rafmacalaba/datause-encoder-data huggingface dataset:
44

# **7.2. Gauging Egypt’s financial system with aggregate data**

Today, Egypt’s financial system is relatively large, in comparison to most peer countries. The benchmarking exercise discussed above shows a financial system corresponding to its level of income per capita and other country characteristics including size, population density and demographic structure. Specifically, Figures 13 and 14 show the actual and predicted values of two aggregate financial depth indicators, corresponding to the two sides of banks’ balance sheets – Bank Deposits to GDP and Private Credit to GDP.

**Figure 13: Deposit collection in Egypt over time**

<!-- Start of picture text --> Domestic Bank Deposits / GDP (%)<br>90.0<br>80.0<br>70.0<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>Value Observed Expected median<br><!-- End of picture text --> Source: Global Financial Development Indicators, own calculations Figure 13 shows that the level of saving mobilization by the banking system has been higher than predicted by country characteristics, although the gap has been recently closing. We also note that the level of Bank Deposits to GDP has actually decreased over the past years. Figure 14 shows that the actual value of Private Credit to GDP has also been above the predicted value for many years, but has moved below it for 2009, both due to the drop in the actual level of private sector lending as to the increase in the expected value. The progress made in financial sector reform has thus not been reflected yet in aggregate financial sector indicators. While savings mobilization as captured by Bank Deposits to GDP has stagnated, 44
14 16 to 25 percent; a 59 % increase in the dollar value of manufactures production was accompanied by a 155% surge in he dollar value of manufactures imports. Far from slowing down after the proclamation of the Single European Act, the growth of maniufactures imports into the EC actually accelerated after 1985.

14. Nor was a wider "Fortress Europe" being formed. EC manufactures imports from EFrA members rose by 146 percent, marginally more than intra-EC trade, but less than imports from all other sources. Manufactures imports from Japan, the target of the broadest and sharpest importresistance, rose by 215 percent over the period, thus increasing the country's share both over non-EC and EC suppliers. The United States did less well, but manufactures imports from that source still rose by 115%, much more than the increase in manufactures production.

15. What is true for manufactures in general is also true for the majority of individual sectors. The GATT database used in this Report lists 6 categories of _prihary products,_ 3 categories of semimanufactures _(Iron and steel, Chemicals_ and _Other)_ and 13 categories of _Manufactures_ (Annex table 2). Intra-EC trade increased more than imports in primary commodities and in semi-manufactures other than iron and steel, but in only two categories of finished manufactures, _other transport equipment_ and _textiles._ In all other categories of manufactures, imports rose more than intra-EC Wade; generally much more (see _Annex Table II._
19 Table 3 continued ...

|Mode|Range Surveyed|Most Likely Range|No. of Studies|
|---|---|---|---|
|**Aggregate**Commnodities|0.82-1.60|0.01.60|3|
|Shipping: Inland Waterway||||
|Aggregate Commodities|(0.74-0.76)|-|I<br>|
|Chemicals|0.75|-|1|
|Coal|0.28|-|I|
|Crude Petroleum|149|-|I|
|Grain|0.4-1.62|0.60-1.60|2|
|Lumber and Wood|0.60|-|I<br>|
|Non-Metallic Ores|0.65|-|1|
|Primary Metal|0.28|-|I|
|Pulp and Paper|1.12|-|I|
|Stone, Clay and Glass Products|1.22|-|1|
|.hlpplng: Ocean'||||
|Dry Bulk Shipment'|0.00.25|-|1|
|<br>Foods|0.20-0.31|-|1|
|Liquid Bulk Shipment|0.21|-|1|
|GeneralCargo|0.00-1.10||I 1|

- 'The high elasticity estimates may reflect a low mar et share of **aggre1ate** freight of the mode when using the translog cost function in estimation.

3There have been very few empirical studies on shipping, hence the elsticity estimates r ported here shr id be interpreted with caution.

'These include coal, grain, Iron ore and concentrates, etc.

Note: Figur in parenthes re mode choice elaticities.

Table 4: Elasticities of Demand for Gasoline

|(|All elasticity figure|s are negative)||
|---|---|---|---|
|Country<br>|Ranged Surveyed<br>|Most Likely Range|No of Studies|
|Austria|0.25-0.27||1|
|Canada<br>|0.11<br>|_1||
|Israel|0.25|_1||
|U.K.|0.1-0.17|_1||
|U.S.<br>|0.04-0.21<br>|_1||
|West Germany|0.25-0.93|_1||
|Multicountry Studies|0.20-1.37-|0.20-0.50|3|

'Included in this range is a long-run elasticity estimate of 0.32-1.37.
Table 5: Fixed-effects regression results for employment elasticity of growth: ECA product market

|Variables||ECA|EU10|BalTur|CIS|
|---|---|---|---|---|---|
|||(1)|(2)|(3)|(4)|
|Firm restructuring||||||
|Output|Coeff.|0.125|0.219|0.073|0.079|
||t-stat.|3.7|3.3|0.4|2.6|
|Other variable|Coeff.|0.015|0.032|-0.024|0.010|
||t-stat.|1.8|1.9|-0.7|1.1|
||N|386|135|71|180|
||R2|0.30|0.51|0.29|0.50|
|Competition policy||||||
|Output|Coeff.|0.13|0.24|0.06|0.08|
||t-stat.|3.78|3.40|0.32|2.72|
|Other variable|Coeff.|0.01|0.00|-0.02|0.02|
||t-stat.|1.3|0.3|-0.7|2.4|
||N|386|135|71|180|
||R2|0.30|0.49|0.29|0.52|
|Banking reform||||||
|Output|Coeff.|0.124|0.202|0.068|0.083|
||t-stat.|3.6|3.0|0.3|2.7|
|Other variable|Coeff.|0.008|0.022|-0.011|0.003|
||t-stat.|1.3|2.1|-0.5|0.4|
||N|386|135|71|180|
||R2|0.30|0.51|0.28|0.50|
|Infrastructure reform||||||
|Output|Coeff.|0.125|0.261|0.083|0.092|
||t-stat.|3.6|3.8|0.4|2.9|
|Other variable|Coeff.|0.005|-0.018|0.005|0.018|
||t-stat.|0.7|-1.7|0.1|1.8|
||N|386|135|71|180|
|Note: The dependent v<br>The output coefficient<br>All regressions also incl|R2<br>ariable is<br>is the para<br>ude coun|0.29<br>employm<br>meter es<br>try and ye|0.50<br>ent grow<br>timate o<br>ar fixed|0.28<br>th.<br>f output<br>-effects.|0.51<br>growth.|

16
(training duration of 2.5 weeks) or to an experienced, professional enumerator, but with less than a day of IPV training.<sup>27</sup> While 21% of the women reported having experienced physical or sexual IPV to the untrained enumerators, 26% reported IPV to the trained ones (Jansen et al. 2004).

Another relevant data quality issue that we want to note from a companion study in the same setting (Jeong et al. 2021) is that time into the survey at the point at which a question is asked appears to adversely impact response quality, a phenomenon known as survey fatigue. While fatigue would be a consideration for any survey, it may be particularly germane for IPV measurement as most surveys place the IPV module at the end - for example, the standard DHS surveys ask about domestic violence at the end; we also chose to always place the IPV module at the end, even as we randomized the location of other survey modules within the survey. While this is usually done to minimize shame or embarrassment stemming from continued interaction with the enumerator after having answered the IPV module, we reiterate our read that concerns about stigmatization from the enumerator are very likely overblown,<sup>28</sup> and purported remedial actions, such as SI or late placement within the survey may be opening up non-obvious channels of bias.

> 27This was done in an effort to speed up the fieldwork midway through surveying after the assassination of then Prime Minister Zoran ~~D~~ in ~~d~~ i ́c in March 2003.

> 28In our study, we asked our enumerators a few debriefing questions after the IPV module and they reported that among respondents who reported any IPV incidence in FTFI, 44% in Liberia and 28% in Malawi shared more about their IPV experience with them than what was asked, suggesting that stigmatization by the enumerators may not be a big concern.

16
# _Elite Capture_

One of the most frequently-raised concerns about locally-run organizations such as the Village Funds is that village elites might “capture” the million-baht windfall and help themselves to the proceeds (Lanjouw and Ravallion 1998; Bardhan and Mookherjee 2000). Well-connected villagers may stand for election to the VF committee, and once there, become self-serving. VF committee members might then pay lower interest rates, obtain larger loans, and/or be more casual about repayment. Alternatively, these benefits might flow disproportionately to the economically privileged, whether or not they serve on the VF management committees.

Despite the temptations, it is plausible that elite capture is unlikely because (i) better-off villagers already have good access to credit, and would not want to jeopardize their credit standing, (ii) if the elite were to appropriate the benefits of the VF, their social standing in the village would fall, (iii) the VF committee is, in principle, accountable to all the adults in the village, and (iv) there is a strong ethic of social solidarity.

The data marshaled in Tables 16 and 17, which are based on the 2010 Socio-Economic Survey, allow us to address the issue of elite capture directly. While just 10% of those in the poorest quintile (as measured by expenditure per capita) have served on a VF committee, 25% of those in the top quintile have done so.

On average, VF committee members paid 5.9% in interest on their VF loans, compared to 5.97% for those who never served. However, a more satisfactory measure is the difference between what a VF committee member in a given village pays (as reported by the individual), and the average interest paid in that village on VF loans. By this measure, VF committee members actually paid 0.015 percentage points _more_ than their peers, although the difference is not statistically significant (p-value of Wald-test is 0.70). VF committee members borrowed 21% more per loan than others, but their on-time repayment rate was 1.5 percentage points higher. By these measures, VF committee members were not strongly self-serving.

An alternative view of elite capture is that favorable loans may flow disproportionately to the more affluent. The figures in Table 17 show that, compared to their peers, borrowers in the top quintile paid slightly lower interest rates (0.013 percentage points lower, not significant), borrowed larger loans (16% higher), and were slightly more likely to repay on time (0.2 percentage points higher). On the other hand, those in the top quintile only got an average of 0.13 loans per adult from the VF, compared to 0.48 loans per adult in the bottom quintile. Again, it is hard to see compelling evidence here of elite capture of the benefits of the VF loans.

26
Appendix Table A.3: Determinant of Total Value of Sales (Median Regression)

|Dependent variable|Log(Value<br>of Sales)|
|---|---|
|Sold at market (dummy)<br>|0.17935<br>(2.96)***<br>|
|Land owned(log)|0.32827|
||(13.04)***|
|Age of household head|0.00089<br>(0.44)|
|Education of Household Head|0.02147<br>(1.45)|
|Dummy=1 if participated in association|0.13506<br>(2.47)**|
|Dummy=1 if dried before sale|0.07019<br>(0.25)|
|Dummy=1 if miled/dehusked/ground before sale|0.76580<br>(5.64)***|
|Dummy=1 if graded/sorted|-0.08187<br>(1.44)|
|Dummy=1 if packaged/crated|-0.21775<br>(3.17)***|
|Dummy=1 if fumigated/treated|-0.02673<br>(0.15)|
|Dummy=1 if pre-cooled/washed|1.16370<br>(6.85)***|
|Dummy =1 if buyer is trader|0.44136<br>(3.11)***|
|Dummy =1 if buyer is commission agent|0.80253<br>(5.40)***|
|Dummy =1 if buyer is other|1.43414<br>(5.66)***|
|Dummy =1 if paid at sale|0.04977<br>(0.17)|
|Dummy =1 if paid at after sale|0.43845<br>(1.49)|
|Constant|10.20500|
||(21.51)***|
|Pseudo R<sup>~~2~~</sup>|0.30|

Robust z statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% Note: Regression includes month of sales dummies, crop dummies and district dummies
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15
determinants of decision‐making as time‐invariant and suggests that other large changes in circumstances that are voluntary in nature (e.g. marriage, childbearing, retirement) may also be unlikely to lead to changes in preferences and beliefs.

The remainder of the paper is structured as follows: Section 2 describes the context in which this migration flow is occurring, and describes the survey and estimation strategy; Section 3 reports on impacts on risk and time preferences; Section 4 covers the impact on pro‐market beliefs; Section 5 reports on the impact on interactions with partners and on household efficiency; Section 6 discusses statistical power and external validity issues, and Section 7 concludes.

# **2. Context and Survey**

# **2.1 Country Context**

The Kingdom of Tonga is an archipelago of islands in the Pacific, with a resident population of just over 100,000. It has a gross national income (GNI) per capita of $5,069 (in 2011 PPP$), slightly less than that of India, and ranks 132<sup>nd</sup> out of 188 countries.<sup>1</sup> Average income in New Zealand is approximately six times as high, at $32,689. Both countries rank 32 places higher in the Human Development Index than they do in terms of per capita GNI.

Tonga has been and continues to be a less market‐oriented economy than is the case in New Zealand. The Index of Economic Freedom ranks New Zealand third in the world, as a free, modern, and competitive economy, while Tonga is ranked as “mostly unfree” at 95<sup>th</sup> in the world, between Swaziland and the Kyrgyz Republic (Heritage Foundation, 2016). Tongan society has historically been highly stratified, with a monarch and aristocracy controlling resources and a largely subsistence‐oriented economy. The extended family plays an important role, with the kinship system expected to provide for everyone’s financial needs and a strong culture of sharing and reciprocity (makafetoli’aki) (Ketu’u, 2014). However, over time the economy has become more market‐oriented than it was several decades ago, with individuals combining remittance income from abroad with income from farming and wage labor to purchase market goods and the Mormon church influence emphasizing a role for individual achievement (Addo and Besnier, 2008).

Emigration out of Tonga has been high, with more than 30,000 Tongan‐born living abroad, mainly in New Zealand, Australia and the United States. Remittances from these migrants were equivalent to 28 percent of GDP in 2014 (World Bank, 2016), the fourth‐highest in the world. Since the 1990s the main channels of

> 1 All statistics in this section are from the _2015 Human Development Report_ (UNDP, 2015).

4
Instead, we pursue an in-depth examination of income growth from different sources and for different categories of households depending on their net position on the rice market and their welfare level. In an examination of these trends we rely on the insights provided by the growth incidence curves, which represent the changes in consumption expenditures of particular groups of population in the period between two household surveys (Ravallion 2004, Ravallion and Chen 2003).

Growth incidence curves, shown in Figures 2.5, reveal that all rural households have experienced a modest increase in total consumption, with the poor experiencing particularly strong income growth. Even among urban households and among rural net sellers, with an average decline in real income of about six percent, the poor have faired far better than the average. In rural areas, households in the bottom 20th to 30th percentile of per capita household expenditures have seen very strong income growth, whether they are net sellers, net buyers of rice, farmers, or non-farmers.<sup>7</sup>

**Figures 2.5. Growth in consumption expenditures in Bangladesh, 1995-96 to 2000.**

<!-- Start of picture text --> Urban Rural<br>40 Growth incidence curve 40 Growth incidence curve<br>35 Mean of growth rates 35 Mean of growth rates<br>30 30<br>25 25<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br>-5 -5<br>-10 -10<br>-15 -15<br>-20 -20<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Percentiles Percentiles<br>Annual growth rate % Annual growth rate %<br><!-- End of picture text -->

> 7 Growth incidence curves for the farmers and non-farmers are not shown here, but they also indicate that expenditure growth has been positive and strong for the poor.

18
**Abatement** Costs

Figure 20 illustrates the extreme variation in marginal abatement cost by pollutant, sector, size class, ownership and degree of abatement. It clearly makes little sense to talk about abatement costs in a general way. The interesting question for Chinese regulators is: Which facilities should be regulated, and how much abatement is socially desirable?

<!-- Start of picture text --> 28(a) 28(b)<br>Cotf AI2lx rt(11OOTaOsMac9 Marginal Cost of Abatement (/100 Tons of S02<br>vs Resut 139*i frop, 202D vs. Present Discounted Benefit<br>. ... . ............... S40,000 ....... .<br>Y UY ) U. U) U) U) .')' U)$35,000. . ,/7<br>-,' *4 C+1S2)* ~ U $30,0(a ./<br>00 ...... ....... 0 ........ i $2d 5,000s 8:-I-cremefit<br>2=~~~~~~ Figur -4:Icemna enft ndCsso<br>To refle t th ovrla bewe TS and S_ as sucsofieprcuas, wepovd<br><!-- End of picture text --> abatement cost evidence for both pollutants in Figure 28. The marginal abatement cost (MAC) schedules are estimated for Beijing by combining our econometric MAC equation xwith current sectoral and ownership information for that city. Large plants are a major source of air pollution **in** Beijing, so the MAC numbers for large facilities are particularly interesting. We begin with the more expensive option, S02 abatement. Our results show that the MAC curve for 100 tons of S02 abatement by large plants in Beijing varies from about $2,900 at 15% abatement to $27,000 at **85%.22** In Figure 28(a), the MAC curve _for_ large plants crosses the $8,000 incremental benefit line at around 60% abatement; by implication, the MAC curve for non-SOGE large plants would cross the incremental benefit line at a much higher abatement level. The MAC schedule for small facilities crosses the incremental benefit line at around 15% abatement.

For particulates, the numbers are much more attractive: MAC scarcely rises above $500 for 100 tons abated, even at 85% abatement for small plants. Costs are substantially less for large facilities. MAC estimates for our other four cities differ somewhat, but have the same order of magnitude. A-useful contrast is provided by NEPA's current estimates of actual abatement in the five cities, displayed in Table 5.2 22 We should note that this MAC curve is an average which reflects the distribution of production between SOE and non-SOE plants in Beijing. As we have seen, the MAC schedule for non-SQE plants is always far lower than the SOE schedule. A targeted regulatory strategy could, of _course,_ exploit this difference to capture the largest returns from stricter enforcement for the least-cost plants.

33
<!-- Start of picture text --> 30<br>21 21<br>20 19<br>o<br>> 10<br>;<br>2 0 | wm_WH_e eowai f= Li). — :<br>= _—_ i a ee ZZ<br>£ 5 | | =n<br>5 lo —3<br>00<br>—20 18 —l7<br>—23<br>30 -26 -26<br>Aug Sep Oct Nov Dec} Jan Feb Mar Apr May<br>2008 2009<br>MEE machinery and transport equipment [EEE manufactured goods<br>(5555 miscellaneous manufacturing articles [____] chemicals and related products<br>VZZZZA primary products ——— total exports<br><!-- End of picture text -->
# **II. Agriculture and Migration in Albania**

Albania presents a uniquely interesting scenario in which to assess the impact of migration on agriculture. Despite an unimpressive performance in recent years and a gradual contraction of the agricultural sector, agriculture remains a sector of vital importance for the national economy and the livelihood of a large share of the population. More than 15 years into transition, Albania remains predominantly rural: 54 percent of the population reside in rural areas (World Bank, 2004) and agriculture still employs approximately 50 percent of the workforce, providing an income source to more than 90 percent of the households in the more remote North-eastern part of the country (McCarthy et.al., 2006).

The agricultural reforms carried out in the post-communist period, and the resulting land redistribution, led to a highly fragmented sector. Albania’s land reform was unique among transition countries for its rapidity and intensity: by the mid-1990s, 94 percent of farmland had been privatized (The World Bank, 2004), with 550 state and collective farms split into 460,000 privately owned farms, averaging only 1.1 hectares per farm (World Bank, 2004; MOAFCP, 2005). Albania has had the highest decollectivization index of all transition economies in Eastern and Central Europe, but differently from these other countries, redistribution was not based on restitution and benefited all rural households (Cungu and Swinnen, 1999).

The sector remains plagued by a plethora of problems, including low productivity and outdated technologies. The labor market for agricultural work is very thin<sup>2</sup> , while formal rural credit and insurance markets remain virtually non existent. Promoting a viable agriculture thus involves removing these constraints, and rural out-migration is seen as potentially playing an important role in ameliorating some of these constraints, while possibly exacerbating others.

Concurrently with this transformation of the agricultural sector, over the past 15 years Albania has experienced one of the most extraordinary migration outflows in recent history. Particularly affected have been rural areas, where the majority of these flows originate. Based on 2005 data, one household in three has at least one former member

> 2 A strong negative social stigma is attached in Albania to being an agricultural wage laborer. This reportedly plays a big role in discouraging the emergence of an active farm labor market.

4