rafmacalaba/datause-encoder-data huggingface dataset:
Figure 11a shows the results on our measure of workforce composition (the difference between the change in average log wages for stayers and the change in average log wages for all workers), again dividing industries into groups based on the magnitude of the shock to direct exports. Again the pre-shock trends across industry groups appear quite similar. Compared to industries that experienced only mild shocks, industries in the other two categories shed lowskilled workers at the end of 2008 and beginning of 2009, but reversed this trend at the end of 2009. Figure 11b shows similar results when grouping industries by the magnitude of the shock to related exports.

We now proceed to the econometric models that implement the continuous treatment design. Specifically, we estimated the following continuous-treatment effects models of the export and related-exports shocks on employment and wages: x (11) ∆Ei,t = βx,t ∙γt ∙∆xi,t∈crisis + βIx,t ∙γt ∙∆Ii,t∈crisis + ∆εi,t, x (12) wi,t = βx,t ∙γt ∙∆xi,t∈crisis + βIx,t ∙γt ∙∆Ii,t∈crisis + ∆εi,t, and x (13) diffi,t = βx,t ∙γt ∙∆xi,t∈crisis + βIx,t ∙γt ∙∆Ii,t∈crisis + ∆εi,t.

Equations 11-13 are estimated using the 99 industries for which both the trade and the labor market data exist for every observation in the sample, (1,089 observations).

In a nutshell, these models provide estimates of crisis-specific deviations from common period-specific shocks across industries. The parameters of interest vary over time. If the export shocks observed during the crisis period did not affect ongoing trends across all industries then βx,t = 0 and βIx,t = 0 across the whole sample period. If the crisis trade collapse induced interindustry dispersion in employment and wages that deviated from common trends, however, then βx,t > 0 or βIx,t > 0 during t∈crisis. In other words, we should observe positive and significant coefficients in (11), (12), or (13) for the quarters of the crisis. If the crisis-induced 28
The thrust of the foregoing discussion is thus twofold. First, although there are solid analytical arguments to suggest that globalization may improve the plight of the poor (in high- and low-income countries alike), there are also equallyconvincing reasons (at least to me) to suggest that the poor may not, after all, benefit much from trade and financial integration-at least without significant govemment interference. Assessing the net effects of globalization on the poor is therefore not a matter of faith, but rather an empirical issue. Second, there are important nonlinearties that may emerge in assessing the link between globalization and poverty. These nonlinearities are not mere theoretical curiosities; on the contrary, it can plausibly be argued that they may be very much at play in the real world. Ignoring them may seriously hamper the reliability of empirical results and may lead to misleading conclusions.

# Ill. METHODOLOGY

To assess the relationship between globalization and poverty I use a crosscountry regression framework, using unbalanced panel data for a group of developing countries for which I was able to collect sufficient data. The dependent variable is the poverty rate _(POV),_ measured by the headcount index for the population as a whole. In addition to measures of trade and financial integration, I include two sets of "control' variables, based on my previous results (see Agenor (2002a)): macroeconomic variables and structural indicators. Specifically, The set of explanatory variables used in the regressions are the following (see Appendix A for more precise definitions): _INFLTAX_ is the inflation tax rate, defined as the ratio of the inflation rate over one plus the inflation rate. This variable is conveniently bounded between zero and implies a concave relation between poverty and the inflation rate; 26
hired into the national teaching workforce, slowing the reduction in the national PTR.

Moreover, teachers are poorly distributed between districts and between schools. The most poorlystaffed eight districts have an overall PTR of 68 or more, and school PTRs can vary within a single zone (sub-district) by a factor of ten or more (Ministry of Education, 2020). The result is that more than 2,200 of Malawi’s approximately 5,800 schools have pupil-qualified teacher ratios (PqTRs) above 90 in lower primary, and more than 800 of these have PqTRs above 190 in lower primary, double the Government of Malawi’s target of 60 (Ministry of Education, 2021).

In addition to being poorly distributed between schools, Malawi’s teachers are also poorly distributed within schools, between grades, with severe under-allocation to lower grades. Average PqTRs range from 83.2 in Grade 1 to 52.1 in Grade 5 and 21.6 in Grade 8 ((Ministry of Education, 2021). These misallocations exacerbate large class sizes in lower grades.

Mixed methods analysis conducted during MESIP confirms that these large class sizes constrain Malawi’s teachers from maintaining certain positive teaching practices, including setting and marking homework and being available for additional support after class (Asim and Casley Gera, 2024).

Severe shortages of classrooms exacerbate large class sizes. The average school pupil-permanent classroom ratio (PpCR) is 98:1, well above the target of 60:1 (MoE, 2022). Moreover, as with teachers, the available infrastructure is inequitably distributed between schools and more than 500 schools have a PCR of more than 160 in lower primary.

These extremely poor conditions lead to high rates of grade repetition and dropout. Most primary schools in Malawi require students to achieve a passing mark in end-of-year examinations to progress to the next grade. In 2021/22, repetition rates nationwide averaged 25 percent (MoE, 2022b). Only 62 percent of students enrolling in Grade 1 survive to Grade 5, and only 39 percent to Grade 8 (ibid.). According to a recent World Bank report, among 12 sampled countries in sub-Saharan Africa, Malawi’s students are the most likely to repeat early grades, and the least likely to survive to Grade 8 (Bashir et al., 2018).

Students who remain in primary school experience poor learning outcomes. At Grade 2 level, fewer than 25 percent of students achieve minimum proficiency levels in the Early Grade Reading Assessment (EGRA) conducted by the United States Agency for International Development (USAID). At Grade 6, fewer than 25 percent of students achieve minimum proficiency levels in the Southern African Consortium for Monitoring Educational Quality (SACMEQ) assessment in Mathematics, placing Malawi near the bottom in the region (USAID, 2013).

_Disadvantaged districts._ In addition to these overall poor conditions and outcomes, Malawi’s public school system has historically been burdened by wide disparities between districts. PTRs and PCRs vary significantly between districts, with certain districts demonstrating consistent disadvantage com6
~~ee~~ <!-- Start of picture text --> Low Educ. Group Survival function: Duration of unemployment<br>a<br>2<br>a<br>><br>ex<br>=|<br>N<br>05 10 15 20 25<br>analysis time<br>—— Plantclosing ——— Quit<br><!-- End of picture text -->

<!-- Start of picture text --> of unemployment High Educ. Group Survival function: Duration of unemployment<br>«©<br>©<br>oo]<br>><br>ex<br>=<br>N<br>15 20 25 0 5 10 15 20<br>analysis time<br>——— Quit —— Plantclosing ——— Quit<br><!-- End of picture text -->
where Y represents the labor market outcome (log of real wages,4 hours of work, formality status or tenure). We include a time-fixed effect, dt, and the matrix X includes a rich set of covariates, including gender, marital status, education, age, age squared, state and industry fixed effects. The variable of interest is TD, which reflects the type of displacement, including plant closings, fired or discharged, quitting, etc. The omitted category is “non-displacement.” We explore the existence of temporary and permanent effects of job displacement on current labor market outcomes. We modify the above specification by adding lagged indicators to the type of displacement: For example, for workers who experience a plant closing event three years before the time of the survey interview, the coefficient _aPC,3,_ captures the difference in current labor outcome, _Yi,t_ , relative to workers in the non-displacement category.

We model the duration of unemployment that follows job displacement with a parametric hazard model. Assuming a Weibull distribution for the hazard function, we estimate the following model, with the survival function, SS(uu) = exp(−(λu)<sup>α</sup> ), which represents the probability that the period of unemployment is a length that is at least U=u; in other words, S(u) = 1 − F(u) = Prob(U ≥ u)

> 4 We use the consumer price index to normalize nominal wages to 2005 prices.

12
17 percent of such credit, with the balance coming mostly from finance companies.15 Expansion of institutional credit was undoubtedly a factor in the expansion of private sector investment in the postreform economy. Private fixed investment as a share of GDP increased from an average level of 8.5 percent in the early 1970s to 18 percent in 1978-82 (Table 15). Between these two periods, the share of fixed investment in private-sector income rose from 10 percent to 18 percent.

To delineate the link between institutional credit and private fixed investment (PRW), an investment function was estimated using annual data for the period 1960-82.16 The explanatory variables were total real (1980) institutional credit to the private sector (ICP), the real GDP growth rate estimated in terms of logarithmic differences (YR), real<sup>public</sup> sector fixed investment (PBIV), an index of rental cost of capital (RC),<sup>1 7</sup> and PRIV with a one-year lag (PRIV(-1)). The inclusion of YR as an explanatory variable implied an accelerator-type relationship between the level of domestic economic activity and capital formation. PBIV was<sup>chosen</sup> as an explanatory variable in light of the debate on the crowding out effect (Blejer and Khan, 1984). PRIV(-l) was included as a regressor on grounds that the adjustment of actual investment to the desired level is not completed within a single period.

The estimated investment function is, ln PRIV = - 0.33 + 0.63 ln ICP - 0.34 ln PBIV(-l) (0.25) (3.52)* (2.26)** + 0.05 ln (1+YR) - 0.07 ln RC + 0.71 ln PRIV (-1) (3.14)* (1.17) (4.73)* R<sup>2</sup> = 0.92 F = 49.3 LM = 0.02 RESET2 = 0.03 JB = 0.96 WH = 0.85 15. Estimated using figures obtained from, Central Bank, Review of the Economy. Figures used in the remainder of this paper, unless otherwise stated, come from this source.

16. The Central Bank has stopped publishing a qeparate private investment series since 1983.

17. This index is constructed as RC = [PIH (1 * NLR)/PJ 100 where PIM = [price of investment goods proxied by the import price index of investment goods (1080 = 10'" (adjusted for the dual exchange rate (FEEC) premium for the perio"d l9I53-, ILR = nominal one-vear (maximum) lending rate of commer. ial I(ank's and F = implicit GDP deflator (1980=100) for non-service a tivities.
# **Figure 4.  Real Exchange Rate Gap and Demand Composition 1990-2012**

<!-- Start of picture text --> 1<br>0.5<br>LAC SSA<br>MNA<br>0<br>EAP<br>ECA<br>-0.5<br>-1<br>-2 -1.5 -1 -0.5 0 0.5 1 1.5 2<br>Demand Composition<br>Real Exchange Rate<br><!-- End of picture text --> ♦LAC 1990-2012 Average ●Other countries per Period ♦ Other groups of countries 1990-2012 Average Sources: Authors' calculations based on UN, WDI and Institutional Investor data. Notes: This figure shows the real exchange rate gaps and the demand composition for the middle income countries in the sample (World Bank classification). Demand Composition is the ratio of external demand (exports) to internal demand (consumption plus investment). Each period is a threeyear average. See Section 3 for the details of how the benchmarks are calculated.

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department of the central government distributes a specific-purpose disbursement, it takes great care not to discriminate against any local government.

The specific purpose disbursements from the central government constitute a fairly large portion of the total revenue of local governments. For prefecture governments, the disbursements constitute about 17 percent of their total revenue in 1989. If we assume the average matching rate of the central government specific-purpose disbursements is one-third, about half of the prefecture government expenditures are for subsidized activities.

Table 2.7. Distribution of Central Government Disbursements for Specific Purpose, 1989 (Yen billion, percent)

||Prefectures|Municipalities|Net total|
|---|---|---|---|
|Subsidies for compulsory<br>eduction|2,564.5 (36.40)|...|2,564.5 (24.7)|
|Livelihood protection|178.3 (2.5)|868.0(26.0)|1,046.3 (10.1)|
|Welfare allowances for<br>children|124.2 (1.8)|239.6 (7.20)|363.9 (3.5)|
|Medical expenses for mental<br>patients|18.4 (0.3)|10.9 (0.3)|29.3 (0.3)|
|Welfareallowances for the<br>aged|43.4 (0.6)|...|43.4 (0.4)|
|Ordinary construction<br>outlays|2,457.3 (34.9)|1,074.2 (32.2)|3,531.5 (34.0)|
|Restoration work<br>after disasters|291.2 (4.1)|105.9 (3.2)|397.1(3.8)|
|Others|1,277.5 (18.1)|897.4 (26.9)|2,174.9 (21.0)|
|Total<br>(100.0)|7,043 (100.0)|3,333.1 (100.0)|10,376.8|

Source: Y'onehara (1993).

The distribution of specific purpose disbursements in 1989 by categories established by the Ministry of Home Affairs is presented in Table 2.7. Nearly 34 percent of the total central government specific purpose disbursements (of both prefecture and municipal governments) are subsidies for ordinary constructions. These subsidies include grants for roads, bridges, parks, river banks, harbors and public housing. As a single program, that subsidizing the salaries of teachers engaged in compulsory education is the largest. In Table 2.6, this disbursement program is included in the subsidy for compulsory education disbursed to prefectures. For municipalities, the largest disbursement program is for ordinary construction.

Almost all special purpose disbursements are cost-sharing grants. They subsidize a certain percentage of the standard cost prescribed by the central government. The rate of subsidy differs from one program lo another. For instance, the grant for teachers' salaries in public primary and junior schools 27
Reading: Barder and Yeh 2006, Global Alliance for Vaccines and Immunization (GAVI) 2004, Lob-Levyt and Affolder See also: International Finance Facility

# **Line of Sovereign Credit**

A line of sovereign credit would provide automatic access to a line of credit at a predetermined interest rate. Whenever a country experiences liquidity problems it could draw from its line of credit. A line of credit would provide a country with flexible access to volatile and unpredictable demand for financing. Other than contingent loans, the access to funding through a line of sovereign credit would not be linked to exogenous shocks and triggers.

Reading: Cordella and Levy Yeyati 2006, Plaut and Melnik 2003 See also: Contingent loans

# **Loan**

In development finance a loan (also credit) is defined as a transfer made in cash, goods, or services for which repayment is required.<sup>103</sup>

# **Local Currency Lending**

Local currency lending refers to loans that are denominated in recipient countries currencies. (Traditionally, development assistance is denominated in donors’ currencies). Local currency lending avoids currency risks of borrowers. Local currency bond markets for sovereign and private debt have developed quickly over the last years but are still not available for some of the least developed countries.

Further information: Burger and Warnock 2006, 2007 See Also: Global Emerging Markets Local Currency Bond Program (GEMLOC)

# **Macro Markets (Macro Derivatives)**

In macro markets, claims on aggregate national incomes, fractions of the GDP, real estate, or income of various groups of a country are traded. Macro markets allow individuals to hedge against risks that derive from the macroeconomic environment (for example, personal income is correlated to GDP and expenses to housing prices).

Reading: Gürkaynak and Wolfers 2005, Jakab 2006, Shiller 1993, 2003b, 2005

# **Microfinance**

Microfinance offers poor people access to basic financial services, such as loans, savings, money transfer services, and microinsurance. The roots of modern microfinance are in the 1970s when first programs in Bangladesh, Brazil, and a few other countries began to provide small loans to groups of poor women, using the group’s savings as collateral. Different types of microfinance providers have emerged including nongovernment organizations; cooperatives; community-based development; commercial and state banks; insurance and credit card companies;

> 103 OECD: http://www.oecd.org/glossary/0,2586,en_2649_33721_1965693_1_1_1_1,00.html#1965544.

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the Zanzibar and Mafia channel, where the shelf extends to some 60 to 80 km (Ruitenbeek and others, 2005). Along most of Tanzania’s coast line, fishing is an open access activity in which anyone can participate.<sup>4</sup> Most marine fishing activity takes place in the shallow near shore waters, using traditional vessels and gear. Table 1 provides an overview of the different types of fishing gear commonly used by artisanal fishermen in Tanzania. About 95 percent of the total marine catch is estimated to come from this type of small scale artisanal fishery (Jiddawi and Ohman, 2002). Fishing is an attractive activity for the poor in particular, as it does not necessarily require the ownership of any assets and has very low start up costs. It also generates food and cash income on a daily basis. While most fishing households in coastal areas are also engaged to some extent in agriculture, there is less dependence on agriculture on the coast due to the lack of suitable soils for farming which is characteristic of certain areas where coral rag soils dominate the terrain. Fishing also provides some insurance for the poor as a fallback source of food and income in between crops or bad crop years, and during times of economic crisis and instability. Fishing is therefore a crucial activity for the livelihoods of many poor rural households along the coast of Tanzania.

The number of fishermen in the coastal areas of Tanzania has increased over time, although reliable time series statistics are difficult to obtain due to the engagement of many part time fishermen and fluctuations due to changing economic conditions (FAO, 2000). Many fishermen in Tanzania attribute the increasing number of fishermen as one of the major causes of declines in fish catches (MANREC, 2005; Leon and others, 2004). They also cite the lack of vessels and gear to fish offshore as reasons for over exploitation of near shore reef fisheries. The lack of fishing gear, many fishermen say, also leads them to resort to cheap but destructive fishing practices (Leon and others, 2004). Many illegal fishing methods, such as dynamite or poison fishing, spear gun fishing, beach seine nets, drag nets, and gill nets with small mash sizes, are still used due to inadequate enforcement of existing fishing regulations.

> 4 As of 2002, only 12 industrial vessels were licensed to fish in Tanzania’s Exclusive Economic Zone (EEZ), although additional vessels were known to fish in the EEZ illegal. Improved efforts to regulate EEZ fisheries, through monitoring, control and surveillance activities, have yielded valuable information on the potential value of fishery resources in the EEZ.

6
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- Bouët, Antoine, and David Laborde. 2010. “Assessing the Potential Cost of a Failed Doha Round.” _World Trade Review_ 9 (02): 319–351.

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