rafmacalaba/datause-encoder-data huggingface dataset:
them produce the goods to export as well as to allow them to finance their extensions of credit to importers.

This is one of the most important sources of short-term financing for firms around the world (Petersen and Rajan, 1997).<sup>3</sup> It tends to be relatively more prevalent for firms located in developing countries (Demirgüç-Kunt and Maksimovic, 2001; Beck _et al.,_ 2008). Its use tends to increase in times of crisis (Calomiris, 1995; Love _et al.,_ 2007). Within the scenario described above, these three stylized facts are striking and suggest that a closer look should be devoted to this specific form of financing.

To this aim, we will start by identifying the main features which distinguish it both from the financing provided by financial intermediaries and the trade financing intermediated by banks.

We will then try to understand whether these features can shield this form of financing from the general credit crunch or rather constitute an extra element of tension, especially from the point of view of developing and low-income countries. Our ultimate objective will be to identify theoretical economic rationales that could underpin policy actions in favor of this form of financing.

Firms simultaneously extend trade finance to their customers and receive it from their suppliers. This exposes them on one side to the risk of non-payment by their customers, and on the other side to the risk of credit stopping by their suppliers. Because of this, trade credit is considered as a mechanism of propagation of shocks. This risk might be stronger in countries with less developed financial markets, and thus in many emerging countries, which have higher proportions of their debts financed with trade credit.

However inter-firm trade finance also presents a number of advantages that justify its widespread use. Unlike standard lender-borrower relationships and intermediated trade finance, inter-firm relationships may be characterized by less severe incentive problems which facilitate contract enforcement and affect both the cost and the access to credit. Intermediated trade finance, for example, involving several agents, is more prone to problems of asymmetric information relative to inter-firm finance. A letter of credit, for instance, involves an importer, the local issuing bank, the exporter confirming bank and the exporter. With several agents there are multiple incentive problems. Buyer’s creditworthiness is crucial for the issuing bank to be willing to undertake the risk. In turn the confirming bank has to be confident that the issuing bank has sufficient funds to extend credit to the importer. In these circumstances, it becomes more difficult to control and align the incentives of the various parties

> 3 According to Global Business Intelligence (GBI, 2007), a consulting firm specialised in supply chain matters, accounts payable and receivable represent 78% of international trade.

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12 1961<sup>12</sup> . When the exemption was passed the first group was 16 to 18 years old, while the second was 20 to 22. Individuals in the first group were not yet eligible for service and thus could change their schooling decisions; those in the second group were instead already beyond high school age. The evidence discussed in the previous section supports the view that differences between exempt and non exempt cohorts reflect the effects of exemption status rather than those of confounding factors correlated with exemption.

In rows A to C we look at the short run effects of exemption status. We draw on the 1981 population census which, importantly, was run in October, at the beginning of the school year. Therefore the evidence describes quite precisely the enrollment decisions of the cohorts affected by the exemption. Youths born in 1963-65 were not yet eligible for service (row A). However, the proportion that was still in high school at the 1981 census date was higher by 3.6 percentage points than that of non-exempt counterparts (p-value 0.027; row B). Correspondingly, a lower percentage entered the labor market (row C), reflecting a choice toward schooling consistent with the mechanism that a longer expected working life increases the incentives to accumulate human capital (Ben-Porath (1967), Jayachandran and Lleras-Muney (2009)). Youths born in 1959-61 had been exempt from military obligations soon after becoming formally eligible to serve. In 1981 the percentage of conscripts was lower by 3.5 percentage points (row A), a proportion similar to that choosing education in younger exempt cohorts. The lower conscription rates basically translated into a higher labor market participation rate rather than school enrollment (row C).

The lower part of table (1) draws on the 1991 population census to describe the long run effects of the exemption by comparing outcomes of exempt and non exempt youths belonging to the two cohorts under analysis ten years after the exemption. The census also includes some retrospective information as of 1986. Ideally, we would like to observe the same individuals in both data sources,

> 12We exclude the 1962 cohort from the comparisons as its members were 19 when the exemption was passed, an age when it is hard to make assumptions about whether they were still in high school or already beyond it.
## **_Distribution of Social Weights by Poverty_**<sup>**_Status_**</sup>

We focus here on how the relative<sup>socialweight attachedto each of thesegroups</sup> varies with the aversion parameter.<sup>Table3.1containsthe resultsof suchan experiment</sup> using household survey data from<sup>Guinea.We find thatthe poor accountfor16 percent</sup> of the mean rank, and the non-poor<sup>for84percent.Thisstemsfromthefactthatthe</sup> overall mean rank (i.e. the mean<sup>of the cumulativedistribution)isequalto0.5.The mean</sup> rank of the poor is about 0.20 while<sup>that of the non-pooris0.70.Theweightedaverage</sup> of the two numbers is equal to<sup>0.5.The weightsusedherearetherespectivepopulation</sup> shares. _When the aversion parameter_<sup>_is equal to one,each group receives,on average, a_</sup> _weight equal to its population share._<sup>As the aversion parameterincreasesabove1, most</sup> of the social weight is shifted<sup>to thepoor.</sup> Table 3.1. Distribution of Average<sup>Social Weightin Guinea by Poverty Statusin 1994 and by</sup> Level of Aversion

||ShareofMeanRank|v=l<br>|v=2<br>|v=3<br>|v=4<br>|v=5<br>|v=6<br>|
|---|---|---|---|---|---|---|---|
|Poor|0.16<br>|0.40<br>|0.64<br>|0.79<br>|0.87<br>|0.92<br>|0.95<br>|
|Nonpoor|0.84<br>|0.60<br>|0.36<br>|0.21<br>|0.13<br>|0.08<br>|0.05<br>|
|Total|1.00|1.00|1.00|1.00|1.00|1.00|1.00|

Source: Computed

# **_Decomposability_** _of_ **_the Gini_**<sup>**_Family_**</sup><sup>_of_</sup><sup>**_Indices by Population Subgroups_**</sup>

Truncation by a poverty line<sup>impliesthat the ordinaryGinicoefficient canbe</sup> written as:

## (3.1)

where the term between parentheses<sup>representswithingroupinequality,whiletheterm</sup> outside stands for between group<sup>inequality.The term</sup><sup>**si**measures the proportionof total</sup> welfare enjoyed by grout i, and<sup>**pi**is the populationshare of thatgroup.</sup> Anand(1983:320) shows that,<sup>in this case, between group inequalityis equalto:</sup>

## (3.2)

Assuming that the first group<sup>is the poor group, then theabove expressionreduces to:</sup>

## (3.3)

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3

**<u>Figure 2: Is There a Hierarchy of Upgrading?</u>**

<!-- Start of picture text --> Process Product Functional Chain<br>Upgrading Upgrading Upgrading Upgrading<br>Trajectory<br>Examples Original<br>equipment<br>assembly (OEA) Moving chains<br>Original Original – e.g. from<br>design brand black and white<br>Original manufacture manufacture TV tubes to<br>equipment computer<br>manufacture monitors<br>OEM<br>Degree of Disembodied content of value added increases progressively<br>disembodied<br>activities<br><!-- End of picture text --> _Source:_ Kaplinsky and Morris, 2001.

# **_1.3. Southern Drivers of Growth_**

It follows from the discussion in Sections 1.1 and 1.2 (but see Kaplinsky and Farooki, 2010 for an extended discussion) that, by hypothesis, global value chains feeding into low-income markets are likely to have different characteristics to those feeding into high-income markets. To take the two issues addressed above – standards and upgrading – we can develop this hypothesis in the following terms.

- Standards are driven by differentiation of consumer tastes so that lead firms have to govern their extended chains to ensure the conformance required to achieve differentiation at low cost. They are also driven by government health and safety considerations, and by civil society concerns with equity and the environment. In each of these cases, it is less likely that these demands will be as evident in value chains feeding into low-income markets. Figure 3 gives some examples of these variant drivers of standards in high and low-income economies, distinguishing between standards driven by firms, governments and civil society, and those addressing products and processes.
# OPEN ECONOMIES WORK BETTER!

Did Africa's Protectionist Policies Cause its Marginalization in World Trade?

Francis Ng and Alexander Yeats*

# 1. Introduction

United Nations statistics show the relative importance of sub-Saharan Africa in world trade experienced a substantial secular decline over the last three or four decades. Specifically, UNCTAD (1993) reports that sub-Saharan Africa accounted for 3.1 percent of global exports in 1955, yet by 1990 this share had fallen to 1.2 percent -- a decline that implies annual trade losses of approximately $65 billion. While the empirical evidence relating to Africa's marginalization in global trade is uncontested, less agreement exists about the responsible factors. Official pronouncements by some African governments or spokesmen indicate that protectionism in OECD markets played an important role. However, many development economists and international organizations like the World Bank or International Monetary Fund maintain that inappropriate domestic policies greatly diminished Africa's ability to compete internationally, and have advocated (often unpopular) structural adjustment programs to reverse unfavorable trade and economic trends. Since these alternative explanations for Africa's trade problems have very different corrective policy implications it is important to determnine which is basically correct.

This study's objective is to produce and analyze empirical information that should help resolve the issue. Specifically, several trade performance indices, such as those relating to changes in a country's competitive position, or to changes in demand for its exports, are employed to assess the impact of "competition" and "demand" factors on Africa's trade. A third measure -- which relates to export Staff members, International Trade Division, The World Bank, Washington, D.C.
and Federal income taxes, and TAXSIM - the tax policy analysis model developed **at** NBER (Feldstein, 1983). Such models take as their nucrodata base cross-sectional information on a representative sample of the population of a country e.g. TAYMOD at the LSE is based on data from the Family Expenditure Survey (Atkinson and Sutherland, 1988).

L)ynamic Population models also take a sample of tht population as their initial microdata base. However, in this instance the sample is then projected forward through time. Each microunit of the sample is aged individually by an empirically based survivorship probability (Merz, 1991). In addition, the occurrence of other demographic events may be simulated. For example, a family unit cou.ld be diminished ir size through divorce or augmented through the birth of a child during the simulation process. By the process of dynamic demographic ageing the size of the cross-section under investigation will be altered. Dynamic population models such as DYNASIM (Orcut+ et al, 1976) are therefore particularly useful for forecasting the future characteristics of the populatiort and thus for modelling the effects of policy change over +he _longer_ period.

Dynamic Cohort models employ the same dynamic ageing process as population models. However the microdata base is not underpinred by the characteristics of a real samn.ple unit but rather the simulation process itself creates 'svnthetic' microunit's and forecasts the whole life-cycle from birth to death. The advantage of this type of micro-simulation is the availability of information for _complete life_ histories for each cohort member. In contrast, dynamic population modelc typically produce incomplete life-histories, mapping only a few decades of the lives of individuals from many different age groups (althougn, the same lifetime profiles could be generated using a dynamic population model where the microunit is children aged 0 and the simulation period 100 yeais!). Dynamuc cohort models are thus particularly suitable for addressing questions concerned with life-times and the life-cycle; for example, accumulation of public and private pension rights. This paper thus relies on a dynamic cohort microsimulation model -LIFEMOD - to provide the longitudinal data necessary.

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by 0.12 points. It means that socioeconomic disparities increased only in countries with relatively short closures, below 22 weeks, and for countries with longer closures the pandemic decreased the relationship between socioeconomic background and mathematics achievement.

Additional analyses using quantile regressions reveal significant differences in the learning losses between students at different achievement levels, but also across countries that vary in the length of school closures. As reported by the OECD, direct comparisons between PISA 2012 and 2022 results in mathematics show similar declines across the achievement spectrum (OECD 2023). Our quantile regression estimates presented in Figure 2 show that, indeed, for countries with the average length of school closures the learning loss is similar for low-, average-, and high-achieving students. However, in countries with relatively short closures, the best students lost very little in terms of achievement and the decline is mostly observed among the average- and low-achieving students. In countries with longer closures, the learning loss is larger for the best students. In countries with the longest closures the lowachieving students lost around 16-17 points, while those at the top of achievement distribution lost 25 points or more.

15
(ii) Indirect effects. As discussed earlier, an adequate stock of rural public goods contributes to sustain growth for agriculture and related rural industries. In general much of the rural industry linked to agriculture is highly intensive in unskilled labor (Lanjouw and lanjouw, 1995). Moreover, it appears that the rural sector in many developing countries is important enough within the broader unskilled labor market to significantly affect the real wages for unskilled workers even at a national level. Thus a dynamic rural productive sector is a vital source of employment for unskilled workers as it may play a role in supporting higher real wages. To the extent that most poor are unskilled workers, these effects may be important in reducing poverty.

# **III. The Model**

_The agriculture per capita value-added function_ . The approach is to specify a reducedform model that explains agricultural GDP per capita as a function of variables that are exogenous to agriculture as well as of policy variables. We start with specifying a production function, where Q is agriculture output, L is labor used in the sector, K is a vector of primary factors of production owned by farmers, X is a vector of purchased inputs, and A is a productivity index. We assume that F( ) is concave in K, L, and X, and that it is subject to constant returns to scale; that is, F( ) is homogenous of degree one in L, K, and X.<sup>15</sup> Agriculture value added or GDP is defined as the returns to primary factors, K and L. Thus, (2) _G_ ( _p_ , _v_ ; _K_ , _L_ ; _A_ ) ≡ max{ _pF_ ( _L_ , _K_ , _X_ ; _A_ ) − _vX_ }, _X_ where G( ) is agriculture GDP, p is output price, and v is a vector of purchased input prices. The function G( ) is a (dual) revenue or value added function and must satisfy certain conditions. The most important, from our point of view, is that, apart from being increasing and concave in L and K, the agricultural value added function is also homogenous of degree one in K and L. This follows directly from the constant returns to scale assumption. We can thus express per capita agriculture GDP as

> 15 The assumption of constant returns to scale in agriculture has not often been rejected by empirical studies using farm household data. See, for example, López and Valdés (2000), who provide estimates for five countries in Latin America.

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INDIA

- 2. Projections NRR-1 by 2015

|**_T1w_**|PapiAtiUon|trati|P_ad_|**rate**|Cmi|Cmi|TF9|**tm**|FmLo|la|Zion|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1990<br>1995|849651<br>930639|70.3<br>67.8|1990-94<br>1995I 9|1.82<br>1.58|28.S<br>25.1|10.2<br>9.2|3.74<br>3.22|59.3<br>61.0|60.2<br>62.6|U<br>77|113<br>96|
|2000<br>2005|1007 22<br>1062479|62.1<br>56.1|2000-04<br>2005-09|1.44<br>1.29|23.0<br>21.1|6.5<br>8.1|2.88<br>2.57|62.8<br>64.2|65.0<br>66.7|65<br>57|79<br>69|
|2010|11S4861|51.6|2010-14|1.13|19.1|7.8|2.29|65.6|68.4|48|S8<br>|
|2015<br>2020|1221692<br>1286806|48.5<br>46.7|2014-19<br>2020-24|1. 4<br>0.97|18.1<br>17.3|7.7<br>7.6|2.19<br>2.15|67.0<br>68.4|70.2<br>72.0|39<br>31<br>|**48**<br>37<br>|
|2025|1350430|46.2|2025-49|0.69|15.0|8.2|2.11|72.3|76 5|16|19|
|2050|1603S93|53.2|2050-76|0.31|13.1|10.0|2.08|76.8|81.6|5|7|
|2075|1732544|6.0|207S-99|0.15|12.4|11.0<br>|2.07<br>|79.3<br>|64.8<br>|3<br>|5<br>|
|2100|1797519|69.2|2100-24|0.10|12.0|11.1|2.06|81.2<br>|87.4<br>|2<br>|4<br>|
|2125<br>**2150**|1841296<br>**1662220**|71.4<br>73.0|2125-49|0.05|11.8|11.4|2.06|81.9|88.4|2|3|

# 3. Age structure (percent): 1990 and 2025

<!-- Start of picture text --> 10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text --> <!-- Start of picture text --> 75 +<br>70-74<br>65-689<br>60-64<br>55-59<br>50-54<br>45-49<br>40-44<br>35.39<br>30-34<br>25-29<br>20-24<br>15-19<br>10-14<br>5-9<br>0-4<br>10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text -->

# 4. Comparison of Country with Region and Income<sup>Group, 1985-90</sup>

<!-- Start of picture text --> TFR e0<br>Co.mtry legion Incams iroip Country RegIon Income groW<br><!-- End of picture text --> Growth rate <!-- Start of picture text --> Country Region Income gro..<br><!-- End of picture text --> 57
|**Cost of Com**<br>**as apercent**|**pliance**<br>**age of an**|**with EU sta**<br>**nualprodu**|**ndards***<br>**ction costs**||
|---|---|---|---|---|
||1|2|3|4|
||pre-|post-|adjustment|post-DCFTA costs|
||FTA|DCTFA|costs|includingadjustment|
|Agriculture,forestryand fisheries|15.8|11.8|2|13.8|
|Mining|0.0|0.0|0|0.0|
|Manfacturing<br>*The change is costs of compliance<br>**Source: Authors' estimates**|21.6<br>for firms|16.2<br>not exporti|2<br>ngto the EU i|18.2<br>s zero.|

## **References**

Jakubiak, M., Maryla Maliszewska, M. Orlova, M. Rokicka and V. Vavryschuk (2006), Non-Tariff Barriers in Ukrainian Exports to the E.U. Case Network Report No. 68. <u>http://www.case.com.pl/upload/publikacja_plik/13388202_rc68.pdf.</u> Maliszewska, Maryla, editor (2008), “Economic Feasibility, General Economic Impact and Implications of a Free Trade Agreement Between the European Union and Armenia,” CASE Network Report No. 80. Warsaw: CASE.

World Bank (2010), “Upgrading the National Quality Infrastructure in the Republic of Armenia,” Washington D.C.: The World Bank.

# **Appendix F:  Estimate of the ad valorem equivalents of barriers in services in Armenia.**

**Appendix F is available as Modebadze and Eroyants (2010) at** https://docs.google.com/leaf?id=0B0V-t-Bs4hXYjA3MTQ5MDctNTAxYy00M2I4LWI3NDAtZGRiNDI3NmYwOGU2&hl=en

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coefficient on education has a similar negative coefficient for caloric intake as for TEE, but per capita expenditures only has a large positive coefficient for caloric intake.

The patterns documented here in the cross-section foreshadow one of the main findings in the time-series, which is that REE often moves in the opposite direction of AL in terms of covariance with observable household characteristics. This dampens the effect of changes in these characteristics on changes in TEE. Appendix Figure S.1 shows that this pattern also occurs across household main industry – households headed by agricultural or construction workers have the highest activity levels in India but also have the lowest REE.

# **3 Estimates of TEE over 1983-2012**

## **3.1 Population estimates**

Table 3 presents estimates of population average TEE for India between 1983 and 2012. Three separate panels present results for all India, rural India only, and for the six states in the TUS only. The first column reports the surveyweighted population average TEE expressed in calories per day. TEE is fairly flat over the entire 1983-2012 period, rising by only 0.8%. There is a small increase over the 1988-1994 and 2000-2005 periods, and a small decrease from 2005-2012, but these changes are small relative to plausible measurement and estimation errors. Columns 2 presents the population average REE which rose by 6% throughout the 1983-2012 period. Column 3 presents the population average AL which fell throughout the 1983-2012 period by about 4%, mostly after 2005.

Recall that the product of population average REE and AL is not population average TEE, and that changes in population AL and REE capture both changes within age/sex demographic groups and changes in the demographic composition of the population. Estimates are reported separately for population TEE and AL for males 15 and over (columns 4-5), females 15 and over (columns 6-7), and children aged 14 and below (columns 8-9). TEE is negatively correlated with household size. However, they still find some “real” decline in caloric intake with household size when using the alternative survey instruments. See also (Beegle et al. 2012) and (Brzozowski et al. 2017) for more evidence on food consumption survey measurement error correlated with household size.

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