rafmacalaba/data-use-mentions-extended huggingface dataset:
# **_Rental Housing Demand and Income Distribution_**

# _Private Rental Sector_

Data on the distribution of households living in urban apartments by a suitable measure of income<sup>52</sup> show that the private rental sector caters to all income groups (Table 6). However, differences can be identified when analyzing individual income groups as follows:

- in Armenia, Russia, and Romania, where private rental supply is scarce, the sector is strongly over-represented - even in relation to owners - in the highest income quintile. Independent evidence from the Lithuania household survey, which does not control for location and type of dwelling, suggests likewise an over-representation of private rental in the highest income quintile. It is somewhat under-represented in the highest income groups in Poland (which, however, has a deep overall private rental sector);

- in Poland, Serbia, and to some degree in Russia, urban private rental sector is strongly over-represented in the lowest income quintile, relative to owners, and even relative to public rental. In Russia, as (see Table 2), private rental sector is also quite large in rural areas, where household incomes are lower. This suggests not only high access costs to ownership, but also an important absorption function of the private rental sector for poor households, which is closer to the pattern in mature market economies.

The interpretation of the data is complicated by the fact that incomes and age are generally correlated over the life cycle, so their effects cannot be separately identified using the current methodology.<sup>53</sup> Urban starters and young families, who are over-represented in the private rental sector, are typically richer rather than poorer in the region, since they generally constitute economic winners of the transition. There are few exceptions: in Armenia, the income distribution is bifurcated - young households are either poor or rich (Table 7). Also, in Poland, outside the largest cities, young urban households are not generally richer than older households.
# **_Male–Female Yield Differences and Production Efficiency_**

Women often achieve lower yields than men in agriculture.<sup>1</sup> In Burkina Faso, Udry et al. (1995) and Udry (1996) find that within the same household, women’s yields were 18 percent lower than the yields of men. Akresh (2005) confirms these results for the same area. In Ghana, Goldstein and Udry (2008) find that women had far lower yields, resulting in far lower profits per hectare, than their husbands who farmed the same crops. These studies provide stark evidence of male and female yield differentials. Using the farming system of West Africa, in which women and men of the same household often farm different plots of land, these studies identify yield differentials _within_ households, thereby accounting for all of the market conditions and imperfections faced by the household as a whole. Moreover, these studies provide evidence that even within a household, households cannot or will not act to overcome the root causes of women’s lower yields.

For broader evidence on gender differences in yields, we can turn to studies that compare yields for male- and female-headed households. Here, evidence from a number of studies points to lower yields on farms run by women. For example, using data from Ethiopia, Tiruneh et al. (2001) find that female-headed households have 35 percent lower value of output per hectare than males. In Zimbabwe, Horrell and Krishnan (2007) find lower yields for households of widows or de jure female-headed households.

However, these patterns are not universal. A number of studies find no statistically significant differences in yields between men and women farmers. For example, in Burkina Faso, the study cited previously by Akresh (2005) finds that male and female yield differentials vary by region; that study finds differences in some regions, but not in others.

3
of micronutrient interventions such as fortification of staples and vitamin distributions during periods of high food prices.

Second, household survey consumption modules often include questions on the quantity of food items consumed or the expenditure on food items, but not on the frequencies with which the food items are consumed. Given the low cost of adding questions on the frequencies of food intake, it may be beneficial to consider augmenting household surveys, particularly for populations that are vulnerable to food insecurity. Measures of dietary diversity are useful tools when detailed food journals or anthropometric data are not available and so micronutrient and macronutrient intake cannot be measured directly. Ruel (2003) discusses some of the benefits and costs of indicators of dietary diversity. In the nutrition literature, Alexander and Thomson (1992) discuss the importance of collecting frequency data in addition to quantity intake data. They demonstrate that both the quantity of food ingested and the intake frequency are important determinants of dietinduced diseases; they argue that looking solely at quantity data could be misleading. Our findings are consistent with this view.

Finally and more broadly, examining household responses using OLS estimation may be misleading since this estimator imposes the restriction that the marginal effects are constant across the distribution. Our analysis indicates that behavioral responses of those at the bottom of several key distributions are substantially different from those at the top of the distribution. This paper demonstrates the benefits of using the unconditional quantile regression estimator to examine marginal effects at various quantiles of the unconditional distribution.

# References

Alexander, C. J., & Thomson, F. J. (1992). The threshold effect: Consequences of change in the frequency of food intake in the presence of a functional threshold. _Medical Hypotheses, 39_ (3), 302-308. doi: Doi: 10.1016/0306-9877(92)90128-y Block, S. A., Kiess, L., Webb, P., Kosen, S., Moench-Pfanner, R., Bloem, M. W., & Peter Timmer, C. (
The study starts with a brief review of developing country trade performance in the 1990's. This uses WTO and World Bank data and covers major developments in developing countries' exporlis and imports utilising growth rates, shares of trade related to output and similar aggregate indicators. The main purpose of this section is to compare, in general terms, the performance of the 42 developing countries, on which the study is based. to that of developing countries as a whole; not to undertake a systematic analysis of trends in developing countries trade performance or the factors that affected it during this period.

The next section presents a comprehensive and detailed review of the state of developing courtry trade policies and institutions based on the latest available Trade Policy Review. For the 1 7economies, for which more than one TPR has been prepared, an effort is made to trace the evolution of various policies and institutions . er the whole period 1989-1997.

The third section is devoted to a discussion of the international environment facing developing countries. This is based on information developed from three main sources: (a) the TPRs of the deve loped - countries, their main trading partners; (b) information generated outside this project e.g. In the OECD/UNCTAD, regarding key indicators of access in developed country markets; and (c) special WTO analyses of market access issues for LDCs ( WT/COMITD/HL/14).

The final section summarizes the main conclusions of the study and their implications for action by the developing countries and the international community on steps that would enhance the integration of the developing countries into the multilateral trading system.

# **II.** Trends in Developing Countries' Trade, **1989-1997**

The period covered by this study, 1989-1997, witnessed a very rapid expansion of world trade, and an even more rapid expansion of developing countries' trade. Between 1989 and 1997, the vallue of country measures with WTO obligations; as well as constraints on what the WT0 Secretariat can accomplish
the composition of the sample countries.

46. A challenge in our study is that very few African nations produce and export autos, particularly in Sub-Saharan Africa. Outside the African continent, we have aimed at finding comparator based on three main considerations about the countries: (i) they are middle income economies producing and exporting autos; (ii) they are going (or have gone) through economic transformation away from agriculture and towards industry and services; and (iii) they have practiced some sort of industrial policy affecting the automotive sector.

47. As in the previous analysis, the D-in-D methodology also requires that the treatment and control group show similar trends prior to the treatment (i.e. the MIDP launch). This is true in both of the samples considered, as illustrated in Figures 5 and 6 in the previous section, where comparable trends in automotive exports before 1995 in all sample countries can be seen.

48. As noted, in a first step we proceed with a panel database of 6 African countries spanning 1993-2007. In a second step, we have increased the sample size by considering other middle income countries around the world that are automotive producers and exporters. Below, we describe in detail the methodology that has been applied in both analyses, as well as the results obtained.

49. The dependent variable is the natural logarithm of automotive exports (US$ real), obtained from UNComtrade. As in the section above, we start with a simple difference-in-difference (D-in D) approach where outcomes are observed for two groups (South Africa, as the treatment group, and all other countries that are automobile producers and exporters, as the control group) and two periods (before 19
)|(0.000)|(0.000)|(0.000)|(0.000)|
|Secondary enrollment rate|0.010***|-0.000|0.002***|0.007***|-0.002|0.003**|
|<br>_(initial level, logs)_|(0.000)|(0.798)|(0.001)|(0.000)|(0.224)|(0.003)|
|ICRG Political Risk Index|0.006***|-0.000|0.005***|0.007***|0.001**|0.005***|
|_(principal components)_|(0.000)|(0.774)|(0.000)|(0.000)|(0.002)|(0.000)|
|Credit to private sector|-0.011***|-0.005***|-0.008***|-0.013***|-0.006***|-0.010***|
|<br>_(% of GDP)_|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Government consumption|0.007***|0.011***|-0.003*|0.005**|0.008***|-0.004**|
|_(% of GDP)_|(0.000)|(0.000)|(0.018)|(0.003)
In this paper our approach is similar to Micco and Panizza (2006) and Iannotta et al. (2011), but unlike these two studies we control for possible endogeneity of GDP growth to credit growth by using system GMM estimation. In addition, we consider a large worldwide sample of banks for the period from 1999 to 2010, including the recent global banking crisis. Furthermore, unlike previous papers we consider the dynamics of the main categories of bank funding and of the accounting for non-performing loans and loan loss provisioning to better understand the various „channels‟ that influence state bank lending over the business cycle. Finally, we also examine differences in lending behavior among domestic private banks versus foreign banks for a large number of countries.

The remainder of this paper is organized as follows. Section 2 discusses the data including our bank ownership classification. Section 3 presents the econometric methodology, and the empirical results. Section 4 concludes.

# **2. Data**

The empirical analysis is based on an international sample of 1,633 banks from 111 countries for the period 1999-2010. See Table A1 in the Appendix for details on the number of banks per country. The main data source is Bureau van Dijk‟s Bankscope, which provides information on statements of banks and their ownership structure.<sup>3</sup> To create time series information on the ownership of banks, we used Bankscope CDs starting from 1999 and Wharton Research Data Services (WRDS) for recent years. The CDs include snapshots of ownership structures in relevant years. In addition, we use various websites to classify the owner as private or state including Bankscope‟s online database, Factiva, Banker‟s Almanac and company websites of the banks. In our sample, we only include banks that we can identify to be owned by another entity with a 50 percent or higher ownership share. Thus, a bank is categorized as a state bank if it is majority-owned by a state-owned entity.<sup>4</sup>
the school attendance computed by the Liberian Census 2008 better than DHS data. The CWIQ (2007) draws from a sample of 3,600 randomly selected households located in 300 randomly selected clusters. Table 1 below presents preliminary results of the incidence/coverage rate of opportunities in Liberia for 2007 and 2010.

# **[Table 1 about here]**

The second relevant consideration in the selection of opportunities refers to coverage. Table 1 shows that a number of indicators previously used in Latin American analyses may not be useful in the Liberian context, as late entry into school appears to be common due to the lingering effects of conflict. Late entry and frequent interruptions also affect timely completion of 6<sup>th</sup> grade, thus questioning the relevance of timely entrance and/or graduation as an indicator to analyze equality of opportunities. In lieu of that, the proposed indicator for the HOI analysis of educational opportunities is the _proportion children of age 6-15 who attend school_ .

# **4.2. Relevant Circumstances in Liberia**

Even when a list of universally accepted circumstances is elusive, circumstances such as gender, parents‘ education, incomes and location are widely considered conditions that should not affect access to key public services. Historical conditions in Liberia justify analyzing inequality along other dimensions as well. Ethnicity (or a proxy for that) can be a relevant circumstance in this multi-ethnic country. Given the recent devastating conflict, whether a child has a missing parent 17
the auditor, consisting of either brochures about the product or, less frequently, personalized amortization tables when credit was requested. The third data source comes from a survey of a sample of credit officers that serviced the auditors. The survey was conducted after the audit study was completed and asked about basic socio-economic characteristics, financial literacy, the marketing of financial products and bonus schemes offered by their employers. The fourth and final data source is what we refer to as the market products dataset containing the terms of all the savings and credit products offered by the institutions in the study at the time of the visits. This dataset was assembled using various sources including the web, market surveys and conversations with the regulatory authorities and staff of the financial institutions. The dataset only includes credit products with loan sizes in the range requested by the auditors, and savings products that can be contracted with the initial deposit amount mentioned. The construction of this dataset proved challenging for two reasons. First, the authorities in charge of collecting market information only require the average of certain terms for the institutions they supervise. As a result, they did not have detailed data on all the terms for all credit 9
Policy Research Working Paper 7556

# **Abstract**

Improving the quality of education is one of today’s main challenges for governments in the developing world. Based on a unique matched student-to-teacher panel data set on test scores this paper presents two empirical results for Indonesia. First, through detailed inspection of teacherlevel responses to test questions, the paper concludes that subject matter knowledge of primary school teachers in Indonesia is low on average and that a 1.0, but also a 2.0 standard deviation increase in teachers’ subject matter knowledge seem to be achievable medium-term goals for education policy making in Indonesia. Second, the paper presents the results of three types of value-added regressions, a (standard) level specification, a school fixed-effects specification, and a flexible student-teacher fixed-effects specification. The student-teacher fixed-effects approach estimates the parameters of a value-added model using test score variation within each student-teacher pair across three different subjects, mathematics, science and Indonesian language. The results suggest that a 1.0 (and 2.0) standard deviation increase in teachers’ subject matter knowledge across-the-board can yield increases in student achievement by 0.25 (and 0.50) student-level standard deviations by the time students complete the six-year primary school cycle.

This paper is a product of the Education 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 author may be contacted at joppederee@ gmail.com.

_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
lack statistically credibility. A properly chosen cap may not only add stability, but may even make the methodology more accurate by eliminating extremes.

Actuarial judgment is required in the choice of cap percentile, as this may substantially affect the degree of smoothing of rates within the state. If the LCCP is set to be too high, the presence of outliers may lead to a low credibility factor Z. If the LCCP is set to be too low, the LCC could be 0% for a large number of districts. Based on the data analysed in this note a LCCP between 85% and 100% is suggested, although this may depend on the specific historical data and historical claim payment frequency. The LCCP could be chosen at the crop/state level.

Since extreme losses are removed from each insurance unit’s experience in this step, it is appropriate to add them back later at a broader level, so that rates are not underestimated. These excess amounts will be added back in the _Capped excess load_ subsection below.

20
**_<u>________________________________________________________</u>_**

# **Conclusions**

There are a number of important cautions and limitations to the data available on the impact of trade costs and facilitation on competitiveness. These include:

- Lack of harmonized definitions and measurement tools which can lead to different indicators of the same barriers;

- Limited country coverage;

- Poor quality data that can be difficult or impossible to replicate;

- Lack of time series data sets;

- Aggregation of various data sources is problematic or impossible;

- Difficulty to use same indicators for cross-country analysis and for measuring project performance.

For example, one measure of trade facilitation is the time required to clear goods through customs. While there exist several sources of such data, there is no uniform and commonly accepted definition of customs clearance times. Measurement methodology differs around the world and there is no standard template or methodology for calculating, on a time series basis, days required to clear customs at national points of entry. The ICAs calculation is based on firmlevel surveys, while TTFSE measures clearance at the border. There is a discrepancy between firm surveys and on the ground interviews and observations. For Uzbekistan, WBES gives an average clearance time of 11 days compared to 5 days by UNESCAP. The question is at what point to measure customs clearance.

The data can suffer from limited or unreliable statistical base, or diverging definitions. Moreover, the involvement of different agencies in the clearance process can yield different results in baseline calculations of customs clearance. Other factors such as origin of goods, mode of transport, loading/unloading time, delays due to phytosanitary standards etc., are factors to be considered, as well.

It is necessary to achieve consensus on definitions, as well as focus attention on achievable goals in this area. It is clearly problematic to capture progress in trade facilitation through one indicator. The sequence of the supply chain can provide a beginning: international transport, border crossings, handling and storage, inland transportation.
est.)|||
|Jordan|Nov 79|<br>2133 F|U.N. 1988 revision<br>(pretim.);<br>11/79 census covered<br>East<br>|<br>Based on 1983 Family<br>Health<br>Survey|Based on U.N. 1984<br>assessment|
||||Bank only|||
|Kaupjchea, Dem|. Apr 62|572° F|U.N. 1988 revision (prelim.)|Based on U.N. 1984<br>assessment|Based on UNPVSR<br>4/87|
|Kenya|Aug 79|15327 F|Bank projection from census,<br>adjusted for5Xundercowut|1980-85, based on va.ious<br>national surveys; 1985-90,<br>Bank projection|1980-85, U.N. 1988 revision<br>(prelim.);<br>1985-90, Bank<br>projection|
|ribati|May 85|64 F|UNPVSR<br>1/88 (official<br>est.)|<br>Based on USS0C 1985|Based on USSOC<br>1985|
|Korea, People's|May|a F|U.N. 1984 assessment|Based on U.N. 1984|Based on U.N. 1984|
|Dam. Rep. of||||assessment|assessment|
|Korea, Rep. of|Nov 85|40432 F|Official<br>est., Economic|Based on U.N. 1984|Based on U.N. 1984|
||||Planning Board|assessment|assessment|
|Kwaait|Apr835|1697 F|U.N. 1988 revision (prelim.)|Based on UNPVSR<br>1/88|Based on U.N. 1984|
||||||assessment|
and time-bound shocks. Additionally, we control for potential country, sector, and time-varying shocks. Finally, we include country-sector-year ∂cst fixed effects, which allow to demean the results, so the coefficients can be interpreted as the distance to the country-sectoral mean and therefore be comparable across countries.<sup>17</sup> Furthermore, we explore the heterogeneous response between BOSs and POEs to economic shocks, particularly to the short-term shock of the COVID-19 pandemic. For this purpose, we evaluate both levels and growth of labor force and fixed assets as a proxy of capital investments using the specification below following Brolhato, Cirera, & Martins-Neto (2023) and Ferro & Patiño-Peña (2023):

# (2) Yit= α+ β1BOSi + β1BOSi ∗2020 + β2Xit + ∂c + ∂s + ∂t +  εijt

Thus, we evaluate whether state ownership can play a role as a stabilizer during periods of crisis. As before, the BOSs variable takes value 1 if the state holds 10% or more state participation, and we also delve deeper into whether there are differences in the response to the shock by the level of state participation.

> 16 Both size and age variables are included as lagged values in t-1.

> 17 Since the BOS variable from the WB BOS database is time-invariant in the sample period, we cannot control for firm-level fixed effects in the assessment. Hence, these results do not claim causality between state ownership and firm performance. Nonetheless, we perform different robustness checks to test different specifications that can help minimize the effect of unobserved firm-level characteristics. The results hold when using shorter timeframes 5-years and 3-years, and when using the subset of firms fully owned by the state. These exercises are conducted to test the robustness of the results in the event some changes in ownership have occurred in the sample period. Estimations are conducted using OLS.

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