rafmacalaba/data-use-mentions-extended huggingface dataset:
35*|
|treatment variables|(0.832)|(0.803)|(0.814)|(0.035)|(0.036)|(0.066)|(4.36)|
|P‐values|0.851|0.498|0.208|0.411|0.596|0.220|0.092|
|Sharpened two‐stageq‐values<sup>μ</sup>|1|1|1|1|1|1|1|

Note : Panel data from midline and endline surveys in 2015 and 2016. All regressions are controlling for strata dummies (dummies for each triplet). Standard errors (in parentheses) are clustered at the firm level. **α:** top‐coded at the 99th percentile. **Β:** controling for baseline value. **μ** : Sharpened two‐ stage q‐values as described in Anderson (2008) ***, **, * indicate statistical significance at 1, 5 and 10% 51
.</mark>

- <mark>Wang, H. H., Y. Wang, and M. S. Delgado. 2014. “The Transition to Modern Agriculture: Contract Farming in Developing Economies.”</mark> _<mark>American Journal of Agricultural Economics</mark>_ <mark>96 (5): 1257–71.</mark>

- <mark>World Bank. 2007.</mark> _<mark>World Development Report 2008: Agriculture for Development</mark>_ <mark>. W</mark> orld Bank, Washington, DC.

- <mark>World Bank. 2013. “What Is the Cost of a Bowl of Rice? The Impact of Sri Lanka's Current ”</mark>

- <mark>Trade and Price Policies on the Incentive Framework for Agriculture</mark> _<mark>.</mark>_ <mark>Agriculture and Environmental Services Discussion Paper 2, World Bank, W</mark> ashington, DC.

- World Bank. 2015. _Sri Lanka_ : _Ending Poverty and Promoting Shared Prosperity. A Systematic Country Diagnostic._ <mark>World Bank, W</mark> ashington, DC.

- World Bank. 2021a. _Sri Lank Poverty Assessment: Accelerating Economic Transformation_ . <mark>World Bank, W</mark> ashington, DC.

- World Bank. 2021b. “Agricultural Productivity, Diversification, and Gender.” World Bank, Washington, DC.

- World Bank. 2021c. “The Rural Nonfarm Sector and Livelihood Strategies in Sri Lanka.” World Bank, Washington, DC.

World Bank. 2021d. World Development Indicators database. <u>https://datacatalog.worldbank.org/dataset/world-development-indicators</u> (accessed March 2021).

Zainudeen, M. H. 2016. “Land Administration in Sri Lanka. Issues and Challenges _._ ” Paper prepared for
- and lowskilled industrial sector occupations fell by nearly 10 percentage points (Goos et al., 2014). In the United States and France, the increased use of robotics in industry is found to be inversely related to industrial employment levels since 1990 and 2010, respectively (Acemoglu et al., 2020; Acemoglu and Restrepo, 2020). Some service sector occupations are also found to have been negatively affected by this trend in both regions, notably middle-skilled jobs such as office clerks. However, codifiable middle- and low-skill jobs have been (at least partially) replaced by higher demand for both lowskill service sector jobs, which are less easy to automate, and higher-skill jobs that complement new technologies. SVAR analysis of sectoral manufacturing data for advanced economies has also found negative effects on total hours worked of developments that have driven persistent positive TFP growth (Chang and Hong, 2006; Park, 2012; Khan and Tsoukalas, 2013).

> 3A wide range of SVAR literature has tackled the response of labor inputs in response to technology to assess the presence of a sticky-price mechanism, including (to name just a few) Christiano et al. (2004), Dedola and Neri (2007), Collard and Dellas (2007), Francis and Ramey (2005), Francis et al. (2014), and Canova et al. (2010). The majority of research has found evidence of decreasing employment from technology shocks in the U.S. context.

5
simultaneously with changes in misallocation. Although a good case can be made that the repeal of ulcra came as a surprise, matters may be less clear for changes in stamp duty taxation. The second worry is that these policies are particular. We expect them to affect mostly the property markets. It is unclear whether our findings for these two policies can be generalized to policies affecting other factors of production.

With this in mind, we now turn to a broader set of policies affecting labor markets, land markets, entry into manufacturing, foreign investment, and international trade.

The regulation of the land and labor markets in India is made at the state level. There is a lot of cross-sectional variation between states with, perhaps at one extreme, a state like Gujarat where the outlook is pro-market, and at the other, West Bengal, which was historically ruled by communists-led coalitions. There have also been many changes over the years. These changes were recorded by Besley and Burgess (2000) for the land market and Besley and Burgess (2004) for the labor markets. These data series were subsequently expanded to 1997 by Aghion _et al._ (2008).

The of labor market and is for our coding changes by Besley Burgess straightforward purpose. Any change in labor market regulations in a state for any given year is assessed to be either or We to create more pro-worker pro-employer. expect pro-worker changes frictions on the labor market and increase misallocation. We expect pro-employer changes to work in the opposite direction.

Matters are more complex for the land reforms. First, in large part they concerned rural land since Besley and Burgess (2000) find much larger effects of these reforms in rural areas than in urban areas. These reforms have affected misallocation in may manufacturing through the location choices of manufacturing establishments and may be part of the explanation behind the decentralisation of manufacturing in India (Ghani, Goswami, and Kerr, 2012). Land reforms are also more heterogeneous and their effects on the functioning of land markets less obvious
Policy Research Working Paper 10285

# **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 first 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 Pacific), 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 be attributed to intergenerational transmission, while there are some countries where the share is more than 80 percent (most in Sub-Saharan Africa). This suggests a dominant role for parents in determining the educational opportunities of their children. Evidence on the evolution of the intergenerational share, however, suggests a declining importance of the intergenerational transmission component in many countries, but the pattern is diverse
br>59.03% 30.00%<br>80.00% 40.00% 40.00%<br>60.00% 30.00% 30.00%<br>40.00% 5.23% 0.61% 0.34% 4.11% 8.68% 1.85% 20.00% 0.12% 0.04% 0.06% 0.12% 0.01% 0.10% 20.00% 2.10% 0.00% 4.10% 1.00% 2.60% 1.60%<br>20.00% 10.00% 10.00%<br>0.00% 0.00% 0.00%<br><!-- End of picture text -->

- **_Chinese firms tend to be wholly Chinese owned. They are relatively small in size.  They mainly complete with firms from other emerging economies of similar size and sector interest._**

Figure 8 based on the six countries’ data show that Chinese investments are mostly wholly Chinese owned, with only a small percentage forming joint ventures with local or other foreign business partners.

**Figure 8: Ownership Structure of Chinese Investment Projects**

14
of being female on employment varies with age at arrival in El Fasher, conditional on age today. Second, we analyze heterogeneity in employment outcomes based on an individuals’ age today, again using a simple probit model (discussed in Section 5.6).

# **3 Data**

**Household survey data** . In El Fasher, the World Bank in collaboration with several partners has collected unique survey data from May to July in 2018 that fulfill the ambitious requirements of our purposes (JIPS, 2019a; Pape and Scharma, 2019). The survey targeted two different groups of IDPs and two groups of comparable non-displaced populations in urban and peri-urban areas ( **Figure 1** ).

The total **survey sample** includes 18,533 individuals from 3,002 households:

- •996 IDP households (5,894 individuals) from the Abu Shouk area;

- •986 IDP households (5,960 individuals) from the El Salam area;

- •509 Non-IDP households (3,303 individuals) from adjacent peri-urban areas; and

- •511 Non-IDP households (3,376 individuals) from nearby urban areas.

The survey data set provides a comprehensive displacement profile of IDPs residing in the Abu Shouk and El Salam areas, including pre-displacement conditions, displacement history and outcomes, as well as detailed additional socio-economic data from both IDPs and non-IDPs.

Notably, a detailed household roster module offers for each member extensive information on sociodemographic indicators (including sex), health (for members aged 5 and over) and employment (for members age 15 and over). While most of this information was provided by one main respondent (a knowledgeable person aged 15 or over, whose sex is also known), an employment sub-module was to be answered by each adult in person, if they were available. The employment sub-module contains detailed information about current and pre-displacement employment outcomes.

8
# **1. Introduction**

The Northern Mali conflict started in January 2012. It was caused by several secessionist groups and led to a coup d’état in March 2012 and to the occupation of the three regions of the North – Gao, Kidal and Timbuktu – by rebels and Islamist factions. These territories were regained following a military intervention by a coalition composed of the Malian Army, French troops and the ECOWASled African-led International Support Missions to Mali (AFISMA) in June 2013 (David, 2013). A Peace Accord was finally signed in May and June 2015 between the government and different actors involved in the rebellion. Nevertheless, in spite of the Accord, the regions of Gao, Kidal and Timbuktu remain in a state of prolonged crisis, with high levels of insecurity and weak governance.

According to the UN Refugee Agency (UNHCR, 2016c), in June 2016 the number of Malian refugees was almost 135,000, while there were more than 36,000 Internally Displaced People (IDPs) and around 23,000 returnees (about 49,000 according to the Mali government (UNHCR, 2016b)). Among these refugees, more than 60,500 were in Niger, 41,000 in Mauritania and 32,000 in Burkina Faso. Including returnees, the population of concern has reached the staggering figure of 570,000 (UNHCR, 2016a). Despite these figures and the (scarce) media coverage (WorldPost, 2016), the UN operations in the regions have been constantly underfunded (UNHCR, 2016d).

The scope of this paper is to analyze some important characteristics of refugees, IDPs and returnees. Furthermore, we investigate the willingness to return to their place of origin for refugees and IDPs after the conflict in Mali. By making use of a unique data set interviewing the head of household (or another member) for as many as 12 consecutive waves, we managed to gather information on welfare and subjective well-being, as well as on willingness to return. Using
5 efforts. We conclude with a discussion section including possible interventions and lessons for policy.

# 2. Data and Methods

## **2.1 Data Sources**

### **2.1.1 Social Norms Sample**

We primarily present findings from a recently completed survey in KRI in 2018, that systematically measures social norms and cultural beliefs, and refer to this as the “social norms sample” in the rest of the paper. A random sample was drawn from the sample frame of the SWIFT survey implemented by the Poverty and Equity Global Practice at the World Bank. Districts from all three governorates of Erbil, Sulaymaniyah, and Dohuk were included, except for districts with a population of less than 30,000. The instrument we use was specifically developed to measure social norms in an urban context, and we, therefore, exclude rural areas. The number of clusters sampled from the remaining districts was in proportion to the population. Finally, the required number of households was randomly sampled from the list of households in each cluster in a given district, along with a replacement list of households.<sup>6</sup> Both a female and male counterpart in each household was included, restricting to spouses, father-daughter, and brother-sister relationships, in order to compare perceptions of the counterpart’s beliefs, with actual beliefs held by the counterpart. We oversampled households with working women, to assess if social norms and beliefs were different for the women and men in these households, and set a target to survey 33% working women (out of all the women we interviewed). Randomly drawn households were called initially to screen whether there was a male-female counterpart as described above, and to meet the target on surveying working women.<sup>7</sup> Given that we restricted household selection to those with women between the ages of 20-55 and with a male counterpart with the relationships described above and oversampled households with working women, we cannot calculate labor force
>_</sup> _hours_<sup>):logarithmofoneplustheratiobetweenthestockofrobotsandthenumber</sup> of work hours (in millions) in developed country _n_ in sector _i_ in year _t_ . The stock of robots was directly observed in the IFR data set.

- _Initial GDPpc_ : GDP per capita (in constant 2000 USD) of developed country _n_ in the first year the country is observed in the estimation sample.

- _Replaceability_ : proportion of US jobs in 1980 that were in occupations that are replaceable by robots.

- _Global robot stock_ : global stock of robots. The stock of robots was estimated using the perpetual inventory method based on the observed stock of robots in the IFR data and using a depreciation rate of 10%.

- _log_ (1 + _material inputs_ ): logarithm of one plus the expenditure on material inputs in developed country _n_ in sector _i_ in year _t_ .

- _log_ (1 + _IT capital_ ): logarithm of one plus the expenditure on IT capital in developed country _n_ in sector _i_ in year _t_ .

- _log_ (1 + _non_ - _IT capital_ ): logarithm of one plus the expenditure on non-IT capital in developed country _n_ in sector _i_ in year _t_ .

- _log_ (1 + _tariff_ ): logarithm of one plus the ad valorem import tariff applied by developed country _n_ to developing country _m_ in sector _i_ in year _t_ .

53
This general improvement in living standards is corroborated by trends in ownership of private assets. The three rounds of the Nepal Living Standard Surveys asked for ownership of 15 private assets, plus ownership of computers introduced in 2003. Figure 3 summarizes asset ownership of these 16 items by three groups based on per capita consumption: bottom 40 percent, middle 40 percent and top 20 percent. Different items exhibit different paths of improvements in ownership and some items were owned by almost all households in 1995 (e.g., kitchen utensils) and others are rarely owned even in 2010 (e.g., cars and washing machines). It appears in general that a sharp increase in ownership is first experienced by the top 20 percent between 1995 and 2003, followed by the middle 40 percent and then the bottom 40 percent in later years. In 1995, few households in the bottom 40 percent and the middle 40 percent owned items such as phones, TV/VCR/VCD and fans but their ownership increased drastically by the middle 40 percent by 2010. On the other hand, ownership of more expensive items, such as motorcycle/scooter, refrigerator/freezer and computer improved but only exclusively among the top 20 percent.

The improvements in living standards along monetary measures of welfare have been accompanied by dramatic improvements in non-income dimensions of well-being. Figure 4 shows progress on reducing deprivation along measures of well-being typically used in the construction of a Multidimensional Poverty Index (MPI).<sup>15</sup> Significant improvements have been made in dimensions such as education, health and access to basic services. For example, households with school-aged children (6 to 15 years old) out of school declined from 55 percent in 1995 to 16 percent in 2010. This is presumably due to improvements in accessibility to and availability of school, as households with children spending more than 20 minutes to reach primary school declined from 84 percent to 33 percent during the same period. But much of it is also likely due to increased ability
sistencies in the measurement of light intensity between the DMSP and VIIRS satellites, the combination of nightlights data from these satellites is a challenge hence limiting the application of nightlights data over the long time horizon. To address this issue, Li et al. (2020) have derived consistent and integrated nightlights data from the two satellites by harmonizing the inter-calibrated nightlights data from the DMSP and a simulated DMSP-like nightlights data from the VIIRS satellite.<sup>12</sup> Using this data, I compute yearly average nightlights intensity for each subnational administrative district boundary as a proxy for the level of economic activity in each administrative boundary per year. I then compute the annual growth in nightlight intensity as the log-differences in the annual average nightlight intensity for each district. In the online Appendix, I conduct several robustness checks to ascertain the robustness of the baseline results using the harmonized data. First, Figure A1 in the online Appendix clearly shows a very high correlation between the harmonized and non-harmonized nightlights data for the period 1999-2013. Also, relying exclusively on DMSP nightlights data between 1998 and 2013, I show in Table A4 that the baseline results hold even if we restrict the data to the pre-VIIRS data.

# **2.3 Complementary data**

I complement the analysis with data on indicators such as natural resource endowments from the IMF, quality of governance from the Polity IV database,<sup>13</sup> distance to the coast,<sup>14</sup> and average annual temperature and total precipitation obtained from the ERA5 Global Reanalysis Database by the Copernicus Climate Change Service.<sup>15</sup> Data on fixed line telephone penetration and access to electricity are obtained from the World Bank’s World Development Indicator database. Summary statistics of indicators used in the analysis are presented in Table A1 in the online appendix.

> 12See Li et al. (2020) for details and access to the dataset using https://figshare.com/articles/dataset/ Harmonization_of_DMSP_and_VIIRS_nighttime_light_data_from_1992-2018_at_the_global_scale/
***|12,348***|3,961***|8.269***|8.449***|8.136***|
||(110)|(1,158)|(65)|(0.020)|(0.050)|(0.018)|
|**N (Observations)**|26,590|26,552|26,596|26,489|26,543|26,587|

_Source:_ Authors’ calculations based on ONDH impact evaluation panel survey. _Note:_ *p < 0.1; **p < 0.05; ***p < 0.01. Standard errors clustered on commune level.

In conclusion, by using regression, RDD, and fixed effects approaches to measure the impact of the INDH on three economic outcome variables (income, consumption, and household wealth), we find limited evidence of a positive economic impact due to the INDH program. Although the estimated effect of the INDH is generally positive, it is moderate in size and often statistically insignificant. This is particularly notable given that we are essentially implementing multiple tests over multiple methods. There may have been some greater impact in 2011 but only on certain economic measures, and effects did not clearly persist into 2013. While the RDD models suggest larger impacts, given their sensitivity to the bandwidth chosen and the observed fluctuations along the forcing variable, these larger impacts are not robust.

23
and the Mohamediya. To compete with these movements, Suharto attempted to create a “regimist Islam” with state-funded mosques staffed by government employees belonging to the Ministry of Religion. Often, all three types – NU mosques, Mohamediya mosques and “Golkar”<sup>13</sup> mosques – exist in Indonesian villages, with competing spheres of authority. In addition, immigrants to Malaysia or the Gulf signal their new-found wealth by building their own mosques. And neighborhoods get together

> 10 Her first name was given her by an Indian—Biju Patnaik a close friend of Sukarno. Patnaik had participated in Indonesia’s war of independence as a fighter pilot, and went on to become one of India’s most prominent, and colorful, politicians.

> 11 All data from UPP2 Evaluation baseline survey 2004.

> 12 Some Imams are particularly proud of their loudspeakers.  I went to interview one in an empty but large mosque in the middle of the afternoon.  The Imam insisted on conducting the entire interview – which was mainly about the role of the mosque in local development activities –  speaking directly into the live microphone, presumably to demonstrate to the neighborhood that he was important enough to be interviewed by a World Bank official.

> 13 Golkar was Suharto’s political party. It remains an important force in Indonesia.

15