rafmacalaba/data-use-mentions-extended huggingface dataset:
|0.36|-0.14***|0.17|0.38|-0.21***|
|towns and suburbs|0.26|0.25|0.01*|0.30|0.32|-0.02***|0.25|0.29|-0.04***|0.24|0.29|-0.04***|
|rural areas|0.42|0.26|0.16***|0.47|0.34|0.13***|0.52|0.35|0.17***|0.59|0.34|0.25***|

Note: Data is at the individual level and weighted accordingly using survey weights. Based on the AROPE status of 2022. Columns (i) – (ii) report t-tests on the equality of means. *p < 0.1, ** p < 0.05, *** p < 0.01. Values reflect mean in observable characteristics, conditioning on being AROPE or non-AROPE. Source: Own estimates based on 2022 EU-SILC. We report the survey year and not the income year. AROPE definition based on EU2030 target.

22
# **1.0 Introduction**

Prior to the liberalization periods in the 1980s and 1990s, many African markets were run on a public monopsony basis, whereby procurement relied heavily on marketing through producer cooperatives and public buying networks. Direct producer to buyer relationships or marketplace transactions had not been fully developed. Post liberalization, these buying networks were dismantled and many producers found themselves geographically isolated and distant from markets. Farm-gate buying became one of the most prevalent marketing arrangements as private buyers quickly set-up buying networks in rural villages.

The liberalization of Tanzania‟s maize market took small farmers through a scenario very similar to the one described above. The cooperatives, which had been their main marketing channel, dissolved and itinerant buyers took their place. Since maize farmers‟ choice of marketing channel depends largely on the way that the market in which they operate is structured<sup>2</sup> , farm-gate buying became the main marketing institution for a large proportion of maize growers, and today only a small proportion of Tanzania‟s maize growers sell their output at a marketplace. Maize growers retain a proportion of their annual output for household consumption<sup>3</sup> . The rest of their maize, the surplus, is sold mostly at the farm-gate to itinerant small maize buyers.

In this paper, we study the transaction costs of farm-gate buying. Using household level data from the most recent Tanzania Household Budget Survey (2007), we estimate the extent to which maize producers‟ distance to market (a measure of household remoteness or access to markets) affects farm-gate prices. We observe that greater distance to market depresses farm-gate prices but that it is a relatively modest effect, and that this effect is pro-cyclical in that it is stronger during the harvest season when prices are lowest. We discuss the role of search costs as a part of this framework.

We begin by describing maize traders and the farm-gate buying process in the first section. The second section presents our conceptual framework. It is followed
Policy Research Working Paper 7915

# **Abstract**

Proxy-means testing is a popular method of poverty targeting with imperfect information. In a now widely-used version, a regression for log consumption calibrates a proxymeans test score based on chosen covariates, which is then implemented for targeting out-of-sample. In this paper, the performance of various proxy-means testing methods is assessed using data for nine African countries. Standard proxy-means testing helps filter out the nonpoor, but excludes many poor people, thus diminishing the impact on poverty. Some methodological changes perform better, with a poverty-quantile method dominating in most cases. Even so, either a basic-income scheme or transfers using a simple demographic scorecard are found to do as well, or almost as well, in reducing poverty. However, even with a budget sufficient to eliminate poverty with full information, none of these targeting methods brings the poverty rate below about three-quarters of its initial value. The prevailing methods are particularly deficient in reaching the poorest.

This paper is a product of the Human Development and Public Services Team, Development Research 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 dvandewalle@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
data. Let β **1** , β **2** , and β **3** be the regression coefficients. The following parameters characterize the general quadratic Lorenz function.

1 _e_ = − ( β 1 + β 2 + β 1 + 1); _m_ = ( β 22 − 4 β 1); _n_ = (2 β 2 _e_ − 4 β 3 ); _r_ = ( _n_ 2 − 4 _me_ 2 ) 2 (2.31) Table 2.3 Size Distribution of Income within the Socioeconomic Groups in the SAM

|Group|Mean|Poorest|2nd|3rd|4th|5th|6th|7th|8th|9th|10th|
|---|---|---|---|---|---|---|---|---|---|---|---|
|||Decile||||||||||
|National|1.00|0.01|0.03|0.04|0.06|0.07|0.09|0.11|0.14|0.18|0.28|
|Rural|0.66|0.02|0.03|0.05|0.07|0.08|0.10|0.12|0.14|0.17|0.21|
|Urban|1.50|0.00|0.04|0.06|0.07|0.09|0.10|0.12|0.14|0.16|0.23|

Source: Author’s calculations The unit record data underlying table 2.3 were generated randomly from the beta distribution. The probability density function is defined by the following expression (Johnson, Kotz and Balakrishnan 1995): where B
37

# CONCLUSIONS AND POLICY ISSUES

This paper has presented a picture of the recent situation in the emerging housing market in Russia. The process of mass privatization of housing, which began in earnest in Russia in 1992, has served as the catalyst to the development of this market. At the same time, as has been seen through the analysis of survey data, the peak of privatization appears to have passed or to be stalled due to some of the conditions that now prevail in the housing situation in Russia. The introduction of policies directed at resolving these problems could further the development of the housing market and the lead to the provision of a wider range of housing market services for which there is at present an unmet demand.

An important shift in household attitudes to the housing market is apparent in the survey data. Recent data indicate a greater willingness to turn to market methods to deal with the housing problem as opposed to the non-market methods that prevailed in Soviet times. This is especially true of provincial towns and to a lesser degree of Moscow. Market methods are clearly the preferred means in households' plans to improve their living conditions. Many households would like to take out long-term loans for the purchase of housing and it is increasingly clear to people that the practice of using waiting lists to obtain free housing, a vestige from the Soviet system of allocation of housing, has not proved effective.

Below follow some broad policy proposals that address the distortions in the development of the housing market that have been examined in this paper.

- Support the transition to predominantly market methods of dealing with the housing problem;

- Abandon the allocation of free newly constructed housing;

- Introduce a category of "social" housing (low-quality dwellings, which in Moscow could include Khrushchev-era buildings, and in small towns, the old low-rise stock without modern conveniences) to address the problem of housing allocation to the poor (possibly free of charge);

- Establish a low rent for the households living in the "social" housing;

- Formulate
**Table 1: Examples of measures, level of restriction and associated score**

|**Level of restriction **|**Examples**|**Score**|
|---|---|---|
|Not restrictive|No restriction on the type of legal entity, no foreign<br>equity limitation|0|
|Not restrictive, but minimal<br>transparencyissue|No provision for prior notice of, or comment on,<br>regulatory changes|0.125|
|Minor restriction|Acquisition of land and real estate by foreigners<br>prohibited|0.25|
|Neither minor nor major|Limit on number of suppliers|0.5|
|Major restriction|Service<br>provision<br>is<br>reserved<br>for<br>statutory<br>monopoly or granted on an exclusive basis|0.75|
|Closed|Commercial presence is prohibited|1|

While in Borchert et al. (2012), five numerical levels of restriction were used (0, 0.25, 0.5, 0.75, 1), this time we decided to use six numerical levels, by adding 0.125 in between 0 and 0.25. The objective was to accommodate those regulatory measures that were not included in the previous World Bank data set (e.g. publication of draft regulation and opportunity to comment). The aim is to take account of measures that are aspects of the regulatory environment rather than restrictions, not even minor ones, in regard of the actual supply of services but that can nevertheless adversely affect the policy or regulatory environment in which firms operate.<sup>22</sup> The full list of measures and associated scores is provided in Annex 5. In addition to mapping scores directly to individual measures, more sophisticated rules are applied for certain measures (e.g. numeric type measures), or
, 2019, _74_ (5), 555.

- **Rød, Espen Geelmuyden and Nils B Weidmann** , “Empowering Activists or Autocrats? The Internet in Authoritarian Regimes,” _Journal of Peace Research_ , 2015, _52_ (3), 338–351.

- **Schneider, Udo, Andreas Becker, Peter Finger, Anja Meyer-Christoffer, Bruno** °

- **Rudolf, and Markus Ziese** , “GPCC Full Data Reanalysis Version 7.0 at 0.5 : Monthly Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historic Data: Gridded Monthly Totals,” `https://bit.ly/3BTrP3O` 2015. Accessed: 2021-04-28.

- **Soufan Center** , “Foreign Fighters: An Updated Assessment of the Flow of Foreign Fighters into Syria and Iraq,” _New York_ , 2015.

22
# **5. Conclusions**

African SMEs are small in number and heterogeneous in performance, which poses a challenge for both experimental and structural methods of estimating the impact of firm policies designed to facilitate employment creation and firm growth. In reviewing African data on firms and on the types of policies governments are using with these firms, it seems there are three sets of firm types, for which different evaluation methods may be suited.

1. The vast majority of firms are microenterprises. Policies which focus on providing training, credit, or other assistance for these firms can often rely on standard randomization of a large number of firms to treatment and control groups, with a small number of surveys taken on each firm.

2. For SME projects and policies targeting smaller number of firms, one should attempt to reduce heterogeneity by focusing on a more homogeneous sub-sample within this target group – such as firms in particular industries or size categories – and then collect data in many survey rounds or time periods on these firms. Randomized experiments can be used to obtain policy impacts for this sub-sample, and the rich data gathered can provide a good basis for structural modeling.

3. For the largest firms there may be no other firms in the country that are comparable, making the use of experimental or non-experimental methods that require a comparison group for estimating the counterfactual not possible. In such cases, rich time series data on multiple dimensions of firm behavior and firm outcomes can enable time series analysis and structural modeling to get some sense of how the policy has performed.

Finally, for years now there has been talk of how we can use experimental methods and structural modeling together to get the best of both worlds – using the experiment to identify structural parameters and impacts of a particular policy, and then the structural model to undertake simulations of how firms would respond under alternative policies (e.g. Todd and Wolpin, forthcoming). At present this is more promise and rhetoric than reality when it comes to firm experiments. The collection
of one if the transaction ZZ happened after February 2022 when war conditions are likely to have significantly altered agricultural land supply and demand; (ii) the number of buyers if a parcel was acquired in joint ownership; (iii) a variable capturing the order of transactions (from 1 to 5) in case a parcel has been transacted multiple times; and (iv) dummy variables for prices below NMV and for missing NMV to capture non-compliance with legal provision prohibiting transactions below the NMV.

Use of fixed effects at the level of 355,663 cadastral blocks, delineated to contain homogenous patterns of land use, is the most appropriate way to control for unobserved heterogeneity in local conditions. However, as agricultural land sales started only in 2021, there may be no or only a limited number of sales transactions at this level, especially in agriculturally marginal areas. Appendix figures A4, A5, A6, A7, A8, and A9, illustrate this graphically by showing that, there were no or only few land sales transactions for many cadastral blocks, zones, villages (KOATUU), communities ( _hromadas_ ), or districts ( _rayons_ ). If there are only few non-representative observations, estimation of fixed effects at any given level may result in biased and misleading prediction of land values.

> 19 Both the CPI and UAH to USD exchange rates are taken from the data of the National Bank of Ukraine <u>https://bank.gov.ua/ua/statistic.</u> 20 As such repetitions are due to a parcel being purchased by multiple owners and the value of each owner’s share is recorded separately, we aggregate the land value over every owner, while keeping other parcel characteristics constant. By contrast, cases where the same parcel is sold multiple times during the period under concern are retained as different observations.

> 21 Extreme values due to very small plot size that result in very high per-ha prices are likely due to data entry error and removed via trimming.

> 22
# **The financial sector is ready to meet the challenges**

We all face great challenges to improve access to financial services: the financial institutions; the government; the regulators and the users of the financial system.

For the financial sector the challenges are: first recovering public trust as financial intermediaries; second, continuing to improve the quality and efficiency of day-to-day service delivery. Recent surveys suggest that there has been tangible progress in this area. Sector studies report that the cost of managing $Col 100 have dropped from $6.2 five years ago to $5.8 today.

Third, maintaining the process of technological innovation in service delivery and meeting the consumers’ needs.

Finally, building a stable institutional framework, since it is clear that a healthy business environment is essential to financial sector depth. And here the government must play a fundamental role.

Factors such as the guaranteed creditor rights, a stable regulation framework, interest rates based on market realities, an efficient judicial system, market transparency based on public disclosure of financial information and clear accounting regulations are crucial<sup>4</sup> for this development. How does Colombia fare in each of these points?

a. According to an IDB study of indicators of credit risk, Colombia has one of the worst ratings of protection to creditors, along with Peru and Mexico<sup>5</sup> ; this necessarily means higher transaction costs and generates inefficiencies in financial intermediation.

b. Regarding a stable regulatory framework, the most impeding issue is the current tax on financial transactions. Initially it was meant as a “short term measure”, with a tax of 0.2% on financial transactions; today, we find ourselves with a permanent tax of 0.4%.

We have repeatedly remarked the negative effects of this tax on the overall economy: it has greatly reduced the average returns on savings, making them less attractive; it has increased preference for cash transactions and has generated an increase in the “
year. About 3 percent have experienced a mortality event in the past year, with only about one percent reporting mortality of a child.

Since diarrhea is the primary public health disease of concern with respect to WSS interventions, we developed a profile of households who have had diarrhea cases and compare them to households who have not suffered from diarrhea in the month prior to the survey. On average, the ‘diarrhea households’ are poorer, more likely to have children under 5, and less educated. In general, they are less likely to be connected to the piped water network, and consume about 30 percent less water per month. They are less likely to store the water in overhead tanks, transfer water by pouring or using spigots, and wash their hands before eating and before preparing food. In terms of toilet behaviors, they are less likely to wash their hands in or just outside the toilet, use appropriate technologies for hand washing, and sanitarily handle and dispose children’s feces.

# **_Econometric Estimates_**

As the sample characteristics above demonstrate, the poor generally experience WSS services differently than the non-poor. In a stated preference study on provider choice for malaria treatment in Nepal, preferences varied significantly based on whether the household was poor. In addition, cost was the major determinant of provider choice and the poor are most cost sensitive (Morey et al. 2003a). The heterogeneity of preferences among respondents represents a challenge for estimating welfare impacts using the results of the SP conjoint questions. Morey et al. (2003b) provide a method of estimating consumer surplus when exact information on income is missing. Here we consider how attributes affect demand and if poor households are different in their preferences for service attributes. We also consider non-linearities for continuous attributes, such as hours of supply and volume of consumption.

Table 4 presents the results of the conjoint analysis used in our Sri Lanka study. This table confirms that households care about attributes of the service, other than costs. A positive coefficient on
this paper. As noted in Blanchard and Perotti (2002) identi...cation of tax shocks depend on purging innovations in revenues of automatic responses to output. This could be achieved by imposing a contemporaneous coe¢ cient on the elasticity of revenue with respect to real output. However, as noted in Auerbach and Gorodnichenko (2010, 2011) this elasticity is likely to vary over the cycle, thereby, introducing a bias of unknown magnitude and direction in our time-varying speci...c estimates. Auerbach and Gorodnichenko (2011) have shown that output responses to tax shocks in di¤erent regimes are sensitive to the assumed elasticity.

Unlike monetary policy decisions, changes in government spending and taxes are typically decided and communicated well in advance of their implementation.

> 14Robustness tests indicate that the fundamental conclusions remain unaltered should one use the detrended GDP growth rate.

9
The model has a very good ...t with a within R-squared at 0.89. The coe¢cient on the trend break variable is statistically signi...cant at the 5 percent level. It is also economically sizable; the size of the coe¢cient suggests that the growth in ...nancial intermediation accelerates by around 4 percentage points per annum post transition to modern payment systems, which is an economically sizable e¤ect. Moreover, all control variables have expected signs.

In line with prior evidence we ...nd the e¤ect of in‡ation to be non-linear, while overall economic activity has a robust positive e¤ect. We also ...nd evidence that ...scal consolidation leads private credit to expand.

Interestingly, the EBRD banking reform index is assigned a negative coe¢cient, suggesting that better compliance with supervisory standards puts a break on credit creation, as might have been expected.

In line with the result reported in Detragiache et al (2006) for high-income countries, we ...nd there to be no signi...cant e¤ect of foreign ownership on credit.<sup>15</sup> However, we ...nd a robustly positive relationship between public ownership and credit, in line with the evidence reported in Bertrand, Schoar and Thesmar (2007) and consistent with the idea that public ownership results in soft budget constraints on banking ...rms.

Finally, as one would expect there is a positive and signi...cant relationship between capital ‡ows into the banking system (measured by changes in foreign exchange reserves over GDP) and the credit over GDP ratio.

Overall, while macro factors (including in‡ation, capital in‡ows, and GDP) are shown to play an important role, this evidence suggests that privatization and increases in foreign ownership are unlikely to have been an important contributor to increases in credit observed in a number of countries over
**Figure 1: Portuguese emigration flows, 1930-2003**

<!-- Start of picture text --> 200,000<br>180,000<br>160,000<br>140,000<br>120,000<br>100,000<br>80,000<br>60,000<br>40,000<br>20,000 End of 2 nd World War<br>Carnation revolution<br>0<br>Legal emigrants Total emigrants Permanent migrants<br>1930 1932 1934 1936 1938 1940 1942 1944 1946 1948 1950 1952 1954 1956 1958 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br><!-- End of picture text --> <u>Notes:</u> Data on legal emigrants and total emigrants until 1988 come from the Portuguese Ministry of Foreign Affairs. Data on permanent emigration from 1992 onwards come from the National Statistics Institute. Data on Portuguese emigration for the period 1989-1991 are not available.

1
: Findings from the Global Competitiveness Index 2007-2008. White Paper, World Economic Forum.

Nitsch, V. (2005). Zipf Zipped. Journal of Urban Economics, 57:86-100.

Pham, M. D. Mishra, K. Cheong, J. Arnold, A.M. Trinh, H. Ngo, H. Nguyen. (2013). Trade Facilitation, Value Creation, and Competitiveness: Policy Implications For Vietnam's Economic Growth: Volume 1. The World Bank.

Redding S. and P. Fagjelbaum (2018) Trade, Structural Transformation and Development: Evidence from Argentina 1869-1914.

October 2018. NBER Working Paper 20217 Rodríguez-Pose, A (2018), "The revenge of the places that don’t matter (and what to do about it)", Cambridge Journal of Regions, Economy and Society 11(1):189–209 Rosen, K.T., and M. Resnick. (1980). The Size Distribution of Cities: An Examination of the Pareto Law and Primacy. Journal of Urban Economics, 8:165-186.

17