rafmacalaba/data-use-mentions-extended huggingface dataset:
happened quite suddenly and been transformational. Such events include decolonization in Africa and elsewhere around 1960; the creation of the Common Agricultural Policy (CAP) in Europe in 1962; the introduction of flexible exchange rates from the 1970s; liberalization, deregulation, privatization, and democratization in many countries from the mid-1980s; the opening of markets in China in 1979, in Vietnam in 1986, and in Eastern Europe (following the fall of the Berlin Wall) in 1989; and the demise of the Soviet Union in 1991. More subtle are the influences of policies that change gradually in the course of economic development, as incomes grow and comparative advantages evolve. But what do the quantitative measures of distortions and reforms reveal?

Empirical indicators of agricultural price distortions (called Producer Support and Consumer Subsidy Estimates, or PSEs and CSEs) have been provided consistently for 25 years by the Secretariat of the OECD (2011) for its thirty member countries. However, the OECD provides no comprehensive time-series rates of assistance to producers of nonagricultural goods to compare with the PSEs, nor of what took place in those advanced economies during earlier decades. Data for these earlier decades from developed economies is needed in order to assess how various countries’ policies evolved during stages of development similar to those of today’s middle-income countries. As for developing countries, almost no comparable time-series estimates were generated in the two decades policies more thoroughly (while giving somewhat less attention to factor market distortions). And third, we place particular emphasis on the substantial body of theoretical and empirical research that has been published during the past fifteen years (that is, since the most recent citations in Binswanger and Deininger).
# **<mark>How cash transfers are targeted and delivered are important program design elements</mark>**

<mark>Recent studies suggest that cash transfers can cause declines in life satisfaction and reductions in psychological well‐being among non‐beneficiaries who live in the same communities as program beneficiaries.</mark><sup>9</sup> <mark>Cash transfers can cause prices of non‐tradable or perishable goods to increase in remote areas with weak links to markets, while in‐kind transfers can have the opposite effect of reducing food prices.</mark><sup>10</sup> <mark>A recent World Bank study of the Philippine CCT shows that such effects can have real consequences: price increases in protein‐rich perishable food items caused substantial increases in stunting among non‐beneficiary children.</mark><sup>11</sup> <mark>Hence, it is important for the World Bank and governments implementing large‐scale transfer programs to pay attention to spillover and general equilibrium effects.</mark> <mark>These findings have several implications for World Bank policies</mark> _<mark>:</mark>_

- A potentially promising way of resolving the tradeoff between CCT and UCT programs is to view them as complements to each other rather than alternatives.  Policymakers could provide a basic unconditional cash transfer to, say, adolescent females or poor households, topped up by conditional cash transfers for human capital accumulation and desired health behaviors – providing an incentive to invest in education and health while still guaranteeing a basic level of protection to those who are unable or unwilling to comply with program conditions.

- The promising evidence of the positive effect of UCTs on children’s height provides an additional reason to consider providing basic UCTs to adolescent girls and young women. Indeed, other researchers in the U.S. context have suggested that targeting transfers towards women of childbearing age would be beneficial,
# V. Discussion and conclusion

In this note we have analyzed the evolution of domestic government spending in health, education and social protection over the last 15 years. Creating a measure of social spending that covers the three sectors has proven to be a challenging task. While for the health sector only current spending is deemed reliable, in education there is no rigorous way to separate current from capital spending. Only for health spending is there high data coverage both over time and across regions. Education and especially social protection display large gaps. It is particularly worrying that in education there is not a consolidated upward trend in data availability. Low data density does not only plague LICs: in many middle-income countries (such as those in LAC), there are large gaps in data availability. Addressing data sparsity represents a clear area of opportunity for the Human Capital Project to leverage its data collection efforts in a manner that encourages more comprehensive data gathering for spending.

Increases in social sector spending have generally been slow and not steady. In fact, while education spending in LICs has seen a stable and steady increase, spending in health has been remarkably flat. Given the strong complementarities between health and education interventions, especially in the first years of life, coordinated investments might help maximize the returns. Some regional trends are also worrying: both EAP and South Asia have seen a reduction in education spending as a share of GDP.

Taking advantage of the newly-designed Human Capital Index (HCI), we go beyond existing analyses that look at the relationship between a single outcome sector-specific spending, and we assess whether a multidimensional measure of human capital is correlated with a multidimensional measure of spending. While this data curation effort was unique, the relationships identified here are challenging to interpret. The (unstratified) relationship between spending and the HCI often plateaus above a certain level of spending and is noisy throughout. While the relationship between spending and HCI appears stronger for HICs than for countries at other income levels, these relationships are not statistically significant.

Our conclusions
# **1.2 Data: SWF investments in Africa**

In this section, we present SWF investments in Africa. We first look at the sources of funding for SWFs that invest in the continent. Second, we take into account the geographical origin of the SWFs, and finally, we classify SWF investments in Africa by sector. We collected data from different sources. We use data taken from Bortolotti _et al._ (2009, 2015), who investigate the relationship between SWFs and their investment patterns and performance levels. Their database includes the total number and value of investments, the number and value of domestic investments, the number and value of listed equity investments, the number and value of unlisted equity investments, and the number and value of real estate investments of SWFs over the period January 1986 to September 2008, for 35 funds. We also used data taken from Chhaochharia and Laeven (2009), who analyze the investments of SWFs by taking into account common cultural traits. They collected information on a total of 29,295 equity holdings by SWFs over the period 1998-2007, comprising 10,282 equity holdings across 51 countries at year-end 2007. We use a database taken from the Sovereign Wealth Center and the SWI, and another database taken from Triki and Faye (2011). Finally, in order to have more information about SWF investments in Africa, we went to different SWF websites, and recorded their acquisitions in Africa. We then used different online sources to find the amounts invested in these companies by SWFs, particularly in financial and economic newspapers. Indeed, this strategy allowed us to track the country destinations of five SWF investments over the period 2000-2013, and then calculate the share of investments in terms of GDP received by African countries.<sup>2</sup> To investigate SWF investments and economic growth, we use per-worker GDP data for 30 African countries over the period 2000-2013 taken from the WDI.<sup>3</sup> We also use private credit
<!-- Start of picture text --> Carbon price by fuel type (2011 USD)<br><!-- End of picture text --> <!-- Start of picture text --> 14,000<br>12,000<br>10,000 —— Carbon price for coal (2011 USD)<br>—— Carbon price for oi! (2011 USD)<br>8,000 —— Carbon price for gas (2011 USD)<br>6,000<br>4,000<br>2,000<br>0 =<br>-2,000<br>80 90 00 10 20 30 40 50 60 70 80 90 00<br>Total emissions/Total emissions in 2017<br>3.0 — Scenario 1: Carbon tax and debt repayment<br>—— Scenario 2: Carbon tax and reduction in direct taxes<br>2.5 —— Scenario 3: Carbon tax and an increase in hh. transfers<br>—— Baseline<br>2.0<br>15<br>1.0<br>0.5E —<br>0.0<br>80 90 00 10 20 30 40 50 60 70 80 90 00<br><!-- End of picture text --> <!-- Start of picture text --> Carbon price by fuel type (2011 PKR)<br><!-- End of picture text --> <!-- Start of picture text --> 1,200,000<br>1,000,000 —— Carbon price for coal (2011 PKR)<br>—— Carbon price for oi! (2011 PKR)<br>800,000 — Carbon price for gas (2011 PKR)<br>600,000<br>400,000<br>200
Section 4). The Capital arm instead added a lump sum cash grant of US$311 (in 2016 PPP). The Full arm included all components.</mark> <mark>Here, we focus on the mechanisms that drove the impacts of the Psychosocial and Full arms (i.e., those that included the psychosocial interventions), which were assessed in a followup survey conducted approximately six months after the end of program and in particular one year after the delivery of the psychosocial interventions. A</mark> nalyses focus on exploring psychosocial mechanisms of effects on economic development, specifically women’s off-farm business development, a primary target of the program. Psychosocial variables include selfreported <mark>intrapersonal beliefs and experiences (e.g., self-efficacy, optimistic future expectations, mental health) and relational beliefs and affordances (e.g., social standing, social support, control over decision-making in the household, social norms). All measures were collected by enumerators blind to condition via in-person surveys.</mark> <mark>In Section 5, we present a pre-registered mechanism experiment (Study 3) that was embedded within the arms of the policy experiment that included psychosocial interventions (i.e. the Psychosocial and Full arms). This mechanism experiment sought to test whether independent agency and/or interdependent agency causally drove observed increases in economic development and assess associated intrapersonal and relational processes. Notably, we expanded the sample to include all program participants, regardless of their participation in the policy experiment, in a subset of 33 villages (N=2,628) and thus the mechanism and policy experiment samples were largely non-overlapping (see Fig. S1). In an individual-level experiment, we tested the effects of receiving one of two brief social-psychological interventions following the program’s community sensitization–a brief independent agency intervention grounded in selfdirection and self-advancement (“Personal Initiative”) or an interdependent agency intervention grounded in social harmony and collective advancement (“Interpersonal Initiative”)
awareness). In addition, public procurement data combined with performance indicators can nurture the spread of best practices in the public sector.<sup>25</sup> In spite of such promises a range of caveats apply. First, information available on official government and watchdog portals is typically on high level of aggregation (e.g. containing total contract value, but no information on the prices of individual inputs of deliverables) and often contain missing or erroneous information limiting its use for civil society monitoring. Especially as information on project execution progress and payments is typically missing from procurement portals, transparency may achieve little on the ground (Cingolani et al., 2015). Second, effective monitoring of complex transactions and projects, typical of public procurement, requires direct observability of performance and mismanagement. This may well be the case in construction, but in services such as education and training provision, lack of easy observability of mismanagement can render transparency ineffective (Banerjee et al., 2010). Third, costs of making data publicly available on transparency and watchdog portals may be substantial with oversight and monitoring costs even higher, significantly reducing net benefits or even turning them into negative.

# _b) Evidence_

Evidence on the effects of transparency and watchdog portals is very limited, it by and large incorporates non-systemic collections of suggestive facts and perception survey data (Šípoš et al., 2015). No experimental or similar study could be identified which would reliably establish causal relationships.

While no rigorous economic analysis underpins the above theoretically sound arguments, some data from the Slovakian watchdog portal provides suggestive evidence (Šípoš et al., 2015). The reach of public procurement information across the Slovakian population has increased dramatically from practically null to 11% of the total population claiming to have checked at least one contract online since 2011. In addition, media reporting of public procurement related scandals increased often making explicit use and reference to the watchdog portal: from 877 articles in 2003-2006 to 1765 articles in 2011-2014. Furthermore, where estimates
_58.2)_|

Source: Authors’ calculations using the Ethiopia SPS (2017), Nigeria IDP Survey (2018), Somalia HFS (2017), South Sudan HFS (2017), and Sudan IDP Profiling Survey (2018).

There is large variation in the MPI incidence and intensity within and across the five countries, both in terms of gender and displacement status. In Sudan, for example, male and female IDPs report an incidence of poverty nearly five times higher than their nondisplaced counterparts, while in Nigeria, the differences are far smaller in magnitude. Still, the displaced populations always experience higher _incidence_ of multidimensional poverty, although in Nigeria, Somalia, and South Sudan, the intensity of poverty is higher among the non-displaced population. Table 6 checks the significance of the differences using an adjusted Wald test of means on the female populations by displacement status (Table 6).

_Table 6: Adjusted Wald test of means by displacement status and gender_

||Fem|ale||IDPs|||Non|-IDPs||
|---|---|---|---|---|---|---|---|---|---|
|Country|IDPs v non-|IDPs||Female v|Male||Femal|e v Mal|e|
||MPI|H|A|MPI|H|A|MPI|H|A|
|Ethiopia|63.75***|63.39***|2.46|10.52***|8.60***|5.97**|0.87|0.87|2.46|
|N.E. Nigeria|2.00|2.78*|4.31**|9.05***|8.27***|0.70|0.34|
that employment in the small companies was shifting to the large ones. Furthermore, the large companies appeared to be paying above the minimum wage, so that increases in the minimum wage would not affect employment levels. On the other hand, Harrison and Scorse (2010) found much more modest effects of the minimum wage increase on the textile industry in Indonesia during the 1990s. According to their estimates, a 10 percent increase in the minimum wage for workers in foreign enterprises and export firms in Indonesia would trigger a 1.2 to 1.8 percent reduction in employment, whereas in small companies the effect was insignificant, mainly because of low compliance with the law.

A more recent study on the situation in Indonesia with data from firms for the period 1996‐2003 found moderate effects on employment, with job losses of less than 1 percent following a 10 percent increase in the minimum wage. Minimum wage hikes led to layoffs of less qualified workers, especially those in areas other than production and those in small companies (Del Carpio et al., 2013). This means that, prior to the minimum wage increases, the small companies, which usually engaged in labor‐intensive activities and had little power over the market, were forced to lay off less‐qualified workers to avoid bankruptcy. In Vietnam, it was found that during 2006‐2010 the minimum wage had a negative effect on employment in domestic companies but none in the larger enterprises (Del Carpio, Nguyen, Nguyen and Wang, 2013).

10
specified level of well-being (e.g., Garner and Short, 2004). Since this method assumes that people know what their true minimum income level is, the hypothetical assessment of the situation may lead to interpretation issues of minimum income questions (Steiger _et al._ , 1997). The second type asks respondents to evaluate their level of satisfaction with life or income, and does not have such disadvantage (e.g., Biewen and Juhasz, 2017; Borah _et al._ , 2018). Our paper is more related to the second approach. But we also offer robustness checks using life satisfaction data that are collected in the same household surveys.

2
intra-province migration|3.82|20.02|29.51|24.88|12.79|7.05|1.92|
|inter-province migration|3.58|20.94|33.35|16.37|12.75|8.78|4.23|

Sources: calculated from Tabulation on 2000 Population Census of the P. R. China, Table L7-6-1b and Table L7-6-2b.

note: 1, literate; 2, primary school; 3, secondary school; 4, high school; 5, vocational school; 6, college; 7, university or above.

We also further breakdown the level of educational attainment of town inmigrants by original place as we are interested in a question of whether inter-provincial in-migrants possess higher level of educational attainment than those in-migrants who are from the same province. Data (Table 10) show that in Zhejiang, among inter-provincial in-migrants, only 10 percent of them had high school education attainment or higher, whereas among intra-provincial in-migrants, as high as 32 percent of them had high school education attainment or higher. Thus the fact that a higher percentage of educated migrants coming from same province than other provinces seems to give some supports to what Zhao (1999) has found in her study that better educated people in rural areas probably were not necessary to leave countryside to migrate to other provinces and were likely to shift their jobs from agriculture to the non-agricultural sector in towns nearby instead.

However, when we further look at the data on educational attainment of inmigrants by original place in Henan and Sichuan, we do not find this kind of sharp contrast between those who came from same province and other provinces. For example, in Sichuan, about 44 percent of intra-province in-migrants had high school education or 11
be blue-collar production workers than inmigrants from same province. For example, 83 percent of in-migrants from other provinces were engaged in blue-collar production jobs, whereas only 43 percent of inmigrant from same province did so. This probably could be explained by the lower educational attainment of those cross-province migrants. Thus the probability of being a blue production worker is twice higher for in-migrants coming from other provinces than the in-migrants from the same province. Likewise, for the white-collar jobs, the chances are much smaller for the in-migrants from other provinces as shown in the Table. Thus labor market segregation not only exists between town in-migrants and local population, but also exists between those who came from same province or came from other provinces.

However, we also find quite different story in other two relatively poor provinces. Data in Table 11 do not show a sharp contrast in percentage distribution of blue-collar production workers between those in-migrants and local population as we have seen in the Zhejiang data. Only slightly higher percentage of in-migrants worked as blue-collar production worker than that for the town local population, 24% vs. 16% in Henan and 23% vs. 19% in Sichuan. Thus, data suggest labor market has been much more flexible in Henan and Sichuan than in Zhejiang.

12
governmental institutions. As far as government expenditures are concerned, the paper only includes social spending, and the subsidies granted in the use of essential utilities, given it is challenging to allocate other types of benefits to the individuals of the households.

The results found suggest that the combined effect of taxes and social spending in Colombia contributes to the reduction of poverty and inequality, even more so than in some of the countries of the region, such as Chile, Ecuador, and Mexico. Furthermore, the finding was that direct taxes are progressive and propoor, while indirect taxes are regressive and contribute to an increase in inequality. Direct transfers, for their part, are progressive and pro-poor. Transfers in kind for education and health services are progressive and contribute to the reduction of inequality. Nevertheless, these results should be interpreted with care since the quality of these services is not considered in the analysis.

The rest of the paper is structured as follows: section II provides a brief description of the structure of taxes and social spending in Colombia. Section III makes a general presentation of the methodology applied in this work, the data used, and the main assumptions. Section IV describes the impacts of fiscal policy on poverty and inequality and presents a methodological comparison with the previous CEQ. Section V shows the impact of taxes and expenditures. Finally, section VI offers the final reflections and future lines of work.

# **II. The tax system and social spending in Colombia in 2017**

This section provides a brief description of the taxation, transfers, and social spending system in Colombia for the year 2017. It merits clarifying that the fiscal situation of 2017 was considered a point of reference given that the households survey used for the exercise correspond to that year. The section begins with direct and indirect taxes and subsequently with the subsidies and social spending of the Colombian system.

## i. The Colombian government's revenues

The revenue structure of the Colombian government is in Table II-1. The total government revenue considering taxes
>acre, (iii) kilograms of total fertilizer per acre, (iv) dummy that takes value of 1 if<br>yield is positive (1 - crop failure) and (v) log total yield.|
|Gives inputs to others|Follow-up|Link level data. Based on self-reported data on network of neighboring farmers in<br>the village that the household head would go for advice of that would come to<br>him/her for advice. Variable contains one observation for each link reported.<br>Dummy variable. Equals to one if the household head reports giving any fertilizer<br>and/or seed to a member of his/her network during 2010/11 planting season.|
Estimating the Economic and Distributional Impacts of the RCEP Figure 4 Average tariff applied in RCEP countries by sector, to RCEP and to the rest of the world Trade weighted average <!-- Start of picture text --> Total Agriculture<br>Total Manufacturing<br>Total Natural Resources<br>Total<br>Meat products<br>Motor vehicles and parts<br>Crops<br>Food and beverages<br>Wearing apparel and leather<br>Non metallic minerals<br>Textiles<br>Other transport equipment<br>Chemical, rubber, plastics<br>Animal products<br>Other manufactures<br>Electrical equipment and machinery<br>Basic pharmaceuticals<br>Metals<br>Wood and paper products<br>Petroleum, coal products<br>Utilities<br>Other extraction<br>Fossil fuels<br>0.0% 5.0% 10.0% 15.0% 20.0% 25.0%<br>ROW RCEP<br><!-- End of picture text --> Source: own elaboration with data from MacMap and Comtrade Barriers to imports may also take the form of NTMs, which range from restrictions on foreign providers of services, to sanitary, phytosanitary, technical, or price control measures, among others. In order to assess the level of protection from NTMs, estimations of ad valorem equivalents (AVE) are found in the literature (Tables AI.1 and AI.2 in Appendix I compare the different estimates available). Recently, two studies estimate AVEs of NTMs for a wide range of countries: Cadot et al. (2018) and Kee and Nicita (2017) (see Appendix I). For services, there are recent estimations by Hoekman and Shepherd (2019). The estimated AVEs of NTMs on services are much higher than on goods (Figure 5). This might be related to the techniques used to estimate the AVE, but