rafmacalaba/datause-encoder-data huggingface dataset:
- 33 -

# Table 3.7. Value of Private Generators and Boreholes as Percent of Total Value of Machinery and Equipment by Region, Indonesia and Thailand

# (Percent)

|____________|<br>In|desk<br>la|_||Tha|land||
|---|---|---|---|---|---|---|---|
|nsIm|Firm Size|Generators|<br>Boreholes||RIo|Firm Size|<br>Generators|<br>loreholee|
|Jakarta|Small**_W_**|4.94|0.79|Bangkok|Small|||
||Large|5.70|0.33||Large|1.84|1.42|
||All|5.65|0.36|_|All|1.84|1.42|
|Metropolitan<br>Cltbk|Small|39.90|2.27|MetropoLitan<br>|Small|70.83|18.58|
|oae)|Large|18.27|0.48|CINR)|Large|4.36|1.77|
||All|18.38|0.50|_|All|4.41|1.77|
|Semarang|Small|7.25|0.92|Chiang Mal|Sonll|75.00|2.87|
||Large|17.67|0.67||Large|23.90|0.68|
||AtAll|16.02|0.71|_|ALL|24.29|0.80|
|Three<br>i|Sell|8.14|0.91|Three<br>|hull|72.73|3.62|
|Regons|Large|13.62|0.41|Region.|Large|5.05|1.60|
|____________|All|13.41|0.44<br>"|_|All|5.12|1.61|

NOTE: (1) The values are for the firm In ech cell **as a** haole (1.e.. ratios of the sua).

s2) The values of generators, borehole, nd radio equipmnt are included in the total vaLue of mchinery nd equipment, but thos of vhicles wnd motorcycles **are** not Included. C3) From AppendIx Tables Fl, F2, G1, and G2.

> Small firm **are** establishments with **less** than 50 eployees.

Source: World Bank Nigeria Infrastructue Project Estabtishmant Survey 1988. World **Sank** Research Project (RPO 676-71) Establishment Survey for Indonean a-nd Thailand 1992.
alone had no impact on female co-ownership of land until the 2010 Land Act made co-ownership the default option for common-law marriages, an awareness campaign had been launched, and procedures for systematic initial registration were designed to put such rights into practice.

This study contributes to several research strands: First, although there is ample evidence of land and credit linkages at the macro level, to the best of our knowledge ours is the first study to document the credit- and land-market activation effects of land titling at the parcel level. Historically, the type of land rights and land ownership structure affected the evolution of cooperative credit institutions (Suesse & Wolf 2020), financial system operation (Rajan & Ramcharan 2011), real estate cycles (Rajan & Ramcharan 2015), and long-term development (Devijlder & Schoors 2020).<sup>2</sup> On U.S. Indian reservations, credit access has been identified as the main channel that supported greater development on parcels with fully, compared to only partially transferable property rights (Dippel et al. 2020). Establishment of property registries and adoption of secure transaction laws by Indian bands has also been linked to greater economic development (Dippel et al. 2021). In India, registry computerization that improved registry access has been shown to expand urban mortgage lending at the level of registry offices (Deininger & Goyal 2012).

We add to such macro-studies by using administrative data to provide micro evidence on the impacts of systematic first registration separately from those of policy and institutional reform. While each of these interventions has significant impact, differences in target variables and the time profile with which effects materialize make them highly complementary: while systematic first registration expands registry coverage, female co-ownership, and investment in the short term, the thrust of policy reform is to translate greater coverage into market activity and credit access, especially for female co-owners. and, in a hypothetical scenario without policy reform, systematic initial registration would actually reduce the number of registered transactions.

A second strand of literature relate to the design and targeting of titling programs. Studies suggest that demand for an urban title is price-elastic (Balan et al. 2020) and informal urban dwellers prefer regular small payments or saving schemes to large up-front payments (Ali et al. 2016). Whether they are due to inefficient registry operation (Nkurunziza 2015), fees far above the cost of service delivery (Manara & Regan 2021), or interest group pressure (Gutierrez & Molina 2020), high fees for titling or other barriers to registry access are likely to reduce demand for titles; coverage of registered rights; and the public sector’s ability to provide local public goods by planning land use and capturing continued and historically large increases in urban land values (Knoll et al. 2017).

> 2 Chaudhary and Swamy (2020) describe the impacts of foreclosure policies and interest rate ceilings in India, as Zegarra (2017) does for Peru.

4
**In recent years, there has been a substantial surge in Romania's real gross minimum wages. Upon joining the European Union, Romania had one of the EU's lowest statutory gross minimum wages.** In 2007, the gross statutory minimum wage stood at 390 RON gross (approximately 115 EUR). This compares to 92 EUR in Bulgaria, 240 EUR in Poland, 666 EUR in Greece, and 1254 EUR in France. While expressed nominally, the minimum wage in Romania was among the lowest in the EU; if adjusted by purchasing power parity (PPP), the minimum wage in Romania was the lowest in the EU (European Commission, 2007).<sup>3</sup> Since then, there has been a rapid increase, with an average annual real increase of 14.3% (inflation-adjusted) between 2007 and 2023. Some years (i.e., 2008, 2016, and 2018<sup>4</sup> ) have witnessed increments close or surpassing the 20 percent mark. Figure 1 depicts the minimum wage's value and annual progression during this period.

**Figure 1. Evolution of Gross Statutory Minimum Wage (Nominal Levels and Annual Changes in Real Terms) Romania, 2007-2023**

<!-- Start of picture text --> 3500 35.0<br>30.0<br>3000<br>25.0<br>2500<br>20.0<br>2000 15.0<br>1500 10.0<br>5.0<br>1000<br>0.0<br>500<br>-5.0<br>0 -10.0<br>20072008200920102011201220132014201520162017201820192020202120222023<br>Year<br>Percentage<br>RON<br><!-- End of picture text --> <!-- Start of picture text --> Gross Nominal Minimum Wage Level (Left axis) Real Gross Minimum Wage Growth (Right axis)<br><!-- End of picture text --> _Source: Own estimates based on the Romanian Ministry of Public Finance and National Statistics Institute. Note: Since 2019, three statutory minimum wage levels were established, so the estimate represents the lowest of minimum wage levels (basic minimum wage). Changes in 2018/2019 also capture the government's imposition of mandatory social contributions transfer from the employer to the employee regarding income from wages. Change in real terms calculated using HICP inflation._

> 3 Source: <u>https://ec.europa.eu/commission/presscorner/detail/en/STAT_07_85.</u>

> 4 The year 2018 stands out as an anomaly in the context of wage increases, including the minimum wage, due to the government's imposition of mandatory social contributions transfer from the employer to the employee in the case of income from wages. Therefore, changes between pre-2018 and 2018 wages are not fully comparable to changes in other years, as they partly captured this policy change. For example, the minimum wage's rise from RON 1450 in 2017 to RON 1,900 in 2018 represented a 31% increase, influenced significantly by the transfer of contributions from the employer to the employee, constituting 22.75% of the total increase.

4
Chart **2.4 EU retotl prices are generalyl well above those of the United States** <!-- Start of picture text -->
>  Average banana retail pices in 1990<br>(World  Bank estimotes)<br>Iwo<br>MIw<br>UnftadYsvGonnmny  b  itod  Fmnce  -_nin  _  ny.<br>s:  ingdo.n  and  4  P_etulu_<br>,  : .:  wOud Siral  has  no bcxr.lh  only of anas cnd  so  lctlO  ef  aipply  am-4<br>,ii  ii  iu In  Ity  World mrket dsterm  US  id ol  p.cts.<br>Sourew.  Bowrel andYang  (199Z.<br><!-- End of picture text -->

Chart **2.5 Most producw prices of preferred suppliers to EU makets are over double the wodd** price **indicator** <!-- Start of picture text --> Fob producer prces for banoas In 1990<br>(World Bank esirrmtes)<br>3:-00:<br>40 w~ price EEEEt<br>100'<br>l;tanArnerIca--Other.ACP1SordIa G3nadetoupe Jamdca&SCoiry&$:<br>* A&:Matilnlque: -. WIndwwrd.:%'~Modeira:<br>. .. . S. . 'rA *...<br>rE = ~~~5.rX Sst tb E~~suppiyqdb h1h-:....<br>. S V71* d ba rK ' ka tJD J; J >;f fS> ;> * ii; M .<br><!-- End of picture text --> _Souce:_ Bowel and Yang (19922 Under a special protocol of the Treaty of Rome, Germany was permtted to increase **its** quota m lne with domestic demand. It therefore did not restrict supply and mntained a virtually free market It had the lowest retail prices in the EU. The German pnce was higher than the US price shown in chart 2.4 because transport costs and other non-traded inputs affecting wholesaling and retaling margms are higher due to the high value of the Deutscbmark. Prices in tariff-only countries were higher than in Germany due to the 20 per cent tarff on imports levied cif'.

# _The new single policy greatly imypedes_ **_the_** _functioning of_ **_an_** _efficient market_

The new unified EU polcy mantans preference to ACP countres and EU territories. A system of EU-wide quotas has been implemented to restrict supplies and maintain consuxner prices at levels high enough to broadly maintain beneffis to select EU marketers, EU **-** ~~~8
<mark>carbon emissions through avoided deforestation.</mark> _<mark>Proceedings of the National Academy of Sciences</mark>_ <mark>, 105(30): 10302-10307.</mark>

- Lokshin, M, M. Bontch-Osmolovski and E. Glinskaya. 2010. Work-Related Migration and Poverty Reduction in Nepal. _Review of Development Economics_ 14 (2): 323-332.

- Luintel, H, Silori, CS, Frick, S, and Poudyal, B.H. 2013. Grassroots capacity building for REDD+: Lessons from Nepal. _Journal of Forest and Livelihood_ . 11(2):1-13.

- Mahanty S, Gronow J, Nurse M, Malla Y. 2006. Reducing poverty through community based forest management in Asia, _Journal of Forest and Livelihood_ 5(1): 78-89

- Manandhar U. 2013.  Forest monitoring, measurement, reporting and verification: from principle to practice. _Journal of Forest and Livelihood_ . Vol. **11** (2):46-54.

- <mark>McKinsey and Company 2010. Impact of the Financial Crisis on Carbon Economics: Version 2.1 of the Global Greenhouse Gas Abatement Cost Curve. Available at</mark> <u><mark>http://www.mckinsey.com/clientservice/sustainability/pdf/Impact_Financial_Crisis_Ca rbon_Economics_GHGcostcurveV2.1.pdf.</mark></u>

- MOFSC. 2013. Persistence and Change: Review of 30 Years of Community Forestry in Nepal. Ministry of Forest and Soil Conservation.

- <mark>Morgera E. 2009. Participation, Balancing of Rights and Interests, and Prior Informed Consent, in J. Costenbader (ed.)</mark> _<mark>Legal Frameworks for REDD</mark>_ <mark>, pp. 35–56. Gland: IUCN.</mark>

- Mundlak, Y. 1978. “On the Pooling of Time Series and Cross Section Data”, _Econometrica_ . Vol. 46, No. 1:pg. 69-85.

- Myneni RB, Hall FG, Sellers PJ and Marshak AL. 1995. The interpretation of spectral vegetation indexes. IEEE Transactions on Geoscience and Remote Sensing. **33** :481-486.

- Ojha HR, Timsina NP, Kumar C, Banjade MR, and Belcher B. (Eds.) 2007. Communities, Forests and Governance: Policy and Institutional Innovations from Nepal. Delhi, Adroit.

- Oli BN and Shrestha K. 2009. Carbon Status in Forests of Nepal: An Overview. _Journal of Forest and Livelihood_ 8(1): 62-66

- <mark>Putz, F.E. and K.H. Redford. 2009. Dangers of Carbon-based Conservation,</mark> _<mark>Global Environmental Change</mark>_ **<mark>19</mark>** <mark>: 400–401.</mark>

- Rights and Resources Initiative. 2014. _What Future for Reform? Progress and Slowdown in Forest Tenure Reform since 2002_ . Washington DC.

- Rosenbaum, P. R. and D. B. Rubin. 1983. “The Central Role of the Propensity Score in Observational Studies for Causal Effects,” _Biometrika_ , 70 (1): 41-55.

- <mark>Saatchi SS, Harris NL, Brown S, Lefsky M, Mitchard TAE, Salas W, Zutta BR, Buermann W, Lewis SL, Hagen S, Petrova S, White L, Silman M and Morel A. 2011. Benchmark map of forest carbon stocks in tropical regions across three continents.</mark> _<mark>PNAS</mark>_ <mark>, 108(24): 98999904.</mark>

- Saxena, A. K. and Singh, J. S. 1987. A Phytosociological Analysis of Woody Species: Forest Communities of a Part of Kumaon Himalaya. Vegetation 3-22.

**43 |** P a g e
**_II.3_ Long-term credit flows have fallen steadily and** **<u>were less pro-cyclical than shorter-term loans</u>** Overall Credit Flows to Non-Fin. European Firms _€ millions (2005Q1-2012Q4)_

**_II.4_ Increasing margins and collateral and decreasing maturities have** **<u>been instruments of choice</u>** Lending Terms and Conditions for Total Loans _Net percentage of banks indicating tightening relative to_ _<u>previous quarter</u>_

<!-- Start of picture text --> 100000 80<br>80000<br>60<br>60000<br>40<br>40000<br>20000 20<br>0 0<br>-20000<br>-20<br>-40000<br>-60000 -40<br>-80000<br>Collateral requirements Margin on average loans<br>Short term Long term Medium term Maturity Non interest rate charges<br>Size of loans<br>Source: ECB Source: ECB Lending Survey 2013Q1<br>II.5 Quarterly changes in long-term credit flows were mainly demand driven. However, during high financial<br>stress conditions, supply factors played a significant role in reducing flows<br>Explaining Long-Term Loan Flows in Europe: Supply vs. Demand<br>€ millions (2005Q1-2012Q3)<br> 4,000<br> 2,000<br> -<br> (2,000)<br> (4,000)<br> (6,000)<br> (8,000)<br> (10,000)<br> (12,000)<br>Supply driven Demand driven<br>Source: ECB; World Bank analysis.<br>2005Q1 2005Q3 2006Q1 2006Q3 2007Q1 2007Q3 2008Q1 2008Q3 2009Q1 2009Q3 2010Q1 2010Q3 2011Q1 2011Q3 2012Q1 2012Q3 2013Q1<br>2005Q1 2005Q3 2006Q1 2006Q3 2007Q1 2007Q3 2008Q1 2008Q3 2009Q1 2009Q3 2010Q1 2010Q3 2011Q1 2011Q3 2012Q1 2012Q3<br>2005Q1 2005Q2 2005Q3 2005Q4 2006Q1 2006Q2 2006Q3 2006Q4 2007Q1 2007Q2 2007Q3 2007Q4 2008Q1 2008Q2 2008Q3 2008Q4 2009Q1 2009Q2 2009Q3 2009Q4 2010Q1 2010Q2 2010Q3 2010Q4 2011Q1 2011Q2 2011Q3 2011Q4 2012Q1 2012Q2 2012Q3<br><!-- End of picture text -->

# **_<u>Emerging and developing economies</u>_**

**As regards foreign lending activities, international banks have scaled back longer-term finance in favor of shorter maturities—it is likely this has also been the case for European banks.** There is no direct information available on the maturity structure of European bank claims in emerging markets. However, the BIS provides data on maturities of international

9
Page

# TABLE OF CONTNTS

|I.|EVOLUTION OF THE WORLDBANK'SLENDING POLICY<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.|
|---|---|
|II.|PRIMARY EDUCATIONs INVESTMENT OBJECTIVES, STRATEGIES,<br>AND LESSONS.<br>.<br>.<br>.<br>.<br>.<br>.<br>...<br>.<br>...<br>.<br>.<br>.<br>.<br>..<br>.<br>.....|
|III.|NONFORMAL EDUCATION AND TRAININGs INVESTMENT OBJECTIVES,<br>STRATEGIES,<br>AND LESSONS<br>.<br>...<br>. . . . . . .<br>..............<br>.<br>26|
|IV.|CONCLUSIONS<br>AND IMPLICATIONS<br>.<br>. .<br>. . . .<br>. . . . . . . . . .<br>32|
||Tables|
|Table<br>1:<br>Table 2:<br>Table 3:|World Bank Lending<br>for<br>Primary<br>Education and Non-formal<br>BasicEducation FY70-90<br>..<br>a.<br>el<br>...**..**...........<br>3<br>World Bank Lending for Primary Education and Non-formal<br>BasicEducation by Region,<br>FY70-90<br>. . ..<br>.<br>*...<br>.<br>.<br>6<br>Number of World Bank Primary Education and Non-formal<br>Basic EducationProjects FY70-90<br>.<br>.<br>.<br>.<br>.<br>.<br>**.**<br>.<br>.<br>6|
|Table 4:<br>Table 5:|Projected World Bank Lending for Primary and Non-formal<br>BasicEducation FY91-93<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>7<br>Dileanas of Large-Scale<br>Programs<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>33|

# Figures

|Figure 1: Composition of World Bank Lending for General||
|---|---|
|Education,FY63-90.<br>.<br>.<br>.<br>.....<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.|.<br>.<br>.<br>.<br>.<br>5|
|Figure 2: A Summary Chart of the World Bank's Primary Education||
|InvestmentObjectivesand Strategies<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.|.<br>.<br>.<br>.<br>.<br>23|
|Figure 3: A Summary Chart of the World Bank's Nonformal Education||
|and Training Investment Objectives and Strategies<br>.|.<br>.<br>.<br>.<br>. 31|
To enhance comparability across countries, we make two further adjustments to the spread of each bank. First, we use consumer price index data to adjust for the impact of inflation on spreads. Second, as we are dividing a flow variable by a stock variable, we calculate the average of the stock variable between t and t-1. In sum, we calculate banklevel lending rate<sup>_i_</sup> _tL_<sup>and the bank-level deposit rate</sup><sup>_i_</sup> _tD_<sup>for period t as follows:</sup> where _II_ is the interest income from the loan portfolio, _IE_ is the interest expense, _L_ represents the total performing loans (being the difference between total gross loans and total non-performing loans), _D_ represents the total borrowed funds (which equals the sum of total deposits, money market funds, and other funds), _P_<sup>_a_</sup> is the average consumer price index, _P_<sup>_e_</sup> is the end-of-year consumer price index, and _t_ indicates year t. The consumer price index data are from the World Bank’s World Development Indicators. All variables are expressed in local currency. The bank-level spread for the year 2000 is computed as the difference between the lending rate _i_ 2000 _L_ and the deposit rate _i_ 2000 _D_ . This average interest rate spread, calculated using bank-level data and aggregating them at the countrylevel, will be defined as SPREAD2.

The relationship between the two indicators, SPREAD1 and SPREAD2, is shown in Figure 1, where the two variables are plotted one against the other together with a 45 8
<!-- Start of picture text --> 40.0% Unemployment Rates 2009<br>30.0% emP| eI<br>20.0% me<br>|<br>10.0%<br>— = =<br>0.0% = —<br>literate Intermediate Intermediate University & above<br>Vocational university<br>SMale =Female & Total<br><!-- End of picture text -->
correlations between these variables and social welfare growth. We find that the relationship between these variables and social welfare growth, to the extent that they are quantitatively important, comes mostly through their effects on growth in average incomes, rather than through changes in inequality. This of course does not imply that there are _no_ policies that can influence inequality in ways that raise social welfare. However, it does suggest that the historical experience of a large set of developed and developing countries does not provide much guidance regarding the set of macroeconomic policies and institutions that might be particularly conducive to promoting growth in social welfare beyond their effects on aggregate growth.

# **References**

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Atkinson, Anthony, Thomas Piketty, and Emmanual Saez (2011). “Top Incomes in the Long Run of History”. Journal of Economic Literature. 49(1):3-71.

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Basu, Susanto, Luigi Pascali, Fabio Schiantarelli, and Luis Serven (2013). “Productivity and the Welfare of Nations”. Manuscript, World Bank and Boston College.

Checchi, Daniele; Garcia-Penalosa, Cecilia. (2008). “Labour Market Institutions and Income Inequality”, _Economic Policy issue 56, pp. 601-34, 640-49._ Chen, Shaohua, and Martin Ravallion. (2010). “The Developing World Is Poorer Than We Thought, But No Less Successful in the Fight against Poverty,” _The Quarterly Journal of Economics._ Ciccone, Antonia and Marek Jacocinski. (2010). “Determinants of Economics Growth: Will Data Tell?,” _American Economic Journal: Macroeconomics 2, 2:4, 222-246_ .

Datt, Gaurav, Ravallion, Martin (1992). “Growth and redistribution components of changes in poverty measures: a decomposition with applications to Brazil and India in the 1980s”. Journal of Development Economics 38(2):275–295.

Deaton, Angus, and Valerie Kozel. (2005). “Data and Dogma: The Great Indian Poverty Debate”, _Oxford University Press 20:177-199._ De Gregorio, Jose; Lee, Jong-Wha. (2002). “Education and Income Inequality: New Evidence from CrossCountry Data”, _Review of Income and Wealth, v. 48, iss. 3, pp. 395-416._ Dollar, David, and Aart Kraay. (2002). “Growth is Good for the Poor,” _Journal of Economic Growth, 7, 195-225_ .

28
20 estimate of the import or export tax equivalents of that distortion are included as well. The range of measures covered in the CTE estimates include both domestic consumer taxes/subsidies plus trade and exchange rate policies, all of which drive a wedge between the price that consumers pay for each commodity and the international price at the border.

# **Estimates of trade and welfare reduction indexes**

Table 1 reports our time series of estimates TRIs for the 28 agricultural commodities, and for four groups of commodities (grains and tubers, oilseeds, tropical crops and livestock products). Generally those TRIs are somewhat above the NRAs and CTEs in Appendix table 1, and especially for tropical products where the trade-reducing effects of import taxes of some high-income countries are reinforced by the export taxes of some lower-income countries. By contrast, for some other products the global average TRI is generally less than the NRA and CTE, reflecting the fact that export subsidies have been in place for some higher-income countries or import subsidies for some lower-income countries, which offsets the trade-reducing effects of tariffs. In some cases (e.g. barley, millet and groundnuts) there are even some five-year periods when the TRI is negative, indicating that policies on net have encouraged international trade in those goods — which can be just as damaging to national and global economic welfare as policies that discourage trade.
4 most skilled workers had the least difficulty finding new jobs, although typically in a new occupation and at a lower rate of pay. Older workers, women, and those with fewer skills and less formal education had the greatest difficulty in locating new jobs (OECD 1990:63-64).

Table 1 Post-Displacement Labor Market Adjustment of Displaced Workers, the U.S. and Canada

|||U.S.||Canada|
|---|---|---|---|---|
|Adjustment experience|Jan. 1984|Jan. 1986|Jan. 1988|Jan. 1986|
|Labor force status|||||
|Employed|60%|67%r|71%|63%|
|Unemployed|26|18|14|20|
|Not in laborforce|14|15|15|17|
|Weeks without work:|||||
|<5|30%|34%0-||63%|
|5to26|37|35|||
|27 to 52|20|18||25|
|>25|13|13||12|
|Wage in new job lower<br>than previous wagea|46%|44%|44%|5W%|

Source: OECD (1990: Table 2.9) aFor those reemployed.

# C. An Overview of DisDIaced Worker Proorams In Industrialized Countries

In view of the substantial adjustment costs illustrated in Table 1, many Industrialized nations have singled out displaced workers for special programs beyond the services fumished to all unemployed workers. The principal argument for targeted displaced worker programs is that while the benefits from increased international competition and technological progress are broadly diffused across