rafmacalaba/datause-encoder-data huggingface dataset:
**Both establishment services and post‐manufacturing services are in large part comprised of more traditional services.** Traditional services include transport, logistics, storage, and distribution, while modern services include telecom, computer, R&D, intellectual property, banking, insurance, etc. The so‐ called modern services tend to be more prevalent in the pre‐manufacturing, post‐sales and back‐office stages. A key difference between traditional and modern services is that modern services can be delivered at arm’s length (e.g. using the internet), without simultaneous production and consumption in the presence of producers and consumers. As a result, modern services often are associated with higher levels of value added and hence productivity (see Ghani et al., 2012; Ghani 2010). Moreover, traditional services are often in sectors that many countries have found difficult to reform, e.g. transportation services which still face many restrictions globally.

**The type of services that manufacturing firms perform is correlated with firm productivity.** Overall, manufacturing firms engaged in post‐manufacturing and establishment services are the least productive in Turkey, whereas those engaged in pre‐manufacturing and back‐office services are relatively more productive. Figure 3.7 shows the TFP distribution for manufacturing firms in Turkey separated by the six production stages. Firms that produce more than one type of GVC services are included as well, but picked up separately in the distribution plots. The distribution plot shows that manufacturing firms with an establishment or post‐manufacturing services affiliate are least productive as the bell‐shaped curves are shifted to the left. These firms are also relatively smaller in Turkey – although much bigger than non‐ servicified firms. Manufacturing firms that integrate back‐office services and manufacturing services seem to be most productive. They also tend to be larger.<sup>13</sup> Similar results are obtained when using four alternative TFP measures (Figure A2 in Annex 1).

**Figure 3.7: Distribution of firm‐level TFP by service category (2014)**

<!-- Start of picture text --> TFP ACF<br>5 6 7 8 9 10<br>Estab Pre-man Manu Post-man<br>Post-sales Back-office Goods only<br>.3<br>.2<br>.1<br>0<br><!-- End of picture text --> Source: EIS data from Turkey; author’s calculations. The vertical axis of this shows the frequency of firms that have a certain level of TFP. The level of TFP is shown on the horizontal axis.

**A higher share of post‐manufacturing services produced in‐house is correlated with lower levels of firm productivity.** Figure 3.8 shows the negative relationship between firm‐level TFP and post‐manufacturing

13 Note that manufacturing firms engaged in pre‐manufacturing services and post‐sales services are the largest in Turkey. All servicified firms are much bigger compared to the non‐servicified goods‐only firms. (The benchmark regression analysis controls for firm size.) 20
35 whereas boys' schools report the opposite<sup>pattern. Of course, these factors</sup> were included in the initial analyses,<sup>but statistical adjustments may not</sup> wholly trap the full result of school size<sup>and time differences.</sup> Another advantage for girls' only schools relates<sup>to the matching of</sup> the genders of teachers and students. In the<sup>girls' schools in this sample,</sup> all teachers in the sampled math classes were female,<sup>which may foster a</sup> symbiotic and supportive relationship between female staff and students. Girls in Nigeria single-sex schools may be inspired<sup>to academic excellence by</sup> female role models they see every day. While girls'<sup>school students see only</sup> women mathematics teachers, the girls in coeducational schools are in contact with only a small number of such females, as<sup>the mathematics staffs of their</sup> schools are less than 20 percent female. The role<sup>model aspect of mostly male</sup> teachers in &1l-male schools possibly makes<sup>less of an impact, since these</sup> boys live in a society which has males dominant<sup>in all professional roles.</sup> Other possible explanations for the findings of this study relate to issues of selectivity which, in a study such as this, is both a statistical nuisance and a social phenomenon. Other research describing<sup>educational</sup> conditions in developing countries (particularly in Africa) has reported lower attendance rates for females, often resulting<sup>in higher social status for</sup> in-school females. While girls in coeducational schools in this study were no more likely to have professional fathers than<sup>their male coeducational</sup> counterparts, girls' school students more frequently reported<sup>professionally</sup> employed fathers. Although we have statistically adjusted for this SES difference -- both for individual students<sup>and with the aggregate measure for</sup> schools -- it is always likely that girls in<sup>single-sex schools are socially</sup> advantaged in other ways not measured in the<sup>IEA study. The fact that girls'</sup>
-93-

(') Sen, A. (1984b) "Economics and the Family," in Resources. Values and Develogment, Harvard University Press: Cambridge.

(59) Sen, A. (1988) "Family and Fooo: Sex Bias in Poverty," in Rurl Pav2e= in South sLia, P. Bardhan and T.N. Srinivasan, eds. Columbia University Press: New York.

(60) Shultz, P. (1981) &coromis.of Ponu1ation, Addison Wesltj: New York.

(61) Solis, L. (1984) "Food Marketing and Income Distribution," in flM Political Economy of Income Distribution in Mexico, Aspe, P. and Sigmund, P., eds., Holmes and Meier: New York.

(62) Sriniva3an, T.N. (1981) "Malnutrition: Some Measurement and Policy Issues," Journal of DeveloRment-Economics, Vol. 8, No. 1, pp. 3-21.

(63) Stiglitz, J. (1988) "Economic Organization, Information, and Development," in HAndbook of Development Economics, Vol. I, H. Chenery and T.N. Srinivasan, eds., North-Holland: Amsterdam.

(64) Stiglitz, J. (1989) "Rational Peasants, Efficient Institutions, and a Theory of Rural Oiganization: M4ethological Remarks for Development Economics," in The Economic Theory of Agrariarn Institutions, P. Bardhan, ed. Clarendon Press: Oxford.

(65) Strauss, J. (1986) "Does Better Nutrition Raise Farm Productivity," Journal of Political Economy, April.

(66) Straeten, P. (1989a) "Poverty: Concepts and Measurement," Institute for Economic Development Discussion Paper # 6, Boston University.

(67) Streeterk, P. (1989b) "Hunger," Institute for Economic Development Discussion Paper # 4, Boston University.

(68) Sukhatme, P.V. (1988) "Energy and Nutrition: On the Autoregulatory Homeostatic Nature of the En.rgy Requirement,' in RuralP P. Bardhan and T.N. Srinivasan, eds. Columbia University Press: New York.

(69) Takayama, N. (1979) "Poverty, Income Inequality, and their Measures: Professor Sen's Axiomatic Approach Reconsidered," Econometrica, Vol. 47, No. 3, pp. 747-59.

(70) Timmer, P. (1988) "The Agricultural Transformation," in Hgndbooh of DeveloRment Economics, Vol. I, H. Chenery and T.N. Srinivasan, ads., NorthHolland: Amsterdam.

(71) Torche, A. (1990) "Food Policy and Nutritional Programs in Mexico," mimeo, The World Bank, August, Washington, D.C..

(72) Van Winbergen, S. (1990) "Mexico's External Debt Restructuring in 1989/90: An Economic Analysis," mimeo, The World Bank, March, Washington, D.C..
# Annex 1: Representativeness of the scraped data on the housing market

|Country|Population<br>(2019)|Average HH<br>size|Number of<br>Households|Number of<br>scraped<br>observations|Individuals<br>using the<br>internet (%<br>of the<br>population)|Percentage<br>of all<br>households<br>covered in<br>scraping|Slum<br>Population,<br>percent of<br>urban<br>population|Ratio of<br>mortgages to<br>GDP, percent|
|---|---|---|---|---|---|---|---|---|
|Albania|2,854,191|3.66|779,688|3,389|72.24 %|0.43|n.a|12.2|
|Costa Rica|5,047,561|3.20|1,579,421|10,376|80.53 %|0.66|4|15.93|
|Morocco|36,471,769|4.77|7,646,128|10,737|84.12 %|0.14|9|23|
|South Africa|216,565,318|6.28|34,500,045|107,652|68.20 %|0.31|26|16.15|
|Pakistan|58,558,270|3.86|15,155,778|91,904|17.00 %|0.61|40|0.23|

Note: Overview on representativeness of the scraped house prices of the formal market. Numbers on average household size are drawn from countries’ latest household survey. Data for total population are drawn from World Bank (2022).

39
**<u>Table 2: Comparison of distance measures for different types of parcels in minutes</u>**

||**Least cost**<br>**between pa**|**path**<br>**rcels**|**Least cost path from**<br>**home to [ ] parcels:**|**Own estimate**<br>**home to [ ] p**|**of from**<br>**arcels:**|
|---|---|---|---|---|---|
||Mean|Sig|Mean<br>Sig|Mean|Sig|
|**Panel A: By rental market participation**||||||
|**Between households**||||||
|Participants (N = 1219, 1964, 1698)|17.23||11.608|15.865|***|
|Non-participants (N=1792, 1816, 1822)|18.14||10.416|11.88||
|**Within household (only participants)**<sup>**a**</sup>||||||
|**Owner-cum-tenants**||||||
|All rented (N = 970, 1290, 1548)|20.76||16.303<br>***|22.498|***|
|Owned|19.09||10.086|12.265||
|**Owner-cum-fixed renter**||||||
|Fixed rented (N =700, 905, 1112)|21.6||17.397<br>***|24.573|***|
|Owned|18.88||9.732|12.821||
|**Owner-cum-free of charge renter**||||||
|Free (N = 383, 546, 652)|18.2||13.371<br>**|18.47|***|
|Owned|18.64||10.389|11.749||
|**Panel B: By sales market participation**||||||
|**Between households**||||||
|Participants (N=1496, 1799, 1816)|19.26||11.397<br>**|15.089|***|
|Non-participants|18.14||8.607|8.614||
|**Within household (only participants)**<sup>**a**</sup>||||||
|Purchased (N=878, 1315, 1469)|22.68|**|16.852<br>***|15.477|**|
|Other owned|17.26||11.128|17.605||

_Source:_ Own computation from 2010/11 Rwanda LTR Household Survey.

N stands for number of households for each data columns in the same order; Sig refer to significance of difference between owned and the category under concern (*significant at 10%; **significant at 5%; *** significant at 1%). An empty “Sig” column implies difference is not significant at 10% or less.

a The least cost path for traded in (e.g., rented or purchased) refers to average distance from these parcels to owned parcels whereas for owned parcels it refers to average distance between owned parcels.

16
## **Table 3.7. Macro SAM III: Additional calculations**

|**# Item or SAM cell**|**Definition**|**Formula/Source**|
|---|---|---|
|_New data inputs to c_<br>|_alculations for Macro SAM III (intermediate da_<br>|_ta)_<br> *|
|1 shrginv|Government share in total investment|IMF Investment and Capital Stock Dataset|
|2 fdi|Foreign direct investment|WB International Debt Statistics Database|
|3 shrgfbor|Government share in total foreign borrowing|WB International Debt Statistics Database|
|4 drf|Change in foreign reserves|IMF BOPS (2017)|
|5 fbor|Total foreign borrowing|=[sav‐inv,row] + drf‐fdi**|
|_New cells in Macro S_|_AM III compared to Macro SAM II_||
|5 [com‐prv,inv‐gov]|Government investment|=[com,sav‐inv]*shrginv|
|6 [com‐prv,inv‐prv]|Private investment|=[com,sav‐inv]‐[c‐prv,inv‐gov]|
|7 [cap‐hhd,hhd]|Household savings|=[sav‐inv,hhd]|
|8 [cap‐gov,gov]|Government savings|=[sav‐inv,gov]|
|9 [cap‐row,row]|Foreign savings|=[sav‐inv,row]|
|10 [inv‐prv,cap‐row]|FDI|=fdi|
|11 [inv‐prv,cap‐hhd]|Household financing of private investment|=[com‐prv,sav‐inv]‐[invng,cap‐row]|
|12 [dstk,cap‐hhd]|Household financing of stock change|=[com‐prv,dstk]|
|13 [inv‐gov,cap‐gov]|Government investment financing|=[com‐prv,inv‐gov]|
|14 [cap‐gov,cap‐row]|Foreign financing of the government|=min{([cap‐gov,gov]‐[invg,cap‐gov]), fbor*shrgfbor}|
|15 [cap‐gov,cap‐hhd]|Household financing of the government|=[invg,cap‐gov]‐([cap‐gov,gov] + [cap‐gov,cap‐row])|
|16 [cap‐hhd,cap‐row]<br>17 [cap‐row,cap‐row]|Foreign financing of household<br>Change in foreign reserves|=([cap‐gov,cap‐hhd] + [invng,cap‐hhd] + [dstk,cap‐hhd])‐[cap‐hhd,hhd]<br>=drf|

*If no data is available, the average for countries in same income group was used.

**If no data is avaiable in World Bank (2017), IMF (2017) is used.

Source: Authors’ elaboration.

# **4. A computer program for building macro SAMs from cross‐country data**

Using the procedure described in Section 3, macro SAMs were put together for 133 countries drawing on data extracted from cross‐country databases. The SAMs and a computer program that applies the procedure are available on request from the authors as an electronic appendix to this paper.

In outline, the program does the following: First, it assembles relevant data from the World Bank, IMF, and GTAP in a computer‐readable format.<sup>1516</sup> On the basis of a check on country‐level data availability, it then identifies the most recent year (2010 or later) with sufficient information to construct each of the three macro SAMs for each country. After this, it defines the three macro SAMs using the procedure presented in Section 3,

> 15 The process was programmed in the GAMS (the General Algebraic Modeling System) software, drawing on raw data entered in a GAMS‐readable format in Excel.

> 16 GTAP is a database with full global coverage, in the version used (version 9) splitting the world economy into 140 regions, most of which are individual countries.

‐18‐
The final set of outcomes of interest for this study is educational enrollment. Educational enrollment rates are calculated for the basic education level age group (6-14 year olds) and the secondary education level (15-17 year olds) in the sample. In addition, three composite indicators for healthcare utilization, nutrition, and access to basic infrastructure are used to measure exposure to multiple risk factors.

# **_Circumstance Variables_**

This study identifies seven circumstances that may affect child's access to basic services. They are:

- a) Location: Metropolitan, urban Lower Egypt, rural Lower Egypt, urban Upper Egypt, rural Upper Egypt, Frontier Governorates (6 categories);

- b) Mother's education: No formal education, primary/preparatory, secondary, higher education (4 categories);

- c) Father's education: No formal education, primary/preparatory, secondary, higher education(4 categories);

- d) Number of siblings: 1-2 children, 3-4 children, 5 or more children (3 categories);

- e) Household wealth (asset) quintiles: 5 quintiles (5 categories);

- f) Gender of child (2 categories); and

- g) Imputed household consumption obtained by combining the DHS and HIECS data (a continuous variable).

# **Empirical Methodology**

The literature on inequality of opportunity emphasizes the differences in outcomes that arise from differences due to predetermined circumstances over which an individual has no control. Differences in adulthood outcomes, such as educational attainment and income may not only be explained by individual efforts and choices but also depend on initial endowments or circumstances. One way to differentiate between the impact of circumstances and individual efforts is to look at inequality in 9
Lebanon. It was the obvious step forward in overcoming these problems and the answer to the increasing demand for evidence around the Syrian crisis.

The study benefitted from a unique and unprecedented set of data. The UNHCR registry data in Jordan and Lebanon were among the better quality registry data available worldwide and the UNHCR also conducted home visits in Jordan that, at the time of the study, covered over a third of all refugees. There were also sample surveys in both Jordan and Lebanon that were small in size but representative of the population present in the registry. The home visits and the surveys included questions on income and expenditure that could be used for the welfare assessment. Using these data, the study addressed ten questions defined as follows: 1) Who are the refugees? ; 2) How different are refugees from “regular” populations?; 3) How poor are refugees? ; 4) What are the main predictors of refugees’ welfare and poverty? ; 5) How vulnerable are refugees from a monetary and non‐monetary perspective? ; 6) Do poverty and vulnerability statuses overlap? ; 7) How effective are refugee assistance programs? ; 8) What is the potential for alternative policies? ; 9) How does welfare compare across countries and data sets?; 10) How transferable are the findings between countries and data sets?

The study findings opened a window on a population little known to economists. Refugees were found to be a population with specific characteristics that derived from a multi‐stage selection process that started well before the conflict in Syria for economic and environmental reasons. Each stage of this selection process further impoverished those who eventually would seek refugee status. The resulting profile of refugees was rather different from that of Syrians before the crisis and that of host populations, a relevant finding to study the impact of refugees on host populations. Poverty was found to be very high. Over 70 percent of refugees in Jordan and Lebanon resulted to be poor if the income threshold used to assign cash grants by the UNHCR was used as poverty line. It was estimated at over 90 percent in both countries if the poverty lines of the host countries were used. It was also found that refugees tend to leave poorer areas in the country of origin to reach poorer areas in the countries of destination, inflating poverty numbers where they arrive. Predictors of poverty were relatively few if one compares with poverty assessments of regular populations. As refugees have few assets and constrained consumption, household size, the ability to pay rent, and few other individual and household characteristics were sufficient to predict poverty well. Predictors of vulnerability were also found to be similar to predictors of poverty and similarly few in number although poverty status did not always overlap with vulnerability status.

The study also found the existing UNHCR cash program and the WFP food voucher to be very effective in reducing poverty. The two programs combined had the potential to reduce poverty to almost 7 percent if 21
which includes town fixed effects. Column (3) shows that women continue to have significantly lower rates of participation in the labour force: thus, working age rural women were 50 percent less likely to participate in the labor force than men. As 71 percent of males were engaged in the labor force for the year under consideration, this suggests that LFP for women in 2015 was just above 20 percent, conditional on individual and district characteristics. While literacy has a modest positive impact on LFP (only after including town FE), the completion of secondary education (or better) continues to have a negative and significant impact on LFP.

When we restrict the sample to rural workers and gauge the predictors of non-farm employment [column (9), Table 11], we see that females continue to have a significantly lower likelihood of engaging in non-farm activities, conditional on participation in the labor force.<sup>14</sup> Thus, rural women continue to have lower rates of participation in the labor force and conditional on participation, significantly lower rates of participation in non-farm activities.

A major departure from the prior results with the NSS data is that marginalized rural workers (SC/ST individuals) have a higher likelihood of being engaged in non-farm activities. The coefficient is economically meaningful, suggesting that a rural SC/ST worker has a 10 percent higher likelihood of being engaged in non-farm activities, relative to non-SC/ST workers. We will discuss this finding in greater detail when considering the specific types of non-farm employment. Finally, while literacy does not predict rural non-farm employment, being secondary educated or better does. The coefficient on literacy is attenuated towards zero, while the coefficient on secondary educated indicates that the completion of secondary education increases the likelihood of being employed in non-farm activities by over 12 percentage points for rural workers.

Moving to the various employment categories in Table 12, we see that secondary education significantly reduces the likelihood of rural workers being employed in farm or informal – non-farm activities the two major sources of employment for rural workers.<sup>15</sup> Expectedly, – it has a large impact on being employed in formal non-farm jobs in particular, as a white collar worker, operating a business, or being a qualified self-employed professional. The – coefficient is particularly large for being employed in white collar activities obtaining a secondary or higher education increases a rural worker’s likelihood of being employed in a white collar occupation by 8 percentage points. Literacy on the other hand does not affect

> 14 While the coefficient remains negative and highly significant across all three columns, it declines in magnitude upon the inclusion of district/town fixed effects, pointing to the role of local factors in influencing women’s participation in non-farm activities.

> 15 Informal non-farm activities include being engaged in wage labor or operating a small business. Together, over 80 percent of rural workers are engaged in either of these two activities.

28
Statistics). But this should not change the aggregate story very much as seen in the more detailed analysis available from Easterly and Serven (2003), which suggests that, at best, decentralization (as well as privatization) compensated for the decline in central government expenditure. In fact, there is a widespread sense that the fact that most urban infrastructure (secondary roads, drainage, sanitation) is increasingly the responsibility of local governments has probably contributed to the drop in public investment. Local governments increasingly depend on local taxation because, with fiscal decentralization and consolidation, many countries have cut central government transfers. To put this discussion in historical perspective, it is worth remembering that in many of the developing countries, total public expenditure levels in the 1970s and 1980s were in the 10-15% of GDP range.

But the decline in public sector financing has been compounded by a sharp fall in ODA. For instance, the commitment level for infrastructure of multilateral development banks (MDBs) all together have somewhat declined since 1995, fluctuating between $18Bn in 1996 and a low point of $13.5 Bn in 1999. These commitments had recovered somewhat to about $16 billion in 2002, and some multilaterals are expanding their support to infrastructure.<sup>xii</sup> Similarly, bilateral development aid declined from $15 billion in 1996 to about $8 billion in 2002 which also represented a decline in the relative share of infrastructure in their total commitments from 27% to 14%.<sup>xiii</sup> The main issue, however, is that the numbers, even at their peak, are too small in relation to the needs. The best that can be hoped for is a significant counter-cyclical effect, at least sufficient enough to offset swings in the expected contribution of the private sector, as well as to leverage funds from private sources.

19