rafmacalaba/datause-encoder-data huggingface dataset:
The paper first illustrates the connection between wage expansion (poverty reduction), labor intensity, and sectoral growth through a multi-sector theoretical model. Then, considering the model’s insights, it conducts a set of cross-country empirical exercises on poverty changes as the dependent variable. The paper finds that the impact of growth on poverty reduction varies from sector to sector and that there is a systematic pattern to this variation. Sectors that are more labor intensive (in relation to their size) tend to have stronger effects on poverty alleviation. Thus, agriculture is the most poverty-reducing sector, followed by construction, and manufacturing; while mining, utilities, and services by themselves do not seem to help poverty reduction.

After this sectoral-driven empirical analysis, the paper conducts a more direct test of the model by considering poverty reduction a function of not only aggregate growth (which would represent growth’s _size_ effect) but also a measure of labor-intensive growth (which would represent its _composition_ effect). The results confirm that poverty alleviation indeed depends on the size of growth. Moreover, they also indicate that poverty reduction is stronger when growth has a labor-intensive inclination. This central result of the paper is robust to the influence of outlier and extreme observations, holds true for various poverty measures (such as the headcount index, the average poverty gap, and the Watt’s index), and is not driven away by alternative explanations --such as the importance of agricultural growth in reducing rural poverty, the role of inequality in dampening the beneficial impact of growth, and the statistical discrepancy between household surveys and national accounts. Finally, analysis on the mechanisms through which labor-intensive growth reduces poverty allows us to conclude that this positive effect does not require or imply redistribution from rich to poor. Although laborintensive growth improves the relative standing of the poor, its main effect on poverty is given by its beneficial impact on their absolute income.

From a positive perspective, these results may help understand the considerable disparity in the poverty reaction to economic growth and, in particular, why in some circumstances poverty is irresponsive to production improvements. This would be the case of, for instance, a country experiencing a mining or oil boom that is unaccompanied by growth in other sectors. From a normative perspective, this study does not provide grounds for “industrial” (or selective) policies as it does not deal with the sources of 22
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medicine: it is there that a financing problem arises, since the public authorities have not pursued an active policy of support--which would involve for example financial assistance--for traditional medicine.

The figures used for the essential variables of the model are not taken from the Health Ministry's statistical data system:

- first, the quality of the data collected is very mediocre; this is particularly true of the statistics on activities and therefore of average cost per activity unit;

- second, the observed values for the activity and cost variables would reflect the conditions of management of the health system during recent years and these conditions cannot serve as an acceptable reference basis for projection: the budget resource and personnel allocations are based on neither the quantitative nor the qualitative level of the services, and the practical conditions of supervision are such that service quality control is totally lacking; there is no purpose in working with the costs of services whose quality is neither acceptable nor at least approximately uniform.

In these circumstances, instead of the observed values for the entire health system over recent years, we have used as our basis a number of particular cases, those for which reliable data are available on activities whose quality can be regarded as acceptable. This does not make the hypotheses used for the simulation unrealistic. Concerning service utilization rates, service technical levels and procurement costs, we shall use simulation bases very close to the practical conditions observed in Mali during recent years. However, the examples used to fix the orders of magnitude of the simulation parameters are often, for a very simple reason--the existence of a minimum of data here and not elsewhere--those of health units that have in one way or another received project aid. For the same reasons we have also decided to describe those activities and costs for which valid data are available rather than activities that appear in job and post descriptions but that nobody is able to carry out. The fact that the technical level of the activities no doubt hardly varies at all when we pass from one level of the pyramid to another diminishes the drawbacks stemming from this constraint.

In practice, we disregard activities associated with confinements that take place in rural maternity hospitals and _arrondissement_ clinics. The hospitalization activities of the _cercle_ health centers and those of the hospitals (including the national hospitals) are grouped together; while this procedure may be surprising, it is justified by the information available on the nature of the activities of these types of establishments (cf. (231 pp. 77-94). Similarly, we shall disregard education and basic and refresher training activities for health staff to be assigned to villages or groups of villages, activities which, despite their important place in certain health projects, have never been shown to have an impact--beyond their start-up phase--on either the level or the quality of the services (cf. [231 pp. 1-18). We shall also disregard nutritional and social activities: the former
The pattern observed at the global level, namely that agriculture-dependent households on average earn less than other households, is replicated _for all_ developing countries in the GIDD ( **Figure 3** ): the share of total population employed in agriculture activities is always larger than its corresponding share of total income. The simple average income premium, i.e. the ratio of non-agricultural to agricultural incomes, is equal to 2.25. Clearly this premium is unconditional in the sense that it does not take into account that in agriculture low-earning unskilled workers tend to be more abundant than skilled workers, or that other factors may explain the observed income gap. However, a simple multivariate regression analysis shows that even controlling for education, age, gender, household size, geographic region, and country fixed-effects, agriculture-related incomes are still 23 percent lower than incomes derived from other sectors.

An important element hidden in Figure 1 and only partially shown in **Figure 3** is the degree of cross-country variation in income inequality. Figure 4 shows that the difference in the Gini coefficient between countries is enormous, with former communist countries like Romania and Hungary showing an index below 0.3 whereas in highly unequal countries such as South Africa and Mozambique the index reaches values above 0.6. Once again, the tendency of higher inequality within the agriculture group observed at the global level is corroborated by the analysis of country-specific inequality. For more than three quarters of the countries included in our data (56 out of 73), Gini indicators of inequality within the agricultural group are higher than those of the non-agricultural group (Figure 4).

**Figure 4: Inequality variation across countries and sectors**

<!-- Start of picture text --> 0.65<br>Overall Gini (  )<br>0.55<br>Gini in agriculture (-) Gini in non-agriculture (x)<br>0.45<br>0.35<br>0.25<br>0.15<br>Denmark Finland Sweden Netherlands Korea, Rep.Austria LuxembourgGermanyAlbania France TajikistanSwitzerland Canada Australia EU SpainGreece ItalyNew Zealand GeorgiaPortugalGhana Israel NigeriaUnited States SingaporeChina Hong Kong,Gambia, TheEl Salvador Mexico NicaraguaColombia Ecuador Bolivia Brazil Guatemala HungaryUkraine Czech RepublicSlovenia Pakistan Belarus Russian Romania KyrgyzLithuania Armenia Kazakhstan EthiopiaBulgariaPoland MongoliaBangladeshEstonia India Indonesia Yemen, Rep.Ireland Uzbekistan Moldova Tanzania Vietnam Lao PDR NepalBenin AzerbaijanTurkeyMacedonia, FYRSri Lanka United KingdomMorocco Mali SenegalGuinea Thailand Mauritania Jamaica GuyanaCambodia Jordan Burundi Venezuela, RBUgandaCameroon KenyaCote d'Ivoire Costa Rica MadagascarPhilippinesBurkina Faso Dominican Peru Chile Honduras Panama ParaguaySouth Africa Haiti<br><!-- End of picture text --> Note: authors calculations based on GIDD dataset using developing countries information only.

8
government, the relationship between the enterprise and the government, production autonomy, the characteristics of the management, and so on.

# **Appendix B. Construction of Variables**

In constructing these variables, we have followed other users of this data set, especially Li (1997), and Gordon and Li (1995). All quantities (value added, capital stock) are valued at the market value in 1989, assuming that the 1989 prices reflected best the opportunity costs of the resources.

# _Capital Price Indexes and Capital Stock_

The survey contains answers to questions about the inflation rate of the mixed price of equipment between the periods 1965-1975, 1975-1980, 1980-1984, and for each year between 1985 and 1988. Based on these answers we computed average inflation rates for equipment. For 1980-1984, equal yearly inflation rates are assumed. For 1989, since we do not observe equipment inflation, we used the output inflation rate in the machine industry in 1989 as a proxy.

Since the survey does not provide information on prices of buildings or plant, for the inflation of building prices we used the percentage increase in aggregate construction costs compiled by the State Statistical Bureau. This series has also been used by Li (1997).

The composite price index for capital goods are then computed by averaging the equipment price index and the buildings and plant price index, the weights being the investment expenditures on equipment and plant.

We based our measure of capital stock on capital assets "for productive use", which includes plant and equipment for industrial production. (In contrast, capital assets "for non-productive use" are mainly buildings and expenditures on dormitories, cafeterias, employee housing, and other social welfare functions.) For our purpose of measuring the contribution of capital, the use of "productive" capital assets is more appropriate. Following Li(1997) and Gordon and Li (1994), we choose not to use the net value of capital stock as the base to compute capital stock because the former "tend to exaggerate the increase in enterprise capita stock during the sample period in which the inflation rate was high, because the accounting rate of depreciation was artificially low and the depreciation was based on historical costs" (Gordon and Li, 1994).

Realized investment at year t is imputed by subtracting the nominal value of productive capital assets at the end of year t-l from that at the end of year t. The reported investment, usually different from our imputed figures, is not used because it measures the value of capital expenditure (rather than capital formation) in a given year. It includes, e.g., expenditure on ongoing construction projects; while it excludes prior investment projects completed in the year.

When assuming that investment occurs smoothly over the course of a year, we can compute the capital stock in 1980( K,80), the initial year, as _Ki,80_ = _0.5(K,79_<sup>**_+_**</sup><sup>_K,)P_</sup> l _K_ 28
TABLE 1. Foreign Trade of the New Independent States of the former Soviet Union, 1991-94 _(millions of current U. S. dollars at market exchange rates)_

|||_1991_||_1992_||_1993_||_1994_|
|---|---|---|---|---|---|---|---|---|
||_Exports_|_Imports_|_Exports_|_Imports_|_Exports_|_Imports_|_Exports_|_Imports_|
|**Trade**with the**rest of**|the**world***||||||||
|Armenia|70|830|40|95|29|188|42|110|
|Azerbaijan|487|1,248|754|333|351|241|360|275|
|Belarus|1,661|1,957|1,061|755|737|777|1,053|690|
|Estonia|50|204|242|254|461|618|730|1,251|
|Georgia|30|480|161|269|222|460|86|189|
|<br>Kazakhstan|1,183|2,546|1,489|961|1,529|1,269|1,327|1,694|
|Kyrgyzstan|23|785|77|71|112|112|112|88|
|<br>Latvia|125|478|429|423|460|339|524|581|
|Lithuania|345|475|557|342|696|486|855|1,063|
|Moldova|180|656|157|170|174|210|121|134|
|Russia|53,100|45,100|41,600|37,200|43,900|33,100|46,974|35,100|
|Tajikistan|424|706|111|132|263|374|319|306|
|Turkmenistan|146|618|1,145|543|1,156|749|371|298|
|Ukraine|8,500|11,300|6,000|5,500|6,300|4,700|4,648|4,347|
|Uzbekistan|1,257|2,048|869|929|1,466|1,280|912|1,106|
|Former Soviet Union|67,581|69,431|54,691|47,977|57,857|44,903|58,433|47,233|
|**Trade with countries o**|**f the former**|**Soviet Union**|||||||
|Armenia|3,823|4,686|243|292|124|159|215|359|
|Azerbaijan|9,091|7,013|521|434|629|461|398|612|
|Belarus|23,151|20,375|1,939|2,128|3,092|3,348|2,085|2,990|
|Estonia|3,836|2,996|147|146|341|326|574|405|
|Georgia|5,594|4,806|144|224|295|433|156|280|
|Kazakhstan|14,285|16,949|2,141|2,463|3,126|3,576|1,958|2,476|
|Kyrgyzstan|5,163|4,293|236|344|282|378|325|402|
|Latvia|5,920|4,365|451|472|587|649|503|652|
|Lithuania|9,268|6,251|505|624|929|1,111|1,160|1,276|
|Moldova|6,190|5,525|244|377|636|743|406|449|
|Russia|108,571|83,333|12,300**|6,200**|15,752|10,546|17,700|15,000|
|Tajikistan|3,456|4,361|93|172|118|198|170|252|
|Turkmenistan|6,314|3,684|616|410|1,731|876|1,252|313|
|Ukraine|49,598|61,217|5,262|6,425|5.669|9,185|5,543|7,593|
|Uzbekistan|13,761|14,100|628|827|2,085|2,225|1,408|1,086|
|Former Soviet Union|268,022|243,954|25,470|21,537|35,396|34,211|33,853|34,145|

_* The rest of the world refers to countries outside theformer Soviet Union._ _** These recent estimates differfrom data in Dikhanov, 1995 *vhich shows exports of 10, 954 mnillion and imports of 9,246 million. These later estimates are thought to underestimate itnports and may wvell be subject to further revision. Source. National official statistics and lMF._
# **Figure 6. GDP Per Capita and Real Exports for Oil Exporters**

## **_(a) GDP per Capita (1950=1) Export Volume (1980=1)_**

<!-- Start of picture text -->
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Note: Countries’ GDP per capita and real exports are divided by the world’s. The comparator group of oil exporters includes Algeria, Bahrain, Cameroon, Gabon, Kuwait, Nigeria, Qatar, and the United Arab Emirates. _Source: Maddison Project and WDI._
<!-- Start of picture text --> 4<br>35<br>3<br>25<br>2<br>1s<br>1<br>os<br>a<br>oPEL9 gh gh Igg FLL2 gr gd LS2 ae LILO BD LIOP oSSIISp LLL2 OOSEIS SEIS$ OwI adSSSso Ae<br>——LAC =——Asien Tigers. ——Southem Europe ——Sub-Saharan Africa<br><!-- End of picture text --> <!-- Start of picture text --> 8<br>6<br>4<br>2<br>t)<br>2015 6 «62017-2018 ~—20: 02, 2021 ©2022-2023, 2024 «= 2025 2026<br>2<br>4<br>6<br>3<br>——AdvancedEcon, —mmmAsia5S mmmLAC MENA eee SSA<br><!-- End of picture text -->
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examples of delinking, until the stock market crash of October 1987, the profitability of specialized financial institutions, such as investment banks, outpaced that of industry and commerce, and the cumulative gains in share prices exceeded those of any other financial or economic indicator. For instance, between 1982 and 1986 the cumulative real GDP growth in OECD countries was 13 percent. By comparison the world index of financial market growth increased by 130 percent over the same period.44/ Although delinking and asymmetry in the rates of growth of financial services and of other sectors of the economy are not bad per se, they are a source of tensions. For example, the fact that financial decisions are often divorced from underlying dievelopments in the real economy raises the level of economic uncertainty. The stockmarket crash of 1987 shows that such risks cannot be underestimated: on October 19 the band-wagon effect of simultaneous action by major institutional investors and feedback between the cash and futures markets precipitated the market collapse and rekindled fears of an imminent global recession.

Admittedly, the continued momentum of growth in industriaL countries, and the limited impact of negative wealth effects--following the fall of share prices--have shown that those recessionary 5ears were largely exagerated at the time.45/ However, the point remains valid that there is a need to 44/ Bernard Snoy: Le decouplage entre la finance et l'economie et la crise financiere d'Octobre 1987; Processed, IEC, Brussels. While this disparity in price performances is not unique--it is a common feature of past "bull markets"--it had become more pronounced then in the past.

45/ The IMF has substantially revised its of real GNP growth in industrial countries. In October 1987, these were estimated at 2.4% for 1987, and 2.6% for 1988. (World Economic Outlook, IMF, October 1987, Table A2, p. 40.) In April 1988, these estimates were revised to respectively 3.1% and 2.8%. (World Economic Outlook, April 1988, Table A2, p. 112.)
The Excel‐based interface, which has been developed for the model (described in Appendix B), facilitates model applications; however, to make good use of the model, it is essential to understand its structure (presented in Appendix A) and learn from applying it.

A companion paper (Cicowiez and Lofgren, 2017a) provides macro SAMs for 133 countries; these provide the bulk of the data needed for macro applications. Given that these SAMs and the rest of the databases needed for country applications are very small, they can easily be adjusted and updated by the analyst.

However, while macro applications meet some analytical needs and may be preferable for training purposes, GEM‐Core and its interface are equally applicable to databases that have finely disaggregated households, factors, and sectors; this is possible thanks to the use of set notation in the model (parameters, variables, and equations) and its database. The degree of disaggregation that is preferable in any applications depends on purpose, data availability, and the time and resources available for the analysis. Given the fact that CGE databases are built from secondary data (i.e., they do not require new surveys) that are available for most countries, the resources needed for the work are relatively limited.

# **References**

Agénor, Pierre‐Richard, Nihal Bayraktar, and Karim El Aynaoui. 2005. “Roads out of poverty: Assessing the links between aid, public investment, growth and poverty reduction.” World Bank Policy Research Paper 3490.

Annabi, Nabil, John Cockburn, and Bernard Decaluwé (2006). “Functional Forms and Parametrization of CGE Models.” MPIA Working Paper 2006‐04, Poverty and Economic Policy (PEP) Network.

Baddock, Emily, Peter Lang, and Vivek Srivastava. 2015. _Size of the Public Sector: Government Wage Bill and Employment_ . World Bank.

Calderon, Cesar, and Luis Serven. 2014. “Infrastructure, Growth, and Inequality: An Overview.” World Bank Policy Research Working Paper 7034.

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# **1 Introduction**

More than 700 million people live in extreme poverty worldwide. This includes an estimated 97 million who were pushed into extreme poverty due to the COVID-19 pandemic, reversing the positive global trend for the first time since 1997 and highlighting the precarious nature of the large development gains achieved over the past 30 years (Lakner et al., 2021; World Bank, 2022a; Kharas & Dooley, 2021). Two-thirds of this extreme poor population are expected to live in fragile and conflict-affected areas by 2030, where social protection is weak and households have limited access to coping mechanisms, making them particularly vulnerable to the increasing frequency of conflict, climate and economic shocks (Corral et al., 2020; Bousquet & Bronkhorst, 2021; Wellenstein et al., 2022). These settings are also where evidence is most limited due to the practical challenges with generating rigorous evidence under these conditions (Tollefson, 2022; Blattman & Ralston, 2015). This raises concerns that, even as global evidence increases, site selection may bias our understanding of the effectiveness of future poverty reduction strategies (Allcott, 2015). Identifying antipoverty strategies that are resilient to conflict, climate and economic shocks will be critical for ensuring that the significant development gains can be recovered and sustained.

In 2016, women in some of the poorest and most marginalized households in Afghanistan’s Balkh province received a multi-faceted intervention (the Targeting the Ultra-Poor Program, or TUP) to help them create sustainable livelihoods and move out of extreme poverty. Aimed at addressing multiple constraints faced by the ultra-poor, the intervention included an asset transfer (typically cows), a monthly cash stipend, and coaching for twelve months. In 2018, two years after the asset transfer, results from a randomized control trial (RCT) showed significant impacts across a range of well-being indicators. The treatment increased households’ consumption and revenues, reduced food insecurity, improved psychological well-being for women and men, increased women’s market work participation (reducing their idle time), improved children’s school attendance and physical health, and reduced their poverty prevalence by 20 percentage points, compared to the control group (Bedoya et al., 2019). Not only did the program improve the lives of treatement UP household members in a fragile context, but it also helped reduce gender gaps and improve women’s empowerment in a setting with particularly binding constraints for women’s participation in social and economic activities.

Shortly following the promising short-term results, the country was beset by multiple crises between 2018 and 2021: severe droughts in 2018 and 2021, escalating violence, and the COVID-19 pandemic fundamentally affected Afghans’ livelihoods, health and security. TUP-type programs have begun to show promising longterm impacts in more secure locations and under more stable macroeconomic conditions (Banerjee et al., 2