rafmacalaba/data-use-mentions-extended huggingface dataset:
country’s political system, or some extraordinary exogenous shock (e.g., major fiscal crisis), which we had earlier concluded was absent in today’s Philippines.

_Weak institutional capacity for collective legislative action_ The Philippine Congress is known for its particularly fragmented institutional structure and its members’ particularistic tendencies.<sup>22</sup> The absence of disciplined political parties capable of mobilizing and organizing votes of individual members means that the transaction cost of orchestrating a major legislative initiative is significantly high. It follows that even when a substantial number of legislators, including those in senior leadership positions, are in favor of a particular bill, regardless of its content, the probability that it will pass as a law through the legislative process is low.<sup>23</sup> This observation holds for any type of legislation whether it is related to decentralization or not. It is also a crude argument because it is obviously untrue that no legislation is ever passed and enacted in the Philippines. Nonetheless the available data does demonstrate that the probability that a bill becomes a law in the Philippines is unusually low. An examination of legislative records shows very low rates of bill enactment – on average, only around 3% of the bills introduced to the House are ever enacted into laws (Table 8). Even in comparison to these low enactment rates, the fate of the bills related to decentralization has been even worse. The enactment rate of the Senate bills on decentralization for the period 1987-2010 is 1.7% (5 out of 302) and that of the House bills is 0.6% (3 out of 465).

**Table 8:  Bill Enactment Rates at the House of Representatives, 1987-2004**

|Congress/Administration<br>|National application<br>(Passed/Introduced)|Enactment<br>rate|Local application<br>(Passed/Introduced)|Enactment<br>rate|
|---|---|
accountability, and performance. Notable examples include the United States and the United Kingdom, who pioneered the release of government data sets in an effort to make government more transparent through platforms such as _data.gov_ and _data.gov.uk_ . Since then, numerous countries have created similar OGD portals to provide a plethora of government information.<sup>1516</sup> At a global level, international institutions and nongovernmental organizations (NGOs) have begun providing technical assistance and capacity building for Open Data initiatives around the world.<sup>17</sup> In particular, the drive toward greater government transparency has been supported by the Open Government Partnership (OGP), which was formed in 2011 to function as “an international platform for domestic reformers committed to making their governments more open, accountable, and responsive to citizens.”<sup>18</sup> Likewise, in order to deepen commitments to government transparency, an Open Data Charter was signed in 2013 by the G-8 countries, setting out the principle of “open by default,” which has subsequently prompted calls for a “data revolution” to take center stage in the post-2015 development agenda, including the dialogue on the Sustainable Development Goals.<sup>19</sup> Other notable initiatives include the Open Data for Development Partnership (OD4D), comprising 65 bi-laterals, multilaterals, foundations, and NGOs; the Open Data Working Group of the OGP; the Open Contracting Data Partnership; and the Global Partnership for Sustainable Development Data; as well as tools such as the Open Data Barometer, which provides information on data availability in more than 90 countries.

> 11 Ubaldi, B. (2013), "Open Government Data: Towards Empirical Analysis of Open Government Data Initiatives", OECD Working Papers on Public Governance, No. 22, OECD Publishing, Paris.

> 12 World Bank, “OGD for Sustainable Development,” Transport and ICT Global Practice Policy Note 1 (2015).

> 13 Ubaldi, B
**Figure 12: Impact on rural areas (children only)**

<!-- Start of picture text --> 60%<br>50% Categorical<br>Imperfectly Targeted<br>40%<br>Targeted<br>30%<br>20%<br>10%<br>0%<br>Change in poverty<br><!-- End of picture text --> Source: Own calculations based on SEDLAC databases The lower poverty gap in urban areas also provides an explanation of why targeted transfers do not necessarily perform better in urban than in rural areas, despite the lower poverty incidence. In fact, the picture remains quite heterogeneous. In Nicaragua, where two thirds of the population in rural areas lives in extreme poverty (against a quarter in urban areas), targeting performs much better in urban than in rural areas: in urban areas, it achieves 2.7 times higher poverty reduction than a categorical program, as opposed to only 1.7 times in rural areas. But in Panama, on the opposite side of the spectrum, poverty targeting performs way better in rural areas. This is because the poverty gap in urban areas remains so small (2.3 against 12 in rural areas), that even a “diluted” categorical transfer would achieve massive poverty reduction. In contrast, targeted transfers in rural areas perform way better because they allow to transfer higher resources to the poor, and hence to overcome more easily the higher poverty gap.

# 5.  Conclusions

From a theoretical perspective, targeting social assistance to the poor always improves the programs’ poverty impact since, for a given budget, targeted programs transfer more resources to fewer beneficiaries. However, the extent to which these gains are substantial enough to overcome some of the drawbacks related to targeting remains an empirical 20
br>‐0.35 Benin ‐0.35<br>Benin<br>‐0.70 SSA ‐0.70<br>‐1.05 Togo ‐1.05 Togo<br>‐1.40 ‐1.40 Nigeria<br>Nigeria<br>‐1.75 ‐1.75<br>2000 2002 2003 2004 2005 2006 2007 2008 2000 2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text --> _Source_ : Governance Database, World Bank.

**The ownership structure and management of state owned enterprises (SOEs) have also been a reflection of the poor quality of governance.** The phosphate sector is operating at less than a quarter of its capacity and IMF (2009a) argues that GDP would increase by as much as 2% if full capacity is reached. The phosphate society OTP/IFG has been replaced by the Societe Nouvelle des Phosphates du Togo (SNPT), but the authorities have so far resisted proposals to identify a new strategic partner which will be needed to bring in the necessary capital and technical expertise. The main cotton company (SOTOCO) was highly criticized and has now been replaced by a new company in which the producers hold 40% of the capital. However, here too the government has delayed plans to

45
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US in the 19<sup>th</sup> century, but given China’s fast demographic transition, it may be quite similar to today’s France in fifty years. So the transition path is very much the same, just at a much faster pace. Furthermore, Milanovic notes that emerging economies also affect C21’s inequality ���. On the one hand, given their lower capital stock, emerging markets continue to offer higher returns, pushing up �, which Piketty recognizes as one of the mechanisms behind the apparent stability in �. On the other hand, their higher growth rates push up � thus delaying the point at which ��� materializes.

Although Piketty’s model deals almost exclusively with developed Western nations, this has not stopped the book from achieving a global appeal. C21 has been widely translated, including into Chinese, Japanese and Korean, and reported in the local press (Brasor, 2015; Denney, 2014). More generally, the concern with inequality goes beyond the developed countries. For example, in a survey of 15 developing countries, 77% of policy makers recognize that the current level of inequality threatens long-term development, while only 7% see inequality as conducive to longterm development (UNDP, 2014). Similarly, in a survey of more than 500 policy makers in Asia, 70% suggest that the concern with income inequality increased in the last 10 years, while just over half disagreed that higher income inequality is acceptable as long as poverty is falling (Kanbur and Zhuang, 2012).

A word of caution is needed right from the beginning. Expanding the analysis to a global level faces formidable data constraints. Due to the unavailability of administrative data for developing 2
Consistent with this, Berry and West (2005), using US data, find a negative effect on future earnings of both a higher school size and a higher pupil-teacher ratio.

The abovementioned studies focus on the estimation of educational production functions where educational performance is the variable to be explained. Other studies estimate **educational cost functions** , in which educational performance is used as an explanatory variable. Studies that use this approach tend to find that _existing schools are smaller than optimal_ (Kenny 1982, Smet 2001, Stiefel _et al._ 2009). But also among these studies there is no clear consensus on the optimal school size. For example, a review of US-based studies by Andrews _et al._ (2002) suggests that 500-600 students may be an optimal size, although there is no consistent evidence of either increasing or decreasing returns to scale. As far as **developing countries** are concerned, Hanusek (1995) summarizes the literature on the effect of class size in developing countries as follows: “The evidence provides no support for policies to reduce class size. Of the thirty studies investigating teacher-pupil ratios, only eight find statistically significant results supporting smaller classes; an equal number are significant but have the opposite sign; and almost half are statistically insignificant. These findings qualitatively duplicate those in the U.S. studies, but are particularly interesting here. Class sizes in the developing-country studies are considerably more varied than those in the U.S. studies and thus pertain to a wider set of environments, providing even stronger evidence that the enthusiasm for policies to reduce class size is misplaced (p.231)”. More recent studies confirm the mixed evidence: Urquiola (2006) finds a negative effect of class size on student performance in Bolivia, while Niaz Asadullah (2005) finds the opposite for Bangladesh. And in a recent review, Hanusek and Woessman (2007, p.66) conclude that “The lack of substantial resource effects in general, and class-size effects in
- 11 -

27. In addition to fiscal policy, there are also other factors that may have contributed to the observed portfolio composition, particularly the high share of Government bonds. Firstly, alternative financial instruments have not been developed, with the exception of the very recent and promising development of the mortgage bond market. The lack of development of financial instruments such as corporate bonds, infrastructure bonds and private equity has been due in part to the expansion of lending by a very competitive banking sector, after a period of contraction in the 1990s due to restructuring and privatization. However, some residual regulatory obstacles may also have hindered the development of these markets, as discussed in Section VII.

28. Secondly, asset managers may have become wary of equity investments, as a result of the very poor performance of equity markets in the early years of the reform. As shown in Figure 3, the second pillar started operating in 1998, the year when the Hungarian equity market (measured by the BUX index) was severely hit by the Russian crisis. Equity prices recovered in 1999 but declined sharply again in 2000 and 2001, in line with the overall decline in international equity markets. During this whole period bond returns (measured by the MAX index) were higher and less volatile. The situation only reverted in 2003, when equity prices initiated a steady recovery, and bond prices faltered due to an increase in interest rates (Figure 4).

**Figure 3: Performance of Bonds and Stocks in Hungary, 1997 – 2004**

<!-- Start of picture text --> 2.2<br>2<br>1.8<br>1.6<br>1.4<br>1.2<br>1<br>0.8<br>0.6<br>0.4<br>BUX MAX VP(2-8)<br>Dec-97 Mar-98 Jun-98 Sep-98 Dec-98 Mar-99 Jun-99 Sep-99 Dec-99 Mar-00 Jun-00 Sep-00 Dec-00 Mar-01 Jun-01 Sep-01
such households.

# SECTION 2. **The Development of the Housing Market** in **the Context of Public Priorities**

This section considers the social and economic aspects of the developrment of the housing market. The findings of sociological surveys of the past few years are used to trace the changes in households' attitudes to the methods of looking for housing over the period of reform. Also examined is the influence of the housing market on household residential mobility. And, finally, the availability of housing depending on
**_<u>________________________________________________________</u>_**

# **Annexes**

## **Annex A. World Business Environment Survey**

Regulations can be burdensome in different ways - they can impose direct costs (formal and informal payments, facilitation costs, expenditures of staff time) or indirect costs (the inefficient allocation of firm resources in response to the incentives created by regulation and regulatory enforcement). In the WBES, firms evaluated the severity of several potential regulatory constraints.

More relevant to trade facilitation, _Customs Procedures and Trade Regulations_ imposed serious constraints on more than half of all firms in LAC and South Asia, 49 percent of firms in Africa, and 44 percent of firms in the MENA region (Batra, Kaufmann and Stone, 2003). More than one-third of firms in the developing East Asia and the CEE regions considered this as a serious constraint. Small enterprises were significantly less constrained by customs and trade regulations in comparison with large and medium-size firms. The average waiting time for goods to be processed through ports and customs varied substantially, ranging from roughly 2 days in OECD nations, East Asia, and China (a firm’s median wait was generally less than half a day), to more than 9 days in Central and Eastern Europe (a median of 5 days), 11 days in South Asia (a median of 7 days), and more than 16 days in Africa (a median of 10 days). These estimates exclude outliers, or processes that take more than 90 days.

## **Percentage of Firms That Rated Regulatory Constraints on Firms as “Major” or “Moderate”**

|Constraints|CEE|LAC|CIS|Africa|<sup>East Asia</sup><br>Developing|<sup>OECD</sup>|South<br>Asia|MENA|East Asia<br>NIC/China|
|---|---|---|-
# **Advanced Regression Analysis (Positive Test)**

Using either the identification strategy encapsulated in expressions (4.07) or (4.08), the high-income or pooled dataset, and the different combined measures of limited government (additive index and confirmatory factor analysis results), it is possible to determine, with greater confidence, whether the predictions of the model can robustly anticipate empirical outcomes. This is because not only is the use of an overall index of limited government more likely to convey a country’s actual (multi-dimensional) EDD/NEDD ratio, but the use of a larger sample also allows all major theoretically and empirically identified determinants of rent extraction to be controlled for, thus reducing the risk of omitted variable bias. However, as noted above, given that the model does not provide a basis for predicting appropriate weights for the different variables, measurement error is a concern. The use of two different measures of the limited government, several different robustness tests, and the ability to compare the results with the results in Table 1.01 can be used to assess the validity of the findings. In this case, if the theoretical predictions are correct, the results below should be significant and not qualitatively different from the above-noted findings.

Table 1.02 reports the results of seven different regression specifications used to examine the theoretical expectations derived from the model. Irrespective of the specification used, the results are the same. As anticipated by the model and the theory, more limited government (a higher EDD/NEDD ratio) in an advanced income democratic context is associated with less perceived rent extraction. Specifically, these results remain robust despite the use of: (1) different independent variables (additive versus factor); (2) different sample size (OECD only versus pooled sample/all observation); (3) different distributional assumptions (GLM versus OLS versus Ordered Probit); (4) different robustness tests (reweighting observations to eliminate outliers); and (5) the
**Inclusive Refugee-Hosting in Uganda Improves Local Development and Prevents Public Backlash**<sup>∗†</sup>

**Keywords** : refugees, forced migration, public goods provision, development, Uganda, GIS **JEL Codes** : D74, F22, H51, H52, O15

> ∗This paper was commissioned by the World Bank Social Sustainability and Inclusion Global Practice as part of the activity “Preventing Social Conflict and Promoting Social Cohesion in Forced Displacement Contexts. The activity is task managed by Audrey Sacks and Susan Wong with assistance from Stephen Winkler. This work is part of the program Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership”. The program is funded by UK aid from the United Kingdom’s Foreign, Commonwealth and Development Office (FCDO), it is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. This work does not necessarily reflect the views of FCDO, the WBG or UNHCR.

> †We are incredibly grateful to the UNHCR Uganda and World Bank Uganda staff for data and insights on refugee-hosting in Uganda; Herbert Musoke for help with obtaining education data; Paul Heysch de La Borde, Ia Mantecon Garcia, Isabella Preite, and Bayley Tuch for excellent research assistance. We thank Christopher Blair, Benjamin Fifield, Vasiliki Fouka, Margarita Puerto Gomez, John Ilukor, Apoorva Lal, Benjamin Laughlin, Carl Müller-Crepon, Melina R. Platas, Hannah Postel, Benjamin Reese, Audrey Sacks, Jeremy Springman, Stephen Joseph Winkler, Alexander Yarkin, Stephanie Zonszein, and participants from the 2021 PDRI Barriers and Bridges to Immigrants’ Integration Conference for helpful comments. Zhou acknowledges funding from SSHRC and CIFAR.

‡Assistant Professor, Department of Political Science, University of British Columbia, Harvard Academy Scholar, and CIFAR Azrieli Global
28 cancer screening. If the raw microdata are not available, we make use of summary statistics in existing reports and papers.

- <u>Figure 6: HEFPI health equity data processing steps</u>

<!-- Start of picture text --> Identify HH surveys<br>Access HH surveys<br>Compute harmonized variables<br>Compute population and quintile mean outcomes, CIs and their SEs<br>Collapse micro‐data to country‐year‐indicator level<br>Conduct data quality checks<br>Drop rejected datapoints Get population and quintile means from published work where microdata inaccessible<br>Merge data from publications into meso data<br>HEFPI HE meso data set<br><!-- End of picture text -->

# <u>Comparisons across subpopulations</u>

The HEFPI database presents not only sample averages but also subpopulation averages and measures of inequality. Households are ranked by either household per capita consumption or income or the Filmer-Pritchett (1999, 2001) wealth index. Subsequent to Gwatkin et al. (2000), the organization responsible for the DHS (then Macro International) decided to include the wealth index in each public-release DHS data set; UNICEF, which is responsible for the MICS, subsequently decided to do the same with the MICS. A handful of earlier MICS surveys<sup>33</sup> and the WHS do not include a wealth index. We therefore created wealth indices for these surveys using principal component analysis (PCA). For the MICS surveys, we included the same asset variables as the

> 33 Namely the Comoros 2000, Lesotho 2000, Eswatini (formerly Swaziland) 2000, Iraq 2006, and Djibouti 2006.