rafmacalaba/datause-encoder-data huggingface dataset:
by county leaders for all villages within their jurisdiction, and varied with village characteristics. Wealthier, more productive, and more agricultural villages were assigned higher quotas. Given that these analyses employ secondary data that report county- or villagelevel averages for quota quantity, there is no analysis of intra-village variation in quota quantity. Furthermore, quota quantities for a given village changed only incrementally once set by county leaders. By contrast, the central government had discretion over the grain quota price and would change it annually (Rozelle et al., 2000; Lin, 1991).

# **2.3 Descriptive statistics around quota income**

This section provides an overview of the quota and market price over time, and describes the key sources of variation in the quota price.

**Quota and market price over time** The RCRE household data used in this analysis provides information on the price for both mandatory grain quota sales and market sales, as well as the volume of grain quota sales at the household level.<sup>5</sup> Figures 1a and 1b report the mean market and quota price of grain by year between 1986 and 2002, and additionally capture spatial variation in prices, showing in each year the 5th and 95th percentile of the within-year, cross-village distribution of prices; Figure 1a shows the cross-village variation in market prices, and Figure 1b shows the cross-village variation in quota prices.<sup>6</sup> Prior to 1991, both prices are low and stable. The market price begins to dramatically increase after 1991, and the quota price likewise increases, though more slowly. Cross-village variation in quota price is relatively limited, while the cross-village

> 5After they have fulfilled their grain quota, rural households also have the option to sell their excess production to the government at a higher, negotiated procurement price. Huang, Rozelle, Ha and Li (2002) show that these negotiated procurement prices are generally intermediate between the quota price and the wholesale market price. In this data, negotiated procurement prices are not reported, and no distinction is made between these two types of sales, as both represent the price that the rural producer would face for the marginal unit of production. The market price is thus used to denote the price for marginal grain sales.

> 6Price data is constructed at the household level for households that report market or quota sales, but a large number of observations are missing, corresponding to cases in which households do not report market sales in particular year. Accordingly, price data is collapsed to the village-year level, as every cell has sufficient data to estimate a market price and a quota price. Observations corresponding to the top and bottom 1% of observed prices across villages are trimmed to avoid undue influence of outliers.

7
- _Log Market Cap:_ Natural log of market capitalization of the issuer in US dollars as reported by Worldscope.

- _Number of analysts:_ As reported by I/B/E/S.

- _Accounting Quality:_ We create an index of firm-level accounting quality (Accounting Quality) which equals the sum of the four separate accounting quality variables: Use of an international Big-5 accounting firm, consolidation of financial reports, receipt of a clean audit opinion, and reconciliation with either U.S. GAAP or International Accounting Standards.<sup>19</sup>

Table 7 reports the estimated models for 1) All ADRs, and 2) Listed ADRs both for the OLS regression (Panel A) and for the Tobit regression (Panel B). Two models are estimated. The first includes only the firm-level attributes discussed above and the second includes both firm-level and country-level attributes. We find that, holding all else constant, higher liquidity of a firm’s underlying stock is negatively associated with the fraction of investment held in the ADR of that firm. This is consistent with the ease of trading arguments and theoretical models that predict that informed traders would tend to concentrate in the most liquid markets. This finding is strongly supported when we run the regression for the subsample of firms that have listed ADRs. Here we can use a more direct measure of liquidity differences between a firm’s ADR and its underlying security by including the _LiquidityRelative_ variable.<sup>20</sup> The coefficient on this variable is positive and significant at the one percent level across all specifications. These findings provide robust evidence that fund manager’s choice of security is significantly affected by the relative liquidity of the two securities. Thus, the mere act of listing an ADR need not move the trading volume from

> 19 Mitton (2002) finds that higher firm-level disclosure quality had a strong impact on firm performance during the East Asian financial crisis. Related studies also find that firms with ADRs appear in better information environments that are then associated with higher market valuations and significantly larger market reactions to earnings announcements (for example see, Foerster and Karolyi, 1999, Lang, Lins and Miller, 2003, and Bailey, Karolyi, and Salva, 2002, Doidge et al., 2004). See Karolyi (1998) for an excellent review of the ADR literature. 20 We also used an alternative proxy for relative liquidity denoted by ADR Fraction which is the fraction of the underlying security that is in form of ADR. The results very similar to those for Relative Liquidity and are not reported. These results are available from authors on request.

31
problems in banks, with higher values indicating greater power. The range of this variable is 4-16, and it has a mean of 11.00.

Following Houston, Lin and Ma (2012), we control for several country institutional variables that may affect international bank flows. Among these, Monitoring (borrower) is an index of the strength of private monitoring of banks through, for instance, certified audits and ratings by international credit rating agencies, with higher values indicating stronger monitoring. This variable ranges between 3 and 11 and is 7.717 on average. Creditor rights (borrower) taken from Djankov et al. (2007) measures the strength of creditors’ rights in case of a bankruptcy in the borrower country, with higher values indicating stronger creditor rights.<sup>10</sup> Creditor rights ranges between 0 and 4, and has a sample mean of 1.913 for borrower countries. Information sharing (borrower) measures rules affecting the scope, accessibility, and quality of credit information available through public or private credit registries in the borrower country, with higher values indicating a greater availability of credit information. This variable ranges from 0 to 6 and has a mean of 3.496.<sup>11</sup> Time to enforce contracts (borrower) is the time required to resolve a commercial dispute in the borrower country, calculated as the average number of calendar days from the filing of the lawsuit in court until the final determination and, in appropriate cases, payment in a country. Higher values indicate weaker contract enforcement. On average, it takes 629 days to resolve disputes in borrower countries.<sup>12</sup> As a final institutional variable, Rule of law (borrower) captures perceptions of the extent to which agents in the borrower country have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property

> 10 <mark>Since the last available data are for 2007, for subsequent years we take the values of this year.</mark>

> 11 <mark>This variable is from the World Bank’s Doing Business database. The World Bank changed the methodology of measuring the Information Sharing Index in 2015. Until then, the index ranged between 0 and 6. Since the revised index ranges between 0 and 8, we multiply the post-2014 values of the index by 6/8. The first available year is 2004, and for prior years we take the values for this year.</mark>

> 12  This variable is from the World Bank’s Doing Business database. The first available year is 2004, and for prior years we take the values for this year.

10
priori theoretical grounds, this is likely to result in depressed wages with obvious distributional consequences, as the income of landless poor households su¤ers. The land market restrictions may also lead to higher dispersion in equilibrium wages across sectors and geographic areas, higher than what one would expect to observe because of standard transactions and search costs in the labor market. Large dispersion in equilibrium wages can create allocational ine¢ ciency, and adversely a¤ect productivity by distorting technological choice and creating technological dualism. Evidence from recent studies shows that misallocation of labor –re‡ected in sustained di¤erences in wages across areas and sectors– can explain a large fraction of international di¤erences in per capita income and overall total factor productivity (Vollrath, 2008; Temple and Wobmann, 2006). In a recent paper, Hayashi and Prescott (2008) argue that the informal restrictions on land inheritance in pre-war Japan had signi...cant negative e¤ects on per capita income growth and employment structure.

We analyze the general equilibrium interactions between land and labor markets, with a focus on the e¤ects of land market restrictions on equilibrium wages and its spatial distribution. There has been a growing interest in recent literature on the e¤ects of land market restrictions on household choices and outcomes (for evidence on labor supply and savings, see, among others, Field (2007), Iyer et al (2009); for evidence on productivity see Deininger et al (2008, 2009), and for evidence on migration see Yang (1997), among others). However, to the best of our knowledge, there is no empirical analysis of the causal e¤ects of land market restrictions on the equilibrium wage in the existing literature. Taking advantage of a quasi-experiment in historical land settlements and restrictions in Sri Lanka, we provide evidence on the e¤ects of land market restrictions on the equilibrium wage and its spatial pattern. We use household survey data for the year 2002 (Sri Lanka HIES 2002) for our empirical analysis.

The restrictions on land sales, mortgage, and rental raise the cost of labor mobility. The inalienability of land rights implies that in the case of migration, households will lose the right to the net present value of future earnings to land. The increased migration cost leads to lower migration and thus to higher labor endowment in a location with higher incidence of land restrictions. This reduces the land-labor ratio compared to the counterfactual where there are no land even after the Hukou restrictions were relaxed starting from mid 1980s. The young men left their family members back in rural areas to keep the entitlement to the land. See also Iyer et al (2009).

2
20

**Table 10: Non-Zambian Employees, bY type of Permit and Maior Occupation Group. 1983**

||Work<br>Permit|Residence<br>Permit|<br>Entry<br>Permit|Not<br>Stated|Total|
|---|---|---|---|---|---|
|Professional,<br>Technical<br>land Related|4,263|944|23|48|5,285|
|IAdministrative<br>and<br>Managerial|364|362|6|12|744|
|,Clerical|127|678|1|9|816|
|Sales|116|20;|1|-|321|
|Service|140|580|5|28|752|
|Agricultural|165|358|2|2|527|
|Production, Transport<br>& Related|840|3,000|100|249|4,189|
|Not Stated|11|22|-|1|34|
|Total in Survey|6,033|6,149|138|349|12,668|

Source: GRZ 1983, Table 10.0.
|P070073|2003|2008|12/3/2007|12 Closed|Africa|
|---|---|---|---|---|---|
|P070073|2003|2008|5/20/2008|13 Closed|Africa|
|P070073|2003|2009|11/27/2008|14 Closed|Africa|
|P070073|2003|2009|5/26/2009|15 Closed|Africa|
|P070073|2003|2010|8/28/2009|16 Closed|Africa|
|P070073|2003|2010|12/31/2009|17 Closed|Africa|
|P070078|2001|2001|3/27/2001|3 Closed|Albania|
|P070078|2001|2002|3/13/2002|5 Closed|Albania|
|P070078|2001|2003|6/9/2003|8 Closed|Albania|
|P070078|2001|2004|6/4/2004|10 Closed|Albania|
|P070078|2001|2005|11/10/2004|11 Closed|Albania|
|P070079|2001|2001|5/31/2001|1 Closed|Bosnia and Herzegovin|
|P070079|2001|2002|5/24/2002|3 Closed|Bosnia and Herzegovin|
|P070079|2001|2003|6/3/2003|6 Closed|Bosnia and Herzegovin|
|P070079|2001|2004|5/28/2004|8 Closed|Bosnia and Herzegovin|
|P070079|2001|2005|5/27/2005|12 Closed|Bosnia and Herzegovin|
|P070079|2001|2006|10/27/2005|13 Closed|Bosnia and Herzegovin|
|P070086|2000|2000|6/30/2000|2 Closed|Bulgaria|
|P070086|2000|2001|4/24/2001|5 Closed|Bulgaria|
|P070086|2000|2002|6/24/2002|7 Closed|Bulgaria|
|P070086|2000|2003|4/2/2003|9 Closed|Bulgaria|
|P070086|2000|2004|5/25/2004|11 Closed|Bulgaria|
|P070086|2000|2005|6/24/2005|13 Closed|Bulgaria|
|P070086|2000|2006|6/24/2005|13 Closed|Bulgaria|
|P070088|2001|2001|5/29/2001|2 Closed|Croatia|
|P070088|2001|2002|6/11/2002|5 Closed|Croatia|
|P070088|2001|2003|6/2/2003|7 Closed|Croatia|
|P070088|2001|2004|6/11/2004|9 Closed|Croatia|
|P070088|2001|2005|12/16/2004|10 Closed|Croatia|
|P070089|2001|2001|6/18/2001|4 Closed|North Macedonia|
|P070089|2001|2002|3/13/2002|6 Closed|North Macedonia|
|P070089|2001|2003|6/4/2003|8 Closed|North Macedonia|
|P070089|2001|2004|6/23/2004|10 Closed|North Macedonia|
|P070089|2001|2005|6/2/2005|12 Closed|North Macedonia|
|P070089|2001|2006|6/2/2005|12 Closed|North Macedonia|
|P070092|2002|2002|5/15/2002|2 Closed|Yemen,Republic of|
|P070092|2002|2003|4/8/2003|4 Closed|Yemen,Republic of|
|P070092|2002|2004|3/31/2004|7 Closed|Yemen,Republic of|
|P070092|2002|2005|4/18/2005|10 Closed|Yemen,Republic of|
|P070092|2002|2006|11/28/2005|11 Closed|Yemen,Republic of|
|P070092|2002|2006|5/12/2006|12 Closed|Yemen,Republic of|
|P070092|2002|2007|12/22/2006|13 Closed|Yemen,Republic of|
|P070092|2002|2007|6/28/2007|14 Closed|Yemen,Republic of|
|P070092|2002|2008|1/4/2008|15 Closed|Yemen,Republic of|
|P070092|2002|2008|6/29/2008|16 Closed|Yemen,Republic of|
|P070092|2002|2009|12/26/2008|17 Closed|Yemen,Republic of|
|P070092|2002|2009|6/15/2009|18 Closed|Yemen,Republic of|
|**Table 5. Summar**|**y of Parameter**<br>Benchmark|**s of the Logistic**<br>Scenario 1|**Function used i**<br>Scenario 2|**n Simulations**<br>Scenario 3|
|---|---|---|---|---|
|Upper asymptote|10.24|5.12|12.28|10.24|
|Lower asymptote|-10.24|-10.24|-10.24|-10.24|
|A|20.48|15.36|22.52|20.48|
|B|1.20|1.20|1.20|5.00|
|C*|0.46|2.23|0.00|4.10|
|D|-10.24|-10.24|-10.24|-10.24|
|Pricegap (θ)<br>* calibrated value<br>Source: own calcul|0.39<br>ations|0.39|0.39|0.39|

In the benchmark, we assume that the upper asymptote is equal to the negative of the lower asymptote (-10.24 bil. GEELs), meaning that half of flex cars consumes hydrous ethanol and the other half consumes fuel in the baseline. The higher is the gap between consumer prices of fuel and hydrous ethanol in a given point in time, the less sensitive flex cars owners are to changes to this gap. Consider, for instance, the actual price gap of $0.39/GEEL observed in 2010/11. In this case, a small perturbation in the price differential is likely to result in a small change in the proportion of fuel and hydrous ethanol use because flex car owners already “incline” to hydrous ethanol. Contrast this with the price differential of $0.08/GEEL observed in 2011/12. It is reasonable to assume that the inclination to hydrous ethanol is not as strong now as in the previous case, hence a greater sensitivity to a change in the price gap.

To reflect the arguments above, we set the value of the shape parameter _B_ to be 1.2 in the Benchmark and Scenario 1 and 2, but set it much higher ( _B_ =5) in Scenario 3.<sup>25</sup> The parameter _C_ is calibrated (Appendix 2) to ensure that given the values of other parameters, the following condition holds: _X_ (θ 0 ) = 0 . The logistic curve corresponding to the benchmark specification is depicted in Figure 8.

In Scenario 1, we set the upper asymptote to one-half of its benchmark level (5.12 bil. GEELs), while in Scenario 2 we set it to 12.28 bil. GEEL, which is the highest possible value

> 25 The values for the shape parameter _B_ were arbitrarily chosen to illustrate the point that the sensitivity of flex cars owners to the observed price gap can differ depending on the size of the gap. The relative size of the two values of the shape parameter matters more that their levels. But it should be noted that the choice of this parameters is not completely arbitrary because if the shape parameters is less than unity, the logistic curve becomes decreasing, which runs afoul of our assumption about the monotonicity of this curve.

27
- Hellerstein, Judith K; David Neumark, and Kenneth R. Tros]he (1996). "Wages, Productivity, and Worker Characteristics: Evidence From Plant-level Production Functions and Wage Equations", _NBER Working Paper_ No. 5626.

- Kanyenze, Godfrey (1995). "Human Resource Development in Zimbabwe: Beyond the Economic Structural Adjustment Program", Zimbabwe Congress of Trade Unions, Harare, processed.

- Knight, John (1996). "Labour Market Policies and Outcomes in Zimbabwe", Oxford: University of Oxford, Centre for the Study of African Economies, Processed: March.

- Lane, Julia; Robert I. Lerman, David Stevens and Qing-Hua Zhai (1996). "Firms, Jobs, and the Dynamics of Earnings Inequality: 1985-94", Unpublished paper, American University, Washington, USA.

- Levey, Frank and Richard J. Mumane (1992). "U.S. Earnings Levels and Earnings Inequality: A Review of Recent Trends and Proposed Explanations", _Journal of Economic Literature,_ vol. 30, 1333-1381.

- Lindauer, David L. and Barbara Nunberg, eds. (1994). _"Rehabilitating Government: Pay and Employment Reform in Africa",_ Regional and Sectoral Studies, Washington, D.C.: World Bank.

- Mankiw, Gregory N; David Romer and David N. Weil (1992). "A Contribution to the Empirics of Economic Growth", _Quarterly Journal sf Economics,_ Vol. 107, No. 2, 407-37.

- Mazumndar, Dipak (1994). "The Structure of Wages in African Manufacturing", Office of the Chief Economist for Africa, The World Bank, Washington, D.C., September 1994.

- Mazumdar, Dipak (1995). "Wage differences by Size of enterprise in African Manufacturing. Further Explorations and Analysis", _RPED discussion Papers,_ African Technical Department, August 1995, World Bank.

- Mincer, Jacob (1974). _"Schooling, Experience, and Earnings",_ New York: Columbia University Press.

- Pack, Howard (1993). "Productivity and Industrial Development in Sub-Saharan Africa", _World Development,_ Vol. 21, No. 1, 1-16.

- Rama, Martin (1997). "Are Labour Market Policies to Blamae?", Working Paper, World Bank.

38
9 Table 3: Potential Explanatory Variables for Direction of Balance-of-Payments Causation.

|_RESC_|_-_|Article XIV status|
|---|---|---|
|_RESD_|-|Special rates for some or all capital transactions and/or some or all invisibles|
|_RESE_|-|More than one exchange rate for imports|
|_RESF_|-|More than one exchange rate for exports|
|_RESG_|-|Restrictions on current account payments|
|_RESH_|-|Restrictions on capital account payments|
|_RESI_|-|Import surcharges|
|_RESJ_|-|Advance import deposits|
|_RESK_|-|Surrender of export proceeds required|
|_PCY_|-|Natural logarithm of per capita income in U.S. dollars|
|_YG_|-|Rate of growth in realGNP|
|_INF_|-|Inflation (continuously compounded rate of change inGNPdeflator)|
|_OPEN_|-|Exports plus imports as a proportion ofGNP|
|_FLY_|-|Cumulated current account deficit as proportion ofGNP|
|_DETY_|-|Foreign debt as a proportion ofGNP|
|_GBLY_|-|Government deficit (-) as a proportion ofGNP|
|_DCGR_|-|Net domestic credit to the government as a proportion of total domestic<br>credit|
|_DCGY_|-|Net domestic credit to the government as a proportion ofGNP|
|_BLA_|-|Black market exchange rate premium|
|_RR_|-|Square of real interest rate|
|_DCPY_|-|Domestic credit to the private sector as a proportion ofGNP|

account, and _FDIX_ for FDI being independent or exogenous. These variables take a value of 1 when a country exhibits the particular Granger-causation specified and 0 when it does not. Since there were only four countries with bidirectional Granger-causation, these were treated as positive for both _CAUSKC_ and _CAUSCK._ The potential explanatory variables are listed in Table 3. The first set of explanatory variables consist of balance-of-payments restriction variables reported annually at the back of the International Monetary Fund's _Balance of Payments Yearbook._ The variables _RESC_
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27
# **Figure B24: Coffee, Arabica**

<!-- Start of picture text --> Log scale<br>3.5<br>Log price Permanent component<br>2.5<br>1.5<br>0.5<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> <!-- Start of picture text --> Log scale<br>1.0<br>Medium-term cycle Transitory component<br>0.5<br>0.0<br>-0.5<br>-1.0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> _— 65 —_