rafmacalaba/datause-encoder-data huggingface dataset:
While the Global MPI has data for over 100 developing countries, not all countries have data for more than one year. As our analysis seeks to also investigate dynamics over time, we required at least two data points for each country—with the aim of an extended time lag in between years (as poverty dynamics can be slow to change). For this reason, we restrict the dataset to those economies that have surveys available for more than one year _and_ have a minimum gap of three years between the first and last survey. This strategy helps us to minimize noise in the changes in multidimensional poverty resulting from short-term measurements. As a result of these selection criteria, our dataset includes information for 67 countries for the period between 2003 and 2020 (Table A1.2 in Appendix 1 lists the covered countries and their survey years).

# **5. Results**

## **5.1. Mapping the Interconnected Structure of Poverty in 67 Countries**

We begin by showing how the Poverty Space and Poverty Centrality can help us understand the structural aspects of poverty within an economy. For illustration purposes, we focus on the countries of the Kyrgyz Republic and Ethiopia which present contrasting cases in terms of the structure and stability of their Poverty Spaces.

Figure 1 displays network visualizations of the Poverty Spaces for the Kyrgyz Republic and Ethiopia, comparing the initial and final years of the survey. In these graphics, nodes signify poverty indicators, and the edges between them indicate important connections (for the visualization, for each network we use a threshold value calculated as the minimum edge weight for which every poverty indicator has at least one connection). The node size corresponds to its poverty centrality (the larger the node, the more central the indicator), while its color matches the censored headcount ratio of the indicator (ranging from a low share of people deprived in the indicator in blue to a high share deprived in yellow).

Focusing on the Kyrgyz Republic (Figure 1 a), we see that Housing was the most central indicator in 2006 (located in the core and denoted by the largest node), while Schooling was the least central (located in the periphery and denoted by the smallest node). This suggests that a household in 2006, if deprived in any other indicator, was most likely to also lack adequate Housing. Conversely, if a 15
constraints and quantitative data available only from the firm itself Hence, it is a waste of tir., to pose speculative or opinion questions whose responses do not tap this pool of information. We advise against question such as: _External debt is a serious national problem. Strong Agree Somehat Agree Somewhat Dsagree Strong Disagrec_ As the object of firm-level surveys is to fill gaps in knowledge and overcome potential biases of existing information sources, they should be organized and worded so that unforeseen responses can emerge. A survey is not simply an instrument to confirm existing understandings, although that may be one outcome, but also to add a new and different perspective on economic policy issues: that of businesses themselves. This leads to two conclusions: there should be at least one question in each area of inquiry open enough to allow unforeseen responses; and questions should never be leading in their wording or ordering.

It is relatively easy to manipulate a survey to obtain desired responses -- but this renders the survey process worthless. For example, if you ask: _A paved four-lane road would be helpful to my business. StronglyAsree Agree Disagree StronglDisagree_ or _There should be more credit at lower interest rates for businesses like mine. Stron Agree Agree Disagree Strongl Dsagree_ you are likely to find grounds to justify your highway or line of credit. Only by weighing transport constraints and finance constraints against others does one get a sense of their relative importance, which should ultimately play a role in determining how resources are all -ated.

Four Tpes of Ouestions Surveys have successfully employed at least four types of questions: short answer questions; open-ended questions; pre-coded multiple choice questions; and questions involving the rating or ranking of constraints. Table II presents examples of each of the four types of questions from actual surveys.

Short answer questions elicit answers of a few words or a single number. (However, they can turn out to be quite time-consuming if the answer requires time-consuming research on the part of the respondent.) They should be pre-tested to assure their answers are truly short, both in length and time.

Open-ended questions are relatively easy to ask, but may elicit long responses that do not lend themselves to easy summarization, much less subsequent statistical description. Some survey forms attempt to overcome this pitfall by having the surveyor categorize the response according to preestablished choices. This can become unwieldy -- one survey had 99 possible categorizations for a single response. However, with pretesting, one can develop a small number of codes for the surveyor to enter that captures most of the common responses. For example, on the Kenya survey, an openended question offered the surveyor 14 ways to code the response. The advantage of this is that the surveyor need not read off 14 potential responses, but merely asks the unbiased question, then circles the appropriate code: _5_
_Table 6 Budget Share of Categories Not Routed Through FABS_

||**Transfers/Loans**|**Debt Service**|**Development spending**|
|---|---|---|---|
|Punjab|10.8%|2.9%|38.0%|
|Khyber Pakhtunkhwa|2.6%|1.8%|38.1%|
|Sindh|7.2%|3.9%|36.8%|
|Baluchistan|5.3%|1.3%|51.1%|

_Source:_ Authors.

# The Problem of Capturing Transactions Ex‐Post

The Government of Pakistan informed while some transactions (as outlined below) may be out of the normal workflow, monthly data on them is still being captured into the SAP data-warehouse of FABS and a dashboard report produced for the Secretary Finance Division. These include (i) Domestic Debt figures are captured on a weekly basis from Central Directorate of National Savings (the data from saving centers across the country is entered into the central system at CDNS on weekly basis and is pulled-in into the IFMIS through an automated solution).

(ii) External debt data is being captured into FMIS at the same time as it is updated in DMFAS, through entry into a SAP terminal made available at Economic Affairs Division (this arrangement has been implemented as an alternative to interface with DMFAS).

(iii) Data on tax receipts from Federal Board of Revenue on a daily basis through optic-fiber linkage with database of FBR.

Daily expenditure data from SAEs like National Highway Authority is also being captured into system ex-post as cheques of NHA come to AGPR for endorsement and data is entered into the SAP system. Daily expenditure statement of Pakistan Public Works department is entered into the FMIS ex-post from its 58 formations. Similar steps are being taken to extend this process to capturing daily expenditure data ex-post from Pakistan Post.

However, while this methodology may suffice for getting adequate up to date reports, these transactions would not have been subject to FMIS based ex-ante budget control. This means that for a particularly of expenditure, e.g. debt or even for the subsidies to the State-owned enterprises and the expenditure carried out by the SAEs no FMIS based budget control is being exercised. While it is understood that this is supposed to be done by the various entities that are generating the transactions in an off-line mode, this 17
Table A.4. Household Demographic Characteristics

|**Variable**|**Full Sample**|
|---|---|
||<br>**Mean/(SE)**|
|**N**|**1,150**|
|Total number of HH members|4.25|
||(1.60)|
|Total number of minors (under 18 years)|1.64|
||(1.18)|
|Total number seniors(above 60years)|0.27|
||(0.57)|
|Total number of disabled HH members|0.05|
||(0.22)|
|Number of respondent's children under 5 years|0.36|
||(0.58)|
|Number of respondent's children under 18 years|1.55|
||(1.15)|
|Number of working-age(>=18 & <=59)HH members|2.30|
||(0.99)|
|Number of female-working-age(>=18 & <=59)HH members|1.27|
||(0.51)|
|Number of male-working-age(>=18 & <=59)HH members|1.03|
||(0.72)|
|Indicator for a male member as head of HH|0.71|
||(0.46)|
|Mother-in-law lives in the HH|0.28|
||(0.45)|
|Father-in-law lives in the HH|0.23|
||(0.42)|
|At least one among MIL and FIL live in the HH|0.32|
||(0.47)|
|Hindu|0.86|
||(0.35)|
|Buddhist|0.09|
||(0.28)|
|Muslim (religion)|0.02|
||(0.13)|
|Christian|0.03|
||(0.18)|
|Khas & Aryan|0.29|
||(0.46)|

36
- 29 -

Sgecific Training for Pedagogic Supervisors. All pedagogic advisors received a common, intensive ten-month training course. This was done to "better define the role of the new corps of pedagogic advisors." Since 1982, new inspectors have also received the same ten-month training. However, there has been no regular follow-up training for inspectors other than an annual meeting, while pedagogic advisors have regular in-service training sessions during the year. These consist of small group sessions scattered over the country (three per year) as well as meeting with all the 100 pedagogic advisors together.

Decentralization of the Pedagogic Advisory Service. The pedagogic advisory, as illustrated in Table 1, was established on a decentralized regional basis. While the ratio of one advisor to 127 teachers is high, we know that advisors only work with teachers implementing the Reform. Thus, the operative ratio is lower, but an exact figure is not available. Nor is information such as the number of school visits per year, the type of assistance the advisor actually provides while at the school, and the geographic dispersion of schools important.

# Sub-contracting Pedagogic Sunnort Service

The initial systam of organizing pedagogic advisors under the auspices of the Institut National de Pedagogie (National Education Institute) is an example of a sub-contracting arrangement by the Ministry of Education. Problems arose between the pedagogic advisory and inspection systems to the extent that the two corps were officially merged under the Ministry of Education. All these professionals are now called "inspectors" and report to the departmental directors. An analysis of their 1985/86 work program showed that the National Education Institute could count on being able to use approximately 15 weeks per year per inspector/advisor on tasks related to teacher training, curriculum evaluation and in-service training of the inspectors' total annual work program. It would appear, from these estimates, that pedagogic support has been subordinated to other functions as a result of the merger. However, this is a hypothesis which needs to be examined empirically.

# Problems Identified

Some of the specific problems identified in the Haitian situation, vis-a-vis the separate inspection/pedagogic support system, include:

- (i) Lack of communication links between the two networks. Although both dealt with the same clientele--teachers in primary schools--they had no direct means of communicating with each other. Moreover, the inspectorate was apparently not well informed about the role and activities of the pedagogic advisors. Nor was it asked to participate in implementation of the Reform which created substantial friction between the two groups. This was exacerbated by the fact that the pedagogic advisors received transportation and office facilities, per diems and gasoline, plus extra materials all financed by the Third World Bank Project.
employment during the second and third years of the foreign investment, this positive effect disappears during the fourth year, which suggests some sort of staff consolidation exercise contributing to productivity increases.

We also look for spillovers on local firms, stemming from the presence of foreign owned companies. Following Fons-Rosen et al. (2014), we define horizontal “competition spillovers” in narrowly defined sectors (four-digit classification), as the entry of highly competitive foreign owned companies is likely to impact on local competitors. At the same time, we define a “knowledge spillover” variable that captures foreign presence in the same two-digit sector, excluding output produced by foreign-owned companies in the same four-digit sector (which would be the direct competitors). We find that, even if there is a small and negative competition spillover impacting domestic companies in narrowly defined sectors, there is also strong evidence of large and positive knowledge spillovers to domestic companies in the broader two-digit sector. This is a very important finding, since Turkey is unique among its neighboring countries in terms of getting full knowledge spillovers from FDI for domestic firms that are not in direct competition with the multinationals. Thus, we argue that there is a strong case for the design of policies aimed at increasing foreign direct investment attraction in Turkey, with the aim of helping unleash economic development.

To reply to our second research question, how Turkey can attract larger FDI inflows, we build a panel of data for 16 Eastern European economies (and Turkey) over the period 1999-2012. Drawing from an eclectic theoretical framework based on existing literature, and following Sánchez-Martín et al. (2014), we include in our empirical experiment a series of macroeconomic, endowment and infrastructure-related, institutional, and risk variables, and conduct a series of cross-country estimations using fixed effects. In line with previous literature focusing on Eastern and Central Europe (Cartensen, 2004, Bevan, 2004), exchange rate stability, mobile phone penetration, government stability, law and order, privatization, and EU candidacy are all significant determinants of foreign direct investment. Nonetheless, it is worth mentioning that the fact that a series of significant determinants of foreign direct investment have been identified in cross-country regressions does not imply the causality of the results.

Turkey is a country that over the past three decades has underperformed with respect to other large emerging economies (BRICs) in terms of foreign direct investment inflows. Only after the beginning of accession negotiations to the European Union, the country was able to significantly increase foreign direct investment inflows. However, as the privatization process comes to an end, and EU accession prospects cool down, attracting FDI is likely to become increasingly difficult. In 28
Our results confirm the importance of the real exchange rate for export growth. We find that its effect is even stronger for exports of services than exports of merchandise; and that it is especially large for exports of modern services. While the evidence for differential effects between advanced and developing countries is weaker, this still suggests that as developing countries shift from exporting primarily commodities and merchandise to exporting traditional and modern services, appropriate policies toward the real exchange rate become even more important.

The rest of the paper is organised as follows. In Section 2 we describe our data and sample. In Section 3 we present the evidence from the levels of exports. In Sections 4 and 5 we define and characterize export surges in merchandise, traditional services and modern services. Section 6 presents evidence on the determinants of those surges, including the real exchange rate. The final section concludes.

# 2. **Data and Sample**

We use annual data for 1980-2009.<sup>8</sup> We include all countries for which significant runs of data on exports of services are available: 66 in all, of which 9 are low income, 15 are low middle income, 20 are high-middle income and 22 are high income.<sup>9</sup> We organize these data into country-year observations of the growth of exports of services in constant U.S. dollars.

Table 1 shows the bivariate correlation between exports of merchandise and exports of different categories of services. The correlation of traditional service exports and merchandise exports is higher than that between exports of merchandise and exports of modern services. This is a first indication that the determinants of exports of modern services may be different from the determinants of merchandise exports.

**Table 1: Correlation between Growth of Exports of Merchandise and Different Components of Services**

||Merchandise|All Services|Traditional<br>Services|Modern<br>Services|
|---|---|---|---|---|
|Merchandise|1||||
|All Services|0.39***|1|||
|Traditional Services|0.42***|0.77***|1||
|Modern Services|0.16***|0.69***|0.18***|1|

> 8 Exports of services being unexceptional prior to the 1980s.

> 9 We use the World Bank definition to classify countries into various income groups, based on the data sheet given by World Bank last updated in April 2012, whereby all economies with populations of more than 30,000 have been classified into various income groups according to their 2010 gross national income (GNI) per capita. The groups are: low income, $1,005 or less; lower middle income, $1,006-3,975; upper middle income, $3,976-12,275; and high income, $12,276 or more. For a few of our countries – Burkina Faso, Brazil, Comoros, Denmark, Hungary, Indonesia, Mozambique and Swaziland –we have data for only a portion of the period.

4
transmission may become less effective because part of the banking sector’s portfolio is associated with direct credit which does not react to changes in monetary policy.

# **B. Considerations for policy makers**

Policy makers should consider focusing on root causes to sustainably lowering lending rates and intermediation spreads. Eight considerations are offered, which should be assessed holistically to formulate a coherent policy mix. These considerations are based on World Bank operational work, the literature, and the analysis presented in this paper.

## <u>Macro-fiscal conditions</u>

1. **Strengthen macro-fiscal fundamentals.** A straightforward recommendation is for policy makers to continue to build strong public balance sheets and sound monetary, exchange rate, and debt management frameworks since these exert a first-order effect on intermediation spreads and bank funding conditions. Overall, EMDEs have strengthened these frameworks in the last few decades. However, fiscal conditions in various EMDEs have recently deteriorated and inflation (expectations) remains vulnerable to global demand and oil shocks (Ha et al., 2019). And for example, in AFR there has been a rise in public debt levels and an increased reliance on domestic sources of funding.

2. **Avoid crowding out the private sector.** Large public sector debt levels financed through bank lending may dissuade banks from lending to a riskier private sector and result in a substantial increase in lending rates, particularly in countries with shallow banking systems.

## <u>Banking sector characteristics</u>

3. **Support competition.** Banking sectors in EMDEs are often dominated by large and profitable players which reduces intermediation efficiency. While competition differs across business segments, policy makers could consider judiciously reducing restrictions on market entry and permitted activities; allowing competition from non-bank financial 21
24 payment expenses were largely recouped through earnings on float by debiting a payor's account one or more days earlier than necessary to make a payment, and earning interest on these funds before a payee's account was credited.

Average prices for different bank payment services in Norway are shown in Table 3 along with their average cost and the percent of total cost recovered in the price. In the future, float revenues are to be reduced further and an even larger share of bank costs are to be recovered through explicit fees. The goal is to reflect better the true resource cost of using the different payment instruments. This enables users, who demand payment services, to make more informed decisions on which instrument has the greatest net benefit, and hence which instrument is best used for point-of-sale or bill payments. It also enables payment suppliers to guide users toward those instruments which have the lowest resource costs, reducing bank expenses. The clear implication in Table 3 is that electronic payments have both a lower cost and a lower price than paper-based payments.

**Table 3: Average bank prices and costs of paper-based and electronic payments** (Norway, 1994)

|_Payment instrument_|_Average price_|_Average ibank cost_|_Price_as_a_<br>_% of cost_|
|---|---|---|---|
||_(In US$)_|_(In US$)_|_%_|
|**Paper-based:**||||
|Mail giro|$0.49|$1.06|46%|
|Giro collection box|0.71|1.55|46|
|Giro at the counter|1.20|2.26|53|
|Giro cash payment|1.27|2.54|50|
|Check|0.92|2.15|43|
|**Electronic**||||
|Direct debit:<br>No notification|0.14|0.49|29|
|With notification|0.42|0.92|46|
|Direct deposit: No notification|0.14|0.21|67|
|With notification|0.35|0.56|63|
|EFTPOS (debit card at point-of-sale)|0.46|0.63|73|
|ATM withdrawal|0.25|0.49|51|

_Source: Flatraaker and Robinson (1995), table 3: exchange rate is NOK709=$1._

**_Explicit payment pricing in other countries._** The clear trend in recovering bank payment costs in Belgium, Finland, Germany, Ireland, Italy, Luxembourg, Netherlands, South Africa, Switzerland, and Sweden has been to implement explicit pricing for payment services (Llewellyn and Drake, _1993).'4_ Two-part pricing is often used, where a transaction fee is combined with a

14 _The U.K. is an exception to this trend, as is the U.S._
# **Figure 4**

# **Sales growth rate, Excess Savings, and Gross Capital Formation**

High-growth vs Low-growth firms <!-- Start of picture text --> 80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>-10%<br>-20%<br>-30%<br>10%<br>5%<br>0%<br>-5%<br>-10%<br>-15%<br>-20%<br>-25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>-5%<br>-10%<br>Decile 1 Decile 2 Decile 3<br>Decile 8 Decile 9 Decile 10<br>Average Sales growth rate<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>Average Excess Savings<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>Average Gross Capital Formation<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text --> Figure 4 shows the evolution of Sales Growth, Excess Savings, and Gross Capital Formation, for high-growth firms (decile 8, 9, and 10) and low-growth firms (decile 1, 2, and 3). Firms were separated into deciles according to its mean sales growth rate during the sample period, and then the average Sales Growth rate, Excess Savings, and Gross Capital Formation were computed for each year by decile.