rafmacalaba/datause-encoder-data huggingface dataset:
# Determinants of Expatriate Workers' Remittances in North Africa and Europe

## 1. INTRODUCTION

The increase in the volume of international migration over the recent decades has led to an unprecedented increase in financial flows to laborexporting countries. Indeed, the flows of workers' remittances increased more than fivefold in real terms during the 1970s and 1980s--from around USS 5.6 billion in 1969 to USS 33.8 billion in 1990.1 The magnitude of these flows has revived the well-known debate on external transfers in the economic development literature. (e.g. Bhagwati (1977), Bhagwati and Brecher (1982), de Macedo (1977) and Pereira (1983)). Migration and remittances have also become major policy issues in the agendas of both labor-sending and labor-receiving countries, as well as multilateral organizations.

From the perspective of labor-sending countries, there is a wide range of important issues related to external migration and remittances. These include the determinants of remittances, the channels through which these remittances flow, and the impact of remittances on the sending country. While international remittances relax foreign exchange constraints and enhance the potential for capital accumulation and growth (see Solimano (1990)), they may also undermine the competitiveness of export industries, thus hurting the labor sending countries in other directions (the Dutch disease effect). Furthermore, the impact of remittances on labor-sending countries may also include lower morale and productivity in the labor force left behind, or a conspicuous consumption behavior in the society.

1 IMF figures of labor-related remittances for 38 labor-exporting countries.
Our results have important implications for policy. While reducing bureaucratic delays and improving ports and customs may have small positive effects on trade, the binding constraint in most African countries to expanding exports is inland transit. Improving inland transit is unlikely to be easy or cheap, but it is likely to boost exports and have broad positive economic effects.

Beyond these direct implications for policy, our results also contribute to the broader debate about the influence of geography versus institutions on income. This literature has focused on the effects of climate versus governance on income, and potential interactions between the two.<sup>2</sup> We focus on a single component of income, exports, and our variables of interest reflect geography and institutions to different extents. The dominance of transit time in hindering exports seems to suggest that geography is the main culprit. To test this, we gather data from a GPS system on geographical distance from the port to the economic center and the estimated time of travel and include them in the regression equation. The difference between travel time in the GPS data and the Doing Business data is that the former are based solely on travel distance and estimated speed of travel by type of road (paved or unpaved). These data do not incorporate delays due to average vehicles, borders, security, traffic, or other road conditions. We find that GPS distance negatively affects exports, but GPS travel time does not. Moreover, neither the economic effect nor the statistical significance of the _Doing Business_ inland transit time variable changes when these variables are included. This suggests that the problem for inland transit lies in the quality and security of the roads, border delays and the efficiency of security checkpoints, the age of the truck fleet and competition in trucking. These are factors which are more closely linked with institutions than geography.

The paper proceeds as follows. The next section discusses the data. Section III presents the estimation strategy. Section IV describes the main results and robustness checks. Section V examines the effect of uncertainty on exports and the importance of geography. Section VI concludes.

# **II. Data**

We use data on trade times based on answers to a comprehensive World Bank questionnaire completed by trade facilitators at freight-forwarding companies in 146

> 2 See, for example, Hall and Jones (1999), Acemoglu, Johnson, and Robinson (2000), and McArthur and Sachs (2001).

4
11 may change over time. In the 19 African focus countries, nontradable products account over time for between 40 and 55 percent of the gross value of production of all covered agricultural products (last column of Table 1).

The range of policy measures included in the NRA estimates in the Distortions to Agricultural Incentives database is wide. By calculating domestic-toborder price ratios, the estimates include assistance provided by all tariff and nontariff trade measures, plus any domestic price support measures (positive or negative), plus an adjustment for the output-price equivalent of direct interventions on inputs. Where multiple exchange rates operate, an estimate of the import or export tax equivalents of that distortion are included as well. The range of measures included in the CTE estimates include both domestic consumer taxes and subsidies and trade and exchange rate policies, all of which drive a wedge between the price that consumers pay for each commodity and the international price at the border.

Anderson and Masters (2009) note several patterns that emerge in the distortions to agricultural incentives in the 21 focus countries. In the 1960s and 1970s, many African governments had macroeconomic, sector and trade policies that increasingly favored the urban sector at the expense of farm households and favored production of import-competing farm goods at the expense of exportables. The policy regime was characterized as pro-urban (anti-agricultural) and pro-selfsufficiency (anti-agricultural trade). Since the 1980s, Africa has reduced its antiagricultural and anti-trade biases, but many distortions still remain.

For the 19 countries in this study, Table 1 and Figure 1 illustrate those patterns. The weighted average NRA for the 19 countries is almost always below zero, indicating that together agricultural price, trade and exchange rate policies have reduced the earnings of farmers in these countries. The average rate of direct taxation (negative NRA) of African farmers rose until the late 1970s before declining by more than half over the next 25 years. Meanwhile, assistance to nonagricultural sectors rose (thereby making farming less attractive in relative terms) and then declined slower than for agriculture, as reflected in the Relative Rate of Assistance (RRA) estimates in Table 1.

Table 2 reports the country-level NRAs for covered products for each of the 19 countries in this sample. It reveals the considerable diversity within the sample. In some countries — such as Cameroon, Ghana, Senegal, Uganda, Tanzania, and Madagascar — there was a reduction in taxing farmers since the regional peak in
- 13 -

<u>Table 2. Sample Distribution by Country</u>

||Number of<br>projects|Number of<br>contracts|Number of<br>bids|
|---|---|---|---|
|Albania|1|1|4|
|Azerbaijan|1|2|5|
|China|7|15|43|
|Egypt|1|2|8|
|India|1|1|3|
|Indonesia|1|3|3|
|Kenya|1|3|14|
|Malaysia|2|2|2|
|Nigeria|1|2|11|
|Sri Lanka|1|1|3|
|Tanzania|1|4|13|
|Uganda|1|2|3|
|Viet Nam|2|6|19|
|Total|21|44|131|

Source: Author’s calculation.

As shown in Table 3, the sample contracts are also considerably heterogeneous in technical terms. The average installed generation capacity is 125 MW with a maximum of 1,200 MW. If a contract is irrelevant to generator erection, then the capacity is set at a very small but nonnegative number (to avoid logarithms of zero). The length of transmission lines is calculated by adding all installed lines, regardless of transmission capacity (kV). The length is about 50 km on average but ranges from several to 760 km. The capacity of substation equipment (including transformer, switchgear and control equipment with low voltage supply equipment) also varies widely between 33 kV and 500 kV. About half auctions adopted the substantive technical evaluation process, such as the two-envelope procedure. The average number of participating bidders is 5.5, but the distribution is very skewed (as seen in Figure 1).<sup>15</sup> About two-thirds are associated with civil works, rather than equipment delivery. In terms of engineering cost estimates some of the contracts are valued at less than one million dollars, but others exceeded 400 million dollars.

> 15 This average is simply calculated in the sample including both winning and losing bids. On an auction basis, the average number of bidder is 4.7 and the median is 4.
||(0.010)|(0.011)|(0.013)|(0.014)|(0.009)|
|---|---|---|---|---|---|
|Muslim|-0.058***|-0.069***|-0.086***|-0.087***|-0.082***|
||(0.010)|(0.010)|(0.014)|(0.012)|(0.011)|
|Other|0.021|0.013|0.004|0.022|0.039**|
||(0.015)|(0.017)|(0.013)|(0.015)|(0.018)|
|Age|0.020***|0.026***|0.027***|0.016***|0.014***|
||(0.004)|(0.004)|(0.005)|(0.005)|(0.004)|
|Age<sup>2</sup>|-0.0003***|-0.0003***|-0.0004***|-0.0002***|-0.0002***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|||||Contin|ues on next page.|

# **Table 2 continued**

|**Pr(Labor force)**|**1987 **|**1999**|**2004 **|**2009**|**2011**|
|---|---|---|---|---|---|
|Children 0–4|-0.022***|-0.018***|-0.024***|-0.036***|-0.039***|
||(0.004)|(0.004)|(0.005)|(0.007)|(0.008)|
|Children 5–14|-0.005**|-0.001|0.008**|-0.003|0.007*|
||(0.003)|(0.002)|(0.004)|(0.004)|(0.004)|
|_District male employmen_<br>|_t shares (Ref. =_<br>|_construction):_<br>||||
|Agriculture|0.255*|0.171*|0.165|-0.008|0.220**|
||(0.132)|(0.097)|(0.116)|(0.108)|(0.096)|
|Manufacturing|0.073|-0.097|-0.039|0.155*|0.268***|
||(0.118)|(0.087)|(0.099)|(0.091)|(0.077)|
|Services|-0.048|-0.090|-0.015|0.120|0.014|
||(0.121)|(0.084)|(0.093)|(0.088)|(0.073)|
|White-collar serv.|-0.036|-0.022|-0.086|0.170|0.052|
||(0.124)|(0.099)|(0.120)|(0.106)|(0.100)|
|District grad. share|0.004|-0.087|0.017|-0.402***|-0.051|
||(0.111)|(0.083)|(0.105)|(0.091)|(0.082)|
|N|29031|32541|29513|27198|27306|
|Pseudo R2|0.145|0.126|0.125|0.114|0.097|
|FLFP rate|0.185|0.179|0.203|0.174|0.179|

_Note:_ Married women age 25–54. All estimations include state fixed effects. District-clustered standard errors in parentheses, *** _p_ < .01, ** _p_ < .05, * _p_ < .10.

Looking at the social group variables, we find that women in SCST households are most likely to work, but the gap between SCST and non-SCST Hindus declined.<sup>17</sup> The impact of religion

> 17 From 1999 onwards, the NSS data also distinguish Other Backward Class (OBC) households. Including a separate category for OBC—rather than including them in the reference non-SCST group—we find that OBC women have higher FLFP than high caste Hindus in 1999 and 2004, but there is no difference in 2009 and 2011. The estimated gap between SCST and high caste Hindus is somewhat larger but still declines over time.

22
5

**_<u>Fig. 1: Asset growth of the Deposit Insurance Fund</u>_**

<!-- Start of picture text --> 18,000<br>16,600<br>16,000<br>14,000<br>12,000<br>10,000<br>8,000<br>6,000<br>4,623<br>4,000 3,595<br>2,000<br>2,000<br>0<br>Jul-04 Oct-04 Jan-05 Jan-06<br>mln RuR<br><!-- End of picture text --> <!-- Start of picture text --> Jul-04<br><!-- End of picture text --> <!-- Start of picture text --> Oct-04<br><!-- End of picture text --> _Source: DIA_

**_<u>Fig. 2: Evolution of RUR and FX deposits since 2000</u>_**

<!-- Start of picture text --> In mln. RUR<br>1,600,000<br>1,200,000<br>800,000<br>400,000<br>0<br>2000 2001 2002 2003 2004 2005<br>Deposits of individuals In Roubles Deposits of individuals In foreign currency<br>Deposits of non-financial companies In Roubles Deposits of non-financial companies In foreign currency<br><!-- End of picture text --> _Source: DIA_ Private deposits have grown strongly since the introduction of the DIS, but interestingly, at a lower rate than in previous years (see Fig. 2). Individual retail deposits grew faster in the regions outside Moscow, especially in the Urals, Volga, and the Russian Far East. Most of this growth seems to be driven by deposits collected by branches of credit institutions headquartered in Moscow, but it also reflects the emergence of healthy and dynamic regional banks. Since the introduction of the DIS, ruble deposits grew faster than deposits in foreign currency, coinciding with the rapid real appreciation of the ruble vis-à-vis foreign currencies. With the notable exception of foreign banks, most of the
Table 4: The heterogeneous effects of input-tariff liberalization on firms’ decision to import capital goods

|Dependent v|ariable: dummy equal to on|e if the frm_i_imports capita|l goods in_t_.||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Input tariff(s)(t-1)|-0.201*||||
||(0.102)||||
|Input tariff(s)(t-1)_×_Low initial TFP||-0.111|-0.145|-0.149|
|||(0.111)|(0.105)|(0.104)|
|Input tariff(s)(t-1)_×_Medium initial TFP||-0.315***|-0.345***|-0.346***|
|||(0.110)|(0.104)|(0.103)|
|Input tariff(s)(t-1)_×_High initial TFP||-0.043|-0.067|-0.058|
|||(0.164)|(0.146)|(0.151)|
|Capital goods tariff(s)(t-1)|-0.181*|-0.150|-0.170*|-0.176*|
||(0.095)|(0.108)|(0.096)|(0.095)|
|Output tariff(s)(t-1)|-0.040|-0.055|-0.041|-0.033|
||(0.047)|(0.052)|(0.048)|(0.051)|
|Imported inputs_>_0|0.344***||0.344***|0.344***|
||(0.029)||(0.029)|(0.029)|
|Age(t-1)|0.022|||0.031|
||(0.057)|||(0.061)|
|Capital intensity(t-1)|0.018|||0.019|
||(0.012)|||(0.012)|
|Herfndhal index(s)(t-1)|0.004|||0.013|
||(0.067)|||(0.069)|
|Firm fxed effects|Yes|Yes|Yes|Yes|
|Year fxed effects|Yes|Yes|Yes|Yes|
|Observations|7,861|7,861|7,861|7,861|
|R-squared|0.087|0.038|0.088|0.088|

_Notes_ : The dependent variable is a dummy for firm _i_ having positive imports of capital goods in year _t_ . Input tariff(s)(t-1) are interacted with quartiles of firm TFP in the initial year of the sample. Firm TFP is estimated using the Levinsohn and Petrin (2003) methodology. All control variables are defined in table 3. Industry control variables (output tariffs, capital goods tariffs and the Herfindahl index) are included in all specifications. Heteroskedasticity-robust standards errors are reported in parentheses. Errors are corrected for clustering at the 3-digit industry level.<sup>_∗∗∗_</sup> ,<sup>_∗∗_</sup> , and<sup>_∗_</sup> indicate significance at the 1, 5 and 10 percent levels respectively.

_VI. ALTERNATIVE EXPLANATIONS_ 36
**Table 3:** Characteristics predictive of infiltration rates

||Physical attributes|Actions|Combined|
|---|---|---|---|
|Seperate kitchen|0.002||0.033|
||(0.066)||(0.069)|
|Size of house (m<sup>2</sup>)|-0.001<sup>_∗_</sup>||-0.002<sup>_∗∗_</sup>|
||(0.001)||(0.001)|
|After 2010|-0.012||0.093|
||(0.168)||(0.218)|
|1990 -2009|-0.016||-0.035|
||(0.135)||(0.135)|
|1960-1989|-0.041||-0.052|
||(0.090)||(0.095)|
|1930-1959|-0.132||-0.117|
||(0.125)||(0.126)|
|Before 1920||||
|Floor of dwelling|-0.005||-0.005|
||(0.008)||(0.008)|
|Consumption index|0.006||0.009|
||(0.025)||(0.024)|
|Has freplace|0.026||0.014|
||(0.408)||(0.367)|
|Greater than secondary schooling|-0.091||-0.092|
||(0.068)||(0.066)|
|Windows open (Hrs)||0.016|0.027<sup>_∗∗_</sup>|
|||(0.011)|(0.012)|
|Cooks with ventilation||-0.140<sup>_∗_</sup>|-0.139<sup>_∗_</sup>|
|||(0.081)|(0.076)|
|Owns air purifer||-0.014|0.180<sup>_∗_</sup>|
|||(0.037)|(0.098)|
|Owns indoor monitor||-0.162<sup>_∗∗∗_</sup>|-0.192|
|||(0.041)|(0.233)|
|N|129|129|129|
|Adjusted R<sup>2</sup>|-0.009|0.007|0.022|
|Total SS|12.94|12.94|12.94|
|Residual SS|11.94|12.46|11.17|
|Pre-period outcome mean:|0.57|0.57|0.57|

Notes: The outcome in all columns the outcome is the household specific infiltration rate, calculated as described in the text, top-coded at 0 and 1. We use the base estimation of the household infiltration rate in this table. The sample includes all households that received an indoor monitor and whose cleaned indoor data resulted in non-zero indoor measurements. The omitted category for date dwelling was built was pre-1920. The omitted category for education is Windows open indicates the typical number of hours the household leaves the windows open in a day. Heteroskedasticity robust standard errors clustered by census district - the sampling block - are in parentheses (* p _<_ .10 ** p _<_ .05 *** p _<_ .01).

22
separated out and do not contribute to the estimation of the reciprocal relationship between weaning and death. That is when these two ages are equal, it is assumed that the child is not weaned. All mortality probability functions include this parameter, which acts as a selection control only and does not have a causal interpretation.

As an alternative to the parametric model employed in this analysis, one might choose a semi-parametric specification. The Cox proportional hazards model is well known in demographic research. In the EFS data, survival times are heavily tied (partially due to heaping), however, which makes the semiparametric model difficult to operationalize. Another limitation of the semiparametric model is that shifts in the hazard that are attributable to the occurrences of the other events (i.e. time dependent covariates) are computationally difficult to estimate. Even with the parametric approach adopted here, estimation is computationally intensive. A final reason for preferring a parametric specification is that the decomposition of effects into direct and indirect effects is more eapi.ly defined and computationally more straightforward.

# C. Analytic approach

Following the discussion in section II of this report, we present results for three samples of births: (1) all births; (2) births to mothers who do not practice contraception prior to the next birth; (3) and births to mothers who do practice contraception prior to the next birth. As argued in section II, we expect the strength of some of the effects to vary according to the presence or 19
When _α_ equals zero, _p_ ( _x, z_ ) = 1 for all _x < z_ , and we obtain the _opportunitysensitive poverty headcount measure_ defined as Following Roemer (1998), _Fi_ ( _z_ ) can be interpreted as the degree of effort - expressed in percentile terms - necessary to escape poverty for a person endowed with circumstances **c** _i_ . An extreme and interesting case arises when _x_<sup>_max_</sup> _i < z_ : in this case type _i_ is said to be ‘poverty-trapped’ in the sense that, given circumstances _ci_ that define **Ti** , no amount of effort is sufficient to escape poverty.

# **6 Poverty, inequality and opportunities in the European Union**

Introducing a concern for inequality of opportunity into poverty measurement acquires practical importance if the assessment of the evolution of poverty in a country - or poverty comparisons across different regions or countries - differ when the concern is incorporated. This section provides an empirical application of the approach proposed in the paper. Using data for eighteen European Union countries, we show that when aversion to inequality of opportunity is explicitly incorporated into the measurement of poverty we are able to uncover some aspects of poverty not captured when using traditional poverty measures.

We use data from the EU-SILC 2005 round (User Database), which includes a special module on intergenerational transmission of poverty that contains information on socioeconomic background characteristics that can be used to define circumstances.<sup>6</sup> Poverty is assessed on the basis of equivalent disposable household income, expressed in Euros at PPP exchange rates and in 2004 prices (Eurostat, 2008).<sup>7</sup> We restrict the sample to household heads - defined as the individual with the highest gross earning in the family - and their spouses, with non-negative disposable household incomes.

We use three circumstance variables to define types: gender, parental education and parental occupation when the respondent was between 12 and 16 years old. Parental education is defined as the highest level attained by either of the two parents (when both are available), and is categorized in two groups: _higher_ when at least one parent completed upper secondary, and lower otherwise.<sup>8</sup> Parental occupation status is based on the highest ISCO 88 occupation status of the parents, grouped into four categories: highly skilled non-manual (ISCO codes 11-34), lowerskilled non-manual (41-52), skilled manual (61-83), and elementary occupation (9193). The total population is thus partitioned into 16 types.

> 6The EU-SILC project aims at obtaining internationally comparable information across EU countries. For more information see `http://epp.eurostat.ec.europa.eu/portal/page/portal/ microdata/eu_silc`

> 7Equivalent disposable household income is computed as the disposable household income divided by the square root of the household size

> 8Educational levels are defined according to the International Standard Classification of Education (ISCED).

11
A second way in which the labor market will be impacted is the high proportion of workers who are engaged in informal work. Informal employment is widespread in Tunisian labor markets. Informal employment, defined as those employed and not affiliated to social security ( _Caisse National de Securite Social_ or CNSS), tends to be higher for lower consumption deciles, ranging from 56 to 71 percent in the lowest three consumption deciles in contrast to between 19 and 34 percent for highest three consumption deciles (Figure 4). Substantial heterogeneity can also be observed among regions with an average share of informal jobs being more than 54 percent in North West region and 62 percent in Centre West region. Interestingly, even among the six “high-risk” sectors identified previously, informal employment is widespread and the highest in construction (68 percent), commerce (51 percent), tourism (45 percent) and textiles (43 percent). Informal employment also tends to be higher for women in contrast to men.

**Figure 4: Share of those employed in informal jobs**

<!-- Start of picture text --> a. By consumption decile b. By region<br>0.71<br>0.62<br>0.58<br>0.56 0.54<br>0.50 0.49<br>0.45<br>0.41 0.42<br>0.39 0.38<br>0.36<br>0.34<br>0.27 0.26<br>0.19<br>poorest 2 3 4 5 6 7 8 9 richest Grand Tunis Nord Est Nord Ouest Centre Est Centre Ouest Sud Est Sud Ouest<br>informal informal<br>a. By “high-risk” sectors b. By gender<br>0.71<br>0.68<br>0.51<br>0.47<br>0.43 0.45<br>0.39<br>0.28 0.27<br>0.21<br>IME Textile Transport Tourism Construction Commerce Agriculture Other masculin feminin<br>informal informal<br>.8 .8<br>.6 .6<br>.4 .4<br>% informal jobs % informal jobs<br>.2 .2<br>0 0<br>.8 .8<br>.6 .6<br>% informal jobs .4 % informal jobs .4<br>.2 .2<br>0 0<br><!-- End of picture text --> Source: Author’s calculations based on EBCNV 2015 8
# TABLE 14 TRADE BALANCE REGRESSIONS

LIST OF VARIABLES: TSGDP: Trade Balance Surplus to GDP ratio.

INF: December to December inflation rate in CPI.

PD: Primary Deficit to GDP ratio of the CPS.

FASGDP: Ratio of Foreign Assets to GDP (built accumulating Current Account Surpluses).

SERGDP: Ratio of the Service Account Surplus to GDP.

MU:Rate of expansion in Ml (instrumental variable for inflation). All regression were done with two stage least squares in order to correct for the simultaneous determination of TSGDP and inflation (PD, FASGDP and SERGDP were considered as exogenous variables).

# REGRESSION (1)

**SMPL** 1963 - 1988 26 Observations Instrunent list: C SERGDP IMF(-1) PD PD(-1) lii IU(-D C 1.6313156 **0,7027608** 2.3212957 **0.030** SERGDP 0.1447856 0.1725432 **0.8391266 8.410** IMF ,0,894151 8.0037333 2.5219420 **019i9 PD -0.2884181** 0.1331306 -2.2163746 0,83 B-squared **0.489888** Mean **of** dependent **uar 1.921335** Adjusted B-squared 0.420327 S.D. of dependent **uar** 2,829074 S.E. **of** regression 1.544861 **Sun** of squared resid 52.50513 Durbin-Watson stat 1.597045 F-statistic 7,042588 Log likelihood -46.028q9

71
World Poverty, 2010

# World Poverty, 2030

<!-- Start of picture text --> Fragile<br>199.7M<br>Non-Fragile<br>1,046.6M<br><!-- End of picture text --> Poverty headcount by country, 2010 estimates. Source: International Futures v7.04 Type i Fragile i Non-Fragile <!-- Start of picture text --> Fragile<br>230.9M<br>Non-Fragile<br>388.6M<br><!-- End of picture text --> Poverty headcount by country, 2010 estimates. Source: International Futures v7.04 Type i Fragile Bi Non-Fragile