rafmacalaba/datause-encoder-data huggingface dataset:
for 2004.

Using a methodology similar to Ingram et al. (1997) and Busato et al. (2006), Argentiero et al. (2008) calibrate a dynamic general equilibrium model to measure money laundering in Italy. They argue that econometric estimations have limitations since the key variable is unobservable leading to technical statistical issues. The authors develop a model that has a formal licit sector and an informal-criminal sector in which three main agents interact: i) licit ...rms that produce licit goods using capital and labor and illicit ...rms that produce illicit goods using labor and land; ii) households that pay taxes, demand both goods, demand money and supply labor and capital to both sectors of the economy; and iii) a government which collects ...scal revenue in the form of taxes from households and ...rms, and includes a central bank that supplies money to the economy. The authors ...nd that money laundering depends optimally on labor in both sectors, capital, prices and quantities of both goods. They calibrate the model and ...nd that money laundered as a proportion of Italian GDP has increased over time. They also ...nd a negative correlation between money laundering and GDP. In Argentiero et al. (2009), they apply their methodology to the United States and 15 countries in the European Union.

# 2.3 Empirical and Econometric Estimations

Unger (2009) describes a series of methods that have been used to assess the volume and mechanisms behind the phenomenon of asset laundering. These include: i) ...eld studies that help understand the way individuals and institutions behave in the presence of crime and asset laundering; ii) interviews and surveys of individuals that work in the di¤erent sectors in which asset laundering occurs, which help understand the di¤erent mechanisms through which asset laundering operates; iii) analyses of suspicious or unusual transactions that help understand some of the mechanisms used by criminals to launder their illicit income; iv) analyses of statistical discrepancies at the macro level in the balance of payments or money supply-demand that help understand the volume of the SE in which asset laundering operates; and v) the latent variable approach, in which asset laundering is viewed as an unobservable variable to estimate indirectly using statistical methods such as Multiple IndicatorMultiple Causes (MIMIC) and Dynamic MIMIC. These methods consist of using the software LISREL to estimate a system of linear structural equations of at least two types: i) a transition equation that relates the unobservable variable, in this case asset laundering, to its determinants, and ii) measure equations that relate observable indicator variables, e.g. money supply, GDP, capital stock, to the unobservable variable, which can be used to measure the impact of the unobservable variable. Unger (2009) also discusses other methods that have been used to estimate the volume of asset laundering like a Gravity Model as in Walker and Unger (2009). For an overview of structural linear models and MIMIC see Manzano and Zamora (2009), Schumacker and Lomax (2010), Kline (2011).

8
**Table A2.    List of product lines at 10 digit level with MFN tariff rates higher than 50% in 2005**

|**Code 10**<br>**digits**|**Product Description**|**MFN**<br>**Tariff**<br>**Rate**|
|---|---|---|
|1604309090|other ready-to-eat or canned roe other than sturgeon caviar and salmon roe, roe substitutes|469.7|
|2208903800|liquor made of plum, pear or cherry, in bottles with volume more then 2 liters|383.6|
|8703239022|other used motor vehicles with spark-ignition reciprocating internal combustion engines with<br>capacity greater than 1800 cub. cm but not more than 2300 cub. cm manufactured more than 7<br>years ago|<br>277.5|
|8703239011|other used motor vehicles with spark-ignition reciprocating internal combustion engines with<br>capacity greater than 1500 cub. cm but not more than 1800 cub. cm manufactured more than 7<br>years ago|<br>266.5|
|8703329091|used motor vehicles with piston compression-ignition internal combustion engines (diesel<br>or semidiesel) with capacity greater than 1500 cub. cm but not more than 2500 cub. cm<br>manufactured more than 7 years ago|251.3|
|8703339091|other used motor vehicles with piston compression-ignition internal combustion engines<br>(diesel or semidiesel) with capacity greater than 2500 cub. cm manufactured more than 7<br>years ago|233.3|
|8703239027|other used motor vehicles with spark-ignition reciprocating internal combustion engines<br>with capacity greater than 2300 cub. cm but not more than 3000 cub. cm manufactured<br>more than 7 years ago|208.6|
|1701991001|white sugar without flavoring-aromatic or coloring agents|208.5|
|9101120000|electric watches, with or without a built-in stop-watch, with optoelectronic display only,<br>with a case made from a precious metal or metal plated with а precious metalv|198.5|
|8703319091|other motor vehicles with piston compression-ignition internal combustion engines (diesel<br>or semidiesel) with capacity of not more than 1500 cub. cm manufactured more than 7<br>years ago|196.8|
|8703249091|other used motor vehicles with spark-ignition reciprocating internal combustion engines<br>with capacity greater than 3000 cub. cm manufactured more than 7 years ago|181.4|
|8703229091|other used motor vehicles with spark-ignition reciprocating internal combustion engines<br>with capacity greater than 1000 cub. cm but not more than 1500 cub. cm manufactured<br>more than 7 years ago|163.3|
|8524530002|other pre-recorded magnetic tapes more than 6.5mm wide|198.0|
|1701991009|white sugar without flavoring-aromatic or coloring agents|196.2|
|8702101921|buses designed for carrying 120 or more persons, driver inclusive, with compression-ignition<br>internal combustion engines (diesel or semidiesel) with capacity greater than 2500 cub. cm<br>manufactured more than 7 years ago|<br>138.4|
|2207200000|non-denaturated ethyl and other alcohols, of any concentration|100.0|
|9105110000|electric alarmclocksnot intendedforpersonalwearorportable use|102.4|

33
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As in other parts of the developing world, microinsurance products could offer benefits in Africa, especially in agricultural areas. For example, rainfall insurance, which pays a set amount when rainfall falls below (or surpasses) a predetermined threshold could be useful to African farmers. In Ghana, Karlan et al. (2014) find that rainfall insurance counters farmers’ risk aversion and improves decision making regarding input mix and land under cultivation. As a result, these agricultural households were less likely to report that they suffered from hunger, though effects were largest when insurance was combined with subsidized capital. However, in that experiment and another one in Africa (Giné and Yang, 2009), the take-up for agricultural insurance products was puzzlingly low, suggesting a weak match between the insurance products and farmers’ needs and/or farmers not fully understanding how these products could benefit them. To date, throughout the developing world there is no example of an agricultural insurance program for small landholders that has achieved scale in the absence of substantial subsidies (Miranda and Nadolnyak, 2023).

In summary, evidence from RCTs in Africa has highlighted how physical proximity to outlets of financial services providers, innovations in financial products, and biometric identification are deepening financial inclusion and spurring greater financial usage among clients that have typically been under-served by providers of financial services. However, these gains have been more pronounced for money management (cash-in/cash-out) and savings than for credit and insurance products.

# **3.3 Mobile Money**

Mobile money transfer (“m-transfer”) systems facilitate financial transactions via mobile phones, 31
12 -

-

1987. 10/ However, these results apply only to the sampled steel products (see Appendix Tables 3 to 13) and, if the same pattern holds for all iron and steel shipments the present value of the associated losses on imports from France approximately would be $2 billion. 11/ This extrapolation is based on the assumption that the same average price premiums are paid for both sampled and nonsampled products. The importance of this ($2 billion) figure is highlighted by the fact that it exceeds the long-term debt of 12 of the associated countries in 1987 and is approximately equal to the debt of the Central African Republic, Chad, C'urkina Faso and Mauritif.s ($2.2 billion).

While the previous analysis examined relative French unit values in the aggregate, Table 3 shows the average premiums or discounts paid by individual associated countries for all imports of sampled iron and steel products. For the full 1962-87 period the individual country premiums average

- 10/ The actual dollar losses reported in Table 2 are the summation of the implied losses in each two year period up to and including 1987. The present value of these losses include their actual value and the interest that would have been earned on each years excess payments up to 1987. In calculating the present value a discount rate of 8 per cent was employed as this appeared to be a close approximation to the average for the 196287 period. It should be noted that when the French associates received an apparent discount, as in 1976-77, this was recorded as a negative entry in both the actual cost and present value calculations.

- 11/ One key question relates to whether the excess pricing pattern observed for iron and steel products also applies to other sectors. There is some tentative evidence in support of this proposition. Yeats (1978, p. 178) compared four-digit SITC product unit values for all French shipments to selected associated and non-associated African countries over 1962-1969 and found that the former averaged between 13 to 18 per cent higher. Assuming that this excess price margin applies to all manufactured imports would mean that the associates were overcharged by approximately $25 billion. A second important point is whether the associates suffered additional losses due to institutional factors that kept them from dealing with an alternative supplier (say Japan) whose prices were generally below the average French export prices. One way to tesc this hypotheses would be to substitute alternative suppliers' unit values for Uij in equation (3) and recompute the associates' gains or losses. i
**Table 2. Microfinance Scale by Region**

|||**Number of**<br>|**Active Borrowers/**<br>|||
|---|---|---|---|---|---|
||**MFI**|**active**|**Working age**|**Gross Loan**|**GLP/**|
||**Providers**|**borrowers**|**population(%)**|**Portfolio(GLP)**|**Total Credit**|
|**MENA **||**2,807,116**|**1.99**|**1,505,414,132 **|**0.47**|
|East Asia & Pacific|160|11,294,660|4.74|5,195,228,561|6.12|
|Europe & Central Asia|263|2,603,513|4.11|9,375,908,169|5.37|
|Latin America & Caribbean|327|12,781,340|4.93|15,334,944,785|7.14|
|South Asia|148|39,274,147|5.90|4,293,510,584|4.72|
|Sub-Saharan Africa|208|6,732,155|1.48|3,162,740,041|4.71|

Source: Microfinance Information Exchange Database and Sanabel. Data for MENA is for 2009, other regions 2008.

**There were 2.81 million microcredit borrowers at the end of 2008, with a total loan portfolio of US$1.5 billion** . Microcredit is concentrated in two countries: in 2009 47 percent of the region‟s microcredit borrowers were in Egypt and 33 percent in Morocco (the order was reversed from 2008, when Egypt had 40 percent and Morocco 44 percent).<sup>6</sup> In terms of clients per population and microcredit portfolio as a proportion of total bank lending to the private sector, Morocco still has the highest rates of microcredit access in the region, although this should be viewed in the context of Morocco‟s low level of access to bank loans. Morocco also stands out in terms of MFI branch networks, comprising 83 percent of the region‟s reported total. (MIX, CGAP, Sanabel, 2010)  Jordan has the highest microcredit coverage of its poor population in the region. Tunisia and West Bank & Gaza have the only other microcredit sectors of any notable size in the region, although microcredit is growing in scale in Yemen, Iraq, and Syria.

**Table 3. Microcredit Outreach by Selected MENA Countries**

||**MFI**|**Number of**<br>**active**|**Percent of**<br>**Women**|**Gross loan**|**Active**<br>**Borrowers/**<br>**Working age**<br>**population**|**Number of**<br>**clients/**<br>**Number of**|**GLP/**<br>**Total**|
|---|---|---|---|---|---|---|---|
||**Providers**|**borrowers**|**Borrowers**|**portfolio**|**(%)**|**poor(%)**|**Credit**|
|Egypt|14|868,315|55.7|165,018,626|1.69|5.8|0.24|
|Iraq|4|27,708|14.7|16,122,827|0.16||0.45|
|Jordan|7|134,037|84.1|121,578,626|3.7|65.6|0.68|
|Lebanon|3|23,678|32.8|20,584,560|0.84||0.1|
|Morocco|9|1,241,957|46.3|695,647,381|5.96|28.1|1.08|
|WB/G|8|34,265|43.2|79,727,913|1.68|||
|Syria|2|22,149|31.4|15,682,015|0.18|7.2|0.04|
|Tunisia|1|94,959|76.7|33,999,508|1.32||0.14|
|Yemen|6|24,976|95.8|3,241,486|0.2|0.2|0.16|

Sources: MIX database, WDI, IFS 2008 data. „GLP‟: gross (microcredit) loan portfolio. Total credit: Domestic private credit to the real sector by banks.

6 Sanabel, 2010. These statistics do not include Mauritania and Sudan, which are included in Sanabel regional totals.
and other ability measures are associated with more rapid recovery. With respect to gender, the shock to the household may affect household bargaining and the allocation of resources. The recovery of males and females may differ as a result. Finally, gifts or loans from family members may be one source of capital for recovery. These are likely to be most commonly received from parents or adult children. Of course, aid from family members is less likely if family members were also affected by the tsunami.

# _Using Experimental Data to Identify Returns to Recovery Funds_

The suddenness of disasters and logistical issues involved in coordinating the aid response make collecting data for households or enterprises receiving assistance a very difficult and rarely undertaken task. Even where such data are collected, identifying the role of capital in the recovery process is complicated by the presence of a number of unobserved factors which are likely to be correlated with both the speed of capital stock replacement and future profitability. Firms anticipating faster profit recovery may be more inclined to replace capital stock or find it easier to persuade family members to lend them resources. More politically connected firms may be better able to access aid flows. And the direction of causation may flow from profit recovery to capital stock, if firms replace damaged capital by reinvesting profits.

To investigate whether high profits provided an incentive for replacing damaged assets, we carried out an experiment in which firms were randomly given grants of cash or in-kind grants to purchase material or equipment, selected by the owner for the enterprise. We describe in detail the results of the experiment among undamaged firms in de Mel, McKenzie and Woodruff (2008). The experiment gives us a clean measure of the role of capital in the disaster recovery process.

Our baseline survey included 227 enterprises in directly affected areas along the coast. The enterprises were selected with a screening survey administered door-to-door in residential neighborhoods in the districts of Kalutara, Galle and Matara. Our intention 16
# **Table 1: Summary Statistics**

|**_Firm Characteristics_**|# Obs.<br>|Mean<br>|Std Dev.|**_Individual Characteristics_**|# Obs.|Mean|Std Dev.|
|---|---|---|---|---|---|---|---|
|Firm Size 0|2865|0.43||Female owner|2865|0.29||
|Firm Size 1 to 4 workers|2865|0.34||Age|2863|37.1|8.8|
|Firm Size 5 to 9 workers|2865|0.12||Married|2865|0.84||
|Firm Size 10 to 24 workers|2865|0.06||Tamil Ethnicity|2865|0.10||
|Firm Size 25 + workers|2865|0.05||Years of Education|2865|10.61|2.92|
|Manufacturing dummy|2865|0.33||Risk Seeking Score|2865|6.25|2.75|
|Retail dummy|2865|0.35||Log Discount rate|2656|2.33|1.30|
|Colombo district dummy|2865|0.35||Hyperbolic Discounter|2569|0.38||
|Number of competitors|1841|21.8|50.0|Digitspan Recall test score|2865|6.29|1.54|
|Number of competitors unknown|2865|0.31||Raven test score|2865|3.59|2.52|
|Customers would take a day or less||||Optimism|2865|0.58|0.54|
|to replace the firm if it closed|2865|0.55||Polychronicity|2865|0.17|1.14|
|Firm exports|2865|0.02||Has had 3 or more previous jobs|2865|0.10||
|Firm is less than 5 years old|2865|0.34||Tenacity|2865|0.74|0.39|
|Firm is legally registered|2865|0.33||Reverse work centrality|2865|-0.20|1.09|
|Firm has received a bank loan|2865|0.37||Plans to leave business in next 5 years|2865|0.06||
|Diversification (proportion of revenues<br>coming from other than main product)|2745|0.27||||||
|Proportion of Goods Custommade|2865|0.14||||||
|Log Monthly Business Profits (Rupees)|1699|9.40|1.31|||||
|LogBusiness Assets(Rupees)|2759|12.73|2.42|||||

Source: Sri Lanka Longitudinal Survey of Enterprises Baseline 2008.

- 27 -
17

**Table 3: Labor and input use, and performance**

|**Variables**|**All**|**TP**|**STP**|**SMP**|**SM **|**P-value**|
|---|---|---|---|---|---|---|
|**Labor allocation**(person-days/ha)|||||||
|Total household labor (Men)|29.31|70.78|29.75|20.98|10.11|<0.001|
|Total household labor (Women)|15.01|35.51|13.32|12.54|5.82|<0.001|
|Total household labor (Children)|5.32|3.09|5.54|5.87|2.39|0.315|
|Total household labor (person-days/ha)|49.6|109.48|48.6|39.4|18.3|<0.001|
|Total hired labor (person-days/ha)|48.2|86.0|61.1|29.4|35.6|<0.001|
|Total labor (HL + HH)|97.9|195.4|109.7|68.8|53.9|<0.001|
|Total hired labor cost (1000 FCFA)|56.3|89.7|64.3|40.2|75.8|<0.001|
|Implicit wage (FCFA/day)|1,166|1,047|1,049|1,379|2,142|<0.001|
|**Other inputs**|||||||
|Use of improved seed (1=Yes)|0.25|00|0.32|0.23|0.53|<0.001|
|Total quantity of seed (Kg/ha)|86.0|77.1|89.1|85.7|80.6|0.811|
|Water source (1=Low-land)|0.68|0.96|0.65|0.66|0.47|<0.001|
|Quantity of Urea (Kg/ha)|62.9|0.00|75.4|66.9|57.4|<0.001|
|Quantity of NPK (Kg/ha)|113.1|0.00|127.6|122.7|157.0|<0.001|
|Total quantity of fertilizers (Kg/ha)|176.0|0.00|203.0|189.6|214.4|<0.001|
|Use of fungicide (1=Yes)|0.31|0.00|0.36|0.32|0.58|<0.001|
|Use of herbicide (1=Yes)|0.90|0.00|1.00|1.00|1.00|<0.001|
|Total quantity of herbicides (L/ha)|8.92|0.00|10.25|9.77|9.20|<0.001|
|Production cost (1000 FCFA/ha)|142.22|101.27|161.49|132.30|173.79|0.251|
|**Productivity and profitability**|||||||
|Yield (kg/ha)|1937.7|1036.7|2081.8|1993.7|2246.6|<0.001|
|labor productivity (kg/person-days)|30.3|6.5|26.8|37.1|51.9|<0.001|
|Profit (1000 FCFA/ha)|148.4|54.2|150.8|166.8|163.2|<0.001|
|Alternative profit (1000 FCFA/ha)|99.71|-52.88|102.69|128.46|143.96|<0.001|
|Return to family labor (FCFA/person-|7142|1440|8060|7065|14463|<0.001|
|days)|||||||
|Return to total labor (FCFA/person-|3146|899|2766|3810|5413|<0.001|
|<br>days)|||||||
|**Farm specialisation, market orientation**|**and food sec**|**urity**|||||
|Share of cash crop area (%)|33.35|54.83|29.63|33.01|16.70|<0.001|
|Rice plot size (ha)|0.98|0.59|0.92|1.10|1.34|<0.001|
|Rice production per ha (kg/per capita)|356.24|162.12|360.58|379.76|570.29|<0.001|
|<br>Proportion of rice commercialized|0.26|0.08|0.30|0.25|0.50|<0.001|
|Rice sold per capita (Kg/capita)|137.40|24.67|151.20|127.48|434.52|<0.001|
|Foodinsecurity|4.28|7.24|4.08|3.93|2.42|<0.001|
|Observations|1039|109|415|477|38||

Notes: Mean values are shown. P-values of continuous variables are obtained by one way anova test for equality of mean values and P-values of binary variables are obtained by Pearson’s _χ_<sup>2</sup> -tests for equal proportions. All labor related variables are expressed in person-days/ha. Implicit wage is obtained as ratio of total expenses on hired labor divided by average person-days hired.

Turning to the labor input in more detail, Table 4 presents a deep dive into the evolution of labor inputs by age, sex, and activity along the four production packages. Notably, as the
“What seems more typical are the comments appearing six months to a year after a disaster, expressing surprise at the speed with which the community has recovered, and the prosperity that now reigns.” (Dacy and Kunreuther, 1969) A series of catastrophic events in recent years has drawn increased attention of both the public and researchers to the plight of those impacted by natural disasters. The number of natural disasters reported by the press and included in the most comprehensive disaster database is increasing. The Intergovernmental Panel on Climate Change (2007) believes that the frequency of natural disasters is “likely” to increase as a consequence of global warming. Some argue that the current increase results from more complete reporting of disasters, and others say that the future trend driven by climate change is uncertain. But there is little disagreement that the impact of natural disasters is felt most severely by households already living on the margins in low-income countries.<sup>1</sup> The death toll from disasters, for example, is typically much higher in low-income countries (Kahn 2005).

There is a large literature on how households in developing countries cope with and respond to disasters and aggregate shocks. (See Skoufias (2003) for an overview.) The poor suffer disproportionately because missing credit and formal insurance markets limit their ability to smooth aggregate shocks. The informal risk-coping strategies that are used to smooth idiosyncratic shocks break down when all members of a risk-sharing group are affected (Morduch, 1999). As a result, transient shocks can have permanent effects, either through a lowered ability of households to provide nutrition or schooling for their children (Maccini and Yang, 2009; Ferreira and Schady, 2009), or through the inability to repurchase productive assets, such as livestock, which are sold to smooth consumption (Carter et al, 2007).

> 1 The trend of an increasing number of disasters shown in the EM-DAT data is likely due in part or in whole to a more complete reporting of disasters. (See, for example, the discussion in Strömberg 2007.)

2