rafmacalaba/datause-encoder-data huggingface dataset:
# **5. Loss of State Revenues Linked to Informal Trade and Customs Responses**

## **Loss of State Revenues Linked to Informal Trade**

**The loss of public revenues is significant. We estimate it to be at least around 1.2 billion TND**<sup>**38**</sup> **(of which around 500 TND for customs duties or more than one-sixth of total customs duties).**<sup>39</sup>

**Although the situation is improving for the local customs authorities on the Libyan border because of the introduction of the flat-rate TND 50 tax, the situation remains volatile, and the balance of power between the various actors has shifted.** A number of concerns have been raised about the potential response—in particular the likely reaction of the people of Ben Gardane—to efforts to control trade flows more tightly.

**The flat-rate of TND 50 TND seems particularly problematic insofar as it does not generate revenue but more worryingly allows goods and smugglers to enter virtually without controls on the Tunisian territory and could thus allow the entry of illegal goods such as weapons and drugs.** This could also partially explain why informal traders have a positive assessment of customs in Ras Ajdir.

**We estimate that the tax levied on each commercial vehicle crossing the border with Libya has raised around TND 3 million. However, this is a tiny fraction (or 2%) of the estimated profits earned by traders.**

## **Customs Responses Against Informal Trade**

Based on field surveys, the most worrying trend seems to be the current situation at the Libyan border with a certain _laissez-faire_ from controlling agencies at the local level.

Despite this, an anti-smuggling plan was announced in July 2013 to address the problem. However, as often happens in such cases, the emphasis is on equipment while it seems that internal controls are weaker and weaker. Leaders (2013) interviewed the former Director of Customs, who had explained that the fight against smuggling will be strengthened with a growing number of vehicles, electronic surveillance, replacing the scanner at Ras Ajdir and rehabilitate buildings at border-posts.

Despite an increase of seizures in 2013, they seem to account for less than 5% of the total estimates. Indeed, in 2012, the customs official seized for 48 million TND (Leaders 2013.2).

# **6. Conclusion**

In this study, we looked at informal trade between Tunisia and Libya and between Tunisia and Algeria. Our aim was to assess the scale of this trade and to try to evaluate the amount lost in taxes and duties as a result as well as to assess the local impact in terms of income generation.

> 38 According to a leader of trade unions in the public company manufacturing cigarettes, 500 million TND of public revenues are lost because of informal imports of cigarettes (or 40% of revenues collected from cigarettes).

> 39 The loss of revenue is calculated using the lowest rate of tax burden on imports for this category of goods, or 72% of its value and 30% for customs duties.

27
Iversen, T. and Rosenbluth, F. (2008). Work and Power: The Connection between Female Labor Force Participation and Female Political Representation. _Annual Review of Political Science_ , Vol. 11, No. 1, pp. 479-495.

Jaramillo-Echeverri, J. (2023) Fertility, education, and social mobility in 20th century Colombia. PhD thesis, London School of Economics and Political Science.

Jaramillo-Echeverri, J.; Meisel-Roca, A.; and Ramírez-Giraldo, M.T. (2019). More than one hundred years of improvements in living standards: The case of Colombia, _Cliometrica_ , vol. 13, No. 3, pp. 323-366.

Junaid, N., Sultana, N., Jabeen, S., and Ali, J. (2019). “Determinants of Female Labour Force Participation Rate in Pakistan”, _Dialogue_ , Vol. 14, No. 2, pp. 218-228.

Knight, B. G., and Ponce de Leon, M.M., and Tribin, A. (2021). "Crime and Gender Segregation: Evidence from the Bogota "Pico y Genero" Lockdown," _NBER Working Papers 28686_ , National Bureau of Economic Research, Inc.

Koch, I. (2013). _Analysis of Multivariate and High-Dimensional Data_ (Cambridge Series in Statistical and Probabilistic Mathematics). Cambridge: Cambridge University Press.

Lanza, S.T. and Linden-Carmichael, A.N. (2021). _A Conceptual Introduction to Time-Varying Effect Modeling. In: Time-Varying Effect Modeling for the Behavioral, Social, and Health Sciences_ . Springer, Cham. https://doi.org/10.1007/978-3-030-70944-0_1 López de Rodríguez, C., and León de Leal, M. (1977). “El trabajo de la mujer”. In M. León de Leal (Research director), _La mujer y el desarrollo en Colombia_ , Asociación Colombiana para el Estudio de la Población (ACEP), Bogotá.

López Castaño, H. (2001). Características y determinantes de la oferta laboral colombiana y su relación con la dinámica del desempleo. Consideraciones teóricas y de política. In Urrutia Montoya, M, (ed.) _Empleo y Economía, Memorias del Seminario_ , Banco de la República, Bogotá.

Luna, L.G. and Villarreal, N. (1994). _Historia, género y política: Movimiento de mujeres y participación política en Colombia 1930-1991_ . Edición del Seminario Interdisciplinar Mujeres y Sociedad, Barcelona. Taken from: http://www.ub.edu/SIMS/libros4.html. Accessed on 4 May 2020.

Magnac, T. (1992). Female labor market participation and wages in Colombia. In: G. Psacharopoulos and Z. Tzannatos (Editors), Case studies on women's employment and pay in Latin America, The World Bank, Waskington, D.C, pp. 169-195.

Mammen, K., and Paxson, C. (2000). “Women’s Work and Economic Development” _Journal of Economic Perspectives,_ Vol. 14, No. 4, pp. 141-164.

32
Note that we write the specification in equation (3) as a nested model, whereby CASH = 1 for predicted beneficiary households in any of the treatment groups, and BCC=1 for predicted beneficiary households in the treatment group assigned to cash transfer and BCC. This specification implies that the estimates of γ in equation (1) and of β in equation (3) both capture the value-added of the behavioral change component. The consistency of estimates for the two specifications provides an indication of the robustness of the results. We also report the p-value from a F-test for the significance of the overall treatment effect ( _α + β = 0_ ) in villages assigned to cash transfers and behavioral change promotion.

Importantly, estimates from specifications (1) and (2) provide precise and unbiased estimates of average treatment effects (ATE) of the value-added of the BCC intervention for cash transfer beneficiaries, respectively of spillovers among cash transfers non-beneficiaries. In contrast, estimates from specification (3) provide intent-to-treat (ITT) estimates for the relative effect of the behavioral change and cash transfer interventions on predicted beneficiary households. As such, estimates from specification (1) are more precise than estimates from specification (3).

# <u>4.3 Baseline Balance</u>

Table 2 documents baseline balance for households surveyed at follow-up. Panel A considers all baseline households surveyed at follow-up. Panel B focuses on actual beneficiaries, and Panel C on households predicted to be beneficiaries when applying the PMT targeting formula on baseline data. Balance is documented for variables from the household survey (top panel, first for household-level variables, then for individual-level variables), as well as variables from the child survey (bottom panel). Results show that the randomization achieved balance between control and treatment groups in the full sample and in the sample of predicted beneficiaries, as well as between actual beneficiaries in the two treatment groups. Observed differences are not more frequent than would be expected, and generally either weakly significant or of small magnitude (less than 10% of the mean in the control group). One meaningful exception is that children aged 0-4 and 5-14 are more likely to be reported sick in the month prior to the baseline survey in the control group. We return to this when discussing the interpretation of the results. Overall, baseline balance indicates that the design was well-implemented. Program impacts can thus be identified at follow-up through single differences in outcome variables across the various treatment and control groups based on the specifications discussed in Section 4.2.

16
<!-- Start of picture text --> Max out of sample MCC Number of plots to reach 90% of max MCC<br>0.34 6000<br>0.32<br>oO83o 0.280.30 ra 400022{e) 5000<br>© 0.26 o<br>“a a<br>© 0.24 5€ 3000<br>S2<br>°<br>020.20 ce 200<br>O18 r 1000<br>corner corner<br>Geolocation strategy Geolocation strategy<br><!-- End of picture text -->
Figure A12: Specification Charts for Longest Dry Spell <!-- Start of picture text --> Ethiopia Malawi<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.04 .06<br>.04<br>.02 .02<br>0 0-.02<br>-.02 -.04<br>* Weather Product * * Weather Product *<br>TAMSAT -.04 TAMSAT<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br>Niger Nigeria<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.1 .1<br>.05 .05<br>0<br>0<br>-.05<br>* Weather Product * -.1 * Weather Product * -.05<br>TAMSAT TAMSAT -.1<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br>Tanzania Uganda<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.03 .04<br>.02 .02<br>.01 0<br>0 -.02<br>-.01 -.04<br>* Weather Product * -.02 * Weather Product * -.06<br>TAMSAT TAMSAT<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br><!-- End of picture text --> _Note_ : The figure presents specification curves, where each panel represents a different country, with three different models presented within each panel. Each panel includes 36 regressions, where each column represents a single regression. Significant and non-significant coefficients are designated at the top of the figure. For each Earth observation product, we also designate the significance and sign of the coefficient with color: red represents coefficients which are negative and significant; white represents insignificant coefficients, regardless of sign; and blue represents coefficients which are positive and significant.

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end or streamline tax subsidies or to broaden the tax base.<sup>3</sup> Institutions remain weak and serious governance issues have occasionally threatened to undermine donors’ confidence (Figure 3).<sup>4</sup>

**The country’s vulnerability to external shocks further complicates economic and fiscal management in the fragile post-conflict environment.** Wide fluctuations in domestic food production—linked to climate change—leave the country vulnerable to volatility in the prices of international food crops. Although subsistence agriculture engages the vast majority of the population, domestic production is insufficient to meet the country’s food needs, and it relies on food imports to cover the deficit. Reliance on a few commodity exports—mainly coffee and tea—further exposes the country to volatility in international commodity prices. Finally, the country’s heavy dependence on foreign aid—the second highest in the region relative to GDP— also negatively affects the policy continuity, with adverse consequence for investment and growth (Figure 4).

**Figure 3. Selected CPIA Scores in 2012**

<!-- Start of picture text --> Country: Burundi Peer: Rwanda Group: Low income AFR<br>Monetary & Exch. Rate<br>Pol.<br>6<br>Overall rating 5 Fiscal Policy<br>4<br>3<br>2<br>Transpar., Account. & Debt Policy & Mgt.<br>1<br>Corrup.in Pub. Sec. Ave<br>0<br>Quality of Public Business Regulatory<br>Admin. Environ.<br>Quality of Budget. & Property Rights &<br>Finan. Mgt. Rule-based Govern.<br><!-- End of picture text -->

**_S_** _ource_ : World Bank.

> 3 More discussions on these topics below.

> 4 For example, an illegal sale of public assets in 2006 which resulted in millions of public revenue losses, and a illegal and fraudulent budget payment to a private company in 2007 which resulted in the suspension of the IMF PRGF program and delays in budget support from other donors (IMF 2011).

4
_Table 21: Short-term measures to cope with floods_

||<br>|**BPL**<br>|**P**<br>|**oor**<br>|**No**<br>|**n-poor**<br>|**Overall**<br>**Range**<br>**(LKR)**|
|---|---|---|---|---|---|---|---|
||%<br>HHs<br>|Average<br>Cost<br>(LKR)<br>(Figures<br>|%<br>HHs<br>in bra<br>|Average<br>Cost<br>(LKR)<br>cket as % of<br>|%<br>HHs<br>month<br>|Average<br>Cost<br>(LKR)<br>ly househo<br>|ld income)<br>|
|Cleaning house<br>surroundings|73.5|1500<br>_(15.0)_|78.6|5000<br>_(12.5)_|79.1|7500<br>_(9.4)_|500-<br>20,000|
|Cleaning canal or gutter|41.2|500<br>_(5.0)_|39.9|5000<br>_(12.5)_|32.6|5000<br>_(6.3)_|100-<br>10,000|
|Repairing roof|8.8|250<br>_(2.5)_|15.6|10000<br>_(25.0)_|11.6|2500<br>_(3.1)_|500-<br>15,000|
|Overhauling vehicle|-|-|7.5|5000<br>_(12.5)_|9.3|5000<br>_(6.3)_|500-<br>20,000|
|Repairs inside house|35.3|1000<br>_(10.0)_|26.6|10000<br>_(25.0)_|7.0|2500<br>_(3.1)_|500-<br>30,000|

The short-term measures listed above are typically undertaken before the rainy season each year to cope with anticipated flooding. The spending of the three categories of households on short-term measures provides very interesting insights into how the costs of these measures might put a burden on their resources. For the BPL households, the overall costs of the short-term measures are lower compared to other households. But as a percentage of their average monthly incomes, these costs are quite significant. These costs are also to be borne every year by them before the rainy season begins. Another important issue is the large percentage of BPL families who have to spend money on these measures every year. For instance, 35% of them have to repair their houses every year, since the overall housing condition may be poor. However, for the non-poor households, the costs incurred on the measures are a small percentage of their monthly incomes. They also do not have to spend large amounts on repairs inside the houses, as their houses are in better condition than those of their poor counterparts. Further, only 7% have reported repairing their houses to cope with floods.

The poor households bear the largest burden of short-term measures to cope with recurrent floods. They spend relatively larger percentages of their average monthly incomes on these measures. The money spent on measures such as repairs inside houses and repairing roofs also seem to be much higher than either the BPL families or non-poor houses. This is not surprising since they would have bigger houses than the BPL families but not in as good condition as the houses of the non-poor.

Besides cleaning and repairs, households stock up on essential items in anticipation of floods every monsoon as seen in Figure 9 below. The most important items to be stocked up are groceries, followed by drinking water and medicines. Many families also stock up on cooking fuel during monsoon.

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18

|**Independent variable**|**Guatemala**|**India**|**Indonesia**|**Kenya **|**Pakistan**|**Sri Lanka**|
|---|---|---|---|---|---|---|
|Constant|–4.68|–16|–12|–7.2|**4.4**|–16|
||(0.34)|(0.21)|(2.7)|(1.3)|**(2.4)**|(0.57)|
||**Consump**|**tion equat**|**ion (regressi**|**on)**|||
|Log of household|0.083|0.19|0.23|0.16|0.77|0.26|
|expenditure|(0.016)|(0.008)|(0.056)|(0.078)|(0.072)|(0.028)|
|<br>Log of LPG price|<br>–0.41|<br>–0.96|<br>–0.67|<br>–0.63|<br>–1.3|<br>–0.91|
||(0.12)|(0.033)|(0.089)|(0.11)|(0.37)|(0.12)|
|Log of firewood price|<br>0.053|<br>0.12|<br>NA|<br>—|<br>—|<br>0.074|
||(0.015)|(0.009)||||(0.023)|
|Log of kerosene price<sup>a</sup>|<br>—|<br>0.46<br>(0.043)|—|—|0.91<br>(0.28)|<br>—|
|Log of kerosene price, other<sup>d</sup>|NA|<br>0.073|NA|NA|<br>NA|NA|
|||(0.025)|||||
|Dummy for house ownership|0.11<br>(0.022)|<br>—|—|—|0.19<br>(0.084)|—|
|Dummy for car ownership|<br>—|0.035<br>(0.010)|—|—|<br>0.23<br>(0.085)|0.064<br>(0.025)|
|Household size|—|0.070|0.048|—|–0.036|0.040|
|||(0.003)|(0.013)||(0.007)|(0.006)|
|Square of household size|—|<br>–0.002<br>(0.0002)|<br>–0.0008<br>(0.0002)|—||<br>—|
|Dummy for engaging in|–0.067|<br>–0.21|<br>—|—|–0.14|–0.22|
|agriculture|(0.030)|(0.009)|||(0.052)|(0.025)|
|Highest level of education for<br>men|—|0.017<br>(0.002)|—|—|—|0.014<br>(0.004)|
|Highest level of education for|0.011|<br>0.003|—|—|0.051|<br>—|
|<br>women|(0.002)|(0.001)|||(0.008)||
|Age of household head|<br>—|<br>0.0004<br>(0.0002)|—|—|<br>—|0.002<br>(0.001)|
|Dummy for male household|—|0.046|—|—|—|—|
|head||(0.009)|||||
|Dummy for urban residence|0.073|0.14|—|—|0.26|0.23|
||(0.027)|(0.007)|||(0.071)|(0.020)|
|Dummy for electricity|<br>—|<br>—|—|—|<br>0.18|<br>0.38|
|<br>connection|||||(0.070)|(0.14)|
|Constant|2.4|1.7|4.5|3.6|<br>–4.4|<br>2.2|
||(0.27)|(0.17)|(0.88)|(0.89)|(1.4)|(0.64)|
||**Inter**|**-equation**|**correlation**||||
|Rho|0.29|–0.17|**–0.12**|**–0.25**|0.37|**0.005**|
||(0.068)|(0.017)|**(0.11)**|**(0.16)**|(0.16)|**(0.057)**|

# _Source_ : Authors’ calculations

_Notes_ : — = statistically insignificant at 5 percent or of the wrong sign; NA = variable not available in the survey. Coefficients are followed by standard errors in parentheses. Numbers in bold indicate that the variables are not significantly different from zero using a 5-percent test.

a. For India, the price of subsidized and rationed kerosene distributed through the Public Distribution System.

b. Number of bedrooms for Sri Lanka, floor area for Indonesia.

c. This variable is not available in Pakistan and is replaced by an alternative dummy that is 1 if the household owned, leased, or rented agricultural land.

d. Price of unsubsidized kerosene in India.
We have two sources of data for aggregate factor incomes: data from National Accounts and data from the HBS. In our Russian data, capital‘s share of factor income is much larger in the National Account data than in the HBS (see table 3.1). This is typical. Ivanic [2004] mapped income from the Living Standards Measurement Surveys (LSMS) in 14 countries into factor shares and compared factor shares with the input-output tables in these countries. Capital‘s average share from the LSMS surveys was 21% of household income, but it was 52% of household income based on National Account information (based on the ―GTAP‖ data set).<sup>43</sup> We must produce a balanced Social Accounting Matrix in order to implement our integrated model, which means we must reconcile those differences. There are biases in both the collection of National Account and Household Survey data so that neither source is clearly correct. A key problem with the factor share data from the national accounts is that capital‘s share is calculated residually in the input-output tables. Then in sectors where labor payments are underreported, as in agriculture (where sole proprietors do not report their labor income and temporary workers are often informal workers) and services, the share of capital is biased up. Unprofitable sectors that receive state subsidies will be reported as labor intensive, despite the fact that in developing countries these are typically the capital intensive sectors. Harrison, Rutherford and Tarr (2003) have shown that this bias can lead to perverse reporting of which sectors are labor intensive in developing countries. On the other hand, income estimates from LSMS surveys are known to be less than income estimates from National Accounts. Deaton (2003) explains that one of the most likely explanations of the difference is that households fail to respond to the survey, and that the probability of non-response plausibly

> 43 Household income (net of taxes and transfers) in Russia exceeds household consumption for almost all households. Part of the reason is savings for investment. But in the case of Russia, an important part of the reason is that Russia has a large current account surplus. Consistency between the macro balances and the household data in construction of the Social Accounting Matrix implies that household factor income must be larger than household consumption for most households to allow for the transfer of capital to foreigners. It follows that the change in factor income as a percent of consumption will be larger than the change in factor income as a percent of household income.

67
_Figure 1. Year of kes source considered_ <!-- Start of picture text --> 120<br>110<br>100<br>90<br>20<br>70<br>00<br>6 0<br>20- 00 -<br>10<br>0<br>N.A. Before 1970 1970-79 1980-84 15955 and later<br>Year of latest emp!rIcaIcl data<br>IMR L LIfe expectancy<br><!-- End of picture text --> Adequacy of mortality estimates also depends on what they are being used for. Estimates of the IMR or life expectancy from a few years back often are suitable for an approximate picture of health conditions in a given country. Indirect estimates of the average IMR for the five years preceding a survey may be valuable input in a population projection. lb document short-term fluctuations, and even more so to link them to a changing socioeconomic environment, requires far greater detail than can often be obtained from surveys or censuses.

8
- [26] Fallon, P. R., & Layard, P. R. G. (1975). Capital ~~-s~~ kill complementarity, income distribution, and output accounting. Journal of Political Economy, 83(2), 279 ~~-3~~ 02.

- [27] Fitzenberger, B., & Kohn, K. (2006). Skill wage premia, employment, and cohort effects: are workers in Germany all of the same type?. University of Freiburg Working Paper.Freeman, R. (1976). The overeducated american. Academic Press, New York.

- [28] Freeman, R. B. (1979). The Effect of Demographic Factors on Age ~~-E~~ arnings Profiles. Journal of Human Resources, 14(3), 289 ~~-3~~ 18.

- [29] Gagliardi, L., & Lemos, S. (2016). Evidence on immigrants assimilation into recipi ~~-~~ ent labour markets using UK longitudinal data between 1981 and 2006. Journal of Economic Geography, 16(3), 547583.

- [30] Glitz, A., & Wissmann, D. (2016). Skill Premiums and the Supply of Young Workers in Germany. Working Paper, Humboldt University Berlin.

- [31] Goldin, C. and Katz, L. (2008). The Race Between Education and Technology, Cam ~~-~~ bridge, MA: Harvard University Press.

- [32] Hendricks, L. (2004). A Database of Mincerian Earnings Regressions. Available at http: //www.lhendricks.org/Mincer.htm.

- [33] Katz, L. F., & Murphy, K. M. (1992). Changes in relative wages, 1963 ~~-1~~ 987: supply and demand factors. Quarterly Journal of Economics, 107(1), 35 ~~-7~~ 8.

- [34] Lee, D. S. (1999). Wage inequality in the United States during the 1980s: Rising dispersion or falling minimum wage? Quarterly Journal of Economics, 114(3), 977 ~~-~~ 1023.

- [35] Lee, R. D. (2014). Macroeconomic consequences of population aging in the United States: Overview of a national academy report. American Economic Review, 104(5), 234 ~~-2~~ 39.

- [36] Leers, T., Meijdam, L., & Verbon, H. A. (2004). Ageing, migration and endogenous public pensions. Journal of Public Economics, 88(1), 131 ~~-~~ 159.

- [37] Lemieux, T. (2008). The changing nature of wage inequality. Journal of Population Economics, 21(1), 21 ~~-4~~ 8.

- [38] Lucas, R.E. (1988). On the Mechanics of Economic Development. Journal of Mone ~~-~~ tary Economics, 22, 342.

- [39] Mahlberg, B., Freund, I., Cuaresma, J. C., & Prskawetz, A. (2013). Ageing, produc ~~-~~ tivity and wages in Austria. Labour Economics, 22, 5 ~~-~~ 15.

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