rafmacalaba/datause-encoder-data huggingface dataset:
# **Migration in Zambia**

## **Migration pressure in Zambia**

International migration is fuelled by economic and socio-political factors typically classified as push and pull factors. The push factors include: poor socio-economic living conditions, unemployment, drops in real income, currency devaluation and rising cost of living, professional isolation, tribal/ethnic discrimination against the qualifications held and competition with expatriates. The pull factors include higher salaries, greater job mobility and professional career; fewer bureaucratic controls, higher standards of living; acquisition of higher skills, foreign scholarships and educational support, active presence of recruitment agents and network effects. These factors work in tandem with each other in generating migratory flows. In a formal empirical study, Hatton and Williamson (2001) analyze the push-pull factors for the sub-Saharan African countries and find that the real wage gaps between sending and receiving countries and the demographic booms in the low-wage sending regions are the two most important factors in driving migration. The study also notes that the situation in the region is similar to the one in Europe in the late 19<sup>th</sup> century which fuelled mass migration.

Emigration from Zambia is mostly driven by the economic motives mentioned above with little role of civil unrest, security concerns, etc., as the country has enjoyed peace and stability since its independence. Fears of mass emigration from Zambia (and continental Africa) to the rich developed countries seem unfounded at least for the present. In fact, African countries show lower levels of labor mobility than others. Table 1 illustrates the point.

**Table 1: Migrants per 10,000 of source country population in 2000**

||**OECD**|**USA**|**Canada**|**UK**|**France**|**Switzerland**|**Netherlands**|
|---|---|---|---|---|---|---|---|
|Angola|87.3|1.7|1.1|2.5|4.6|2.7|2.0|
|Botswana|15.8|5.9|0.2|6.0|0.7|0.1|0.4|
|Congo,DR|18.0|0.9|1.1|1.1|3.3|0.6|0.7|
|Lesotho|3.3|0.5|0.3|1.3|0.1|0.4|0.2|
|Malawi|11.0|1.1|0.3|8.9|0.0|0.0|0.1|
|Mozambique|33.7|0.8|0.5|1.5|0.4|0.4|0.2|
|Namibia|10.6|0.5|0.9|3.9|0.4|0.7|0.4|
|South Africa|61.1|11.1|6.2|20.5|0.3|0.9|1.2|
|Swaziland|18.0|9.4|0.7|4.9|0.0|0.4|0.3|
|Tanzania|18.1|2.7|5.2|8.5|0.1|0.2|0.2|
|**Zambia**|**26.5**|**4.3**|**1.4**|**16.1**|**0.1**|**0.2**|**0.3**|
|Zimbabwe|47.1|6.5|2.2|27.1|0.2|0.4|0.4|
|AllSADC|35.2|4.1|2.9|9.7|1.3|0.7|0.7|
|Africa|55.9|8.0|2.8|7.7|17.8|0.8|2.9|
|World|94.0|40.0|7.7|5.8|6.2|2.3|2.1|

Source: Docquier & Marfouk (2004).

6
4 proximate determinants, changes that are in turn driven (though not necessarily in the same directions) by changes in socioeconomic conditions. By identifying the changes likely to occur in proximate determinants as a result of adjustment policies, we can derive hypotheses about how such policies will affect health, partIcularly differences in health among groups in a society.

The Mosley-Chen model identifies four categories of proximate factors that determine underlying child health status: maternal factors (age, parity, prior birth interval, subsequent birth interval); environmental contamination (waterborne, airborne, food-borne, and insect-borne infectious or parasitic agents); nutrient deficiency (intake of carbohydrates, proteins, vitamins, and trace elements); and personal injury. A fifth category of factors, personal illness controls, both preventive and curative, determine the relation between underlying health status and actual health status. Post-childhood health status is determined by all the same categories of proximate factors but maternal factors (except for maternal mortality).

We now consider the likely effects of adjustment policies on each of these categories.

(a) Maternal factors. The historical studies and limited contemporary studies available suggest that an economic deterioration has an effect over two years or so of reducing fertility, followed by a less-than-compensat **in** g crease. For population subgroups adversely affected by adjustment (those facing higher food prices as a result of reduced or eliminated food subsidies, government employees sacrificed to efficiency, and subsistence producers facing higher prices for
**Table 4.2. Results for highland livelihood zone**

|||**Soil moistur**|**e**||**NDVI**|||**SPEI**||**Tot**|**al seasonal ra**|**infall**||**WRSI**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|(13)|(14)|(15)|
|**Landed h**|**ouseholds**|||||||||||||||
|Hazard|**0.045*****|**0.044*****|**0.044***|**0.020***|0.017|0.025|**0.041****|0.001|**-0.049***|**0.030****|0.017|0.031|**0.003***|0.017|0.065|
||**[4.495]**|**[4.350]**|**[1.653]**|**[1.649]**|[1.154]|[1.382]|**[2.426]**|[0.060]|**[-1.649]**|**[2.575]**|[1.172]|[1.307]|**[1.782]**|[1.332]|[0.902]|
|Hazard<sup>2</sup>||-0.004|-0.004||-0.003|-0.013||**-0.055*****|**-0.041****||**-0.017***|**-0.021***||-0.000|-0.001|
|||[-0.427]|[-0.333]||[-0.338]|[-0.917]||**[-3.940]**|**[-2.496]**||**[-1.895]**|**[-1.813]**||[-1.147]|[-0.783]|
|Hazard<sup>3</sup>|||-0.000|||-0.005|||**0.024****|||-0.005|||0.000|
||||[-0.007]|||[-0.853]|||**[2.153]**|||[-0.694]|||[0.699]|
|**Landless**|**households**|||||||||||||||
|Hazard|-0.005|-0.006|-0.030|-0.002|-0.027|-0.026|0.020|-0.011|0.031|0.017|0.016|0.042|0.002|0.021|0.111|
||[-0.214]|[-0.231]|[-0.533]|[-0.063]|[-0.565]|[-0.556]|[0.765]|[-0.294]|[0.600]|[0.747]|[0.622]|[0.876]|[1.046]|[1.368]|[1.577]|
|Hazard<sup>2</sup>||-0.002|-0.000||-0.023|-0.024||-0.031|**-0.111****||0.001|-0.009||-0.000|-0.001|
|||[-0.100]|[-0.002]||[-0.852]|[-0.496]||[-1.080]|**[-2.233]**||[0.062]|[-0.358]||[-1.303]|[-1.425]|
|Hazard<sup>3</sup>|||0.010|||-0.001|||**-0.051***|||-0.009|||0.000|
||||[0.523]|||[-0.045]|||**[-1.746]**|||[-0.625]|||[1.308]|
|Obs.|15,046|15,046|15,046|15,046|15,046|15,046|15,046|15,046|15,046|15,046|15,046|15,046|12,159|12,159|12,159|
|R-sq|0.265|0.265|0.265|0.260|0.260|0.261|0.261|0.266|0.268|0.262|0.263|0.263|0.258|0.259|0.259|

_Source:_ World Bank.

_Note:_ NDVI = Normalized Difference Vegetation Index; SPEI = Standardized Precipitation Evapotranspiration Index; WRSI = Water Requirement Satisfaction Index. Results are from a weighted regression such that each household is assigned its survey-specific population weight. Countries and years included are Ethiopia (2011, 2016), Lesotho (2017), Mozambique (2014), and Zimbabwe (2017). Dependent variable is the log of per capita (or adult equivalent) consumption. Other variables included and not shown are education of household head, gender of household head, whether the household location is remote, whether the household was surveyed during the lean season, survey year, country, country x survey year, and administrative fixed effects for district (Ethiopia. Lesotho) and province (Mozambique, Zimbabwe). Robust t-statistics are shown in parenthesis. Significance level: * = 10 percent, ** = 5 percent, *** = 1 percent. When variable is significant the coefficient and standard error are in bold font.

19
|Piauí|3.92|3.34|3.83|
|---|---|---|---|
|Ceará|0.69|5.27|3.66|
|Paraíba|4.53|1.78|3.08|
|Maranhão|2.96|2.38|2.80|
|Pernambuco|1.97|2.72|2.60|
|Rio Grande do Norte|0.32|2.96|2.04|
|Bahia|-1.19|1.83|0.65|
|Alagoas|-1.49|1.91|0.57|
|Sergipe|-5.93|1.99|-1.34|

_Source_ : Calculated with data from _Contas Regionais_ and Central Bank of Brazil.

Per capita growth has been impressive in recent years. Between 1990 and 1998, annual GDP growth in RN was 3.0 percent, compared to 2.7 percent for the Northeast and 2.5 percent for Brazil (Table 4.1). It was higher than in the richest states of the Northeast, Bahia and Pernambuco, but below those of some of the poorest states, Ceará and Piaui.

The strong relationship between Brazil’s aggregate growth and RN’s economic progress in the last five decades reflects the response to common macroeconomic forces. Countrylevel policies (for example, macromanagement) imply a strong correlation between state growth and country growth. Growth in the Northeast, outperformed by other regions such as Center-West, has been lower than the Brazilian average. Nevertheless, RN’s growth performance has been among the better in the Northeast during the last decade, suggesting that some convergence took place in the 1990s. The main factor in both RN’s and Brazil’s growth performance in the 1990s was the macroeconomic stabilization that followed the inflation-beating Real Plan of 1994. This may suggest that RN’s economy experienced a very limited transformation in the past decade, which positively affected the growth path of the state.

# **Sectoral Sources of Growth and Employment**

An understanding of the sectoral sources of growth and employment is useful in designing a strategy to accelerate growth and employment. Over the last two decades the economy has shifted away from agriculture, industry, and mining toward services (Figure 4.2 and Table 4.2).

8
to have lowered child labor. This finding is in line with the evidence synthesized by Kremer and Holla (2009), suggesting that relatively small costs (for example, the costs of school uniforms) are sufficient to keep children out of school and that relatively small subsidies can generate “sizeable movements” in the take-up of health and education interventions. Together with the finding that cash transfers have a stronger impact on the poor, this finding lends further support to the hypothesis that the mitigation of credit constraints is a key channel through which cash transfers affect child labor. Possibly, once sufficient cash has been transferred and credit constraints are no longer binding, transferring additional cash to the household has no effect on child labor.

# **<<B>> 3.7. Protection from Shocks**

There is substantial evidence that households in developing countries use child labor to cope with income shocks. Beegle, Dehejia, and Gatti (2006) use data from a household panel survey in Tanzania to show that households increase child labor in response to transitory income shocks. Similarly, Duryea, Lam, and Levison (2007) find that unemployment shocks experienced by male household heads in Brazil significantly increase the probability that a child will enter the labor force. Guarcello, Mealli, and Rosati (2010) show that in Guatemala, exposure to negative shocks strongly influences household decisions and pushes children into work. An important question is whether social protection programs, such as conditional cash transfers, can serve as a safety net preventing income shocks from causing children to drop out of school and enter work.

De Janvry et al. (2006) test whether the conditional cash transfers provided by the Oportunidades program in Mexico protect children from household level shocks, including illness of the head of household, loss of employment by the head of household, and natural disasters such as drought and harvest failure. The authors find evidence of state dependence in schooling: children may leave school as the result of a shock (particularly illness of the household head and localitylevel natural disasters) and there is a substantial risk that they will not return to school at a later stage. Oportunidades strongly reduced the risk that pupils would leave school as a result of such 19
Maurer, L., Barroso, L., 2011. “Electricity auctions: an overview of efficient practices”. World Bank. Washington.

Mayr, D., J. Schmidt, et al. (2014). "The potentials of a reverse auction in allocating subsidies for costeffective roof-top photovoltaic system deployment." Energy Policy **69** (0): 555-565.

Maurer, L. T., and L. A. Barroso. 2011. _Electricity Auctions: An Overview of Efficient Practices._ Washington, DC: World Bank.

Menanteau P., Finon D., Lamy M., 2003. “Prices versus Quantities: Choosing Policies for Promoting the Development of Renewable Energy”. Energy Policy 31: 799-812.

Mir-Artigues, P. and P. del Río (2014). "Combining tariffs, investment subsidies and soft loans in a renewable electricity deployment policy." Energy Policy **69** (0): 430-442.

MNRE (Ministry of New and Renewable Energy), 2010. “Jawaharlal Nehru National Solar Mission – Towards Building Solar India”.

Nielsen, S., P. Sorknæs, et al. (2011). "Electricity market auction settings in a future Danish electricity system with a high penetration of renewable energy sources – A comparison of marginal pricing and payas-bid." Energy **36** (7): 4434-4444.

Nilsson, M., L. J. Nilsson, et al. (2009). "The rise and fall of GO trading in European renewable energy policy: The role of advocacy and policy framing." Energy Policy **37** (11): 4454-4462.

Pengfei, S., 2008. “Booming Wind Power Market and Industry in China”. Chinese Wind Energy Association Pillai R., Banerjee R., 2009. “Renewable energy in India: Status and potential”, Energy, Volume 34, Issue 8, August 2009, Pages 970-980.

Pollitt, M.G., 2010. “UK renewable energy policy since privatization”. University Energy Research Group Discussion Paper. No. 40. Cambridge.

Porrua F., Bezerra B., Barroso L., Lino P., Ralston F., Pereira M., 2010. “Wind Power Insertion through Energy Auctions in Brazil”. IEEE Qiu Y., Anadon L. D., 2012. “The price of wind power in China during its expansion: Technology adoption, learning-by-doing, economies of scale, and manufacturing localization”. Energy Economics, Volume 34, Issue 3, May 2012, Pages 772-785, ISSN 0140-9883, 10.1016/j.eneco.2011.06.008.

REN21 Renewable Energy Policy Network. 2005. “Renewables 2005 Global Status Report.” Washington, DC: Worldwatch Institute.

REN21, 2013. “Renewables 2013 Global Status Report”. Paris: REN21 Secretariat.

Rudnick H., Barroso L., Llarens D., Watts D., Ferreira R., 2012. “Transmission challenges in the integration of renewables in South America” IEEE Power & Energy Magazine, Vol.10, issue: 2, pag 2436, March/April 2012 Stadelmann M., Frisari G., Konda C., 2014. “The role of Public Financing in CSP – Case Study: Rajasthan Sun Technique, India”. Climate Policy Initiative, March 2014.

36
ticipate in a comprehensive household survey. In the vast majority of cases (84% of all surveyed households), the respondent was the actual borrower identified by the bank record, as well as the user of the loan and the household’s main financial decision-maker. When somebody else in the household was the financial decision-maker and the loan’s true user, we interviewed that individual instead. Household members other than the borrower identified on the beneficiary list were only interviewed, once the actual borrower had been located and this borrower confirmed that another household member was both the financial decision-maker and the actual user of the loan in question. This occurred in a small number of cases, where the loan was taken out in the father’s or wife’s name, who legally owned the land, but the son or husband was the true financial decision-maker and user of the loan.

There were two main sources of attrition. First, although considerable effort was made to locate all borrowers identified on beneficiary lists, this proved quite difficult for loans that were disbursed several years prior to the program. Second, because only imperfectly recorded and transliterated names were available from banks, many villages had multiple individuals with the same name, which sometimes created obstacles to the identification of individuals if no additional bank data on the beneficiary account was available. No interview was conducted if a borrower could not be identified with certainty based on bank records. To verify that attrition was balanced across treatment and control groups, Table D.2 (a) compares located households with households that could not be found and shows that the probability of a household being located was indeed independent of treatment status. As an additional test, Table D.2 (b) compares basic characteristics of located and non-located households (as available from the bank data). Tested jointly, balanced attrition cannot be rejected at conventional levels of significance ( _p_ = 0 _._ 24). Similarly, attrition is not systematically related to landholding or relief amount.

The average surveyed household is a family of seven with total landholdings of 1.82 hectares, an annual gross income of Rs 72,429 (US$ 1,610) and total pre-program debt of Rs 92,676 (US$ 2,059). For all households in the sample, agriculture is the main source of income. Surveyed households are extremely dependent on credit to support investment in agricultural inputs, such as irrigation, fertilizer and pesticides. Households in the sample spent an annual average of Rs 13,254 (US$ 295) on agricultural inputs, and 72% of households relied on external borrowing to finance this investment. Before debt relief, 86.7% of this credit was provided by banks and credit cooperatives.

17
The number of the laws and decrees might not capture the most important laws and decrees. In other words, it might not reflect the depth and significance of economic reforms. We use news media to provide a robustness check by focusing on reform announcements in the press. The argument is that media chatter about reform announcements can pick up reforms that are perceived as important. Similar results are obtained when reform announcements are used instead of the number of laws and decrees. Figure II.3 shows scatterplots between the reform announcements about a policy pillar versus its lagged Distance to Policy Frontier (i.e., the distance to the policy frontier _in the previous year_ ) between 2007 and 2017. A policy pillar’s lagged distance to the frontier is not significantly correlated to reform announcements (see Appendix A2 for more detail).

## **Figure II.3: Number of Laws and Decrees Passed and Reform Announcements**

<!-- Start of picture text --> Bahrain Kuwait Oman<br>Qatar Saudi Arabia UAE<br>0 20 40 60 80 0 20 40 60 80 0 20 40 60 80<br>Distance to Frontier, Lagged (100: farthest away from the frontier)<br>Diversification Financial Market Development Goods Market Efficiency Health and Primary Education<br>Higher Education and Training Infrastructure Innovation Institutions<br>Labor Market Efficiency Macroeconomic Environment Technological Readiness<br>2<br>1.5<br>1<br>.5<br>0<br>2<br>1.5<br>1<br>Reform Announcements Index .5<br>0<br><!-- End of picture text --> _Note_ : A higher ‘Distance to Policy Frontier’ implies being farther away from policy best practices. GCI data are from 2007 to 2017 due to data constraints from WEF’s GCI database (the GCI scores are only methodically consistent and comparable across years during this period).

# III. Population’s Engagement and Support for Economic Reforms: Insights from Social Media

Drawing insights from social media, this section examines the people's engagement and support for current economic reforms in the GCC. As demonstrated by the long-lasting consequences of the Arab Spring, this aspect is critical for the long-term sustainability of economic reforms. Our thesis is that social media can reflect the population’s true sentiment toward economic reforms. To our best knowledge, this paper is the first systematic effort to shed light on the general attitude towards economic reforms in the GCC countries.

6
period, and there were large improvements in all five of the indicators in the rest of the world. There were also large improvements in other resource-intensive countries over the same period, which also likely significantly benefitted from the high global commodity prices. Thus, the link between resource-led growth in PNG appears even more tenuous than in other resource-intensive countries.

And lastly, we are able to explore the link between economic activity and well-being in PNG to both non-resource and resource-intensive countries across the world. Not only did the share deprived not change between 2009 and 2016-2018, but also the well-being measures analyzed here are poor in PNG. Approximately 75 percent of the population is multidimensionally poor according to the MPM, which ranks 118 out of 122 countries for which the measure is estimated in the World Bank’s Poverty and Inequality Platform (PIP); and the population faces significant deprivations in every dimension captured. Of the countries that have similar levels of deprivations across all the indicators captured here, PNG has the highest level of economic activity, and the disparity is larger than for other resource-intensive countries.

Given that very little of the population is employed in the resource sector in PNG (e.g., PNG NSO and UNFPA 2023), resource-led growth would either have to spur economic activity in other sectors or the government would have had to redistribute the gains in order for the population to significantly benefit. However, neither development is supported by the results presented here. There are several factors potentially behind the disconnect between economic growth and improvement in household well-being, including a difficult business environment (e.g., World Bank 2023; Howes et al. 2022; etc.), low levels of human capital among the population (e.g., World Bank 2018; Pandey and Howes 2018; etc.), and a high degree of clientelism (e.g., Walton and Dinnen 2022; World Bank 2023; etc.). However, many of these issues have been shown to actually be consequences of developing primarily through resource-led growth (e.g., Ross 2015), and the lack of any benefit to the population is surprising.

Combined, these results build on previous work in PNG and other resource-intensive settings and more concretely show how little of economic activity in resource-dependent 4
ment Practices Across Firms and Countries”, Academy of Management Perspectives, 26 no. 1 (2012).

- BLOOM, NICHOLAS, BENN EIFERT, DAVID MCKENZIE, APRAJIT MAHAJAN, AND JOHN ROBERTs, “Does management matter: evidence from India,” _Quarterly Journal of Economics_ , 128, no. 1 (2013), 1-51.

- BOTTICELLI, PETER, AND LOUIS BARNES, “Family firms in the newspaper industry,” Harvard Business School Case 898-075 (1997).

- BURKART, MIKE, FAUSTO PANUNZI, AND ANDREI SHLEIFER, “Family Firms,” _Journal of Finance,_ 58, no. 5 (2003), 2167–2202.

- BUSTOS, PAULA, “Trade Liberalization, Exports, and Technology Upgrading: Evidence on the Impact of MERCOSUR on Argentinian Firms,” _American Economic Review_ , 101, no. 1 (2011), 304-40.

- CALIENDO, LORENZO, AND ESTEBAN ROSSI-HANSBERG, “The Impact of Trade on Organization and Productivity,” _The Quarterly Journal of Economics_ , 127, no. 3 (2012), 1393-1467.

- CASELLI, FRANCESCO, AND NICOLA GENNAIOLI, “Dynastic Management.” _Economic Inpuiry_ 51 (1): 971-996 (2013).

- CHANEY, THOMAS, “Distorted Gravity: The Intensive and Extensive Margins of International Trade,” _American Economic Review_ , 98, no. 4 (2008), 1707-21.

- CLAESSENS, STIJN, SIMEON DJANKOV, AND LARRY H.P LANG, “The separation of ownership and control in East Asian Corporations”, _Journal of Financial Economics_ 81-112 (2000).

- CLAESSENS, STIJN, SIMEON DJANKOV, JOSEPH FAN, AND LARRY LANG, “Disentangling the Incentive and Entrenchment Effects of Large Shareholdings,” _The Journal of Finance_ , 57, no. 6 (2002), 2741277.

- CHEN, CHENG, “Management Quality, Firm Organization And International Trade,” Working Paper, Princeton University, 2013.

- EISENMANN, THOMAS, “The US cable television industry: Managerial Capitalism in Eclipse”, Business History Review 1-40 (2000).

- FACCIO, MARA, AND LARRY H.P. LANG, “The ultimate ownership of Western European corporations”, _Journal of Financial Economics_ 365-395 (2002).

- FAHLENBRACH, RUDIGER, “Founder-CEOs, Investment Decisions, and Stock Market Performance”, _Journal of Financial and Quantitative Analysis_ 44 (2): 439-466 (2009).

- FOLEY, C. FRITZ, AND ROBIN GREENWOOD, "The Evolution of Corporate Ownership after IPO: The Impact of Investor Protection”, Review of Financial Studies 23, no. 3 (2010).

- FRANKS, JULIAN, COLIN MAYER, PAOLO VOLPIN, AND HANNES F WAGNER, “The life cycle of family ownership: International evidence”, _The Review of Financial Studies_ (2012).

- GENNAIOLI, NICOLA, RAFAEL LA PORTA, FLORENCIO LOPEZ-DE-SILANES, AND ANDREI SHLEIFER,

24