rafmacalaba/datause-encoder-data huggingface dataset:
Fifth, misclassification of goods may create huge revenue losses. Low-tax headings should therefore be closely monitored.

Against this backdrop, a new multipronged strategy is being implemented in Madagascar, with the aim not only of curtailing fraud, but also of facilitating legal trade. A reform strategy for the 2015–2018 period was adopted in September 2014.

Several significant reform measures have recently been adopted or are in the process of being adopted:

- A system has been instituted whereby the performance of each customs office will be monitored based on monthly indicators. This should enable better scrutiny of the Customs Authority and make monitoring more effective, thus facilitating trade.

- The post-clearance audit unit has been reinforced with the recruitment of additional auditors, and several major importers have since been audited and have been found to have committed fraud.

- A new circular has been signed by the Minister of Finance revising the criteria under which companies are eligible for fast-track customs clearance.

- Moreover, in the context of the “quick results” approach adopted, the Customs Authority is making every effort to scale back physical inspections, given their negligible impact.

- Finally, the risk analysis and statistics departments have been reinforced to provide more targeted monitoring.

# **VII. Concluding Remarks**

This paper proposes a simple methodology aiming to identify some risky import operations. The mirror analysis first helps to identify and target products or sectors with the greatest risk. Analysis of declaration patterns (that is, mining of customs data) is then performed to identify and target the most high-risk companies. In the specific case of Madagascar, and within the current context of customs reform, this paper suggests that there was sizeable fraud in the areas of customs valuation and tariff classification in 2014.

Recent studies (Yang (2008), Carrillo et al. (2014)) have highlighted that many tax evaders respond to the implementation of corrective measures (such as increased monitoring, establishment of cross-checking systems, etc.) not by changing their behavior, but by changing their tax evasion method. Therefore, if the authorities are to succeed in reducing fraud, they must make every effort to anticipate how fraudulent actors are likely to adapt their behavior.

18
58

## Table 2

# **Public Expenditure and Participant Inflows in Labour Market**<sup>**Programmes**</sup><sup>_(%)_</sup> Czech Republic

||1991|1992|1993|1991|1992|1993|
|---|---|---|---|---|---|---|
|Programme categories|A|A|A|B|B|B|
|1. Public employment services and administration|0.08|0.10|0.11||||
|2. Labour market training|0.01|0.01|0.01|0.10|0.34|0.20|
|3. Work experience for school leavers|0.01|0.05|0.03|0.27|0.42|0.14|
|4. Subsidised employment|0.08|0.16|0.05|0.89|2.27|0.47|
|<br>a) Subsidies to regular employment in private|0.05|0.10|0.02|0.40|1.22<br>|0.16<br>|
|<br>sector|0.02|0.03|0.01|0.22<br>|0.50<br>|0.08<br>|
|b) Support of unemployed persons starting<br>enterprises<br>c) Direct job creation (public or non-profit)|0.01|0.03|0.02|0.27|0.55|0.23|
|5. Supported work for the disabled|..|0.01|0.01||0.03|0.02|
|6. Unemployment compensation|0.23|0.20|0.16||||
|TOTAL|0.41|0.53|0.36|1.26|3.05|0.83|

A Total expenditure as percent of GDP B Participant inflows as percent of labour force _Source:_ OECD
||(0.12)|(0.118)|(0.11)|(0.051)|(0.094)|
|---|---|---|---|---|---|
|Remittances from family within the same||||||
|enumeration area (EA)|-0.064|-0.014|-0.084|0.075**|0.038|
||(0.044)|(0.059)|(0.051)|(0.038)|(0.046)|
|Observations|873|726|726|655|710|

Note. Results are presented in margins and clustered standard errors in parentheses. Individual-level survey weights used. Statistical significance at: *** p<0.01, ** p<0.05, * p<0.1. Enumeration areas fixed effect included. In Malawi, the number of youths who expressed a desire to relocate to rural areas and to another country was quite limited, with only 3 6 and 143 individuals in th ese categor ies respectively . Consequently, several observations had to be excluded from the regression analysis due to the presence of perfect collinearity with the dependent variable. We also controlled for the following variables: Youth is female, 19-22 years, Currently working (either paid job or farming), Interesting job to do, STEM related work, Years of education, Working in agriculture, Own account worker, Remittances from family within the country and Remittances from abroad within EA. Results for these variables were either not statistically significant or not discussed in the analysis. Full results are available under request.

**Table 5. Youth’s migration aspirations in Nigeria, marginal effects from logistic regressions**

|**Main variables**|**Migrate**|**Moving to**<br>**capital city**|**Moving to**<br>**town/city**|**Moving to**<br>**rural area**|**Moving**<br>**to**<br>**another**<br>**country**|
|---|---|---|---|---|---|
|**Characteristics of the youths**||||||
|Youth is female|-0.116***|0.0244|0.00910|0.008|-0.17***|
||(0.031)|(0.047)|(0.045)|(0.043)|(0.042)|
|23-25 years|-0.005|-0.121*|-0.01|0.081|-0.04|
||(0.046)|(0.069)|(0.063)|(0.061)|(0.067)|
|Years of education|0.012***|0.01**|-0.013**|-0.011***|0.003|
||(0.003)|(0.005)|(0.006)|(0.004)|(0.005)|
|Currently working (either paid job or farming)|0.008|0.017|-0.151**|-0.227***|-0.028|
||(0.047)|(0.069)|(0.063)|(0.067)|(0.057)|
|Currently doing the ideal job|0.04|-0.008|0.054|0.093|-0.212*|
||(0.073)|(0.09)|(0.11)|(0.089)|(0.120)|
|Lack of money|0.117***|0.054|-0.01|0.08*|0.032|
||(0.037)|(0.049)|(0.049)|(0.045)|(0.049)|
|Job that family and friends regard highly|-0.065*|0.029|-0.004|-0.003|-0.074|
||(0.036)|(0.054)|(0.055)|(0.051)|(0.063)|
|**Characteristics of the adult head of HH**||||||
|Years of education|0.003|-0.0005|-0.003|-0.01***|-0.003|
||(0.002)|(0.004)|(0.003)|(0.0035)|(0.004)|
|Wage job employee|-0.077**|-0.018|-0.036|-0.024|0.088|
||(0.036)|(0.059)|(0.056)|(0.054)|(0.063)|
|Working in agriculture|0.031|-0.034|-0.032|-0.114**|-0.029|
||(0.031)|(0.051)|(0.053)|(0.045)|(0.054)|
|**Characteristics of the household**||||||
|Quintile 2|-0.054|-0.058|-0.011|-0.041|0.136**|
||(0.046)|(0.063)|(0.068)|(0.071)|(0.057)|
|Quintile 3|0.0003|-0.085|-0.132*|-0.158**|0.212***|
||(0.04)|(0.069)|(0.068)|(0.067)|(0.068)|
|Quintile 4|-0.039|-0.031|-0.178**|-0.28***|0.069|
||(0.048)|(0.071)|(0.074)|(0.071)|(0.071)|
|Quintile 5 (richest)|-0.094*|-0.024|-0.109|-0.191**|0.231***|
||(0.049)|(0.084)|(0.082)|(0.090)|(0.077)|

23
Table 3: First differences of donor and non-state actor service provision on willingness to accept the tax department’s authority

|First Diference|Lower 95 % C.I.|Upper 95 % C.I.|
|---|---|---|
|UN: helps a lot vs. not at all<br>0.08|0.04|0.12|
|China: helps a lot vs. not at all<br>0.07|0.04|0.11|
|International donors/NGOs: help a lot vs. not at all<br>0.07|0.03|0.11|
|US: helps a lot vs. not at all<br>0.07|0.04|0.10|
|International donors/NGOs: too much infuence vs. too little infuence<br>0.05|0.03|0.07|
|Domestic organizations/NGOs: too much infuence vs. too little infuence<br>0.04|0.02|0.06|
|International businesses: too much infuence vs. too little infuence<br>0.03|0.01|0.05|
|International donors/NGOs: right amount infuence vs. too little infuence<br>0.03|0.01|0.05|
|Traditional authorities responsible for taxes vs. state responsible<br>-0.06|-0.10|-0.02|

Table 4: First differences of donor and non-state actor service provision on willingness to accept authority of the courts

||First Diference|Lower 95 % C.I.|Upper 95 % C.I.|
|---|---|---|---|
|No corruption among judges vs. a lot of corruption|0.14|0.08|0.19|
|Approve government eforts to maintain roads vs. disapprove|0.04|0.02|0.06|
|Government treatment of ethnic group: fair vs. unfair|0.04|0.01|0.07|
|Approve government eforts to collect fees vs. disapprove|0.03|0.01|0.05|
|Gov’t treatment of citizens: fair vs. unfair|0.03|0.00|0.05|
|International donors/NGOs: too much infuence vs. too little|0.03|0.01|0.05|
|Domestic organizations/NGOs: too much infuence vs. too little|0.02|0.00|0.04|
|Approve government eforts to maintain water and sewage vs. disapprove|0.02|0.00|0.03|
|Community responsible for law and order vs. state responsible|-0.03|-0.06|0.00|
|State enforcement of law among citizens likely vs. unlikely|-0.06|-0.08|-0.04|

Table 5: First differences of donor and non-state actor service provision on willingness to accept authority of the police

||First Diference|Lower 95 % C.I.|Upper 95 % C.I.|
|---|---|---|---|
|No corruption among police vs. a lot of corruption|0.11|0.05|0.16|
|Gov’t treatment of ethnic group: fair vs. unfair|0.05|0.02|0.08|
|Domestic organizations/NGOs: too much infuence vs. too little|0.05|0.02|0.08|
|International businesses: too much infuence vs. too little|0.04|0.02|0.07|
|International donors/NGOs: too much infuence vs. too little|0.04|0.02|0.07|
|Approve Gov’t eforts to maintain water and sewage vs. disapprove|0.04|0.02|0.06|
|Approve Gov’t eforts to collect fees vs. disapprove|0.04|0.02|0.06|
|Approve Gov’t eforts to maintain roads vs. disapprove|0.03|0.02|0.05|
|Traditional leaders responsible for law and order vs. disapprove|-0.03|-0.06|0.00|
|Community responsible for law and order vs. state|-0.04|-0.07|-0.01|
|State enforcement of law among citizens likely vs. unlikely|-0.05|-0.07|-0.02|

38
The indicators that focus on the labor aspect of informality are the prevalence of self-employment and the lack of pension coverage. The former is given by the ratio of self to total employment, as reported by the International Labor Organization. The latter is given by the fraction of the labor force that does not contribute to a retirement pension scheme, as given in the World Bank’s World Development Indicators. Appendix 3 presents some descriptive statistics on the four informality indicators. In particular, it shows that, as expected, they are significantly positively correlated, with correlation coefficients ranging from 0.59 to 0.90 –high enough to represent the same phenomenon but not so high as to make them mutually redundant.

Using data on these four indicators, we can assess the prevalence of informality across Latin America. For comparison purposes, Figure 1 presents data on the four informality indicators for individual countries in Latin America and the Caribbean (LAC). The USA and Chile are used as benchmark countries. The USA is the developed country to which the region is most closely related. Chile is the Latin American country often taken as a model for economic reforms and sustained growth in the region.<sup>3</sup> It is clear from the figure that there is considerable variation in informality across countries in Latin America. However, in all of them, the degree of informality is much higher than in the USA; and for some countries (e.g., Bolivia and Haiti) it is comparable to the most informal countries in the world. For the median country in Latin America, about 40% of GDP is produced informally. Informal employment is more difficult to ascertain. Using the measure based on pension contributions, about 70% of the labor force is informal in the median country in Latin America.<sup>4</sup>

# **_II. The Cost of Informality_**

Informality is a distorted, second-best response of an excessively regulated economy to the shocks it faces and its potential for growth. It is a distorted response

> 3 The LAC countries under consideration are those included in any of the four regressions where informality is a dependent variable (Table 3).  They are 20 countries plus Chile, which functions as a comparator country, unless otherwise noted.  Trinidad and Tobago is also excluded since the World Bank classification (as of July 2007) considers the country as a high-income country.  See Appendix 1 for sample of countries in each regression.

> 4 Self-employment is arguably a lower bound for the measure of informal labor given that tax and regulation evasion occurs massively in all types of firms.

4
POVERTY AND INFRASTRUCTURE ACCESS IN SLUMS

# Piped water: tenancy and location matter, but poverty does not

Appendix tables 21 and 22 summarize the spatial variation in piped water coverage in the two cities. As with electricity, all divisions in both cities report at least some households with private piped water connections, indicating that the trunk infrastructure extends to all the divisions. The variation in coverage levels across divisions within Nairobi is higher (s.d.: 22 percent) than in Dakar (s.d.: 16 percent). For Nairobi, the minimum division level coverage is 8 percent (Embakasi) and the maximum level is 38 percent (Westlands). The corresponding figures for Dakar are 59 percent (Dalifort) and full coverage at 100 percent (Nolivet/Barak).

As with electricity, the access to piped water connections also differs significantly in the two cities. In Nairobi, we find that 75 percent (6 out of 8) of the divisions have at least one or more EAs that completely lack private piped water connections and about 31 percent (27 out of 88) of all EAs have no access. In stark contrast, none of the 10 divisions in Dakar have an EA that completely lacks access to piped water connections. So, for both electricity and piped water, we find that Nairobi exhibits a significantly lower access level than Dakar all through its slum settlements, and its spatial access is concentrated in about 70–75 percent of its slum settlements.

Appendix tables 23 and 24 present the results from the household level logistic regression results. In contrast to our findings for electricity, the results show (at 10 percent significance level or less) that poverty level does not play much of a role in a household’s access to a piped water connection in either city. The tenancy status matters in Dakar but not in Nairobi. Specifically, the likelihood of a tenant household having access to piped water is reduced to about half (46 percent) that of a nontenant household in Dakar. As with electricity, the results again show that, after controlling for demand side factors, residential location also matters in determining access to piped water. Overall, the results indicate that a household’s access to piped water is affected essentially only by supply side factors in Nairobi, but both tenancy status and supply side factors play a role in Dakar. Unlike the case of electricity, water connections are not influenced by a household’s poverty level in either city.

22
# **Sustainability** of Private Capital Flows to Developing Countries: Is **a** Generalized Reversal Likely?

In recent years there has been a sharp increase in private capital flows to developing countries. In fact, the capital account surplus-including net errors and omissions-for developing countries as a whole increased from US$25 billion in 1988 to US$163 billion in 1993, implying an annual growth rate of 46 percent. During the same time period, the current account deficit for developing countries as a group increased from about US$20 billion to US$98 billion, implying a growth rate of 37 percent a year (IMF 1994).

Yet the sharpest increase has occurred in private rather than in official capital inflows (table 1). Thus total long-term private flows-which include foreign direct investment, loans and bond issues, and equity portfolio investment-increased from US$36 billion in 1988 to about US$159 billion in 1993, a growth rate of 34 percent a year (World Bank 1994). At the same time, real official flows increased slightly-from US$45 billion in 1988 to US$54 billion in 1993 (about 4 percent a year).

The similarity between conditions in the early 1980s and those in 1994-an increase in interest rates in industrial countries after several years in which developing countries have received sizable capital inflows,' and the recent Mexican criSiS2 -has reopened the question of the sustainability of these inflows and the appropriate policy responses to an adverse external shock to avoid a reversal of flows. In other words, the recent Mexican experience highlights the risks of a generalized reversal of flows and opens the question of sustainability in a magnified way.

The issue of sustainability is particularly relevant because several developing countries remain highly indebted and are still completing the economic reforms necessitated by the debt crisis, which erupted more than a decade ago. Within this context a generalized reversal of flows, by requiring a sharp

- I Since the start of 1994 the U.S. Federal Reserve Board has raised short-term interest rates six times, from 3 percent to 6 percent. 2 Recall that the debt crisis was triggered when Mexico declared a moratorium in its external debt in 1982.

l
previously acquired in the same industry both increases the likelihood of founding a new business and contributes to a firm’s better performance.

Nichter and Goldmark (2009) noted an additional channel by which learning on the job may positively affect the survival rate of newborn firms: previous work experience may expand entrepreneurs’ social network, which, in turn, can positively affect post-entry performance (see also Barr 1998; Kantis, Angelli, and Koenig 2004). However, the authors stress the differences between DCs and advanced countries with regard to this link, and the evidence about DCs is quite controversial.<sup>12</sup> Finally, turning our attention to a managerial and organizational perspective, new founders who had previously been employed as top managers in the same sector and who have better access to relevant information are expected to exhibit better post-entry business performance due to their better ability to run and organize complex activities (for an empirical validation of these relationships, see Cooper, Gimeno-Gascon, and Woo 1994; Cressy 1996; Arrighetti and Vivarelli 1999; Shane 2001; Vivarelli 2004).

# _Financial Constraints_

Credit constraints and a lack of financial capital in general should limit the rate of entry of new businesses as well as their likelihood of survival and rate of growth (Becchetti and Trovato 2002; Carpenter and Petersen 2002; Aghion, Fally, and Scarpetta 2007). However, some recent microeconometric studies have shown that the role of credit rationing has been somewhat over-emphasized and that entrepreneurial saving plans may be able to overcome borrowing constraints (Cressy 1996, 2000; Parker 2000; Hurst and Lusardi 2004).<sup>13</sup> At any rate, new entrepreneurial initiatives in DCs are credit-rationed in the vast majority of cases due to a lack of collateral, informational asymmetries, and largely imperfect 15
**<u>Table 10: Maize, farm-level indicators</u>**

||**%**<br>**Foreign**|**Tax as %**<br>**DVA**|**Variable**<br>**Costs**<br>**(USD/ha)**|**Net Profit**<br>**(USD/ha)**|**Return to**<br>**Variable**<br>**Costs**|**Farm-gate**<br>**SV**<br>**(USD/MT)**|
|---|---|---|---|---|---|---|
|**Malawi (2007/08)**|||||||
|Rainfed|||||||
|FAM-OPV|44%|14%|116.29|70.67|0.61|158.35|
|FAM-low|54%|19%|201.43|<br>65.53|0.33|173.46|
|FAM-high|61%|28%|381.43|<br>70.38|0.18|183.85|
|Irrigated|||||||
|FAM-low|44%|20%|200.23|250.38|1.25|171.72|
|FAM-high|54%|25%|413.57|238.23|0.58|183.88|

58. Several other points are worth noting from the farm-level indicators as follows.

- Irrigation can result in a significant improvement in farmer incomes, but does not contribute to improved trade competitiveness as measured by farm gate SV. Tonfor ton, in fact, open pollinated varieties (OPV) maize is the lowest cost product and therefore most competitive.

- With irrigation, low-input maize provides slightly more net profit and a far better rate of return than with high input management. Similarly, without irrigation, the rates of return to variable costs deteriorate at each level of higher input management.

- Taken together, these poor results for supposedly “improved” management suggest that Malawi is already at the point of diminishing marginal returns to fertilizer and other yield enhancing technologies. The high cost of fertilizer contributes to this poor result and the analysis shows that efforts to reduce this cost are not only important for income and trade competitiveness, but also for achieving higher yields and domestic food security.

- At 44-61%, the foreign exchange content of maize is largely accounted for by the imported costs of fertilizer. Tax as a share of DVA increases with higher input management as a result of incremental fertilizer use and, in the case of irrigated maize, because of pumping and foreign costs associated with irrigation development.

59. The chart in Figure 13 shows the per hectare cost components in more detail. As shown, farm costs increase significantly at each level of improved management in which fertilizer accounts for the majority of total expenditure. Although yields also increase with higher management, the additional expenditure on fertilizer and other inputs is likely to be difficult for poor households to afford without financial support. In the case of irrigated maize, the value in the top part of the bar for irrigation costs cover pumping and water user association (WUA) fees only. Depreciation on the irrigation infrastructure is assumed to be paid for by government and/or a donor funded projects and are not included as part of these financial calculations.

25
outcomes. For example, small rewards in the short‐term may be more appealing than large rewards in the long‐term depending on the conditions under which we make choices (McClure et al., 2004 and 2007).

The EU model and the introduction of uncertainty is evidently a good starting point to model forced displacement. Decision makers (refugees and IDPs for example) experience a shock (conflict, violence) and suddenly face sets of uncertain events that will lead to alternative outcomes in terms of personal utility. Prospect theory is also important in this respect because it introduces the notion of the weight function. Given the short‐term nature of the decision to be taken on whether to stay or move during conflict, people may behave very differently as compared to normal situations and attribute very different weights to these probabilities as also shown by recent advances in neuroeconomics and behavioral economics. Therefore, while the specificities of a forced displacement model based on expected utility theory have yet to be designed, the fundamental framework is in place and it will be a question of tweaking this framework to identify the utility maximization process that best describes agents taking decisions during a conflict.

# Modeling choice

If utility theory applies to forced displaced populations, we should expect choice models that derive from this utility theory to be suitable to study forced displacement situations. This reasoning reflects indeed the economics behind migration studies. The economics literature on migration originates from the labor economics literature on job seeking and wage differentials. Essentially and similarly to people seeking or changing job, migrants choose between moving and not moving and move if the difference between income prospects in the origin and destination areas (net of the moving costs) are positive (Harris and Todaro, 1970, Borjas, 2014). The natural step forward would be to use the economics of migration literature for the study of forced migration. In doing so, however, we are hampered by some of the specificities related to forced displacement situations.

In labor or migration studies, the choice set is between opportunities across occupations or geographic areas and the pay‐offs are largely known to the decision maker. In such contexts, one can use job search models borrowed from the labor economics literature, an approach that has also been used for refugees in the past (Damm, A.P. and Rosholm, M., 2010). However, forced migrants are “forced” to leave the place of origin because of conflict, violence or more generally a life‐threatening hazard. In economics terms, their choice set is more limited than the choice set of an economic migrant. Some people may just have to run and leave everything behind, while some other people under the same circumstances may opt to stay and face the hazard. Therefore, it is correct to model forced migration in a choice framework but the 12