rafmacalaba/datause-encoder-data huggingface dataset:
NAS and population census; and (iii) Net Domestic Product (NDP; "income" for short) per person, also from the NAS and census. In all cases, the elasticities are estimated by regressing the log poverty measure on the log mean consumption or income. We also give an "adjusted" estimate in which a control variable was added for the first difference of the log of the ratio of the consumer price index for agricultural laborers to the national income deflator (i.e., the difference in the rate of inflation implied by the two deflators). This was included to allow for possible bias in estimating the growth elasticity due to the difference in the deflator used for the national accounts data and that used for the poverty lines.

For 1958-2006 as a whole, the national poverty measures responded significantly to all three measures. This also holds when we use lagged survey means and national accounts and price data as instruments for the current survey mean, in an attempt to reduce the potential for spurious correlation due to common survey measurement errors. The (absolute) elasticities are higher if one uses the NSS estimate of mean consumption, rather than the national accounts estimate. The elasticities are lowest for per capita income. This may be due to inter-temporal consumption smoothing, which may make poverty (in terms of consumption) less responsive in the short-term to income growth than to consumption growth. Imperfect matching of the time periods between the NSS and the NAS could also be playing a role in attenuating the elasticities using NAS growth rates. But probably the most important reason for lower (absolute) elasticities with respect to NAS consumption or income has to do with the increasing divergence between NSS and NAS growth rates of mean consumption or income. Note that: An elasticity of  w.r.t. _C_ (NAS consumption per capita) of around 0.5 (Section 3) would yield a poverty elasticity w.r.t.  that is about double that w.r.t. _C_ —roughly in accord with Table 3.

When we split the period into two at 1991, we find an appreciably higher (absolute) elasticity of the headcount index with respect to the survey mean in the post-1991 period; the difference in the estimated elasticities (1.58 and 2.07 respectively for the two periods) is statistically significant.<sup>32</sup> However, for the poverty gap measures, the difference in the

> 32 See Table 3.  These results are based on regressions of log poverty measures on log survey mean interacted with dummy variables for pre- and post-1991 periods, and a dummy variable for MRP surveys.

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# EXECUTIVE SUMMARY

**Migration, both internal and external, continues to be a dominant livelihood strategy for households in Lesotho, with almost half (43%) of households reporting at least one member living away (DHS 2014).** Female migration is on the rise, as a result of two concomitant trends. First, since the 1990s the number of male migrants to South African mining jobs has dwindled, leaving households who were dependent on remittances struggling to make ends meet. Concurrently, there has been a tremendous expansion of job opportunities primarily for women in the export garment sector in Maseru and Maputsoe, driven in large part by the preferential trade terms under the United States’ African Growth and Opportunity Act (AGOA). Coupled with a continued large demand for domestic work, the consequent increase in female migration – which has not entirely replaced male migration but occurs increasingly alongside and, more recently, in place of it – has fundamentally altered gender roles and family structures, with important implications for the welfare of migrants and their families.

This study analyzes female migration using existing nationally representative data sources and primary data collected by the research team in March‐April 2015. The study proceeds in two parts. **Part I offers an in‐depth exploration of the migration experience from the perspective of rural households and migrants themselves.** Due to the limited scope and budget for this study, the fieldwork focused exclusively on women migrating internally within Lesotho. After reviewing the patterns of migration in Lesotho today, we examine the perspective of rural communities on the role of migration in sustaining households. We also elaborate the experience of female migrants, highlighting the benefits and challenges of moving away from one’s family to work.

From the households’ perspective, three main findings emerge: First, female migration in Lesotho is primarily driven by economic “push” factors, often due to shocks to the family household, such as job loss, death, or crop failure, rather than “pull” factors. Second, rural perceptions of migration are overwhelmingly negative: many understand migrants to be those who move away and come home just to die from illness, and they believe that migrants do not make enough money for the hard work and rough treatment to be worth it. And third, even with remittances, households that send internal migrants are struggling financially and are not seen as better off than non‐sending households. Households only experience substantial improvements in living standards when they have a male working in the mines in South Africa, consistent with previous evidence suggesting that migration is only effective in sheltering 4
**Figure 4 – Income Gains: Production during Years Lost, by Decile, by Decile**

<!-- Start of picture text -->
1.0<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>1 2 3 4 5 6 7 8 9 10<br>Deciles<br>Low-bound elasticity Medium elasticity Upper-bound elasticity<br>Income Gains (%)<br><!-- End of picture text -->

_Source:_ Estimates based on Russia Longitudinal Monitoring Survey–HSE 2016. _Note:_ Estimates assume a price shock of 25 percent.

# **Net effects: Total distributional impact**

In this subsection, we add the previous results to estimate the aggregate effect of an increase in tobacco taxes (see equation 1). The aggregate effect of an increase in tobacco taxes is positive and progressive; in the long run, poorer deciles benefit more than richer ones. The positive effect of reduced medical expenses and years of life gained offsets the negative price effect.

Under any tobacco price elasticity scenario, the overall effect for cigarettes is always positive and progressive (table 9 and figure 5). Moreover, the benefits are amplified if we compare the lower-bound elasticity with the upper-bound elasticity. Apparently, a population in which medical expenses are high and which is sensitive to tobacco price changes will reduce consumption sufficiently to allow work and health benefits to offset cost increases. This is exactly what happens in Russia. Furthermore, the assumptions in this model do not include other possible policies, such as smoking cessation programs, antismoking advertising, youth outreach, or policies financed through the new tax revenue. Therefore, these results are in line with the literature, showing the important role that taxation plays in lowering tobacco usage.

**Table 9 - Net Effect on Household Expenditures (%), by Decile**

|_Price shock scenario_|_1_|_2_|_3_|_4_|_5_|_6_|_7_|_8_|_9_|_10_|
|---|---|---|---|---|---|---|---|---|---|---|
|Low-bound elasticity|1.44|1.03|0.78|0.69|0.61|0.67|0.51|0.45|0.41|0.31|
|Medium elasticity|2.08|1.56|1.23|1.14|1.04|1.16|0.91|0.85|0.77|0.62|
|Upper-bound elasticity|2.71|2.09|1.68|1.59|1.47|1.66|1.31|1.25|1.13|0.93|

_Source:_ Estimates based on the Russia Longitudinal Monitoring Survey–HSE 2016. _Note:_ The table reports the share of total consumption for each decile.

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The data for the model consist of a Social Accounting Matrix (SAM) and other parameters, such as import and export trade flows by region, sectoral tax and tariff rates, and elasticities of substitution and transformation. Because there is little empirical evidence on relevant elasticities for the Tunisian market, we make standard assumptions about their values. In particular, labor-capital substitution is set at unity in a Cobb Douglas value added production function. Benchmark trade elasticities are drawn from Rutherford, Rutstrom and Tarr (1995) and Konan and Maskus (2000). The various trade elasticities are 2.0 for substitution between domestic and imported goods, 5.0 for substitution among regional imports and for transformation between domestic output and exports, and 8.0 for transformation among regional export destinations. These data are assembled into a consistent set of relationships between intermediate demand, final demand, and value-added transactions using the 1995 input-output table for Tunisia on a diskette provided by the Institut National De La Statistique (INS) along with the 1998 INS _Les Comptes de la Nation_ report.

Trade and tariff data, provided in Table 2, were aggregated to the input-output sectoral basis using import weights consistent with the concordance between the input-output table and the tariff classification. Tariff rates were determined by collections data for 1995 and vary across regions due to duty drawback provisions as well as preferential treatment of the EU and the Arab League. There are no data on tariff collections on services, and we take their tariff rates to be zero.

Table 3 lists our estimate of price wedges due to service barriers, which are the key parameters to be altered in the simulations. Mode 1 barriers on cross-border trade are treated as ad valorem tariff-equivalent NTBs. These are simply set to zero in the liberalization exercises. Sectors that are listed as non-traded (indicated by _nt_ ) remain non-traded post-liberalization. In terms of mode 3, we would ideally like to estimate the impact that services barriers have on both price markups and on resource costs so as to distinguish between the pro-competitive effects and income totaled negative 680 million Dinars according to the IMF _Balance of Payments Statistics Yearbook_ .

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# **Exporter Behavior, Country Size and Stage of Development: Evidence from the Exporter Dynamics Database**<sup>*</sup>

## **Ana M. Fernandes      Caroline Freund      Martha Denisse Pierola**

## **JEL Classification codes** : F14, O1.

## **Keywords:** exporter dynamics, exporter growth, firm-level data, allocative efficiency.

* Ana Fernandes and Martha Denisse Pierola are at the World Bank; Caroline Freund is at the Peterson Institute for International Economics and CEPR. The authors would like to thank two anonymous referees as well as Nathan Nunn. We are grateful to Tolga Cebeci for his collaboration on an earlier version of this paper that focuses on describing the Exporter Dynamics Database in more detail.  Research for this paper has been supported in part by the World Bank’s Multidonor Trust Fund for Trade and Development and through the Strategic Research Partnership on Economic Development. We also acknowledge the generous financial support from the World Bank research support budget and the Knowledge for Change Program (KCP), a trust funded partnership in support of research and data collection on poverty reduction and sustainable development housed in the office of the Chief Economist of the World Bank (www.worldbank.org/kcp).  We thank Erhan Artuc, Richard Baldwin, Peter Klenow, Aaditya Mattoo, Sam Kortum, and Andres Rodriguez-Clare for many useful comments and discussions. We thank workshop participants at the Empirical Investigations in International Trade at Santa Cruz, the Graduate Institute in Geneva, the World Bank, the World Trade Organization, the International Trade Center, and Nova School of Business and Economics for useful suggestions. We thank Mario Gutierrez-Rocha, Jamal Haidar, Alberto Behar and Madina Kukenova for their contribution to this project and Francis Aidoo, Paul Brenton, Maurizio Bussolo, Huot Chea, Julian Clarke, Alain D’Hoore, Pablo Fajnzylber, Elisa Gamberoni, Sidiki Guindo, Javier Illescas, Mehar Khan, Sanjay Kathuria, Josaphat Kweka, Erjon Luci, Mariem Malouche, Eric Manes, Yira Mascaro, Mamadou Ndione, Zeinab Partow, Stefano Paternostro, Jorge Rachid, Richard Record, Jose Guilherme Reis, Jose Daniel Reyes, Nadeem Rizwan, Liviu Stirbat, Dimitri Stoelinga, Daria Taglioni, Maria Nicolas, Erik von Uexkull, Cristian Ugarte, and Ravindra Yatawara for facilitating the access to the data for developing countries. We thank Lina Ahlin, Martin Andersson, Juan de Lucio, Emmanuel Dhyne, Luc Dresse, Cédric Duprez, Richard Fabling, Jaan Masso, Raul Minguez, Asier Minondo, Andreas Moxnes, Francisco Requena, Lynda Sanderson, Joana Silva, Priit Vahter, and Hylke Vandenbussche for providing us with measures for developed countries.
indicator due to a change in the reference period. In the APIS surveys up to the round for 2011, the reference period for this question was the “past month”; however, in the 2013 round this was changed to the “past six months”, thus leading to much higher rates of reported morbidity. In light of this, our main results up to 2013 exclude “ _health2_ ”, while we also report estimates including it, but the results for these are valid for the period up to 2011 only.

The first five indicators related to the standard of living are also very similar to those in the global MPI, and do not require any special comment. The global MPI also includes a deprivation indicator based on the use of inferior sources of cooking fuel. The APIS data do not offer a direct measure for such an indicator. It is possible to proxy this with information on whether the household owns a gas range or stove with oven – one of the asset ownership items included in the questionnaire. However, this seems to be a rather noisy indicator with implausible fluctuations over certain rounds of the survey; for instance, the proportion of population in households not owning a gas range or stove with oven registered a sharp increase between APIS 2011 and 2013, from 78% to 87%. While the reasons for this are not entirely clear, the possibility that there was a sudden and marked deterioration in the use of improved sources of cooking fuel seems unlikely. Hence, we opted not to include this indicator in this assessment.

Note that the final indicator in Table 1 relates to employment – a dimension that is not in included the UNDP MPI nor typically in most applications. Thus, a brief comment on the rationale for its inclusion is apposite. Employment generates income, so its pecuniary significance is obvious. But employment may also be considered an independent dimension in its own right as it represents more than just a means to earning a living. Sociologists have long recognized the importance of employment as a basis for social differentiation; see for instance, the centrality of “weak labor force attachment” as a defining characteristic of the “underclass” in the pioneering work of Wilson (1987). The linkage of employment with life satisfaction, happiness, self-esteem, social status, mental and physical health and wellbeing has also been widely documented.<sup>7</sup> There is also a well-established tradition of using employment-based indicators in social exclusion measures in OECD countries; see for instance, indicators of quasijoblessness included in EU-2020 measures/ targets for poverty and social exclusion. Employment-based

> 7 See for instance, Dooley, Catalano, and Wilson (1994), Dooley, Prause, and Ham-Rowbottom (2000), Helliwell and Putnam (2004), Stutzer and Lalive (2004), Lundin and Hemmingsson  (2009), Stuckler et al. (2009a, 2009b), ILO (2010), Blanchflower and Oswald (2011).

7
**_Energy demand models for policy_** **_<u>formulation</u>_**

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females with the social network effect being higher for adult males than females in the rural areas.<sup>9</sup> Our analysis of the role of social network effects in the participation of indigenous peoples in cash transfer programs such as _PROCAMPO_ and/or _Oportunidades_ reveals that social networks do not play a significant role in the participation these programs. Part of the explanation for this result may rest on the fact that the _Oportunidades_ program is targeted in two stages, first at the locality level, and then at the household level with explicit selection criteria that leave little room for self-selection and social network effects to play a role in the participation in this program. It is also possible that the time elapsed between the year of the Census and the start of the program in late 1997 is too short for the social network effects to start having a visible role. These findings contrast with those of Bertrand et al. (2000) who present evidence that social networks based on language spoken at home play a significant role for participation in the US welfare system. It is still possible, however, that high and continuing participation rates in cash transfer programs such as _Oportunidades_ have the risk of creating over time a “welfare culture” that could ultimately distort work incentives, inhibit economic mobility, and could perpetuate rather than alleviate poverty.

> 9 Existing evidence on gender differentiated impacts of migrant networks is not conclusive. Empirical analyses based on cross-sectional data from Mexico suggests that male migrant networks are more important determinants for international migration for men than for women (Curran and Rivero-Fuentes 2003), while other studies suggests that young men’s decision to migrate is motivated by a ‘rite of passage’ factor, while kinship ties exert more influence on the migration of young women (Kandel and Massey 2002) .

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THE CENTRAL AFRICAN REPUBLIC’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE contracts from being awarded and completed within a 12-month budget cycle (Gwilliam and others 2009).

**Figure 6. Optimal and existing fuel levy in select Sub-Saharan African countries**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Existing Optimal for maintenance<br>US cents per liter<br><!-- End of picture text --> _Source:_ Gwillliam and others 2009.

Traffic levels are extremely low, making it difficult to justify heavy road engineering. Traffic, mainly concentrated in the Bangui-Douala and Point Noire–Brazzaville–Bangui corridors, consists of 200 vehicles per day on the paved segments. This is only 25 percent of the average—843 vehicles per day—in the fragile countries and 15 percent of the average—1,288 vehicles per day—in low-income countries. The volume of traffic in the CAR’s unpaved network is also low, with only 14 vehicles per day versus 31 vehicles per day on average for comparable fragile states (table 4). Around 86 percent of the traffic in the the CAR section of the Douala-Bangui corridor consists of fewer than 300 vehicles per day, the estimated minimum economic threshold for paving. In the case of the Point Noire–Brazzaville–Bangui corridor, about 80 percent of the traffic in the CAR’s section consists of fewer than 300 vehicles per day. With 61 percent of the primary network under asphalt and an average annual daily traffic of below 300 vehicles, the CAR’s level of overengineering is 47 percent higher than in comparable fragile states (table 3). Nevertheless, due to recent conflict it may be that traffic volumes are artificially depressed due to security concerns.

Primary and secondary networks have been sensitive to this reality by adopting lower-cost surfacing. The CAR’s total road network, consisting of 25,235 km, is largely surface treated, both pavement (63 percent) and gravel (34 percent). The primary or national network (about 5,000 km) consists of mainly surface treatment pavement (4,322 km) and asphalt (654 km), and represents about 20 percent of the total network. The secondary or regional network primarily consists of surface treatment pavement (4,364 km) and gravel roads (589 km). The tertiary or rural network (about 15,000 km) consists of 100 percent gravel roads. Adopting more appropriate standards would reduce the costs of network maintenance and ease the financial pressure on road maintenance.

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