rafmacalaba/data-use-mentions-extended huggingface dataset:
*|**641**|**577**|**453**|

Notes: All the earnings are in PPP ($,2011). Teachers include survey respondents who self-identified as a primary or secondary school teacher and a wage worker. Other wage workers include respondents who self-identified as a wage worker in other corresponding occupation groups; university faculty, special education teachers and other teachers were excluded from the group of other wage workers. ISCO-08 group definitions are used to classify occupation groups (ILO, 2012). Elementary occupations involve the performance of simple and routine tasks which may require the use of hand-held tools and considerable physical effort. Earnings in _italics_ are estimated based on fewer than 10 observations. Sample only includes workers with post-secondary education. The top and bottom 1% earnings are trimmed. Source: Surveys listed in Table 1.

38
TABLE 1. Total Number of Database Sources

|**Census round**|**Birthplace**<br>**sources**|**Nationality**<br>**sources**|**Total national**<br>**sources**|**Birthplace**<br>**by gender**|**Nationality by**<br>**gender**|
|---|---|---|---|---|---|
|**1960**|124|67|149|103|64|
|**1970**|112|52|133|92|49|
|**1980**|145|86|164|117|80|
|**1990**|151|114|175|129|99|
|**2000**|134|122|161|118|100|
|**Total**|666|441|782|589|392|

_Source:_ Authors‘ calculations based on data described in text.

36
nonfinancial private sector to total domestic credit (excluding credit to money banks); and _BANK,_ the ratio of deposit money bank domestic assets to deposit money bank domestic assets plus central bank domestic assets.<sup>7</sup> While all of these measures are positively associated with financial development, each one captures a slightly different aspect of it. _LLY_ is a measure of the overall size of the financial system, while _BANK_ measures the relative importance of banks within the financial system. _PRIVY_ and _PRIVATE_ measure the extent to which financial services are provided to the private sector, with _PRIVATE_ being a more direct measure of how credit is allocated to the private vs. the public sector. Thus, _LLY_ and _PRIVY_ are more general measures of overall development, while _PRIVATE_ and _BANK_ attempt to gauge the nature of the development.

We use annual data from 70 countries for the period 1956 to 1998. The majority of our data is extracted from the IMF's International Financial Statistics. The exceptions are the average level of real GDP, real consumption and real investment per capita _(MEAN),_ which are calculated from the Penn World Tables 5.6, and the political regime and stability variables _(POLITY),_ which come from the 1996 version of the POLITY III Dataset.<sup>8</sup> In order to calculate our volatility measure, the standard deviation of the growth rate of GDP and its components, we need to collapse several years of data into one time period. An ideal characterization of the amplitude of the business cycle in each country and each time period would require a large number of annual observations to capture both the upturns and downturns of the business cycle. However, we face a tradeoff: as we increase the number of years in each period, perhaps increasing the accuracy with which we characterize volatility, we reduce the number of periods we can use in our fixed effects estimation, reducing its efficiency. Nonetheless, we are able to create 4 time
Panel c. Elasti|city of Fundin|g Conditional on|Being Funded|
|Disruptive|0.422*|0.343|0.278|0.216|
||(0.231)|(0.288)|(0.320)|(0.493)|
|_R_<sup>2</sup>|0.437|0.436|0.439|0.436|
|Observations|548|481|548|481|
|Countries|9|9|9|9|

Clustered errors in parentheses. *** 10%, ** 5% and * 1%. All regressions include as controls age (in IHS transformation), dummies for 9 categories of employment size, number of similar firms (in IHS transformation), dummies for the number of overlapping sectors where the firm operates (one or two additional sectors). The dependent variable is either a dummy variable indicating if the firm received funding (panel a), or the IHS transformation of the amount of funding in US Dollars received in the last funding round (panels b and c). Standard errors clustered at the country level.

# **4.4 Disruptiveness and performance**

To analyze the correlation between the incorporation of disruptive technologies in firms’ products and services offering and firm-level performance, we rely on Pitchbook data, which includes more detailed information in terms of valuation and growth. Due to data restrictions, in this case, we are only able to compare Africa with LAC.

In Table 5, we estimate similar models to the ones shown in previous tables but using some performance-related indicators as dependent variables. As shown in panel (a) of the table, disruptive firms in Africa receive funding 0.66 years earlier than non-disruptive 24
sending and receiving text messages, making phone calls, and using mobile money applications. Adults in villages in which an education program was offered were tested immediately after the program and received test scores in writing and mathematics that were 0.19 – 0.26 standard deviations higher than those that did not participate. Moreover, standardized math test scores remained higher seven months after the end of the program. These results suggest that simple information technology can be harnessed to improve educational outcomes and build skills even among remote rural populations.

Mobile phone technology also contributes to labor market outcomes. Klonner and Nolen (2010) study the labor market effects of the rollout of mobile phone coverage from 1995 to 2000 in rural South Africa. The findings show that localities that received network coverage experienced a 15percentage point increase in employment, primarily among women no longer burdened with large childcare responsibilities. The authors also find a significant shift in occupational patterns in rural areas; with agricultural employment decreasing substantially, especially among males.

Mobile connections not only accelerate economic growth but also improve inequality in the income distribution. Using panel data of 177 countries for the period 1990 – 2019 and GMM11
years olds have achieved upper secondary education<sup>9</sup> and the average number of years of schooling for this cohort is just over 12.5 years.<sup>10</sup> Analyzing the education attainment across cohorts enables us to compare the speed at which accumulation of human capital took place over time. Of particular interest is the fact that Costa Rica, Panama and Nicaragua had “plateaus” in the accumulation of human capital, the first two during the period 1960 to 1970 (ie for the 40-49 year age group), and Nicaragua in the period 1970 to 80 (30-39 year age group). For El Salvador and Guatemala, successive cohorts have steadily increased their investment in education, albeit starting from a low level.

> 9 OECD Education at a Glance 2011.  Population with at least upper secondary education (2009)- Year of reference is 2002.

> 10 OECD Family database.   Educational Achievement expressed in average number of years in formal education (2004), Year of reference is 2003. Available online: <u>http://www.oecd.org/document/4/0,3746,en_2649_34819_37836996_1_1_1_1,00.html.  Data accessed: June</u> 15, 2012.

9
above, and considered respondents living in the community for less than five years as migrants. After this selection process, their sample included 2,883 individuals aged 15 or above, only 6% of whom migrated from one Tanzanian district to another in the period between 2004 and 2008. Kubik and Maurel (2016), instead, in the absence of an explicit question on permanent migration in the data set, exploited the second wave of the survey collection (2010-2011) to identify migrants, by directly comparing the place of residence of all household members in the first and the second waves of the survey and identifying residential moves based on the GPS coordinates of the place of origin and destination. Following this strategy, they defined their outcome variable of interest as a migration dummy equal to one for households with at least one member who permanently moved out of the original village between 2008/09 and 2010/11, and found that 14 percent of households had at least one migrant between the two waves. Note also that Kubik and Maurel (2016), by design, observe internal migration only.

In the subsequent waves of the Tanzanian NPS collection, some changes were implemented. From Wave 2 (NPS 2010-2011) on, Module G on children living outside the household was dropped. In Wave 3, NPS 2012-2013, an amendment was added among the roster of possible replies to the following question in the _Shocks_ Section R: R.6: “ _What did your household do in response to this [SHOCK] to try to regain your former welfare level?_ ” Among the possible replies, there is the following choice: “ _Household member migrated_ ”. This option, however, was not present in the subsequent, and currently final, wave of the collection, NPS 2014-2015. The Tanzania example, chosen as its surveys were used by the studies reviewed above, is emblematic of the limitations and internal inconsistencies involving migration data in most surveys of the LSMS-ISA collection.

Prompted by the reply featuring migration as a coping strategy
period considered for this paper.

Data was far less frequent for Nepal and Bhutan, for which we only had three surveys. We used data from the Nepal Living Standards Survey (NLSS). Although the NLSS has three rounds, only the last two were post-2000. Nepal conducts its LFS sporadically, with the latest available from 2008. For Bhutan, we used three waves of the Living Standards Survey (LSS) covering employment characteristics in detail.

In India, the National Sample Survey (NSS) collects labor market information by using a separate employment-unemployment module (Schedule 10) every five years. In the intervening years, the government gathers basic employment information, along with the household consumer expenditure module. The quinquennial surveys have large samples and are referred to as “thick rounds”, whereas intervening surveys are known as “thin rounds” because of their relatively smaller sample size. Overall, data is less frequent in India, and the most recent data point is the “thick round” NSS conducted in 201112. The annual Periodic Labor Force Survey (PLFS), first conducted in 2017, aims to fill this gap by providing frequent, timely, nationally and regionally-representative labor market data.

In Afghanistan we used the 2007-08 and 2011-12 waves of the National Risk and Vulnerability Assessment (NRVA) surveys and its successor Afghanistan Living Conditions Survey (ALCS) of 2013-14. These surveys are the country’s core instrument to collect household level data, and capturing development indicators

> 6Note that all the data used in this paper are identical to those used for employment rate estimates _in Jobless Growth, 2018_ . This was done to obtain consistent estimates for aggregate female employment rates. In addition, definitions for employment status (employed, unemployed, inactive), employment type (regular, casual, unpaid, self-employed or employer), sector of activity (agriculture, manufacturing, services, mining and construction), ownership structure (private or public) and location (rural or urban) are consistent with those used in _Jobless Growth, 2018_
# **4.3. COUNTRY FINANCE AND GOVERNANCE INDICATORS**

Private credit to GDP ratio is used to proxy of financial sector development (Table 9 in the Appendix). This is in line with the literature which has generally utilized private credit to GDP ratios to proxy for financial sector development (Beck, 2003; Braun, 2003; Classens, Stijin, and Laeven, 2003; Fisman and Love, 2003a). Financial indicators are worse in MENA than in highincome countries, and are lower than expected given their income levels (Figure 4). For example, the private credit to GDP ratio in high-income countries is 117.2 compared to only 49.25 in MENA.

In the area of governance, property rights index is used as a proxy, which has also been used by Claessens and Laeven (2005), and Hur, Raj, and Riyanto (2006). Global Competitiveness Reports offer a wide range of scores ranging from 1(worst) - 7(best) on a country’s development. These scores are strongly correlated with income levels. High-income countries score the best across all topics, in most cases at least one-point higher than any other income group. Low-middle-income countries also have the lowest GCI scores in the group. The GCI property rights index in MENA is higher in than non-MENA countries, but much lower than in high-income countries.

A second proxy for governance is the set of World Governance Indicators (WGI), developed by the World Bank (See Kauffman, Kraay, and Mastruzzi, 2009). World Governance Indicators values are normalized between -2.5 and 2.5 with a higher value being associated with better governance. The WGI Voice and Accountability indicator captures the quality of interaction between citizens and government and is particularly relevant for the MENA region, as well as being instrumental towards promoting a stable environment for production and investment (World Bank, 2003a). Among all six WGI indicators, the MENA region averages the lowest in this measure. Voice refers
|**Computer-electronic**|Legal-accounting-consultancy|
|---|---|
|**Electrical-equipment**|Architectural-engineering|
|**Machinery-Equipment-nec**|Scientific-research-development|
|**Motor-vehicles-trailers**|Advertising|
|**Other-transport-equipment**|Other-professional-Veterinary|
|**Furniture-Other-manufacturing**|Rental-leasing|
|**Repair-installation**|Employment-activities|
|**Transmission and Distrib.**|Travel-agency-Tourism|
|**CoalBL**|Other-serv|
|**GasBL**|Public-administration|
|**WindBL**|Education|
|**HydroBL**|Health|
|**OilBL**|Residential-care|
|**OtherBL**|Creative-arts-cultural|
|**GasP**|Sports|
|**HydroP**|Membership-organisations|
|**OilP**|Repair-of-computers-and-personal-goods|
|**SolarP**|Other-personal-serv|
|**Gdt**|Households|
|**Water**||

# **_Taxes_**

Information on major taxes in Italy is obtained by reconciling tax return data with information from Istat (the Italian National Institute of Statistics) national accounts. Among commodity taxes in the model, the following are explicitly considered:

- Value Added Tax (VAT), whose aggregate amount is published by the Istat, is allocated by commodity according to the VAT Microsimulation Model developed by the Department of Finance (DF). The VAT DF Microsimulation Model estimates VAT by commodity distinguishing between final consumption of households, intermediate goods and capital goods.

- Excises, published by Istat with the details of the goods they are imposed on, have been allocated by commodity accordingly and associated to both intermediate and final consumption using the use table from Istat.

- Tariffs on Imports are computed by applying estimated import tariffs to imports.

- Other Net Tax on Products (other indirect taxes less subsidies on products) are calculated as a residual by subtracting VAT, excises and tariffs on imports by commodity from the net indirect
31.5}|{24.4}|{27.9}|
|Daughter Dummy|-71.2|181.1|22.4|267.5|
||(400.2)|(386.8)|(413.7)|(397.5)|
||[327.1]|[348.5]|[334.6]|[364.5]|
||{275.1}|{305.8}|{252.7}|{318.8}|
|Intercept|827.4|4137.0|1034.3|4592.9|
||(278.8)***|(337.8)***|(264.5)***|(330.1)***|
||[300.0]***|[397.1]***|[295.5]***|[401.7]***|
||{316.8}**|{436.8}***|{292.6}***|{488.9}***|
|No. of Children||-2222.0||-2401.2|
|||(132.7)***||(154.4)***|
|||[211.3]***||[233.4]***|
|||{366.3}***||{412.4}***|
|No. Observations|2784|2784|2435|2435|

Notes: (1) The data used are CFPS 2010. (2) Robust standard errors in parentheses; standard errors clustered at county-level in square brackets; standard errors clustered at province-level in braces; *
# **I. Introduction**

The 2030 Agenda for Sustainable Development, as articulated through the Sustainable Development Goals (SDGs), has raised important questions regarding the readiness of countries and the broader international development community to monitor progress. Tracking the SDG targets and the associated indicators places even more pressure on national statistical systems than the Millennium Development Goals (MDGs). For example, many SDG indicators rely heavily on household survey data.<sup>2</sup> However, the investments for enhancing country capacity to design, implement, analyze and disseminate household surveys have often lagged behind the demands for more and better data. Monitoring progress and development as part of Agenda 2030 will require a concerted effort on the part of both countries and their international development partners to improve the availability, quality, timeliness and relevance of the data needed to track the SDGs.

The influential United Nations Report titled _A World That Counts_ discusses how nothing short of a “data revolution” is one of the preconditions for achieving sustainable development (IEAG, 2014). Several studies have estimated the cost of monitoring the post-2015 development agenda, arriving at divergent figures. Jerven (2014) estimates the _total_ cost of monitoring the 17 SDG goals and the associated 169 targets at US $254 billion. Demombynes and Sandefur (2014), on the other hand, project the _additional_ costs of monitoring the SDGs to be a more modest, at US $300 million per year, given the existing international survey programs. Finally, Global Partnership for Sustainable Development Data (GPSDD) (2016) projects the estimated _total_ cost of an expanded program of surveys and censuses, and improvements in administrative data in 77 IDA-eligible countries over the period of 2016-2030 to amount to USD 17.0 to 17.7 billion.<sup>3</sup> Within this agenda, the report asserts the need for six additional surveys, at the scale of a Demographic and Health Survey or a Multiple Indicator Cluster Survey, per IDA-eligible country from 2016 to 2030
who left to work abroad during this period were from Muslim households. In contrast, Muslim households were among the least likely to see a member migrate to another Indian state.<sup>21</sup> In terms of economic status, households with members who migrated internationally between 1998 and 2003 came from relatively wealthier households (based on pre-migration land, averaging 71.5 cents,<sup>22</sup> and overall asset holdings). Domestic migrants came from households with holdings similar to non-migrant households (mean of 58.5 cents). I measured asset holdings using a composite index derived from a first principal component weighting of 6 indicators of durable assets ownership and 11 indicators of housing quality.<sup>23</sup> Further elaboration of my method for constructing the asset indices appears in Section 4.B.2.

# _B. Empirical Strategy_

# <u>B.1. Specification</u>

I examined asset gains by estimating a regression across migrant and non-migrant households, using a difference-in-difference specification with the panel of Kerala households surveyed in 1998 and 2003. The goal of the exercise is to determine the effect that the labor migration of household members between 1993 and 2003 had on the accumulation of assets, all else being equal. Measuring changes in household-level outcomes naturally yields a first-difference regression specification: where is the change in household asset holdings between survey rounds in 1998 and 2003 for household , is the number of household members who migrated overseas for employment between survey rounds, and reflects the number of household members who

> 21 Scheduled caste families were the least likely to have a domestic migrant during this period (7 percent). The largest group of households whose members moved within India for employment during this period was Hindus (38 percent). 22 Landholding is the sum of agricultural and non-agricultural land measured in cents, where 100 cents is equivalent to an acre.

> 23 I confined the indicators used in the construction of the overall asset index to a common set
26.56|15.79|3.64|6.57|
|Upper middle<br>income countries|57.56|49.65|4.09<br>I_I|3.87|
|OECD countries|76.28|88.51|2.68|2.73|

Notes: Liquid Liabilities = currency plus demand and interest-bearing liabilities of financial intermediaries as share of GDP; Private Credit = total clains on private nonfinancial sector by financial institutions as share of GDP; Net Interest Margin = accounting value of a bank's net interest revenue as share of total assets; Overhead costs = accounting value of a bank's overhead cost as share of total asset; Net interest margin and overhead cost numbers include both private and public and both domestic and foreign banks. All numbers are for 1997. Source: Beck, Demirgfii -Kunt and Levine (1999).

In addition to the low level of private credit, the average maturity of loans is low in Brazil. The ratio of long- to short-term debt for the median Brazilian company is 1.23, compared to an average of 2.20 for OECD countries.3 Anecdotal evidence suggests that most small and medium size companies in Brazil only obtain short-term bank credit with maturities of at most 90 days. Sources of long-term finance are very limited and mainly from state-owned development banks, such as BNDES.

> 2 See Beck, Levine and Loayza (2000) and Levine, Loayza and Beck (2000).

3These numbers are for 1997 and from a firm-level database for 46 countries. The database only includes listed firms. Any debt that is due in six months or less is considered short-term. The average for Latin American countries in the sample is 1.30.

2
- 52 -

GDP estimate is taken from World Tables 1995 and relates to 1993. Wages and salaries are from Esteban Garcia de Motiloa (AF5CO) and relate to 1994.

# Kenya

Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1991. It reflects data gathered through Establishment surveys, that is, data on the number of workers on establishment payrolls. This in turn may result in an underestimation of employment. Central government, Non-Central government, and Education data are taken from 1995 Economic Survey published by the Central Bureau of Statistics. Data are for 1994. Health employment is taken from the Republic of Kenya's Central Bureau of Statistics' Statistical Abstract. 1991 and relates to 1991. Public Health employment statistics must be read with caution. Since there is a fee for registration and no annual license, respective registers may include those who do not practice or who have left the country. Employment in state-owned enterprises is taken from 195 Economic Survey published by the Central Bureau of Statistics and relates to 1994. Employment in State-owned enterprises is made up as follows: 106,900 persons working in parastatals fully owned by the Republic of Kenya (Includes Kenya Railways, Kenya Ports Authority, Kenya Posts and Telecommunications Corporations, Kenya Airways Ltd), and 48,800 persons in those institutions with a majority control by the public sector. GDP and wages and salaries of Consolidated Central Government are from IMF Government Finance Statistics, 1995 and relate to 1994. Data on wages in manufacturing are taken from the International Labor Office's Yearboo of Labor Statistics 1995 and refer to 1991.

# Lesotho

Unemployment rate and non agricultural employment estimates are for 1993, and are taken from Report No 13171LSO, Poverty Assessment and reads: "The unemployment rate in 1993 - counting unemployment and underemployment - was estimated to be an alarming 35 to 45%". We have taken the median value. This unemployment rate, however, clearly overstates the actual unemployment in Lesotho, particularly compared to other