rafmacalaba/datause-encoder-data huggingface dataset:
# **Reported portfolio quality in MENA is generally good, with the exception of Morocco and**

**West Bank/Gaza.** Portfolio at risk (measured at 30 days, a relatively conservative measure typical of microfinance) has been broadly stable in recent years, and averaged 3.9 percent for MENA in 2009. (Sanabel 2010) This is despite the decline in the quality of the Moroccan microcredit portfolio. MFIs in MENA are relatively „profitable‟, with a higher average return on assets than MFIs in other regions.<sup>8</sup> This is driven by low expenses, high efficiency levels, and initial (zero cost) donor funding.  Financial revenues are also low, and this relative profitability does not seem to be a factor of high interest rates (which do not tend to be high by global standards). Annex A2.3 presents efficiency, profitability, and portfolio quality data by country.

_Postal Networks_

**Postal savings banks and post offices continue to be the primary providers of savings and payment services for low income people in the region.** There are 6 postal networks providing financial services in MENA, which were all state entities.  Egypt Post is the largest provider of savings products to Egyptian micro entrepreneurs and lower income population, serving over 17 million clients through 3,700 outlets nationwide, including with savings, payments and domestic transfer services, although not with microcredit. The largest bank network, PBDAC (the state agricultural bank) has over 1200 branches and outlets, by comparison. While the numbers in Yemen are smaller, the Post and Postal Savings Corporation has 310 branches, which is more than the total number of branches of commercial banks. Tunisian MFIs and the postal service combined have 20 times more branches in rural areas than banks do. (CGAP and World Bank Financial Access 2009; M Khaled, pers. comm.)

**Post office branch networks are in many cases among the most extensive delivery channels**

**for financial services in MENA, as noted earlier.** The Moroccan post office, for example, provided basic financial services (savings passbooks and checking accounts) to a large customer base (more than 2.5 million people). In recent years, it has successfully leveraged on the large postal network (1,762 branches in 2008) to attract a significant number of unbanked and rural customers (14 percent of the Moroccan population have postal accounts in 2008, up from 10 percent in 2005).

# _Financial Inclusion and the Global Financial Crisis_

**Loans to GDP declined in MENA** in 2009 as a result of the crisis (with Tunisia a notable exception) for both loans and deposits, due to falls in commercial bank lending. Deposit volumes also fell, with some exceptions, while the number of deposit accounts continued to rise. This

> 8 Microbanking Bulletin data. It should be noted that MFIs reporting to the Microbanking Bulletin, as to the Microfinance Information Exchange, do so on a voluntary basis. Stronger and larger MFIs are therefore more likely to provide data than small and poor performing MFIs.

11
**Figure 12: Distribution of health self-assessment among formal and informal workers**

<!-- Start of picture text --> 100% 6% 54%<br>90%<br>52%<br>80% 5%<br>70% 50%<br>60% 4%<br>48%<br>50%<br>40% 3% 46%<br>30%<br>44%<br>2%<br>20%<br>42%<br>10%<br>1%<br>0% 40%<br>Formal Informal<br>0% 38%<br>Very good Good Average Bad Very bad Formal Informal Formal Informal<br>Source: Authors’ estimation using RLMS 2016.<br>Note: This figure shows the distribution of self-assessed health levels for formal and informal<br>workers. Central panel defines bad health as categories bad and very bad health. Left panel defines<br>good health as sum of categories very good and good health. Using RLMS, the authors define<br>informal employment as workers that are employed at an enterprise without a contract in their<br>primary job and workers employed in firms with no more than one employee (i.e., self-employed).<br>Workers with a secondary job or in no job but in irregular economic activities are not included. This<br>RLMS-based measure is henceforth referred to as “RLMS (primary only)”.<br>self assessed in bad health<br>self assessed in good health<br><!-- End of picture text --> 38 Informal Employment and Worker’s Wellbeing in Russia / June 2019
database that covers a large number of countries over a long time period and includes a wide range of measures of financial integration. This allowed us to examine how the relationship has changed over time and across countries. Our results indicate that, consistent with theoretical predictions, a higher degree of financial integration is associated with a lower sensitivity of consumption to income over time. We also provide a discussion of several alternative interpretations of changes in the sensitivity of consumption and income over time.

These results point to two areas for future research. First, country-specific features can affect the relationship between international financial integration and the sensitivity of consumption to income. For example, our simple robustness analysis suggests that trade integration and financial sector development can affect this relationship. It would be useful to conduct a comprehensive study of the linkages between financial integration and trade and financial development, and how these linkages endogenously affect the proportion of rule-of-thumb agents in an economy, using a framework similar to Davis (2014). Second, it would be useful to study how the changes in the sensitivity of consumption to income affect the transmission of international business cycles using the benchmark statistics we provide here.

# References

- [1] Asdrubali, P. and S. Kim. 2008. “Incomplete Intertemporal Consumption Smoothing and Incomplete Risk Sharing.” Journal of Money, Credit and Banking 40(7), 1521—1531.

- [2] Bacchetta, P. and S. Gerlach. 1997. ""Consumption and credit constraints: International evidence." Journal of Monetary Economics 40(1997) 207-238.

- [3] Baxter, M. and M. J. Crucini. 1995. “Business Cycles and the Asset Structure of Foreign Trade.” International Economic Review, November, Vol. 36, No. 4, 821-854.

- [4] Bilbiie, F.O., 2008. “Limited asset markets participation, monetary policy and (inverted) aggregate demand logic.” J. Econ. Theory 140 (May (1)), 162—196

- [5] Campbell, J. Y. and N. G. Mankiw. 1989. "International evidence on the persistence of economic fluctuations," Journal of Monetary Economics, Elsevier, vol. 23(2), pages 319-333, March.

- [6] Campbell, J. Y. and N. G. Mankiw. 1991. "Permanent Income, Current Income, and Consumption," NBER Working Papers 2436, National Bureau of Economic Research, Inc.

- [7] Carroll, C. D., J. Slacalek, and M. Sommer. 2011. “International evidence on sticky consumption growth.” The Review of Economics and Statistics 93 (4), 1135—1145.

- [8] Chinn, M. D. and H. Ito, 2006. “What Matters for Financial Decelopment? Capital Controls, Institutions, and Interactions,”Journal of Development Economics, Volume 81, Issue 1, Pages 163-192 (October).

- [9] Cochrane, J. 1991. “A Simple Test of Consumption Insurance.” Journal of Political Economy 99:5 (October 1991) 957-976.

- [10] Crucini, M. J., 1999. “On International and National Dimensions of Risk Sharing.”The Review of Economics and Statistics, February, Vol. LXXXI, No. 1, 73-84.

13
Table 3: The growth elasticity of poverty

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|VARIABLES|Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|
|gr_change|-1.826***|-1.974***|-2.307***|-2.762***|-2.204***|-2.491***|
||(0.164)|(0.199)|(0.326)|(0.501)|(0.345)|(0.516)|
|Constant|-0.0152**<br>(0.00731)|-0.109<br>(0.0841)|0.0132<br>(0.00976)|-0.0883<br>(0.0825)|-0.00144<br>(0.0122)|-0.125*<br>(0.0759)|
|country fixed effect|No|Yes|No|Yes|No|Yes|
|Observations|575|575|575|575|505|505|
|R-squared|0.432|0.590|0.174|0.390|0.208|0.386|
|Notes: Table shows results o<br>survey means (Columns (1) a<br>consists of all consecutive sp<br>percent. Robust standard erro<br>significant at 10%.|f a regression of th<br>nd (2)) or GDP pe<br>ells between 1981<br>rs in brackets. ***|e annualized lo<br>r capita (Colum<br>and 2021, drop<br>: Statistically s|g change in po<br>ns (3) and (4)<br>ping spells wit<br>ignificant at 1%|verty headcoun<br>or HFCE per c<br>h baseline and<br>; **: Statistica|t on the annual<br>apita (Columns<br>endline poverty<br>lly significant|ized log change in<br>(5) and (6). Sample<br>rates lower than 2<br>at 5%; *: Statistically|

20
without access to short-term bank credit, as well as large firms that can negotiate trade credit terms cheaper than alternative sources of external financing.

These financial and operational decisions introduce a strong link between the use and extension of trade credit and suggest that accounts receivable could be used to finance accounts payable. Furthermore, trade credit terms offered to customers should be set to match trade credit terms received from suppliers. Lastly, the timing of payments to suppliers should match the receipt of payments remitted from customers. If this was the case, it would suggest a symbiotic relationship between the decision to supply trade credit to customers and the demand of trade credit from suppliers. Studying the two sides of the trade credit relationship simultaneously could therefore illuminate some important questions, for instance, why both small and large firms, constrained and constrained, take and offer trade credit.

However, because of data limitations, previous related literature focuses only on one-side of the trade credit relationship in isolation. In this paper, we use a novel data set which allows us to take a two-sided approach. We use a large firm-level survey of Chinese firms, which is a unique source of data for two main reasons. First, it provides detailed information on the market power and competition of both output and input market. Second, it contains information on the amount, terms, and payment history of both trade credit extended by firms to their customers (accounts receivable), as well as the receipt of trade credit by firms from their own suppliers (accounts payable). Third, since only about 30% of firms in our sample have a line of credit, we can discuss the unique role of trade credit for firms in an emerging market.

2
**Table 5A: Manufacturing value added as a share of GDP and total private credit as a share of GDP**

|Dep. Var.:<br>manuf._share|2SLS<br>(1)|2SLS<br>(2)|2SLS<br>(3)|2SLS<br>(4)|2SLS<br>(5)|2SLS<br>(6)|2SLS-FE<br>(7)|
|---|---|---|---|---|---|---|---|
|ln_totalcredit|2.4197*|1.5264|2.0091|1.4576|0.9954|6.2060**|0.7597|
||(0.067)|(0.239)|(0.110)|(0.247)|(0.445)|(0.027)|(0.554)|
|fdi|-0.1068**|-0.1046**|-0.0966**|-0.0793**|-0.0913**|-0.1451***|-0.0985**|
||(0.010)|(0.016)|(0.020)|(0.046)|(0.026)|(0.003)|(0.026)|
|enrolment|-0.0362|-0.0327|-0.0411*|-0.0153|-0.0001|-0.0492|0.0220|
||(0.134)|(0.173)|(0.085)|(0.589)|(0.998)|(0.170)|(0.522)|
|ln_initialgdppc|-3.0062***|-2.6310***|-3.1220***|-4.0587***|-3.4333***|-5.4944***|-3.3768***|
||(0.000)|(0.001)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|tradeopenness|-0.0158|-0.0130|-0.0206|-0.0282*|-0.0148|0.0054|-0.0110|
||(0.161)|(0.271)|(0.118)|(0.081)|(0.265)|(0.693)|(0.425)|
|financialcrisis||-0.8744|-0.7774|-0.7662||||
|||(0.478)|(0.526)|(0.561)||||
|ln_population|||-0.5342<br>(0.178)|-0.9563*<br>(0.062)||||
|tot||||-0.0970***<br>(0.000)|-0.0881***<br>(0.000)||-0.0891***<br>(0.000)|
|exchangerate||||||0.0408<br>(0.333)||
|Constant|21.3094***|24.0256***|30.0359***|47.3383***|34.1718***|7.1932|31.7566***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.308)|(0.000)|
|Observations|169|167|167|124|126|137|126|
|R-squared|0.291|0.292|0.303|0.406|0.394|0.158|0.415|
|Prob > F|0.000|0.000|0.000|0.000|0.000|0.000|0.000|

Regressions (1) – (6) are 2SLS regressions with legal origin and religious composition as instruments. Regression (7) is a 2SLS regression with the same instruments adding with year fixed effects. Credit variables, initial GDP per capita and population are in logs. All the other variables are in levels. Five-year average data are used. Robust p-values are reported in parentheses. *, **, *** respectively indicate significance at the 10%, 5% and 1% level.

18
these indicators on the basis of econometrically estimated equations that include GDP, urbanization and time as arguments. This analysis needs to be complemented by forecasts for GDP and urbanization. The cost analysis, which covers the same set of indicators (except for poverty), is based on fairly detailed modules that consider input needs and assessments about future wage changes. The module for analysis of fiscal sustainability compares estimated costs of achieving targets to available public funding (based on assumptions about GDP growth, tax collection, and the level of sustainable deficits). The target setting module is clearly a useful tool for assessing target realism. On the other hand, the fiscal sustainability component is relatively weak, reflecting the fact that a set of independent tools cannot capture interdependencies between GDP growth, different MDG targets, program costs (including wage changes), and alternative financing approaches.

The different publications of the UN Millennium Project report represent a more detailed sectoral approach (see for example United National Millennium Project 2005). Its main feature is to estimate and add up the costs of specific interventions in areas such as education, health, and public infrastructure. As implemented, this is essentially a fixed-coefficient-fixed-price planning exercise. This approach—rich in detail—has typically ignored or simplified the synergies across the MDGs and, more importantly, the interactions with the broader economy.

Agénor et al. (2005a) apply a novel approach to MDG analysis to Niger.<sup>2</sup> Recognizing the need for an economywide perspective, they combine a macro model with an MDG module. Government spending has different repercussions depending on whether it is identified as being used for education, health, or infrastructure. The MDG module is used for post-calculations of MDGs and other social indicators: poverty, malnutrition, the literacy rate, infant mortality, life expectancy, access to safe water. The determinants of most of these indicators are estimated using cross-section, cross-country data for a sample of developing countries (when feasible limited to sub-Saharan Africa), allowing for links between MDGs. A key strength of their approach is that it requires relatively little data and draws on econometrically estimated parameters. On the other hand, the macro model is highly aggregated: it has only one production sector (meaning that one dollar of additional government demand for investment in infrastructure has the same direct effect on production and imports as one dollar of additional government demand for education) and it does not include intermediate inputs, factor markets, or factor wages (rents). This limits the ability of the model to analyze key aspects of MDG strategies such as the labor market repercussions of scaled-up government services and Dutch disease effects. Its high level of government and labor market aggregation (only one type, “educated labor”, is used in production) makes it more

> 2 Agénor et al. (2005b) includes more detail on the macro model.

- 3 -
# **Table 7: Regressions in Changes**

|Regression|1|2|3|4|5|6|7|8|
|---|---|---|---|---|---|---|---|---|
|<br>dates available|1970-90|1975-95|1970-95|1970-95|1970-95|1975-95|1970-95|1975-95|
|In(u60)|-0.011 **|-0.012**|-0.012**|-0.012**|-0.11 **|-0.01 1**|-0.012**|-0.012**|
|year|-0.00010|-0.00013**|-0.00015**|-0.00013**|-0.00047|-0.00011 *|-0.00014**|-0.00012*|
|Africa*pre80|0.00077|0.0048**|0.0054**|0.0036|0.0023|0.0025|0.0031|0.0019|
|Africa*post8O|-0.0062**|-0.0043**|-0.0062**|-0.0050**|-0.0037**|-0.0050**|-0.0052**|-0.0052**|
|zYags|-0.0085|-0.020|-0.023|-0.021|-0.0057|-0.011|-0.023*|-0.012|
|zYinds|0.012*|0.014**|0.20**|0.16**|0.012*|0.010|0.015**|0.0092|
|lagruw|-0.0011|-0.0018**|-0.001|-0.0013|-0.0013*|-0.0011|-0.0013*|-0.0010|
|cereal||-0.000064|||||||
|bmp|||0.0000006||||||
|lag[ln(noeduc)]<br>|-0.00031||||||||
|lag[ln((noeduc) <sup>2</sup>)]|0.00011||||||||
|lag[ln(secondary)]|-0.00083||||||||
|Poscropsh||||-0.00052|||||
|<br>Negcropsh||||0.00022|||||
|Sivard|||||||-0.0020*|-0.0021*|
|Democracy||||||-0.0016*||-0.0020**|
|ln(foodaidc)|||||-0.00043||||
|ln(foodaidnc)|||||_0.00050_||||
|Adj. R<sup>2</sup>|.593|.603|.588|.589|.554|.580|.588|395|
|N|368|412|382|432|329|395|437|.583|

Dependent variable is change in % urban population. Notes: see Table 2..

24
# **Table B6. Estimated collected tariff rates at 2 digit Harmonized System (HS) level**

|**HS**<br>**code**||||**Year**|||
|---|---|---|---|---|---|---|
|**2 digits**|**_Type of products_**|**2001**|**2002**|**2003**|**2004**|**2005**|
|||70|70|70|70|70|
|1|_LIVE ANIMALS_|3.9|3.9|3.6|3.3|3.5|
|||_(2.1)_|_(2.1)_|_(2.2)_|_(2.3)_|_(2.3)_|
|||256|256|292|292|300|
|2|_MEAT EDIBLE OFFAL_|19.6|20.2|26.1|27.0|28.2|
||_,_|_(7.6)_|_(7.9)_|_(18.7)_|_(18.2)_|_(20.1)_|
|||338|338|338|339|339|
|3|_FISH CRUSTACEANS_|10.1|10.8|10.0|9.9|9.8|
||_,_|_(4.2)_|_(20.6)_|_(5.1)_|_(3.3)_|_(2.2)_|
|||176|176|176|176|176|
|4|_DAIRY PRODUCTS_|14.6|14.6|14.3|14.6|13.9|
|||_(5.7)_|_(4.1)_|_(5.3)_|_(5.3)_|_(5.5)_|
|||33|33|33|33|33|
|5|_ANIMAL PRODUCTS NES_|7.6|8.2|7.8|7.8|7.4|
|||_(3.1)_|_(2.4)_|_(2.8)_|_(2.8)_|_(3.1)_|
|6|_LIVE TREES PLANTS_|42<br>13.6|42<br>15.3|42<br>15.7|42<br>14.9|42<br>14.3|
||_,_|_(9.0)_|_(8.6)_|_(10.2)_|_(8.5)_|_(7.5)_|
|||121|121|121|121|121|
|7|_EDIBLE VEGETABLES_|13.0|13.7|13.2|12.9|13.2|
|||_(4.1)_|_(4.5)_|_(5.1)_|_(5.0)_|_(4.0)_|
|||137|137|137|143|143|
|8|_EDIBLE FRUIT NUTS_|5.4|8.7|9.0|8.7|8.6|
||_,_|_(3.7)_|_(9.1)_|_(10.2)_|_(8.7)_|_(9.2)_|
|||42|46|54|54|54|
|9||5.8|6.6|7.2|6.6|6.7|
||_COFFEE, TEA, SPICES_|_(3.7)_|_(6.0)_|_(6.0)_|_(4.5)_|_(4.8)_|
|||57|57|57|57|57|
|10|_CEREALS_|6.9|7.1|9.0|7.4|6.8|
|||_(3.8)_|_(3.6)_|_(9.5)_|_(6.7)_|_(5.2)_|
|||85|85|85|85|85|
|11||93|90|95|90|93|
||_MILLING PRODUCTS_|.<br>_(2.0)_|.<br>_(2.6)_|.<br>_(2.9)_|.<br>_(3.5)_|.<br>_(3.4)_|
|||84<br>|84<br>|84<br>|84<br>|84<br>|
|12|_OIL SEED_|4.4|4.5|4.4|4.3|4.5|
|||_(1.4)_|_(1.2)_|_(1.4)_|_(1.6)_|_(1.3)_|

47
93 ESTIMATES OF COSTS AND BENEFITS controlled renter consume more than that the median resident landlord; one fourth of resident landlords consume less than the median tenant.

Malpezzi (1986) presented similar evidence for Cairo. Again, richer landlords certainly own more buildings and more rooms. But in Bangalore, India, Malpezzi and Tewari (forthcoming) were able to control for the amount of housing capital owned; while it widened the gap in average incomes between landlords and tenants, significant overlap remained.

Table **5.11:** Landlord **and** Renter Consumption

||**Mean**|Standard<br>Deviation|Median|Q3-Ql|Mode|**I**|
|---|---|---|---|---|---|---|
|Resident Landlords|18,381|12,124|15,668|11,024|**10,500**|92|
|Controlled Tenants|12,808|6,356|11,563|6,791|10,700|725|
|Uncontrolled Tenants|17,554|9,151|15,260|10,665|9,850|**105**|

Figure **5.11:** Distribution of Consumption by Tenure Controlled Renters Uncontrolled Renters Resident Landlords <!-- Start of picture text --> p p s .......... . ............... p<br>,. e r ~~~~~~~~~~~~~~~~~r e . .... I............<br>C C 2............ ................ C<br>S ~~~~~~~~~~~~~~~nn<br>II 6OO0 I-lb K -IS 5-0k 00251 6521 I6000 5-l51 I015r i. 10 20156 CttS1 LIt 600 1t. 1 5 0 5r Ct t<br>Median: 11,563 Cedis Medlac: 15,260 Ceadr Medan: 15,668 Ceds<br><!-- End of picture text -->
Policy Research Working Paper 9870

# **Abstract**

Access to infrastructure supports economic development through both capital accumulation and structural transformation. This paper investigates the links between investments in electricity, Internet, and road infrastructure, in isolation and bundled, and economic development in the Horn of Africa, a region that includes countries with different levels of infrastructure and economic development. Using data on the expansion of the road, electricity, and Internet networks over the past two decades, it provides reduced-form estimates of the impacts of infrastructure investments on the sectoral composition of employment.

Bundled infrastructure investments cause different patterns of structural transformation than isolated infrastructure investments. The impact of bundled road and electricity investments on reducing the sectoral employment share in agriculture is found to be 2.5 times larger than the impact of roads alone. The paper then uses a spatial general equilibrium model to quantify the impacts of future regional transport investments, bundled with electricity and trade facilitation measures, on economic development in countries in the Horn of Africa.

This paper is a product of the Infrastructure Chief Economist Office. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at mlebrand@worldbank.org and mdappe@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
22 term on the leverage ratio (a3a) should switch sign and become negative. If liberalization helps in mitigating informational problems that cause an increase in the premium for external finance, then we would expect O3a to be smaller in absolute values than a3b, its value before liberalization. If both a3a and ax4a are not significant this would mean that the perfect market paradigm cannot be rejected after liberalization.

A different way of assessing the impact of financial reform would be to see whether the marginal significance level of the Sargan/Hansen test of over-identifying restrictions changes when the equation is estimated allowing the coefficients to differ before and after liberalization. The Sargan/Hansen test statistics in Tables 2 and 4 do not suggest that there evidence of mispecification against either set of equations, and the marginal significance levels do not change in any informative way.

In the previous section we have argued that capital markets imperfections affect mainly small firms. The relevant question is then whether financial liberalization has improved their access to external finance. Results are presented in column (A) of Table 4 for the model that corresponds to column (A) in Table 2, but allows the coefficients on the financial variables to differ pre and post liberalization. The evidence suggests that small firms suffered from a ceiling constraint and an increasing cost of borrowing both before and after liberalization. Moreover we cannot reject the hypothesis that the coefficients capturing capital market imperfections are identical in the two sub periods.

Finally, if we impose the restriction that -a4Ob = **-a4Oa** = **al,** as the theory suggests, we can recover the structural parameters. The results for the restricted version of (17) are presented in column (B). The estimates in column (B) suggest that the slope of the marginal agency costs function for small firms, c, decreases slightly after liberalization (from .175 to .155), and it is less significant. However, the difference is not statistically significant.
# Figure 1

<!-- Start of picture text --> EMPLOYMENT TRENDS 1989 - 1993<br>Irdex t4Nrers 1989 - 1OO<br>105<br>100-<br>95<br>so<br>95<br>so<br>75<br>70<br>Es<br>60<br>55 X *<br>.1t989 1990 1991 1992 1993<br>o Tots I EconomY 4 Enter, tlse Sector 0 Budget Sector<br><!-- End of picture text --> 4