rafmacalaba/datause-encoder-data huggingface dataset:
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33
Table 2b presents the same analysis as in Table 2a, but using GLSS2 and GLSS3, instead. Main results remain consistent with that of GLSS1 and GLSS3 comparison. First, inequality has increased in Accra, but decreased in Other Cities and Rural Areas for most part. Second, Accra has the highest average living standard among all localities. Third, contributions to the inequality at the lower end of distribution has increased for Accra, but decreased for Other Cities and Rural Areas.

Table 2b. Ghana: Inequality by locality, GLSS2(1989) and GLSS3(1992), adjusted data

|_Measure_||_E(O)_|_%_||_E(l)_|_%_||_E(2)_|_%_|
|---|---|---|---|---|---|---|---|---|---|
||1989|1992|_Change_|1989|1992|_Change_|1989|1992|_Change_|
|**Accra**||||||||||
|Mean expenditure<br>|207439<br>|211949<br>|2.2<br>||<br>|<br>||||
|_E(cx),_|22.~~|~4i 235|5.2..|:~22.4:|~<br>_25 .6_|..<br>14.3|28.3: '|~:~ 37.1|31.0IL|
|Weighted|2.0|1.9|-4.9|2.1|2.1|-2.3|2.8|3.0|5.6|
|_CoMMbto_(|_90_|_9.3_|_3.8_|_120_|_1.6_|_-3.-5_|_14.5_|_14.0~_|<br>_3.6_|
|**Other Cities**||||||||||
|Mean expenditure|167510|188283|12.4|||||||
|_E(a)_|4.3|20.0|7.5|27.9|21.4|-232|46.|29.1|_-3 753_|
|Weighted|5.6|_5.0_|-11.4|5.5|4.7|-14.3|7.7|5.6|-27.7|
|_con2tribution: (4_|_-25.0_|_24.1_|<br>_-3.3_|_31.01_|_-0000:_<br>_26.3_|_-1.3_|_40,0_|_.26.4_|_-34._|
|**Rural**||||||||||
|Mean expenditure|132218|161853|22.4|||||||
|E(a),|2L|2.6|-63|22.2|<br>2L0|~<br>9|29-|_32_|1.|
|Weighted|14.9|13.8|-7.7|10.0|11.1|10.4|8.8|12.6|43.5|
|_Contribution_<br>_(%)_|_66.1_|_66.6-_|_0.7_|_1.0_|_62.1_|_9.1_|_45.5_|_59.6_|_31.0_|
|**Ghana**<br>_E(a)w_|22.6|20.7|-8.3|17.6|17.8|1.2|19.3|21.1|9.5|

Figures 2 to 4 present the stochastic dominance analysis for the three localities. Figure 2 shows in Accra, 1988 distribution dominates 1992 distribution, indicating a decline in social welfare. The decline in social welfare is especially severe for the population of lower and middle incomes, represented by the wider gap between the two cumulative functions in the lower and middle part of the distribution. Figure 3 shows that there is a general improvement in social welfare for the population residing in Other Cities. As for rural areas, Figure 4 shows that the expenditure per capita has improved for majority of the population. There was, however, little improvement for the upper 10 percent of the population. The same analysis for GLSS2 and GLSS3 shows very similar patterns for Rural Areas and Other Cities. However, for Accra, GLSS2 and GLSS3 comparison shows that the welfare in 1989 is better off for some parts of distribution, but worse off for others. Graphs are presented in Appendix A.

6
# **Figure 5. FMIS implementation costs plotted against number of users**

<!-- Start of picture text --> 60<br>50<br>Vietnam<br>40<br>30 Indonesia Pakistan<br>20<br>Azerbaijan Kazakhstan<br>Moldova<br>10 Mongolia<br>Albania<br>Maldives<br>Kyr 0 gyz Republic<br>0 1,000 2,000 3,000 4,000 5,000 6,000<br>Number of users (COTS licenses)<br>US$ millions<br><!-- End of picture text --> _Note_ : COTS = commercial off-the-shelf. _Source:_ Hashim 2014.

3.80 **Costing data for custom-developed systems and recurrent costs is unavailable.** The results of a comparable exercise for custom-developed implementations may not be as reliable because a combination of local government staff and consultants often develops the software and does not capture the associated costs accurately during the long systems development life cycle. In such cases, the implementation is often incremental and is constantly changing, with many iterations done during several years, each yielding a slightly better functionality. Similarly, comparable data for recurrent costs are unavailable for the actual recurrent costs associated with ongoing system operation and maintenance.

**_Finding 22: The World Bank team needs to have experienced specialists available to advise the client on important technical issues during the design and implementation phases. The specialists should also be familiar with procurement practices and procedures for information technology procurements and the World Bank rules under which they will need to be applied. Continuity of World Bank staff from project design throughout implementation is a crucial success factor._**

3.81 **It is important to take a proactive role in project design and implementation.**<sup>**64**</sup> Having experienced World Bank specialists available to advise clients on important technical contract management and procurement issues during the design and implementation phases were identified as a critical success factor in many cases. In Cambodia for example the availability of expertise was a turning point in

> 64 See project documentation for FMIS operations in Bolivia, Cambodia, Cameroon, China, Indonesia, Kenya, Timor-Leste, and Zambia.

**53**

**53**
to quantify issue). Not surprisingly, such complexity has led to different views and mixed empirical evidence. The remaining of this section elaborates on these concepts (see Appendix A for a discussion on speculation in commodity mar‐ kets).

Broadly speaking, the “new” money—which is not associated with physi‐ cal commodity transactions—can be linked to three sources:

- **_Diversification of investment vehicles._** During the past decade or so, invest‐ ment fund managers noticed that existing asset classes were becoming increa‐ singly correlated. In their search for new (uncorrelated) assets they broadened their portfolios by including assets from emerging economies. When these as‐ sets became themselves correlated (because of the systemic effect of all funds invested in the same assets) fund managers began investing in commodities, thus setting the stage for the so‐called ‘financialization of commodities’—a role typically reserved for gold, after 1973. To the extent that commodities are viewed as another asset class, diversification may have a permanent charac‐ ter.

- **_Rebalancing of investment portfolios._** The rebalancing of investment portfo‐ lios by shifting funds from US$‐denominated (and other) holdings to com‐ modities added further inflows into commodity markets. The strong inverse relationship between the US$ and commodity prices—especially crude oil— has been invoked often as evidence for such rebalancing (see Medlock and Jaffe 2009).<sup>**5**</sup> The effect of rebalancing is less permanent than diversification and depends largely on how long investors’ risk attitudes will favor com‐ modities or disfavor other assets.

- **_Excess liquidity._** The low interest rate environment supported by many cen‐ tral banks resulted in excess liquidity, part of which found its way to com‐ modity markets (on top of the price increase due to expansion of physical demand for commodities). It is believed that excess liquidity has been the key reason behind the boom in real estate markets. The effect of excess liquidity is likely to last for as long as interest rates remain low.

The key channel through which the “new” money found its way into commodity markets is index funds. The most widely used and closely watched indices are the Dow Jones‐AIG and S&P Goldman Sachs Commodity Index (also known as DJ‐AIG and S&P‐GSCI). About 95 percent of funds indexed to com‐ modities are replicated by these two indices. The funds take long positions in commodity futures exchanges by buying contracts and, prior to expiration, roll‐ ing them over. While there are no precise estimates on their size, a broadly ac‐ cepted range as of mid‐2008 was $250 to $300 billion (Masters 2008). A report by a major commercial bank estimated that an additional $60 billon went into com‐

**_Page 6_**
kernel estimates for several of the distance to frontier variables for 2005 (or 2004) and 2013. In the vast majority of cases there is convergence over time, meaning that on average countries are closer to the frontier and countries that were originally further from the frontier were the ones that make the most progress in getting closer. This convergence is particularly noticeable for starting a business, dealing with construction permits, registering property and getting credit indicators. In these cases the distribution for 2013 stochastically dominates the distribution for the initial year (2005 or 2004). In the indicators on resolving insolvency and protecting investors there is some level of convergence but not as strong. In the indicator on enforcing contracts the convergence is almost inexistent. Once all the nine indicators are integrated into the aggregate distance to the frontier score, there is convergence and the distribution for 2013 stochastically dominates the distribution for 2005.

# **3. Econometric models**

This paper examines the link between business regulations and firm creation and growth. We use two different econometric models to study this relationship.

Our first empirical test consists in investigating whether, on average, the efficiency of countries’ business regulations is correlated with a higher number of newly registered businesses. With this aim we analyze within country variation using our panel data and estimating the following model: In this specification DTF stands for the distance to the regulatory frontier. We estimate the model using the distance to the frontier in alternative regulatory sets. We include country fixed effects (αi) in our specification in order to capture unobserved between-country differences that might affect our dependent and explanatory variables. Moreover, we control for lagged GDP per capita growth in order to capture the impact of the business cycle on the number of new businesses registered. Following Klapper and Love (2011) we include time dummies to control for any global changes in the macroeconomic environment likely to affect business entry worldwide. This is particularly important given that our panel covers the global financial crisis. Firm registration dropped significantly due to the crisis and time dummies will capture the average drop in registration due to such event so that this does not distort our estimations. We also control for a vector X, which includes a set of country-specific characteristics that can influence the business environment such as per-capita GDP, trade openness, corruption, economic and political risk as well as civil freedoms. Finally, we include time dummies to control for global macroeconomic changes affecting all countries (δt) and the random error term clustered on the country level to capture potential serial correlation of residuals within country (εit).

6
Proposition 7: In equilibrium, the city is organized in four zones. Denoting the boundary zone threshold ̊ _x_ ( _k, J_ ) =<sup><u>1</u></sup> _t_ � _y −_ (1 _−kq_ )(1 _−J−π_ )<sup>_−u_</sup> � , we have:

- Zone 1 <u>(mixed formal</u> and informal residential zone): On [0 _,_  ̊ _x_ ( _k, J_ )[ , all owners (irrespective of the initial tenure security level of their plot) participate in the market. Some _q_ -owners register their plot before the sale (in quantity _Lfq_ ( _x, k, J_ ) = 1 _−_ (1 _−πq_ <u>(1)(</u> _−yq−_ <u>)(</u> _xty−−xtu_ )+ _−uJ_ <u>)</u> _−k_<sup>),thoughsome</sup> do not and sell exclusively to their cousins. 1 -owners do not register their secure plots and sell them exclusively to their cousins. The informal price in each location _x_ is _R_<sup>_c_</sup> ( _x_ ) = _y−xt−u−k_ + _J_ and the formal price for registered plots is _Rf_ ( _x_ ) = _y − xt − u_ .

- Zone <u>2 (informal</u> residential zone, <u>full</u> market <u>participation):</u> On ] ̊ _x_ ( _k, J_ ) _, x_ <u>(</u> _J_ )[ , all owners ( _q_ - and 1 -owners) sell their plot informally and exclusively to cousins. In each location _x_ , the price for these informal sales is _R_<sup>_c_</sup> ( _x_ ) = ( _π_ (1 _− q_ ) + _q_ )( _y − xt − u_ ) .

- Zone <u>3 (mixed</u> informal residential and agricultural zone, <u>partial</u> market <u>participation</u> of _<u>q</u>_ -owners): On <u>]</u> _<u>x</u>_ <u>(</u> _J_ ) _,_  ̄ _x_ ( _J_ )[ , all 1 -owners and some _q_ -owners sell their plots exclusively to cousins. The rest of _q_ -owners drop out of the market. The mass of _q_ -owners selling their plot in _π_ <u>(1</u> _−q_ <u>)(</u> _<u>y−xt−u</u>_ <u>)</u>

- _x_ to cousins is _L_<sup>_c_</sup> _q_<sup>(</sup><sup>_x, J_) =</sup> _Ra_ + _J−q_ ( _y−xt−u_ )<sup>_−π_.Thepriceineachlocation</sup><sup>_x_is</sup><sup>_Rc_(</sup><sup>_x_) =</sup><sup>_Ra_+</sup><sup>_J_.</sup>

- Zone <u>4 (mixed</u> informal residential and agricultural zone, all _<u>q</u>_ -owners dropping out of the market): On ] ̄ _x_ ( _J_ ) _, x_<sup>_∗_</sup> _a_<sup>[,all1-ownersselltheirplotexclusivelytocousinsandall</sup> _q_ -owners drop out of the market. The price in each location _x_ is _R_<sup>_c_</sup> ( _x_ ) = _y − xt − u_ .

- The city boundary is at _xb_ = _x_<sup>_∗_</sup> _a_<sup>=</sup><sup><u>1</u></sup> _t_<sup>[</sup><sup>_y −Ra −u_].</sup>

# Proof: See Appendix D.

The structure of the city and the corresponding equilibrium land prices are represented on Figure 5.<sup>35</sup> In the central residential zone (Zone 1, which has mixed registered and non-registered plots), the registered price curve has slope _−t_ , reecting the standard trade-o between proximity to the center and land prices under full tenure security. There is a constant markup between the registered price curve and non-registered price curve equal to _k − J_ . This ensures that _q_ -sellers are indierent

> 35In Appendix D, we present a gure that plots the payos of sellers underlying their participation, registration and cousinage decisions (see Figure 18).

30
Policy Research Working Paper 7758

# **Abstract**

Ghana is an exceptional case in the Sub-Saharan Africa landscape. Together with a handful of other countries, Ghana offers the opportunity to analyze the distributional changes in the past two decades, since four comparable household surveys are available. In addition, different from many other countries in the continent, Ghana’s rapid growth translated into fast poverty reduction. A closer look at the distributional changes that occurred in the same period, however, suggests less optimism. The present paper develops an innovative methodology to analyze the distributional changes that occurred and their drivers, with a high degree of accuracy and granularity. Looking at the results from 1991 to 2012, the paper documents how the distributional changes hollowed out the middle of the Ghanaian household consumption distribution and increased the concentration of households around the highest and lowest deciles; there was a clear surge in polarization indeed. When looking at the drivers of polarization, household characteristics, educational attainment, and access to basic infrastructure all tended to increase over time the size of the upper and lower tails of the consumption distribution and, as a consequence, the degree of polarization.

This paper is a product of the Poverty and Equity Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at vmolini@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
provider surveys and expert surveys. Section 6 concludes by considering where the major gaps appear to lie. I argue that the different approaches to data collection are complementary. They also involve sharply differing collection costs, which is an aspect not fully explored here. I also note that some existing data sources have not yet been as widely used as they could be.

# **1. The data gaps and how to approach filling them**

# **1.1** **_Existing shortcomings_**

Perhaps the most widely asked question in this area is: “What proportion of the population in each country have access to financial services?” Yet, despite the work of Christen et al. (2004) – who have gone further than anyone else in attempting quantification of this type – we really don’t have a good answer to this question. There are several reasons for this astonishing gap (Honohan, 2004).

They include the fact that many different types of financial institution provide such services and they record their activities in a variety of different ways that are hard to aggregate. Even poor households may use formal commercial banks, national postal savings or agricultural banks, cooperative credit unions and other entities which do not consider themselves to be specialized microfinance institutions. It is therefore not enough to count the number of customers of specialized microfinance institutions. And, while specialized microfinance entities may generally try to keep records of how many customers they have, this is often not true of mainstream financial institutions such as banks, which often organize their data around accounts rather than account holders, and which are more concerned with dollar aggregates than numbers of customers.

Even to the extent that financial service providers have, or could have, the elements of this data, regulatory agencies such as central banks generally have little interest in collecting it. That is because these agencies have different primary functions such as inflation control and preserving financial stability. These functions do not require collecting information about the large number of small deposits or loans; it is chiefly the total value of monetary and credit aggregates, and the large individual borrowings, that matter for these functions.

Furthermore, even if we knew how many active<sup>3</sup> saving or depositing customers each institution had, we don’t know how many of these have accounts at multiple institutions.

An alternative perspective begins by asking “how widespread and effective is _microfinance_ ”. Getting an answer to this variant of the question is no easier. Besides, it is less interesting. Indeed, defining the appropriate dividing lines between what should be treated as microfinance, whether in terms of the providers, the type of users and the type of financial services, raises a whole new set of problems (discussed in more detail in Appendix A of Honohan, 2004). Arguably this way of posing the question is less fruitful

> 3 Savings accounts often remain open with a small, but perhaps forgotten balance – including these can be misleading.

3
both because of greater job destruction in the economy and greater incentive to stay unemployed longer to look for a new job. The increase in the unemployment rate over the period was however small (from 2 percent to 2.6 percent) reflecting either a low take-up of unemployment benefits or simply a lack of net job loss, or both.

The fact that both labor force participation and the employment-to-population ratio increased over the period suggests that there was a net increase of entrants in the labor force and that job creation nearly kept up with this net influx of workers. However, adjustments may have been done elsewhere, in particular in the type and quality of employment created.

The unemployment rate among youth is much higher than for adults but has remained stable between 2007 and 2009 (Table 2). Youth unemployment is generally higher than the adult rate of unemployment because youth tend to have shorter spells of employment while gaining experience and searching for a more permanent job. In middle-income countries, this may also be due to the fact that they are more likely to work in the informal sector, which has greater turnover. However, as will be shown below, this is not the case in Vietnam. Interestingly, the youth unemployment rate has not changed since 2007, while the unemployment rate for adult increased (from a low level). Moreover, these results are found for both young men and women.

**Table 2: Youth labor market performance (%)**

||**Labor**<br>|**force part**<br>|**icipation**<br>|**Emplo**<br>|**yment t**<br>**ratio(**<br>|**o population**<br>**15+)**<br>|**Une**<br>|**mployme**<br>|**nt rate**<br>|
|---|---|---|---|---|---|---|---|---|---|
||All|Men|Women|All|Men|Women|All|Men|Women|
|**2009**||||||||||
|Youth (15-24)|60.5|62.5|58.5|56.7|58.6|54.9|6.2|6.2|6.1|
|Adults (25+)|81.5|87.0|76.3|80.0|85.6|74.9|1.8|1.7|1.9|
|**2007**||||||||||
|Youth (15-24)|55.9|56.9|54.8|52.5|53.4|51.6|6.0|6.2|5.8|
|Adults (25+)|80.4|86.0|75.4|79.5|85.3|74.6|1.0|1.1|1.0|

_Source: LFS, 2007, 2009_ Overall, many new entrants joined the labor force probably in order to complement income; the youth labor market is dynamic and youth have been relatively successful in securing employment. The labor market of youth saw an increase in labor force participation and employment-to-population ratio and a stable unemployment rate over the period 2007-2009. In addition, there is evidence that although a large proportion of 7