rafmacalaba/data-use-mentions-extended huggingface dataset:
The preceding discussion is based on a static, cross-sectional view of poverty. As noted, Malaysia does not collect panel survey data on household income or consumption, so there is limited knowledge of poverty and income dynamics at the micro-level. However, some studies have investigated socioeconomic intergenerational mobility based on retrospective data (Khalid 2018, Hamid et al. 2019). Khalid (2018) finds that the majority of younger-generation Malaysians (birth cohort 1975–1985) have higher educational attainment and better occupational skills than the previous generation (the cohort born between 1945–1960). Income mobility patterns across generations are mixed, but mobility was found to be highest for children born to parents in the lowest income quintile. This is consistent with the image of reductions in poverty and inequality in official Malaysian statistics. For example, Khalid (2018) finds that children born in the poorest households will in general not be poor as adults, suggesting that long-run chronic poverty is uncommon. However, he also observes that upward intergenerational mobility was less likely in rural areas than in urban ones. Despite positive findings about past mobility, Khalid argues that such patterns are unlikely to be repeated for children born in the 1990s and later, e.g., due to changes in Malaysia’s education policy. This connects with the criticisms discussed above that preferential policies favor those who are already richer than the average Malaysian.

7
**Figure 2: Difference in Capabilities, South Africa (LHS) and Other SACU Countries (RHS) versus ASEAN**

**South Africa v. ASEAN (Avg)**

**Other SACU Countries (Avg)  v. ASEAN (Avg)**

<!-- Start of picture text --> Proximity to Markets ‐0.53<br>Natural Capital ‐0.33<br>Human Capital ‐0.16<br>Physical Capital ‐0.29<br>Institutional Capital 0.25<br>Logistics/Connectivity ‐1.02<br>Wage Competitiveness 0.08<br>Market Access ‐0.49<br>Access to Inputs 1.60<br>‐3.00‐2.00‐1.000.00 1.00 2.00<br><!-- End of picture text --> <!-- Start of picture text --> Proximity to Markets ‐0.78<br>Natural Capital 0.23<br>Human Capital 0.26<br>Physical Capital ‐0.20<br>Institutional Capital 0.33<br>Logistics/Connectivity 0.65<br>Wage Competitiveness ‐0.83<br>Market Access 0.67<br>Access to Inputs 1.25<br>‐3.00‐2.00‐1.00 0.00 1.00 2.00<br><!-- End of picture text --> Source: Authors’ calculations from data sources listed in Table 1

# B. How do SACU’s capabilities compare to requirements of GVC sectors?

Overlaying the global requirements of each GVC sector described previously and the capabilities present in SACU economies, Figure 3 depicts the alignment between SACU’s current endowments and the requirements in the largest sectors of GVC trade. The units of the axes are standard deviations from the average endowment.
(2002) note that the initial fiscal year 1998-1999 did not provide for broad emergency programs to cope with negative impacts of the crisis. In July 1998 the current budget was revised to include an explicit Social Safety Net Program.

> 13One author of this paper was a member of the IFLS3 survey team. A discussion of sampling and survey methods for IFLS2 and IFLS3 can be found in Frankenberg, Beegle and Thomas (1999) and Strauss et al (2004b), respectively.

9
that follows, I first use the 2007 TLSS data to estimate the determinants of completion of “basic level of schooling or higher” (that constitutes eight or nine grades depending on when an individual entered schooling) and “secondary school or higher” levels of education. The choice of these dependent variables is based on the system of education in Tajikistan where the basic level of education (nine grades) is compulsory. Students who completed secondary education level qualify for a secondary school diploma. Only students who completed secondary school level can apply for admission 16
use of ICT also increased their productivity.

As Figure 2 shows, firms that exhibited changes in their ICT use above the median showed much higher increases in sales-per-worker. Furthermore, as shown in the second panel of the figure, this is the case even when we analyze the residuals of a regression of the change in sales over firm size, age, investment in fixed assets per worker and the share of white-collar workers. That is, even controlling for firm characteristics, firms that increased their use of ICT became more productive.

Figure 2: Mean change in sales/worker 2008-2012 (a) _Mean change in sales/worker_ <!-- Start of picture text --> Computers−per−worker **<br>Share of labor with internet<br>Share of labor with computer ***<br>−.1 0 .1 .2 .3<br>Low increase in ICT use High increase in ICT use<br><!-- End of picture text --> (b) _Mean change in sales/worker controlling for firm characteristics_ <!-- Start of picture text --> Computers−per−worker **<br>Share of labor with computer ***<br>Share of labor with internet<br>−.2 −.1 0 .1 .2<br>Low increase in ICT use High increase in ICT use<br><!-- End of picture text --> Source: Authors’ calculations with data from ENTIC 2009 and 2013, INEGI. Differences in means significant at the: * 10% level, ** 5% level, *** 1% level Note: In panel (a) the means of the differences in ln(sales/worker) are shown for each ICT-use group. In panel (b) we take the residuals of a regression of the difference of ln(sales/worker) over size dummies, age dummies, ln(investment
be expected (Pomfret, 2007). Note, however, that the results reported in the next section are robust to the inclusion of ECO.

Of all the trade agreements we considered only one includes MENA countries that can be classified as resource poor and resource rich, and that is PAFTA. For a description of countries under each category, see footnote 8.

The Herfindhal indices of export concentration and the number of export lines at the six digit HS level are computed using HS 6 digit data from United Nation’s Comtrade. They are measured before the entry in force of the agreement and are based on total export by product of each country to the world.

# **5. Empirical results**

Table 1 reports the results of the estimation of equation (1) for seven preferential trade agreements involving MENA countries. Both intra and rest-of-the-world (row) effects are reported for each of the seven agreements. The first column reports results using total imports, whereas the second column reports results for non-oil imports. The first point to notice is that there are no statistically significant differences between the coefficients reported under the two columns for total imports and non-oil imports.

In all agreements except AGADIR (involving Egypt Jordan, Morocco and Tunisia) and GCC (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and United Arab Emirates) we found a positive, large and statistically significant coefficient on intra-regional trade. The fact that AGADIR and GCC do not find a statistically significant coefficient for intra-regional trade can be partly explained by the fact that all AGADIR and GCC countries are part of PAFTA and entered into force after PAFTA. So the advantages in terms of intra-regional liberalization that AGADIR and GCC offer may be limited.

12
Republic of the Congo*, Djibouti, Guinea*, Haiti,<br>Kyrgyz Republic, Cambodia, Lao PDR, Mongolia, Mauritius*, Panama*,<br>Philippines,Russian Federation, Sri Lanka*,Thailand,Turkey, Uganda*(18) 7|2012 only|
|Chad*, Republic of the Congo*, India*, Niger*, Senegal*, Sierra Leone*,<br>Tanzania*,Togo*(8)|2011 only|
|Bangladesh*, Ethiopia*, Guinea-Bissau*, Lesotho*, Madagascar*, Malawi* ,<br>Mali*, Nepal*, Nigeria*, Rwanda*, São Tomé and Príncipe*, Tunisia, Vanuatu,<br>South Africa*,Zambia*(15)|2010 only|
|Burkina Faso*, Botswana, Maldives, Nicaragua*, Papua New Guinea, West Bank<br>and Gaza, Sudan*, Swaziland*,Tajikistan,Tonga (9)|2009 only|

*Countries for which GMD has surveys’ PSU.

# **Lineup**

The lineup procedure followed three steps: 1) Calibrate the sample of each survey to represent the national population in 2013 according to UNDESA. The calibration is made to represent UNDESA’s population divided by 5-year age groups and gender. The survey weights are calibrated using maximum entropy estimation, and then rescaled to equal the exact UNDESA figures.<sup>14</sup> The number of children aged 15-17 within the 15-19 age-group is determined by each survey’s age distribution within the group. For Kosovo we use population estimates

> 14 Command maxentropy in Stata®14. This technique is used so as, to the extent possible, maintain constant weights within households. See Wittenberg, Martin. “An Introduction to maximum entropy and miminum cross-entropy estimation using Stata”. The Stata Journal 10 (2010) for a description of the methodology.

20
SOUTH SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE percent, the Central African Republic and Chad are in the order of 30 percent, and Sierra Leone is close to 40 percent.

There is also very limited mobile penetration in South Sudan. Household surveys from 2009 suggest that only 18 percent of South Sudan’s households owned a cell phone. In contrast, over a third of the population in Sudan owns a cell phone. Estimates for 2011 set mobile ownership up to 25 percent of the population still below African averages.

South Sudan records a low 7 users of the Internet per 100 people (table 16). Very low literacy rates in part drive low Internet usage. Further, less than 1 percent (0.7 percent) of the population owns a computer.

**Table 16. Benchmarking South Sudan’s ICT performance indicators**

|Indicator|Units|South<br>Sudan|Resource-<br>rich<br>countries|East Africa|Low-<br>income<br>countries|Sub-<br>Saharan<br>Africa|
|---|---|---|---|---|---|---|
|||Various<br>years|2008|2008|2008|2008|
|**Access**|||||||
|GSM coverage|% population under signal||78|0.02|63|72|
|International bandwidth|bits/person||18|11|25|39|
|Internet|users/100 people||9|5|3.5|5.5|
|Landline|subscribers/100 people|0.11|0.9|0.24|0.8|1.4|
|Mobile phone|subscribers/100 people|12|38.2|21|24.4|33.1|

_Source:_ Data for
and 3 were conducted after September 2014. The time lapse between the first and the second visits was much greater than originally planned (3 months in average instead of 2 weeks) because of logistical constraints, and because it often required several trips to the business to complete the second visit successfully.

During the two years following program launch, 302 businesses registered with CGA (13 percent of the total in treatment group 2 and 15 percent of the total in treatment group 3), and 272 businesses participated in a group training session at CGA (12 percent of the total in treatment group 2 and 14 percent of the total in treatment group 3). Since businesses had first to register for the _entreprenant_ with GUFE in order to be eligible to register at the CGA, and thus receive the trainings, the percentage of eligible businesses that did register with CGA is sizeable. In fact, 83 percent of the businesses in groups 2 and 3 that formalized (362 businesses in total) decided to register with the CGA, and 75 percent decided to obtain trainings. Business owners in groups 2 and 3 who decided to register as _entreprenant_ had also the possibility to open a bank account at BoA or Orabank. After two years, 131 businesses opened an _entreprenant_ bank account (6.2 percent of total).<sup>8</sup> Panel A of Table 2 summarizes achievement for each program implementation step.

Qualitative information collected with beneficiaries during program implementation suggests that the program was implemented following the study protocol and in particular that the formalization process with the _entreprenant_ status was considered as simple and fast (in addition to being free of charge). Panel B of Table 2 shows quantitative data from the follow up survey and confirms that the formalization process was fast and cheap for businesses in treatment groups. 82 percent of businesses that benefited from the program and formalized declared that they did not pay anything in the process (those who paid something in the treatment groups formalized with a different status than the _entreprenant_ status).
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# Growing Up Together: Sibling Correlation, Parental Inuence, and Intergenerational Educational Mobility in Developing Countries

Md. Nazmul Ahsan, St. Louis University<sup>1</sup> M. Shahe Emran, IPD at Columbia University Hanchen Jiang, University of North Texas Qingyang Han, Johns Hopkins University Forhad Shilpi, DECRG, World Bank First Drat: June 22, 2022; This Version: January 16, 2023 ABSTRACT This paper presents credible and comparable evidence on intergenerational educational mobility in 53 developing countries using sibling correlation as a measure, and data from 230 waves of Demographic and Health Surveys. It is the rst paper to provide estimates of sibling correlation in schooling for a large number of developing countries using high quality standardized data. Sibling correlation is an omnibus measure of mobility as it captures observed and unobserved family and neighborhood factors shared by siblings when growing up together. The estimates suggest that sibling correlation in schooling in developing countries is much higher (average 0.59) than that in developed countries (average 0.41). There is substantial spatial heterogeneity across regions, with Latin America and Caribbean having the highest (0.65) and Europe and Central Asia the lowest (0.48) estimates. Country level heterogeneity within a region is more pronounced. The evolution of sibling correlation suggests a variety of mobility experiences, with some regions registering a monotonically declining trend from the 1970s birth cohort to the 1990s birth cohort (Latin America and the Caribbean and East Asia and Pacic), while others remained trapped in stagnancy (South Asia and Sub-Saharan Africa). The only region that experienced monotonically increasing sibling correlation is the Middle East and North Africa. The recent approach of Bingley and Cappellari (2019) is used to estimate the share of sibling correlation due to intergenerational transmission. The estimates show that when the homogeneity and independence assumptions implicit in the standard model of intergenerational transmission are relaxed, the estimated share is much larger. In the sample of countries, on average 74 percent of sibling correlation can
in the context of transportation infrastructure, because such investments might also affect locations further away. The market access measure captures such spillovers. As with our previous empirical approaches, we rely on Kebeles as our primary unit; however, to test the sensitivity of results, we also repeat the analysis using Woredas (N = 780). The 1x1km pixels are less suitable for market access analysis.

The measure of market access is constructed using the following equation (Donaldson and Hornbeck, 2016): where population of the other markets ( _popj_ ) is held constant at 2000 levels and where we measure the travel time _ttij,t_ between the origin unit _i_ and the destination _j_ at time _t_ (see Appendix S11 for details on estimating travel time using the RSDP road network data). We use the most populous pixel within each unit as the location for estimating travel times. In our primary models, we use an elasticity ( _θ_ ) of 3.8, following Donaldson and Hornbeck (2016); however, we test the robustness of our results to alternate measures.

We aim to understand whether market access growth is associated with changes in economic activity, urbanization, or cropland. As our primary model, we use a long-difference framework adopted from Alder (2017), who estimates how changes in market access are associated with changes in nighttime lights in India. We regress changes in our outcome variables on changes in market access, as specified in the following equation: where ∆ _IHS_ ( _yi_ ) represents changes in outcome variables as specified in the above section, and we take the inverse hyperbolic sine transformation before differencing; _MAi_ is the measure of market access; _Ni_ is a vector of indicator variables for different levels of baseline nighttime lights; _Ci_ is a vector of controls, including initial market access, average nighttime lights at baseline, pre-trends in luminosity, and pre-trends in the number of urban pixels; and _γz_ are Zone fixed effects (where these represent Zone-year fixed effects in a panel framework). In computing the long
vertical lines in the two graphs). The PL for urban Maputo is 93 percent higher than that of rural N-CD, while the PL for rural Maputo exceeds the PL for Nampula by 73 percent (see Table 4, column 6, in Appendix). As shown in Figure 3 the magnitude of the PL differentials completely offsets the dominance of per capita consumption in Maputo, both for urban and rural areas. The key question, therefore, is whether these sizeable

> 18 The location-specific PLs used in the official poverty statistics group homogeneous urban and rural areas into 13 regions, sometimes combining areas from different provinces, as is the case with Niassa and Cabo Delgado Provinces (see Table 1). 19 In both cases, we truncated the upper side of the distribution in order to close up on the lower percentiles: this way we simply disregard the upper 1<sup>st</sup> percentile. What concerns us is the rest of the distribution, in particular the part where the poor are identified.

13
examining different measures of savings. In spite of that, his view supports the conceptual theory that low savings or higher consumption accelerates economic growth; however, both the minimal and poor quality of savings data makes it quite difficult to reach such conclusion when based solely on official savings aggregates.

While analysis of savings is significant to guide development policies in developing economies, it is challenged by quality of data and strength of the analytical framework and interpretation of empirical outcomes. With that being said, this paper will study savings behavior in the Arab Republic of Egypt. The next section provides a literature review of recent empirical literature on savings behavior in addition to earlier research on modeling savings behavior in Egypt. It is followed by a section describing the data and the empirical methodology. The last two sections include the analysis of the empirical findings and the conclusion.

# **Literature Review on Modeling Savings Behavior**

The Life Cycle Hypothesis (LCH) enriched the theoretical literature on savings. The key assumption of this hypothesis is that individuals spread their lifetime consumption evenly over their lives by accumulating savings during earning years and maintaining consumption levels throughout retirement. Therefore, tests of the life cycle hypothesis are mainly concerned with the effect of demographic variables such as age groups, birth rates, and dependency ratios on savings 2
Table 4: Estimation of propensity score, using pretreament data (2007)

||Panel A:|Fifth-grade schools|Panel B: N|inth-grade schools|
|---|---|---|---|---|
||Coefficient|_P_value|Coefficient|_P_value|
||(1)|(2)|(3)|(4)|
|∆ Portuguese grade|-0.02|0.00|-0.02|0.00|
|∆ Mathematics grade|0.04|0.00|0.02|0.00|
|Portuguese grade|0.03|0.00|0.04|0.00|
|Mathematics grade|-0.05|0.00|-0.05|0.00|
|Dropout rate|0.04|0.00|0.00|0.73|
|IDEB Index|-1.02|0.00|-1.34|0.00|
|∆ IDEB|0.48|0.00|1.09|0.00|
|Municipal population|0.00|0.00|0.00|0.00|
|Students' enrollments|0.00|0.00|0.00|0.00|
|IDHM|19.11|0.00|17.32|0.00|
|Urban|0.33|0.58|-0.65|0.06|
|Basic sanitation|-1.10|0.00|-1.13|0.01|
|Library|0.16|0.02|0.29|0.00|
|Sports court|-0.06|0.30|-0.17|0.02|
|Number of classrooms|-0.05|0.00
Rozo and Winkler, 2021), employment (Amin, 2021), productivity (Amin and Okou, 2020), quality of products (Banerji and Jain, 2007), and innovation (Avenyo et al., 2021). Lastly, Distinguin et al. (2016) provide evidence that the presence of informal competition makes formal SMEs’ more likely to be creditconstrained. Our analysis contributes to this literature by showing that the impact of informality on the formal sector depends on the _perceived_ threat that formal firms associate to informal competition. This, in turn, has relevant effects on firms’ expectations, investment decisions, and borrowing choices. As such, our paper provides an important piece of the puzzle in the understanding of the effect of informality on the functioning of the formal economy.

Second, our paper speaks to the literature on the determinants of firms’ access to finance in developing countries. Several studies have analyzed how availability of finance is linked to firms’ characteristics (Beck et al., 2005, 2008) and emphasized the existence of obstacles to the supply of credit (Banerjee and Duflo, 2014; Kersten et al., 2017). Bigsten et al. (2003) use data from African countries to document how inefficiencies in the credit market lead micro-sized and small firms to a lower probability of loan access compared to larger companies. Kuntchev et al. (2014) make use of WBES data to show that credit availability is inversely 2
in the case of a Labor Force Survey (LFS) which typically is _not_ conducted over 12 months, these differences could matter even in the aggregate. For this reason, it is extremely important for LFS and other surveys not carried out over 12 months to include, and rely on, the 12 month recall data.

**Table 7: Comparison of primary employment status in 2009/10: 7 day vs 12 months recall period**

||**UNHS**|**2009/10**|
|---|---|---|
||**_7day recall_**|**_12month recall_**|
|**_Primary Employment Status_**|||
|Private wage - non agriculture|11.8|11.7|
|Private wage – agriculture|5.7|5.8|
|Government wage|3.3|3.2|
|Own account - non agriculture|13.1|13.1|
|Family worker- non agriculture|2.2|2.1|
|Family worker - agriculture|61.7|59.2|
|Unclassified<sup>c</sup>|2.1|4.9|
|Total|100.0|100.0|

_Source: Authors calculations based on the UNHS 2009/10. Notes: a) Unadjusted rates based only on screening questions for employment and inactivity and corresponding recall period self reported main employment status b) The composition of primary employment is based on the unadjusted employment status responses for the working age bracket (15-65 years), with the exception of the inclusion of domestic servants in the private wage category. c) Refers to people who reported a economic activity in the respective recall screening questions but have missing responses in the main employment status questions._ When analyzing individual or household behavior however _, the difference between reported status by recall period matters a lot more_ . The differences