rafmacalaba/datause-encoder-data huggingface dataset:
# **6. Conclusion**

Price controls, although also used in advanced economies, are particularly ubiquitous in EMDEs and LICs, especially for energy commodities. While often implemented with the best social intentions in mind, these policies often distort markets and their consequences for growth, poverty reduction and government policies grow over time. Countries can replace price controls with expanded and better-targeted social safety nets, coupled with reforms to encourage competition and a sound regulatory environment. Evidence suggests that such comprehensive price control reforms can be both pro-poor and pro-growth. However, historical experience highlights the need for careful communication and sequencing of reform efforts to ensure political and social acceptance. Where they exist, authorities can ensure that price control regimes are transparent and supported by well-capitalized stabilization funds or national hedging strategies to ensure fiscal sustainability.

# **References**

Aaron, H. 1966. “Rent Controls and Urban Development: A Case Study of Mexico City.” Social and Economic Studies 15 (4): 314-328.

Aksoy, A., and F. Ng. 2010. Food Prices and Rural Poverty. Washington, DC: World Bank.

Alleyne, T. 2013. Energy Subsidy Reform in Sub-Saharan Africa: Experiences and Lessons. Washington, DC: International Monetary Fund.

Baffes, J., M. A. Kose, F. Ohnsorge, and M. Stocker. 2015. “The Great Plunge in Oil Prices: Causes, Consequences, and Policy Responses.” World Bank Policy Research Note 15(01). Bank for International Settlements (BIS). 2003. Fiscal Issues and Central Banking in Emerging Economics. BIS Paper 20, Bank for International Settlements, Basel.

Cardoso, E. 1991. “Deficit Finance and Monetary Dynamics in Brazil and Mexico.” Journal of Development Economics 37 (1-2): 173-197.

Clements, B., H.-S. Jung, and S. Gupta. 2007. “Real and Distributive Effects of Petroleum Price Liberalization: The Case of Indonesia.” The Developing Economies XLV (2): 220-237.

Coady, D., M. El-Said, R. Gillingham, K. Kpodar, P. Medas, and D. Newhouse. 2006. “The Magnitude and Distribution of Fuel Subsidies: Evidence from Bolivia, Ghana, Jordan, Mali, and Sri Lanka.” IMF Working Paper 06/247, International Monetary Fund, Washington, DC.

Contreras, J., and N. Guarata. 2013. “Inflacion y Variacion de Precios Relativos en Venezuela.” Economia 38 (36) : 85-122.

Coomer, J., and T. Gstraunthaler. 2011. “The Hyperinflation in Zimbabwe.” The Quarterly Journal of Austrian Economics 14(3): 311-346.

13
16 increasing participation by SMEs has not yet taken hold in Colombia with anything like the vigor that we observe for Korea.

-donesia (again). For all three ndonesian subsectors, our surveys yielded evidence on the evolutdon of export channels. As Table 9 shows, relative to both Korea and Colombia, initiatives by buyers turn out to be unusually dominant in all time periods.

- -

---

.~~~nmswr For the pioneering periods, the explanation for buyer dominance vanes across subsectors. For garments, the presence of pre-existing networks among the extended Chines community along the lines nated earlier provides sufficient explnation, since the vast majority of that industry's garment exporters are Chinese in background. By contrast, most of the wooden fumiture exporters in the sample were pnibumi, based in the traditional woodcarving center of Jepara, who took advantage of the area's abundant supply of carving skills to produce reproduction antique furniture for export markets. Paralleling ihe pattern hypothesized for Colombia, Jeparan producers had not initially set their sights beyond the domestic market. So, as the case study details, and as is signalled by the score for buyer initiatives im Table 9, the initial impetus for exports came from outsiders.

For rattan funiture, it is necessary to distngish between the "prehistory and the acuala period of export take-off. in the 1970s and early 1980s, rattan furniture exports were pioneered by a small number of predominandy prbumi entrepreneurs. They are the finns grouped in the "pioneer" category and, as can be seen in Table 9 and consistent with the discussion earlier, were disproportionately dependent on collective support for the initial export efforts. However, take-off of rattan fritmre exports came only in the latter 1980s, at about the time the Indonesian govermnent banned the export of raw and semi-processed ratan. The majonty of new exporters during this phase were Chinese (many of them former traders in raw rattan) who, as in garments- were able to rely on their extended network to penetrate export markets. Thus, as Table 9 shows, at export start-up non-pribumi "follower" firns identified buyer initatives as their most important source of support. To be sure, some of the "follower" -entrants were pibumi. However, they lacked access to the network and hence, as is evident inneieti
# **5. References**

Bank of International Settlements (2017). Chapter VI: Understanding Globalization. _87_<sup>_th_</sup> _Annual Report 2016/17_ . Bank of International Settlements. Basel, Switzerland.

Beck, T., R. Todorov, and W. Wagner (2013). “Supervising Cross-border Banks: Theory, Evidence and Policy.” _Economic Policy_ 28 (73): 5–44.

Bruno, V., and Hauswald, R. (2013). “The Real Effect of Foreign Banks.” _Review of Finance_ 18 (5): 1683-1716.

Basel Committee on Banking Supervision (BCBS) (2012a), Joint Forum Principles for the supervision of financial conglomerates, http://www.bis.org/publ/joint29.pdf Basel Committee on Banking Supervision (BCBS) (2012b), Core Principles for Effective Banking Supervision, <u>http://www.bis.org/publ/bcbs230.pdf</u> Committee on the Global Financial System (CGFS) (2014), EME banking systems and regional financial integration, CGFS Papers No 51.

<mark>Cetorelli, N., and L. Goldberg (2012a). Liquidity management of US global banks: Internal capital markets in the great recession.</mark> _<mark>Journal of International Economics</mark>_ <mark>, 88(2), 299-311.</mark> <mark>Cetorelli, N., and L. Goldberg (2012b). Banking globalization and monetary transmission.</mark> _<mark>The Journal of Finance</mark>_ <mark>,</mark> _<mark>67</mark>_ <mark>(5), 1811-1843.</mark> Choi, M., E. Gutierrez, and M. Martinez Peria (2016). Dissecting Foreign Bank Lending Behavior During the 2008–2009 Crisis. _Financial Institutions, Instruments and Markets Journal_ , 25: 361– 398.

Claessens, S., and N. Van Horen (2015). The impact of the global financial crisis on banking globalization. _IMF Economic Review_ , 63(4), 868-918.

Claessens, S., A. Demirgüç-Kunt, and H. Huizinga (2001). “How Does Foreign Bank Entry Affect Domestic Banking Markets?” _Journal of Banking and Finance_ 25: 891–911 Claessens, S., O. Hassib, and N. van Horen (2014). “How Foreign Banks Facilitate Trade in Tranquil and Crisis Times: Finance or Information.” International Monetary Fund, Washington, DC.

Claessens, S. (2016). “Global Banking: Recent Developments and Insights from Research.” _Review of Finance_ .

Cull, R., and M. Martinez Peria (2010). “Foreign Bank Participation in Developing Countries: What Do We Know about the Drivers and Consequences of This Phenomenon?” World Bank Policy Research Working Paper 5398.

De Haas, R., and N. van Horen (2013). “Running for the Exit? International Bank Lending during a Financial Crisis.” _Review of Financial Studies_ 26 (1): 244–85.

27
value of their position at market close. The data set also includes demographic information for a smaller subsample of households, such as income, age, gender, occupation and marital status.<sup>6</sup> A comparison of this data set with Survey of Consumer Finances, IRS and TAQ data has shown it to be representative of U.S. individual investors (Ivkovic, Sialm, and Weisbenner 2008, Ivkovic, Poterba, and Weisbenner 2005, and Barber, Odean, and Zhu 2006).

To address concerns that our findings are specific to the data we employ, we repeat the analyses using a similar data set from Finland which reports the complete trading records of all market participants.<sup>7</sup> This data set comes from the central register in the Finnish Central Securities Depository (FCSD). The register officially records all the trades of all Finnish investors - both individual and institutional- on a daily basis from January 1995 to December 2003. For the purposes of the current study, we only utilize the trades of individual investors in Finland. Similar to the U.S. data set, the Finnish data set reports for each transaction, the number of shares traded and the daily closing price. Furthermore, we observe the initial holdings for each account at the beginning of the data set, allowing us to keep track of the holdings of households on a daily basis. The data set also reports individuals’ demographic information, such as age and gender. We do not have information about income, occupation, and marital status. A more detailed description of the Finnish data set can be found in Grinblatt and Keloharju (2000, 2001).

# **3.2 Measures of Transaction Costs**

> 6 For a more detailed description of this data set please refer to Barber and Odean (2000, 2001). We thank Terrence Odean for providing us with this data set.

> 7 We thank Jussi Keppo for providing us with this data set.

11
- 25 -

mean farm size (less than 1/3 hectare) has the highest<sup>estimated returns to scale</sup> (1.116). In Jurong county the returns to s ale are<sup>1.056. These results are</sup> thus compatible wlth the observatlon that capital indivisibillty<sup>implies Increasing</sup> returns to scale within a range of smaller farm sizes.<sup>4</sup> The demand for croppingrelated capital In the countles with very small farm<sup>sizes Is thus smaller</sup> by more than a proportionate factor as compared to a<sup>hypothetical region with</sup> Identical agro-clmatic conditlons but larger farm sizes. Furthermore,<sup>the estimated</sup> productlon elasticity of capital In the two counties (Tal<sup>and Jurong) is not</sup> significantly different from zero. This suggests a low<sup>marginal productivity of</sup> capital In crop production, apparently due to the IndivisibIllty<sup>of capital and the</sup> very small size of farms.

We proceed next to analyze the determinants of Investment<sup>in productive</sup> assets. In order to allow for a better correspondence between the tenure security variables and the type of Investment considered, only<sup>Investment In</sup> cropping-related capital Is con3idered (e.g., livestock Is excluded).<sup>The estknated</sup> model corresPonds to the reduced form of the system<sup>of first-order conditions</sup> (6) - (7) as swumarized in Table 8, with modifications<sup>to allow for a somewhat</sup> richer model. One modification Is the Incluslon of a<sup>third form of Investment,</sup> namely, non-crop-related capital, In addition to housing<sup>and crop-related capital.</sup> Its effect or crop-related Investment Is expected to be<sup>In the same directlon as</sup> housng (i.e., positive). While values of housing In<sup>the beginning of the perlod (Ho</sup> In the notation of the preceding section) were not<sup>available, Indicators of housing</sup> quality In 1982 were used to construct a composite<sup>Index of Initial house quality.</sup> 4 A study employing province level time serles data<sup>for the period 1984-1988 obtained an</sup> estimate of returns to scale of 1.07. See Lin (1989).
Economic growth appears to be positively correlated with gender equality (Figure 5).<sup>24</sup> This latter finding is robust to changes in the length of the period over which per capita GDP growth rates are averaged and to one alternative measure of gender equality (the GDI-HDI ratio). When gender equality is measured by the gender empowerment measure (the GEM), however, the relationship is not statistically significant.<sup>25</sup>

**Figure 5: Per capita GDP growth rates and gender equality**

<!-- Start of picture text --> 10<br>8<br>6<br>4<br>2<br>0<br>0.6 0.7 0.8 0.9 1 1.1<br>-2<br>-4<br>R2 = 0.1376<br>-6<br>Female-to-male ratio in HDIs, 1997<br>Average annual GDP per capita<br>growth rate 1997-2004 (in percent)<br><!-- End of picture text --> Note: Scatter plot for 103 countries. The sample of countries is restricted to Bank client countries. GDP per capita growth rate statistics are obtained from the World Bank's World Development Indicators (WDI) database. Male and female HDIs were calculated using data from the 1999 HDR statistical database. See Klasen (2006) for the formula used to obtain the gender-specific HDIs.

In the remainder of this section, we summarize what is known about the link between gender equality under the law (rights), political voice, and access to and control over resources (opportunity), on the one hand, and rates of economic growth, on the other.

_Gender equality under the law and growth_

> 24 The correlation coefficient is 0.35 and is highly statistically significant.

> 25 In theory, the GEM—a measure of equality in agency—captures “women’s and men’s abilities to participate actively in economic and political life and their command over resources” (HDR 2006, p. 280).  The three indicators on which the index is based are: (1) the male and female shares in parliamentary seats; (2) the male and female shares in administrative, professional, technical, and managerial occupations; and (3) male and female earned income levels.  As a result of the way income disparities are incorporated in the index (levels instead of shares), a poor country is constrained to obtain a low GEM value, even if earned income shares are equal across genders.  By the same token, a rich country with unequal earned income shares can obtain a high GEM value by virtue of its higher earned income.  The GEM has been criticized because of this characteristic (HDR 2006).

32
Table 1: Exposures of different populations, measured as total DDT or DDE concentrations

|**Tissue**|**Occupation**<br>**(IRS spray**|**al exposure**<br>**operators)**|**Resident**<br>**treated**|**s in IRS-**<br>**homes**<br>|**General**|**population**|
|---|---|---|---|---|---|---|
||_Total DDT_|_DDE_|_Total_<br>_DDT_|_DDE_|_Total DDT_|_DDE_|
|Blood serum<br>(lipid<br>adjusted)|Mean: 77.8<br>(8.7–241.1)<br>μg/g lipid|Mean:<br>41.8 (7.1–<br>131.8)<br>μg/g lipid|Mean:<br>9.8<br>(1.09–<br>21.8)<br>μg/g lipid|Mean:<br>19.7<br>(0.8–<br>77.9)<br>μg/g<br>lipid|Mean: 5.0<br>(0.38–26.1)<br>μg/g lipid|Mean: 1.0<br>(0.2–3.18)<br>μg/g lipid|
||Median:<br>58.6 μg/g<br>lipid|Median:<br>25.6 μg/g<br>lipid|Median:<br>5.18 μg/g<br>lipid|Median:<br>9.7 μg/g<br>lipid|Median:<br>0.93 μg/g<br>lipid|Median: 0.77<br>μg/g lipid|
|Umbilical<br>cord blood|—|—|Mean:<br>29.9 μg/l|Mean:<br>60.3<br>μg/l|Mean: 15.0<br>μg/l|Mean: 3.1<br>μg/l|
||—|—|Mean:<br>4.6 μg/g<br>lipid|Mean:<br>9.3 μg/g<br>lipid|Mean: 0.44<br>μg/g lipid|Mean: 0.36<br>μg/g lipid|

Source: WHO 2011c. DDT in Indoor Residual Spraying: Human Health Aspects<sup>21</sup>

> 21 Citation of WHO 2011c as the source for this table was inadvertently omitted in an earlier version of the working paper.

20
may not be. In the United States, RM were subject to somewhat incoherent regulations at the state level until recently. Moreover, elderly homeowners risk losing their access to need-based assistance like Medicaid, depending on the RM disbursement method and eligibility criteria of social services (Caplin, 2002; Chatterjee, 2016). In some countries (e.g. Australia) where pensions are asset or means tested, there could also be a risk of losing entitlement to social security benefits.

Many of the supply-side constraints mentioned above are particularly salient in developing countries. Lenders may face legal uncertainties regarding property ownership, which raises the transaction costs of mortgage origination, foreclosure, and adds to cross-over risk. Patchy housing registry and valuation data represents another impediment to RM market development. Moreover, actuarial information may be limited in many developing countries, making it harder for lenders to correctly assess and price longevity risk. Insurance markets are also less deep than in advanced economies, which exacerbates the risk-pooling constraints inherent in the RM market in rich countries. It is indicative that lenders in Singapore and South Africa have exited the market in the 2000s because they considered RM economically unviable.

Developing countries are also facing greater constraints on the demand side. Financial literacy and consumer protection are already major concerns in advanced economies, they are even more likely to operate against the development of a healthy RM market in middle and low-income countries. Creating an RM market in an environment of relatively weak consumer protection regulation and oversight may open a new door to fraudsters and financial criminals. In addition, non-interest fees for RM origination are likely to be even higher than in rich countries. Anecdotal evidence suggests that upfront fees for the closing of an RM in South Africa amount to about 15% of home equity, roughly twice as much as in the United States (Fisher-French, 2018). Moreover, bequest motives and the desire for inter-generational capital accumulation may be stronger in many developing countries, further constraining demand for RM.

High transaction costs and interest rates cast the value of RM as an instrument to fight poverty into doubt. Independent of jurisdiction, RM target not the poor but cash-strapped people with housing wealth. Due to higher transaction costs and interest rates in developing countries, the minimum home equity required to make an RM attractive may be so high that only upper income brackets of the population would benefit. In other words, RM would serve as another financial tool for the already financially included, wealthier parts of society rather than as an instrument of poverty alleviation.

21
# **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 by sections presenting the data used and a quantitative analysis using household budget survey data<sup>4</sup> , before briefly concluding.

> 2 Proximity of markets, formal and informal rules governing buying and marketplaces and transaction costs such as search and transport all affect this choice.

> 3 Field interviews with farmers and mills owners revealed that this maize is stored throughout the year, and milled into flour at the local “ _posho_ ” mill at a high cost: approximately 15 to 30 percent of the value of the maize

> 4 As a part of the research for this paper, field work was conducted in two major maize producing regions in Tanzania, namely Iringa (Southern) and Arusha (Northern) and in Dar es Salaam, the major urban trading centre for

2
# **1.1. Key Labor Market Indicators**

Similar to many countries of the region, Serbia has experienced a painful transition from a centrally planned to a market economy. Restructuring of economic activities resulted in massive job losses and the unemployment rate remained high over the past decade. Reform processes in Serbia were delayed by the political turmoil of the 1990s and the country still lags behind other Eastern European economies on key employment outcomes (Table 1). Only half of Serbia’s working age population reports being employed, which is much worse than the EU average and far from the European Union Lisbon target of 70% to be achieved by 2010. The unemployment rate in Serbia is almost double the EU-27 average and much higher than in other Eastern European economies.

**Table 1. Labor market indicators for working age population (15-64) in Serbia and selected countries of Eastern Europe, 2009**

||ActivityRate|Employment Rate|Unemployment Rate|
|---|---|---|---|
|EU 27|71.1|64.6|8.9|
|Bulgaria|62.5|62.6|6.8|
|Czech Republic|70.1|65.4|6.7|
|Hungary|61.6|55.4|10.0|
|Romania|63.1|58.6|6.9|
|Croatia|62.4|56.6|9.1|
|Poland|64.7|59.3|8.2|
|**Serbia**|**60.5**|**50.0**|**17.4 **|

Source: for Serbia, LFS, 2009; for other countries Eurostat LFS Database <u>http://epp.eurostat.ec.europa.eu/portal/page/portal/employment_unemployment_lfs/data/database</u> While the overall labor market situation is difficult, women appear to be disproportionately affected (Figure 1). Despite similar education levels, women’s employment rates are 26% lower than men’s (with no changes between 2008 and 2009). Women also tend to be overrepresented in the category of inactive population and the unemployed<sup>1</sup> . Furthermore, a large number of the unemployed women – 12.5% have been looking for a job for over ten years, with negative consequences for their skill levels and welfare.

**Figure 1. Labor market indicators for working** **<u>age men and women in Serbia, 2008 and 2009</u>**

<!-- Start of picture text --> 80 68.4<br>60 51.7 60 43.9 54.2 62.446.1 60.5 52.8 50 57.442.7<br>% 40<br>20 14.1 12.316.3 17.4 16.119.1<br>0<br>Activity Rate Employment Rate Unemployment Activity Rate Employment Rate Unemployment<br>Rate Rate<br>2008 2009<br>Total Male Female<br>Source: Serbia LFS 2008 and 2009<br><!-- End of picture text -->

> 1 Restricting the analysis of labor market indicators to the 25-64 age group, to account for the relatively large share of population obtaining secondary and university education, improves labor market indicators to some extent. Activity rate in this age category is 67.2%, employment rate – 57.1% and unemployment rate – 15.1%. Yet, gender disparities are similar to those observed in the broader working age group: women’s activity rate is 23.9% lower than men’s, employment rate is 26.9% lower than men’s and unemployment rate is 19.5% higher than men’s.

2