rafmacalaba/datause-encoder-data huggingface dataset:
# **Annex 2: Identifying Labor and Capital Remunerations Using Household Survey Data**

In household surveys, the primary source of income information, labour remunerations and returns to capital are lumped together in the income figures reported by selfemployed. In many instances it is important to distinguish the proportion of personal income of self-employed that is accrue to the individual's labour inputs from what is attributable to capital. The objective of this note is to show how to identify the value added (VA) of capital using micro data at the personal level.

Let us define the income of self-employed individuals as the sum of labour remunerations _l k_ and returns to capital: _Y_  _Y_  _Y_ . Assume that _A_ and _B_ are two randomly-drawn individuals from the population who are identical in all characteristics except for their occupational category. _A_ is a wage worker and _B_ is self-employed. Furthermore, assume that self-employment activities require an investment in physical capital greater than zero. Under competitive labor markets, _B_ could earn a wage as high as the wage earned by _A_ . Therefore a good proxy for the unobserved value of _Y l_ for individual _B_ is the expected wage given his/her personal characteristics. Under this simple setting, income gaps between _A_ and _B_ are attributable to returns to physical capital.

Define wages ( _w_ ), as the sum of personal characteristics related to labor productivity ( **_X_** ) valued at their market rate (  ) plus a random component (  ): 2 where  ~ _N_ (,0  ) and _i_  ( _earner_ ) . The parameters in equation (1) can be use to estimate the expected value of the log of labor income for self-employed workers, if and only if the wage equation parameters apply to out-of-sample observations. A necessary condition to fulfill this requirement is that the partition between wage workers and selfemployed is the outcome of a random process. In other words, workers in the wageearning sectors should be _similar_ once controlling for _Xj_ , than self-employed workers. If this condition is not met and wage workers are distinguished by certain unobservable characteristic that makes them _self-select_ into the wage-earning sectors,  _j_ cannot be use to obtain an estimate of labor remunerations for the self-employed. A simple modification to equation (1) can correct for the selection problem (Heckman, 1979): where **_Z_** is a vector with the variables determining the probability of being a wage worker;  (  ) and  (  ) represent the probability and cumulative normal distribution * * functions, respectively; and (  ,  ) are the parameters for the _population_ regression model. Notice that vector **_Z_** contains all personal characteristics **_X_** plus, at least, one extra 28
_Beyond Unequal Development_

# **III. Inequality, Growth and Development: Complementarities and Trade-offs**<sup>**8**</sup>

Let's move from the (complicated) questions of distributive justice to the macro interactions (trade-offs and/or complementarities) between inequality, economic growth, and long-run development. Is income and wealth inequality the price to be paid for accelerated economic growth? Or, conversely, does inequality retards economic growth? How do inequality evolves during the course of economic development? These are key questions that need to be addressed.

# 3.1 **Growth and Inequality Links**

The relationship between economic growth and social inequality at macro level depends on how the growth process is specified<sup>9</sup> . In models of saving-driven growth, if profits-recipients save in greater proportion than wage-earners (linear saving functions), a pattern of income distribution more concentrated towards capital will increase national savings and accelerate the rate of economic growth (all what is saved is invested). This model supported the "conservative" notion that a more equitable distribution of income retards economic growth through a negative effect on the national savings ratio; pointing, then, the existence of a trade-off between growth and equity.

Conversely, neo-Keynesian and endogenous growth theories view growth mainly as an investment-driven process and emphasize complementarities between growth and social equality. In Neokeynesian models in which aggregate demand plays a role in the determination of long-

> 8 This section draws on Ch. I and Ch. 4, Solimano (1998).

> 9 See Solimano (1996).

-10-
in the relative importance of services trade globally, whether measured on a gross or net basis. On the goods side, chemicals and metals account for an increasing share of total trade in value added, while the share devoted to transport and trade services has fallen consistently. Interestingly, public services (including education and health care) account for a rising share of both gross trade values and embodied trade. Where one has more detailed data on these sectors (from EU-KLEMS for example), this may point to a path for promising research quantifying the impact of rising health costs have had on cost structures as reflected in the value added content of trade.

Table 4: **Sector shares of direct value added exports – World Trade**

||1992|1995|1997|2001|2004|2007|
|---|---|---|---|---|---|---|
|Agr, Forestry, Fisheries|0.050|0.046|0.039|0.036|0.034|0.031|
|Processed Foods|0.017|0.026|0.025|0.024|0.026|0.024|
|Energy Extraction|0.077|0.061|0.075|0.069|0.119|0.153|
|Minerals nec|0.019|0.016|0.007|0.006|0.013|0.017|
|Beverages and Tobacco Products|0.007|0.007|0.008|0.007|0.007|0.006|
|Textiles|0.018|0.019|0.022|0.021|0.020|0.015|
|Wearing Apparel|0.019|0.020|0.018|0.019|0.015|0.012|
|Leather Products|0.010|0.010|0.009|0.009|0.007|0.006|
|Wood Products|0.014|0.015|0.014|0.015|0.014|0.013|
|Paper Products, Publishing|0.021|0.024|0.021|0.020|0.019|0.016|
|Chemical, Rubber, Plastic Products|0.058|0.083|0.076|0.081|0.095|0.090|
|Mineral Products nec|0.008|0.011|0.016|0.016|0.010|0.010|
|Ferrous Metals|0.013|0.019|0.015|0.012|0.016|0.018|
|Metals nec|0.011|0.012|0.016|0.014|0.017|0.022|
|Metal Products|0.015|0.017|0.017|0.017|0.019|0.019|
|Transport Equipment|0.075|0.069|0.066|0.072|0.072|0.066|
|Machinery and Equipment nec|0.203|0.231|0.231|0.230|0.194|0.176|
|Manufactures nec|0.022|0.020|0.019|0.024|0.017|0.017|
|Electricity, Gas, Water|0.001|0.002|0.005|0.005|0.005|0.006|
|Construction|0.002|0.004|0.006|0.004|0.005|0.007|
|Trade and Transport Services|0.210|0.179|0.158|0.143|0.117|0.111|
|Other Commercial Services|0.117|0.073|0.111|0.125|0.134|0.143|
|Public Services|0.011|0.036|0.028|0.029|0.026|0.023|
|Dwellings|0.000|0.000|0.000|0.000|0.000|0.000|
|total|1.000|1.000|1.000|1.000|1.000|1.000|

While most of the discussion in this paper is focused on forward linkages, in Table 6 we have focused instead on backward linkages. In other words, when we focus on actual exports of metals or transport equipment or chemicals, and recognize that these embody value added from upstream activities, what is the value added 11
|**Table 5.4**<br>Support|Functions for Spectrum Management|**Inte**|**nsity o**|**f use**|
|---|---|---|---|---|
|**Support function**|**Outline**|**ADM**|**RGT**|**COM**|
|Data|Detailed information on current spectrum use|+++|++|+|
|Analysis|Analytical skills, methods, and tools—engineering,||||
||economics, legal|+++|++|+|
|Administration|Administrative processes and methods to handle<br>routine tasks|+++|+|+|
|Monitoring &<br>enforcement|Ensuring compliance with allocations, rules, and<br>authorizations|+++|++|+|
|International|Participating in ITU and other organizations on||||
|affairs|spectrum matters|++|++|++|
|ADM<br>Improved admin<br>RGT<br>Tradable rights<br>COM<br>Commons|istration||||

The design of the building blocks depends on the initial focus and balance among approaches chosen, and adjustments may be required along the way to reflect the changing balance. Consider, for example, the different ways in which the risk of interference is managed. Under the administration approach, harmful interference is largely handled _ex ante_ as part of the authorization process followed by monitoring and enforcing compliance with detailed technical and operating rules. In a spectrum rights regime, the risk of interference is higher, especially when changes of use are allowed. Addressing interference problems, when they happen, may require assessing whether interference levels (at the geographical and frequency boundaries of the spectrum rights) have been violated, corrective action by the parties themselves, and perhaps formal dispute resolution. In the commons approach, minimal technical rules (e.g., maximum power transmitted) provide some protection against interference levels building up among commoners as a group. But interference-free operation is not assured, and users rely primarily on technologies, protocols, and codes of conduct that themselves reduce the risk of interference among users and handle problems when they arise.

Five support functions are required for the critical building blocks to work well. These support functions provide data, analytical, administrative, monitoring, and international capabilities.<sup>128</sup> The scope and importance of each of these support functions also vary considerably among approaches to spectrum management. Table 5.4 outlines these support functions and gives an impression of their relative intensity of use under each approach to spectrum management. To some extent the choices among approaches are influenced by their support requirements and the extent to which these can be met under given country conditions. Rights and commons

> 128Support functions are discussed in considerable detail in module 5 of infoDev’s online toolkit for ICT regulation (infoDev 2006).

**48**
<!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15<br>Interest rate for VRF borrowers (% p.a.)<br>% of total cases<br><!-- End of picture text --> Figure1. Interest Rates Charged by Village Funds, 2004 Source: Thailand Socio-Economic Survey 2004 Eight percent of VRF borrowers reported that they were overdue on repayments, and the proportions were similar for poor, and for rural, households. However, a sixth of those who obtained VRF credit in turn borrowed elsewhere in order to repay their VRF loan. The interest rates charged by those alternative sources of credit were high, averaging 46 percent (on an annualized basis).

Despite the challenges that some faced in repaying the VRF loans, seven out of ten borrowers said that their economic situation had “improved” as a result of the program and just 2 percent said that it had worsened. However, less than a third of borrowers said that the VRF system should be left unchanged; substantial numbers wanted the loan amounts to be larger (34% of respondents), longer (34%), cheaper (37%), or to be focused more on the poor (25%).

In 2004, women were slightly less likely than men to have borrowed from the Village Fund: 15.5% of adult women borrowed from the fund, compared to the overall average of 16.6%. Women asked for, and received, slightly smaller loans; paid a slightly higher interest rate; and were less likely to borrow to buy agricultural inputs or equipment. However, in most other respects, female borrowers are indistinguishable from male borrowers, as may be seen by comparing the first and last columns of numbers in Table 2.

A 2005 survey undertaken in the northeast of Thailand by the Thailand Development Research Institute (TDRI) found that about 40% of households had borrowed from the VRF, and among those who borrowed, slightly over 90% said they were satisfied with the process. There is, however, anecdotal evidence that in some cases the injection of credit has led villagers to borrow too much, leading to difficulties when the _Village Fund, draft of June 29, 2009_ _Page 8 of 34_
**<u>NON-TECHNICAL SUMMARY</u>**

In this paper we focus on the consequences of skilled migration for developing countries. We first summarize the recent findings of Docquier and Marfouk (2004) on the international mobility of the highly-skilled; their estimates, based on immigration data collected from nearly all OECD countries in 1990 and 2000, show that the “brain drain” has gained in magnitude over the period covered, but that substantial differences remain across regions and income groups.

The central section presents the theoretical arguments of the ''new'' and ''old'' brain drain literatures in a fully harmonized framework. We first review the early brain drain literature, which tends to emphasize the losses to those left behind. We then ask whether the traditional detrimental effects stressed in the early literature may be offset by potentially beneficial effects emphasized in more recent contributions (remittances, return migration, creation of trade and business networks, and possible incentive effects of migration prospects on human capital formation at home), and provide empirical evidence on these different channels where available. According to most existing studies, it is unlikely that remittances, return migration or other ways through which highly-skilled emigrants continue to impact on their home country's economy are significant enough to compensate sending countries for the losses induced by the brain drain. By contrast, cross-country comparisons provide supportive evidence that migration prospects foster domestic enrollment in education. Additional investigations are clearly needed to assess the net effect of emigration on human capital formation at home.

The last section explores some of the policy implications of the analysis, with emphasis on migration policy and education policy. We show that from the perspective of developing countries, the "optimal" emigration rate for the highly-skilled is zero where liquidity constraints are strongly binding and then conforms to an inverse-U shape pattern with respect to the country's level of development. As to education policy, we discuss the role of taxes and education subsidies when human capital is mobile internationally and show how optimal policy responses to such mobility are affected by the parameters of the model.
and as a result led to a more subjective interpretation, which made the comparability of answers across business functions and companies harder to interpret.

# **A.1.4 Barriers and Drivers**

In addition to the information on the technologies used by firms, the survey also collects information on potential drivers of and barriers to technology adoption. First, the survey asks whether the firm acquired new machines, equipment, and software in the last three years; and in the case of machines or equipment, whether these were leased, purchased as new or secondhand. The survey also asks questions on links to larger firms and multinationals, either via value chain linkages as a supplier or buyer, or via the CEO previous experience working in a MNE or a large firm.

The survey also asks questions about access to finance and trade status. The first question is about having secured a loan in the previous three years for purchasing equipment, machinery or software. On more general access to finance, the survey asks how many times the establishment needed to borrow money to expand production but could not obtain finance. On trading status, the survey asks whether the firm is an importer, an exporter; and if an exporter, what is the share of sales that is exported.

A key complementary factor for technology adoption is the quality of management. The survey pays special attention to management by collecting information on the top manager’s background and on management practices. Specifically, FAT asks about the level of education attainment of the top manager in the establishment, whether she has studied abroad, and whether she has experience in multinationals. In addition, the survey contains four questions about management practices. These include four questions from MOPS (Bloom et al., 2016) on the number of KPIs, the frequency with which they are monitored, the horizon of production targets and a question on the use of formal incentives. Though the information we collect on management practices is more restricted than the sixteen questions in MOPS, we have used information from the Mexico ENAPROCE survey and show that the index that emerges from the small number of variables collected is highly correlated with the full MOPS index and it captures a large fraction of the cross-firm variance in the quality of management practices.<sup>29</sup> To investigate also the potential role of policies on technology adoption, the survey asks questions about awareness about existing public programs to support technology upgrading;

> 29 Specifically, we use data from Mexico ENAPROCE survey and calculate the correlation between a management quality index with the 4 questions in FAT and the overall index using all questions of MOPS that are in ENAPROCE. The correlations are 0.74 for 2015 survey and 0.73 for 2018; which suggests that with less questions we are still able to capture most of the variation in management quality.

76
# Deflnltlons and **Sources** of Variables

Y/Y: Average annual growth rate of GDP. Source: World Tables, World Bank, 1992. Sf / Sf: Average annual change of the share of foreign capital import in GDP. This variable is calculated based on data from two sources: data on the dolUar value of foreign capital are from United Nations' Intrtional Trade Statistical Yearbook; and the share of total import in GDP is from Summers and Heston (1991).

Sh / Sh: Average annual change of the share of home investment in GDP. This is calculated as the log difference of share of home investment in GDP, and the share of home investment in GDP in urV in calculated by subtracting Sf from the share of total investment in GDP. The latter is frm Summers and Heston data set.

i N _I_ N: Average annual growth rate of population. Soure: Summers and Heston data set. INFLAT: Average amual inflation rate, computed as the log-difference of CPI. Source: _Intermational Fmawcial Statisdics,_ CD-ROM, June, 1993. GDP deflator data from the World Bank were used to extend inflation senes for Malawi.

EXCPREM: Average black market exchange rate premium. Source: World Bank, _World Development Report,_ 1991. [Computer file] TOT: Change in terms of trade, calculated as the log difference of the net terms of trade in a time period. Sources: World Bank, _World Development Report, 1991_ dataset.

F.CONSUM: Annual average change in the share of foreign consumption iMport in GDP. Sources: UN's International Trade Statistical Yearbook, and Summers and Heston.
**Table 6a. Decomposition of concentration index for child undernutrition, non-linear models**

||**Stunting**|**Wasting **|**Underweight**|
|---|---|---|---|
|**Standardizing (demographic) variables**||||
|Child age in months|-0.003|0.004|-0.004|
|Child sex (Male=1, Female=0)|0.000|0.001|0.000|
|Birth order number|0.001|-0.003|-0.006|
|**Subtotal**|**-0.002**|**0.002**|**-0.009**|
|**Control variables**||||
|Quintiles of wealth index|-0.062|0.001|-0.038|
|Education/ Mother's education level|-0.017|-0.032|-0.037|
|Education of HH Head|-0.010|-0.037|-0.017|
|Improved water source for drinking|0.010|-0.022|-0.006|
|Improved toilet facilities|-0.002|0.016|0.007|
|Number of household members|0.002|0.008|-0.004|
|Number of children 5 and under|0.001|0.007|-0.002|
|Area of residence (Urban=1, Rural=0)|-0.016|0.015|-0.002|
|Region|-0.004|-0.035|-0.022|
|**Subtotal**|**-0.098**|**-0.086**|**-0.118**|
|Residual: regression error|0.008|-0.008|0.011|
|Residual: missing data|0.008|-0.007|-0.002|
|Inequality (total)|-0.085|-0.099|-0.118|
|**Inequity/Unjustified inequality**|**-0.091**|**-0.094**|**-0.107**|

Note: ADEPT software (health outcome module) is used to produce the results. The decompositions are based on mini-DHS 2014 Data.

Another observation from the decomposition is the differential effect of spatial variables. The effect of place of residence (being urban) is more important in services than in health status outcomes. This might be expected because there are proportionally more health facilities with skilled professionals in urban than rural areas. Also, for equity in MNCH services, the rural-urban classification is more important than region.

32
could publish or broadcast politically sensitive materials, whereas outlets targeting large audiences were systematically censored. Moreover, over the last decade, in contrast to the early transition optimism, following general trends in democratic backsliding and security threats, ‘old-time’ media censorship trends have dramatically increased around the world, affecting both democracies and autocracies, with negative consequences for the overall quality of political institutions and civil liberties.

Yet domestic variations in strategies of censorship and its targets are currently undertheorized. Who are the censors? Who is likely to be most affected by censorship and why? Are all media outlets equally exposed to capture? Are media capture tools based on coercion (censorship) or inducements (bribes and economic control) mutually exclusive or do they work jointly to insure information control? We argue that individual exposure to punitive measures depends on the size of a media outlet’s audience and on the transaction costs of censorship. Irrespective of political regime type, censors are likely to allow free information in narrow, elite segments of the media market while suppressing it for larger audiences that, as the historical quotation above suggests, include a critical enough number of voters to pose a political risk for the incumbent when exposed to compromising information left uncensored. Building on the seminal Besley and Prat (2006) model of media capture, the paper proposes two major sets of hypotheses. First, censors economize coercion by targeting only the politically threatening segments of the media market. Second, punitive and inducement-based capture are substitutes, except for media outlets that pose the highest political threat, where capture tools act jointly to insure compliance.

Since currently there are no individual-level data that allow us to test empirically whether the segmentation of news consumption can lead to heterogeneous levels of censorship within a country’s media market, we generated a new cross-national data set containing around 9,000 3