N N<br>cPV BDI BFA MRT CMR ETH NER KEN LsO MW BEN SWZ ERI UGA ZMB TZA MDG SEN NAM ML DZA BWA ZAF MAR MUS EGY<br><!-- End of picture text --> <!-- Start of picture text --> B. IC block contribution to the mean of each block<br>100%<br>7<br>80% |<br>TT<br>70%<br>60%<br>40%<br>30% N N S&S NN NS N y y .<br>NNNNNNNNNSNNABSB RK 7<br>MNNNNNNNNVNNNVVNVNNVVNVNNVVVNANANN A N VN VN VN VN VN AN AN VN AN WN WN WN AWN AW AWN AWN WN VN AN CN AN ESA<br>CPV CMR BFA BOI ETH NER KEN BEN MRT MW TZA ZMB LSO MDG UGA SEN SWZ Mu ERI DZA NAM ZAF BWA EGY MUS MAR<br><!-- End of picture text -->
31 Other developing countries benefit from national GSP schemes. The extent of GSP benefits varies among EFTA countries. According to Table 7 these are relatively limited. In 1991, for example, only 15% in Finland or about 1% in Norway and 21% in Sweden of imports from developing countries would have actually received GSP benefits.

Level of protection in **EFA** countries. The structure of protection in the EFTA countries makes preferences valuable in sensitive sectors because of their higher level of protection. Average duties of manufacures in the EFTA countries are relatively low, ranging between 6-9%, which will be futher reduced by the Uruguay Round. The high level of duties in a number of sensitive, labor-intensive sectors in the Nordic EFTA countries implies a large preferential margin (Table 8) A number of sensitive sectors such as fish, textiles and clothing, and footwear are in addition subject to a range of non-tariff barriers increasing the protective impact of duties on third countries that remain outside the preferences. Non-tariff barriers, Table 7: EFTA GSP Benefits (1991)

|Importers|Share of Imports from<br>Developing Countries|Share of Imports frk m<br>that receive|Developing Countries<br>d Preferences|
|---|---|---|---|
||Covered by GSP(%)|% of GSP Covered|% of Total Imports|
|Austria|45|100|45|
|Switzerland|69|71|28|
|Finland|26|59|15|
|Norway|23|64|1|
|Sweden|32|66|21|

# Source: UNCTAD TD/B/SCP/3

especially in Sweden and Norway, affect resource and capital-intensive sectors as well (Table _8)I1._ This increases the importance of preferences in market access especially in products that '<sup>7</sup> Data on non-tariff barriers should be read carefully. Latest data available is 1988 and the situation may have changed during the last five years. As most Nordic countries use MFA restictions on textiles and clothing, and the data shows very low levels of NTBs in these items, it may not portray very accurately the actual situation.
## **Figure 10. Services trade restrictiveness by services sector and region**

<!-- Start of picture text --> Europe and Central Asia<br>OECD<br>Latin America and Caribbean<br>Sub-Saharan Africa<br>East Asia and Pacific<br>Middle East and North Africa<br>South Asia<br>Gulf Cooperation Council<br>0 10 20 30 40 50 60 70<br>Services trade restrictiveness index<br>Professional services Transportation Retail Telecommunications Financial<br><!-- End of picture text --> _Source:_ Borchert, Gootiiz, and Mattoo 2014.

_Note:_ This figure compares the restrictiveness of services trade policy across countries based on the World Bank Services Trade Restrictions Index, which ranges from 0 (completely open) to 100 (completely closed). The information for the database was collected between 2008 and 2010.

# **_International cooperation and services in global value chains_**

Much of the openness in services markets and the emergence of services-related GVCs has come from unilateral liberalization. Services trade negotiations have generated an abundance of rules and commitments that have enhanced the transparency and security of market access but have not produced much additional liberalization. One reason is a form of “negotiating tunnel vision,” which has led to a focus on reciprocal market opening rather than on creating the regulatory preconditions for liberalization. More could be achieved if negotiations offered regulators the opportunity not just to tie their hands (through agreed-on commitments) but also to secure assistance to deal with problems they cannot solve on their own.<sup>20</sup> One obvious reason for international cooperation is that poorer developing countries do not always have regulatory institutions equipped to deal with international competition. These countries would participate

> 20 The capture of regulatory barriers by established services providers may also partly explain the limited progress on this front.

22
traditional segmentation view would argue that in periods of economic expansion, the increased availability of formal sector jobs, and the reduction in separations, should lead to increased flows from informality toward formality, and reduced flows in the opposite direction.

To keep the analysis compact, we examine only the Mexican market from 19872004, a period that includes two periods of recovery and crisis.<sup>13</sup> Figure 6 shows the evolution of the share of the work force in unemployment, as well as the share of formality over total employment sector’s share of the labor market<sup>14</sup> The increase in informality in both periods of high unemployment suggests a very traditional view of the behavior of the role of the informal sector as a shock absorber for the formal sector and perhaps a kind of disguised unemployment.

However, again, the simple stock variables hide important information. Figure 7 shows the flows in and out of formal employment. As expected, we observe pro-cyclical transitions from informality to formality. But, contrary to the segmented view of the labor market, we find virtually identical pro-cyclical transitions from formality to informality, especially to self-employment. In fact, the HP filtered correlation between the formalself-employment bilateral flows is 0.9. There is an unusually high transition rate from formal employment into self-employment during the 1987-91 boom that mirrors, in fact, exceeds the reverse movement from the formal sector. This suggests that there was a particularly strong re-matching between these two types of employment during that recovery. There is a decline in sector to sector search going into the crisis and then a recovery again mirrored, although more weakly, in the reverse transition. Similar

> 13 In this case we take full advantage of the ENEU and compute the quarterly transitions across employment status as described in section II. We also smooth the series using a moving average smoothing with a three quarter window.

> 14 The share of the formal sector remained reasonably constant from 1987 to 1991 period showing a slight decrease in the share of formality from 59% to 57% of total employment, despite a continued decrease in unemployment rate. Thereafter, it remains stable around that level up to the eve of the crisis in which it bottoms out at 53%.  After the devaluation, it began a sharp recovery, regaining its earlier highs by 2001. Finally, despite the fact that the 2001 recession was substantially milder than the Tequila Crisis, formality rates fell again to around 54%. These movements are largely mirrored by the movement of unemployment from 3% in 1989 to 8% during the crisis and then again down to the lowest levels in the sample in 2001, with a slight increase in the last three years of the sample.

21
**Table 5 Ghana:  Projected MDG achievements in the baseline scenario**

|||1990|2004|2015|Target|Distance to target|
|---|---|---|---|---|---|---|
|Poverty headcount|(%)|52|31|11|26|382%|
|Primary completion rate|(%)||47|88|100|76%|
|Under-5 mortality|(per 1,000)|122|112|97|40|20%|
|Maternal mortality|(per 100,000)|740|503|440|185|20%|
|Access to safe water|(%)|54|56|62|85|21%|
|Access to sanitation|(%)|21|35|47|85|23%|

It should be noted that the projected narrowing of the income distribution in the baseline scenario is at odds with Ghana’s inequality trends of the last 15 years (see Coulombe and Wodon, 2007). However, according to that study, growing spatial inequality has been one of the main drivers of increases in total inequality (the national Gini coefficient rising from 0.35 in 1992 to 0.39 in 2004). In particular, the poor in the northern regions—a historically poor area in Ghana—have largely been left out of the growth process that has been benefited other parts of the populating during the last decade and a half. Since the current version of the Ghana MAMS model does not include a regional dimension, this historically important element of the evolution in inequality has been left out of the analysis. Instead, the simplifying assumption is that all poor, regardless of geography, can benefit from the robust growth in unskilled wages in the baseline scenario.

**Figure 3 Ghana: growth incidence curve for the baseline and MDG scenarios**

<!-- Start of picture text --> 0 20 40 60 80 100<br>Share of population ranked by per capita income<br>Growth incidence: base Average income gain: base<br>Growth incidence: MDG Average income gain: MDG<br>75<br>70<br>65<br>60<br>55<br>Per capita income growth in %<br>50<br><!-- End of picture text --> To assess whether the decrease in inequality is particularly helpful in enhancing the poverty reducing effect of growth, the micro-simulation model is used to generate an additional counterfactual scenario where every individual gets the same average increase in his/her income (i.e., a hypothetical situation where growth is distributionally neutral). In this case, poverty would be reduced less, with the 2015 poverty headcount reaching 12.1 percent instead of 11.4 percent in the baseline scenario. The relatively small 12
relatively a very small change in welfare in the past decade<sup>30</sup> . In effect, the relative winners were actually the poor and lower-middle class households in both the rural and urban population (up to the 45<sup>th</sup> percentile of the respective population distribution) who saw their real consumption significantly improve, while losers can be identified as the rural rich (over the 70<sup>th</sup> percentile) and the urban rich (over the 80<sup>th</sup> percentile) households. Such an outcome is the result of many socio-economic factors that characterize the growth process in Jordan over this time period. These factors range from changes in productivity and economic returns observed in various sectors, to access to services such as education and health that contribute to human capital formation, to more micro aspects related to the household composition and individual characteristics. The paper will not dwell here on the determinants of welfare changes in the 2000s and will leave it to subsequent sections of the study.

Meanwhile, the popular discontent cannot be solely explained by the general perception of worsening wellbeing and the relative losses and gains in welfare growth. One needs to examine carefully the welfare patterns post-2008, a period that saw a severe slowdown in economic activity and a rise in unemployment. To do so, the paper re-calculates the welfare, poverty and inequality indicators for the period 2008-2010, using the respective household surveys, and discusses the observed trends next. As is discussed later, the period 2008-2010 threatens to reverse the welfare gains observed during the decade.

> 30 The incidence curves become flatter for middle percentiles.

13
(2011). The framework suggests an identification strategy that relies on the following reasoning: the more a country competes with China in a third market, the more a given depreciation of the renminbi is likely to hurt its exports in that market. We develop indices of competition between China and its competing exporting countries at the exporter-importer-product level to implement this strategy. The empirical specification, with a battery of very general fixed effects that control for all observable and unobservable importer-exporter-product, importer-exporter-time, exporterproduct-time and importer-product-time varying characteristics, helps us overcome to a large extent the problems of omitted variables that plague estimation of trade-exchange rate equations using aggregated data. Moreover, our estimates are less susceptible to reverse causality concerns as exports, measured at a disaggregated level, are unlikely to affect a macroeconomic variable like the exchange rate, more so when the latter is the exchange rate of another country, China.<sup>2</sup> We find robust evidence for the existence of a statistically and economically significant spillover effect. In particular, exports to third markets of countries with a greater degree of competition with China tend to rise/fall significantly more as the renminbi appreciates/depreciates. Our estimates suggest that a 10 percent appreciation of the renminbi increases a developing country’s exports at the product-level on average by about 1.5-2 percent. For high indices of competition, the increase could be as large as 6 percent. The results imply that, going forward, an appreciation of the renminbi could provide a substantial boost to developing country exports. Our spillover estimates are robust to a variety of statistical tests, to alternative measures of exchange rates, to alternative disaggregation of the trade data, and also across exporting and importing regions. They are also robust to incorporating the effect of competition from countries (other than China) whose currencies move with the renminbi.

We also find that the magnitude of the spillover effect is consistent with the predictions from the analytical framework. For example, as implied by theory, the spillover effect is greater for homogenous products with greater substitution possibilities than differentiated products. Further, the spillover effect is attenuated for products that rely more on foreign inputs (and hence have a lower degree of Chinese domestic value added).

> 2 See Engel (2009), for a discussion of how hard it is econometrically to separate out the effect of exchange rates on trade.

3
# **The Impact of Interest Rate Caps on the Financial Sector: Evidence from Commercial Banks in Kenya**

Mehnaz Safavian and Bilal Zia<sup>1</sup>

## **JEL Codes: G18, G21, G28**

**Keywords:** Interest Rate Caps, Financial Inclusion, Banking Sector

> 1 Both authors are from the World Bank, msafavian@worldbank.org and <u>bzia@worldbank.org, respectively. This</u> paper is the culmination of a year-long independent study of the impact of interest rate caps in Kenya, commissioned by the World Bank country office in Kenya at the request of the Central Bank of Kenya (CBK). We are very grateful to CBK for providing data from commercial banks that are used in this paper. We also thank the World Bank team involved in the study, in particular Rajiv Daya, Allen Dennis, Sarah Sanya, and Caroline Wambugu. Finally, we thank all financial sector participants who accepted our interview invitations and responded to CBK’s data request. All analyses, conclusions, and recommendations presented in this paper are our own, as are any remaining errors.