rafmacalaba/datause-encoder-data huggingface dataset:
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- [19] De la Fuente, A. and R. Domenech (2002): Human capital in growth regressions: how much di¤erence does data quality make? Un update and further results, CEPR Discussion Paper, n. 3587.

- [20] Docquier, F. and A. Bhargava (2006): Medical brain drain - A New Panel Data Set on Physicians’Emigration Rates (1991-2004), Report, World Bank, Washington DC.

- [21] Docquier, F., O. Lohest, and A. Marfouk (2007): Brain drain in developing countries, World Bank Economic Review 21: 193 - 218.

- [22] Docquier, F. and A. Marfouk (2004): Measuring the international mobility of skilled workers - Release 1.0, Policy Research Working Paper n. 3382, World Bank (August 2004).

- [23] Docquier, F. and H. Rapoport (2007): Skilled migration - The perspective of sending countries, In J. Baghwati and G. Hanson (eds), Skilled migration: prospects, problems and policies, Russell Sage Foundatation: New York, forthcoming.

- [24] Dollar, D. and R. Gatti (1999): Gender inequality, income and growth - Are good times good for women?, Policy Research Report on Gender and Development, Working paper series, n.1, World Bank.

- [25] Dumont, J.C. and Lemaître G. (2004): Counting immigrants and expatriates in OECD countries: a new perspective, Mimeo: OECD.

- [26] Dumont, J.C., J.P. Martin and G. Spielvogel (2007): Women on the move: the neglected gender dimension of the brain drain, IZA Discussion Paper, n. 2920.

- [27] Easterly, W and Y. Nyarko (2005): Is the brain drain good for Africa?, Mimeo: New York University.

- [28] Grogger, J. and G.H. Hanson (2007): Income maximization and the sorting of emigrants across destinations, Mimeo, University of Chicago.

- [29] Haveman, R. and B. Wolfe (1995): The determinants of children’s attainments - A review of methods and ...ndings, Journal of Economic Literature 33(4), 18291878.

- [30] Hatton, T.J. and J.G. Williamson (2002): What fundamentals drive world migration?, NBER Working paper, n. 9159.

31
# **Figure B23: Cocoa**

<!-- Start of picture text --> Log scale<br>3<br>Log price Permanent component<br>2<br>1<br>0<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 --> _— 64 —_
# **Table A18: Item-level Regressions**

|Before deleg|ated randomiza|tion of inspect|or assignment|||
|---|---|---|---|---|---|
|Dependent variable|**Log initial**<br>**unit price**|∆**log unit**<br>**price**|**Log fnal**<br>**unit price**|∆**log**<br>**weight**|**Hyp. tax**<br>**revenue**<br>**losses**|
||(1)|(2)|(3)|(4)|(5)|
|Excess interaction share|-0.408*|-0.078***|-0.484***|-0.006|0.294|
||(0.237)|(0.023)|(0.226)|(0.004)|(0.190)|
|Inspector-HS 8-digit product fxed efects|Yes|Yes|Yes|Yes|Yes|
|Declaration characteristics|Yes|Yes|Yes|Yes|Yes|
|Item characteristics|Yes|Yes|Yes|Yes|Yes|
|Broker fxed efects|Yes|Yes|Yes|Yes|Yes|
|Source country fxed efects|Yes|Yes|Yes|Yes|Yes|
|Month-year fxed efects|Yes|Yes|Yes|Yes|Yes|
|Observations|134,013|133,775|134,013|134,000|134,013|
|R-squared|0.624|0.200|0.620|0.148|0.359|

_Notes:_ Standard errors clustered two-way by inspector and by broker presented in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. _Excess interaction share_ is the difference between the share of given broker’s declarations handled by an inspector in a given semester and the hypothetical share she would be expected to handle if the allocation of declarations to inspectors were random conditional on their productivity, as prescribed by official rules, calculated using calibration methods (as explained in Section 4). Item characteristics include the tax rate. Declaration characteristics include the risk score, a dummy for the red channel, a dummy indicating whether the declaration was mixed, and a dummy indicating the declaration was subject to valuation advice. "Observations" refers to the number of non-singleton observations. OLS estimation is used. The sample covers the period January 1, 2015 to November 17, 2017 (i.e., before the delegated randomization intervention).

78
height-for-age _z_ -score scales a child’s height relative to a healthy population of that child’s age and sex. We use the 2006 WHO international reference population of healthy children.

**Summary statistics.** Table 1 presents summary statistics about the international dataset. Over 6 percent of children in the data died before their first birthday. The average child in our data is notably shorter than children in the healthy reference population. About one-third of the average child’s neighbors defecate in the open. Although it is not used in the regressions (because it would be a country-year fixed effect, which we use as a control), we include GDP per capita from the Penn World Tables in table 1 for illustration. The median child in this dataset is poor; she is growing up in a country-year with a GDP per capita per day of $1.44.

Population density varies widely in our sample, with an interquartile range from 31 to 239 people per square kilometer. Appendix figure 3 plots a kernel density estimate of the distribution of population density among children in our international sample. Throughout our analysis, we transform population density to a log scale. A normal distribution with the same mean and standard deviation is included for comparison; population density appears to match a lognormal distribution.

# **3.2 Motivation: Urban place, sanitation and infant mortality**

Urban places have higher population density on average, but also lower open defecation rates, on average. How do these three factors interact to predict infant mortality? In this section, we motivate the analyses that follow by using the international dataset to present results from regressions of the form: where _mortality_ for child _i_ living in place _p_ is scaled for infant deaths per 1,000; _sanitation_ is open defecation in the child’s local area (PSU); and _place_ will be implemented either as 10
exclusion of informal, or irregular workers is likely also be a function of banks’ preference for clients with steady employment, reflected in requirements for references and proof of work (see Chapter 2). At the same time, almost 50% of salaried workers are unbanked, suggesting that informality is not a primary reason for financial exclusion.

**Figure 3. 4  Employment profile of the unbanked**

<!-- Start of picture text --> Banked and unbanked by type of activity Banked and unbanked by type of job<br>100% Banked 50% 50% Banked<br>90% 48% Unbanked<br>80% 78% 76% 73% Unbanked<br>70% 60% 40% 36%<br>60% 52%<br>50% 40% 48% 30% 24%<br>40%<br>30% 22% 24% 27% 20%<br>14%<br>20%<br>9%<br>10% 10% 7%<br>0% 2% 4% 1% 3% 3% 0% 0%<br>0%<br>%<br>%<br>Housing chores Students Unemployed Other Employed salaried worker in private sector self-employed domestic worker employer family worker without pay salaried worker in public sector other<br><!-- End of picture text --> _Source: DANE household study of Bogotá, 2003_ 9. **The unbanked have lower educational attainment levels than those with bank accounts.** Figure 3.5 shows a clear relation of education to the use of formal banking products. As educational levels increase, the percentage of unbanked falls and the percentage of banked persons rises. Regression analysis shows a high correlation between educational levels and likelihood of being banked or unbanked. At the same time, it is noteworthy that approximately 15% and 25% of the group with less than elementary and elementary school only respectively do have bank accounts in formal sector institutions.

**Figure 3. 5  Education levels of the banked and unbanked**

<!-- Start of picture text --> 100%<br>90% 85%<br>79%<br>80% 75%<br>70% 61%<br>60% 50%<br>50%<br>50%<br>39%<br>40%<br>30% 25% 21%<br>20% 15%<br>10%<br>0%<br>Incomplete Complete Complete Incomplete Complete<br>Primary Primary - Secondary Superior Superior<br>Education and Incomplete Education Education Education and<br>less Secondary More<br>Banked Unbanked<br>%<br><!-- End of picture text --> _Source: DANE household study of Bogotá, 2003_ 48
but a 2% increase in per capita personal consumption, between 1997 and 1998 estimated from the national accounts (NSCB, 1999). The impact on the headcount index is of the order of about 9% (an increase from 29.1% to 3 1.7%), while the poverty gap index is about 11% higher due to the crisis, and similarly, the squared poverty gap index is indicated to be about 13% higher. The effects on living standards and poverty are of course larger for the crisis-affected population.

The measured magnitude of the impact of the crisis on poverty of course also depends on the poverty line. To examine this, we plot in Figure 2 how the entire cumulative distribution function (CDF) shifted as a result of the crisis. The Figure plots the difference between the actual ("post-crisis") and counterfactual ("pre-crisis") CDFs against log per capita consumption. Thus, for instance, at the poverty line, we can read off a value of 2.6, which confirms the result in Table 4 that the crisis induced an increase in the headcount index by 2.6% points. But the key point that Figure 2 shows is that the impact of the crisis could have been significantly higher or lower depending upon where the poverty line is drawn. This comes out even more strongly in Figure 3 where we plot the _ratio_ of the actual-to-counterfactual distribution functions. Again, while at the poverty line we have used, the crisis appears to induce about a 9% increase in the headcount index (as noted above), the _percentage_ impact could be much higher for lower poverty lines (up to 16% if the poverty line were halved), and much lower for higher poverty lines (falling down to 3% if the poverty line were doubled).

The crisis appears to have had little effect on measures of overall inequality (Table 4). With the exception of the third-degree generalized entropy measure, the impact ranges between less than 1% and 2.5%.

We also looked at whether the shock was progressive, i.e. whether the absolute impact as a proportion of pre-crisis (counterfactual) consumption was an increasing function of pre-crisis 19
# II. An Econometric Framework

Additional credit supply can raise output, input use, and hence investment, employment and wages when the farmer faces a credit constraint. This is the liquidity effect of credit. Credit has another role to play. In moat developing countries where agriculture still remains a risky activity, better credit facilities, by enabling the smoothing of consumption, can increase the willingness of farmers to take risk and hence increase agricultural investment, output, employment and wages. This is the consumption smoothing effect of credit. Thus, better rural credit markets may lead to a volume of agricultural investment and output and consequently rural employment and wages which may not be attainable with a less developed or less efficient credit system.

The informal credit sector consisting of a large professional money lenders, commission agents, traders, relatives and friends plays an important role in rural India (Timberg and Aiyer, 1984). However, with the growth of formal credit market development, the importance of the private lending has reduced. According to All India Debt and Investment Survey, the proportion of farmers' cash debt from formal sources rose from 18 in 1961 to 32 percent in 1971. In contrast, the proportion of loan fron rural money lenders has declined from 83 to 36 percent over this period. Although Indian official statistics provide district-level information on institutional credit advanced, the information on informal credit does not exist. Thus, without information on informal credit it is difficult to quantify to what extent this transition in farmer's source of credit from private to government haq helped increase agricultural investment, output, rural employment and wages in India.
Table 11: Network regressions: log Sales Pooled

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||logSales|logSales|logSales|logSales|logSales|logSales|
|SOE|0.175***|0.175***|0.174***|0.174***|0.175***|0.175***|
||(0.019)|(0.019)|(0.019)|(0.019)|(0.019)|(0.019)|
|Connect|0.310***|0.310***|0.180***|0.223***|0.392***|0.279***|
||(0.052)|(0.052)|(0.054)|(0.070)|(0.040)|(0.050)|
|BigI|0.148**|0.053||-0.081|||
||(0.062)|(0.068)||(0.107)|||
|HasBetween||0.189***||||0.254***|
|||(0.065)||||(0.082)|
|logDegree|||0.167***|0.102*|||
||||(0.032)|(0.060)|||
|BigI_×_logDegree||||0.088<br>(0.075)|||
|Politician|||||0.075|0.128*|
||||||(0.056)|(0.067)|
|Politician_×_HasBetween||||||-0.082<br>(0.119)|
|Constant|10.236***<br>(0.007)|10.236***<br>(0.007)|10.236***<br>(0.007)|10.236***<br>(0.007)|10.236***<br>(0.007)|10.236***<br>(0.007)|
|Observations|11,178,831|11,178,831|11,176,694|11,176,694|11,178,831|11,178,831|
|R-squared|0.499|0.499|0.499|0.499|0.499|0.499|
|Sector Dummies|✓|✓|✓|✓|✓|✓|
|Size Dummies|✓|✓|✓|✓|✓|✓|
|Year FE|✓|✓|✓|✓|✓|✓|
|Country FE|✓|✓|✓|✓|✓|✓|

Robust standard errors in parentheses clustered at the level of firm

*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1

35
up. We use different age ranges for men and women to reflect marriage patterns in Tanzania. We censor the ages for the sample of women to age 23 because after age 23 only a very small proportion of women have never married.<sup>10</sup> Among men, on the other hand, roughly 2 percent were married at the age of 18 (NBS, 2005); this is also consistent with the findings in the KHDS data. Therefore, the sample of men in this study consists of those between the ages of 20 and 28 in 2004.

We have two proxy measures for being affected by HIV/AIDS after the baseline round: being orphaned and having experienced a prime-age death (ages 15-49) of an individual in the households who is not the parent. We will focus on the sub-sample of children who were not orphaned at baseline. Children orphaned at baseline are excluded since we do not have data on pre-orphanhood characteristics of these children. These are characteristics which may be correlated with current orphanhood status the omission of which is likely to lead to endogeneity and omitted variable bias; this is the standard problem encountered by studies which only use cross-sectional data to evaluate the impact of orphanhood. We then assess the impact of orphanhood and deaths of prime-age household members between 1991-94 and 2004 on the probability of being married by 2004, controlling for child and household characteristics at baseline.

Among the sample of non-orphans at baseline, 22 percent became orphaned before the age of 15 (Table 1). Most of these are single-parent orphans; only 3 percent of the sample experienced the death of both parents before they were 15 years old. Fewer children experienced the second type of mortality shock – a non-parent, adult mortality event - than a parental death; 13 percent experienced the death of a prime-age adult before the age of 15, of which 7 percent suffered the death of an adult male and 7 percent suffered that of an adult female. Overall, 30 percent of the sample of children experienced at least one of these four mortality shocks; 9 percent suffered more than one of these four mortality shocks.

Table 1 presents the set of control variables which includes, among others, indicators for wealth/poverty status (head’s education, per capita expenditure, flooring material). Even when parents are alive, children do not necessarily reside with them. As

> 10 Nationally, the median age at marriage in rural Tanzania is 18 for women and 24 for men (NBS, 2005). More than 87% of women ages 25-29 were married by age 25. Among men, this statistic is 63%. Less than 5% of women 15-19 years were married by age 15.

7
Effectively, in Egypt emigration is negatively associated with the progress of education, while in Morocco a positive association is found (Table 3). Whether the antecedent is education or emigration––that is whether regional differences in education are a cause or a consequence of regional differences in emigration —cannot be established with data that were available for preparing this paper.<sup>22</sup> Evidence from Turkey goes in the same direction (Figure 7). If one excludes the most developed provinces of the country—those where an early demographic transition was recorded in the wake of a modernization process which took place during the first half of the twentieth century, i.e. before emigration to the West started (Duben & Behar 1991)—one finds a negative correlation between cumulated emigration before 1990 (as reflected into the proportion of expatriates per 1,000 inhabitants in the 1990 population census) and the child/woman ratio in 2000 (reflecting fertility in the 1990s). As in Morocco, this negative association corresponds to the combination of a positive correlation between emigration and education (education index in 2000) and a negative correlation between education and fertility (Table 3).

> 22 Emigration indexes used here are stocks, i.e. cumulated numbers of emigrants (return migrants in Egypt) at the time of the survey/census, which thus refer to migration movements that took place during the open period of time before the survey/census.  Education indexes are those provided by Human Development Reports and all refer to 2000.

23
<!-- Start of picture text --> Ltn Amc th Cube<br>Mie Est Not Ace,<br>Europe & Central Asia —<br>East Asia: & Pacificp ee:Finance<br>o 10 20 30 40 50 ‘60 70<br>Porcent of firms<br>Note: Figure shows the share of fams thal report access to finance, and any of electricity, telecom<br>MUNications, or transportation as “major or “sevend” obstacles to the operation and qrowth of their<br>business.<br>Source: Workd Bank Invastment Climate Surveys.<br><!-- End of picture text -->
- NSE Emerge provides subsidies across the spectrum, including not charging listing or transaction fees. With such low fees and small number of companies listed, NSE Emerge needs to be subsidized by the main board during its startup phase.

_ii. Reducing ongoing costs:_ These costs can be high, such as for information disclosure and investor relations. Disclosure costs and investor communications can be reduced by using the exchange to disseminate information and waiving requirements for printed documents.

_iii. Reducing financial services fees:_ Although the SME exchange can lower some costs, advisor, legal, and brokerage fees can be high, and the exchange cannot often directly influence them. It can, together with the capital markets regulator and other capital market entities, help create a competitive environment for intermediaries and advisors by encouraging a number of these intermediaries to enter the market.

||Table 8. Cost Structure of SME Exchanges<br>|
|---|---|
|Exchange|Cost Structure|
|NSE India<br>Emerge|About 40% of main board costs.|
|JSE AltX|A separate fee structure exists for initial listing of securities and annual listing<br>fees. For 2014, the cost of initial listing is capped at $2,800, and annual<br>listing fees are capped at $3,000. Documentation fees remain the same as<br>main board applicants.|
|GreTai|The cost of initial listing is a flat rate of $16,667 (NT$500,000). The after-<br>listingannual fee is based on companycapitalization.|
|Bovespa Mais|Exempts the company from a number of fees, such as register fee, offering<br>liquidityfee,andgradual discount on annual fee until fourthyear.|
|WSE<br>NewConnect|Fees related to trading are based on flat rates and do not depend on company<br>capitalization,at around$1,000 ayear.|
|BIST ECM|Exchange and depository fees reduced to 1/10th of the main market. Capital<br>markets board fees reduced to 1/20th. Registration fee of 0.01% of offering<br>value(0.2%for main market).|

_Source:_ WBG compilation of exchange information.

# 2. Reducing Regulations and Protecting Investors

A key challenge with SME markets is how to create a regulatory regime that SMEs can meet and that achieves the prudential needs of the investors as well. The regulatory requirements cover performance, disclosure, and governance requirements. These may be lowered, compared with the main market, to reduce costs to the issuer. SMEs can be more risky than larger companies, although they are also less complex; lowering regulatory requirements can reduce investor protection and investor confidence.

# _i. Easing Entry Requirements_

To facilitate the listing of smaller companies that have been in existence for a short period of time with short profit histories, many SME exchanges relax entry requirements compared with the main board, as shown in Table 9. Requirements that can be lowered concern: 22
# **2.  Prior Research**

Empirical research has provided many useful insights about the determinants of forest clearing. The results are generally consistent with an economic model in which the conversion of forested land varies with potential profitability. Nelson and Chomitz (2009) and Rudel et al. (2009) have studied this relationship across countries over multi-year intervals. Within countries, numerous econometric studies have estimated the impact of economic, social and geographic drivers on deforestation during multi-year intervals. Some studies have used aggregate data for states, provinces or sub-provinces (e.g., studies for Brazilian municipios by Pfaff (1997) and Igliori (2006), and Mexican states by Barbier and Burgess (1996)).

Many studies have also used GIS-based techniques to obtain multi-year estimates at a higher level of spatial disaggregation (e.g., Cropper et. al. (1999, 2001) for Thailand; Agwaral et al. (2002) for Madagascar; Deininger and Minton (1999, 2002), Chowdhury (2006) and Vance and Geoghegan (2002) for Mexico; Kaimowitz et al. (2002) for Bolivia; and De Pinto and Nelson (2009) for Panama). In rarer cases, studies have used annual national or regional aggregate time series for extended periods (e.g. Zikri (2009) for Indonesia; Ewers et al. (2008) for Brazil).

While econometric work on long-run deforestation drivers is well-advanced, previous data problems limited treatments of economic dynamics to theoretical work and simulation. Arcanda et al. (2008) and others studied the theoretical relationships between macroeconomic drivers and forest clearing. Notable simulation exercises include Cattaneo (2001) for Brazil and San et al. (2000) for Indonesia.

Recently, the advent of monthly and annual remote sensing databases has led to the first spatial estimation exercises that explicitly incorporate economic dynamics (Wheeler et al., 2011; Dasgupta et al., 2014). Direct impact studies in Latin America using the new databases have 3