rafmacalaba/data-use-mentions-extended huggingface dataset:
level. These datasets with large country-sector coverage also enable us to draw conclusions with high general validity.

We find a positive but weak association between the FDI and labor productivity at the countrysector level. Unlike the existing literature, we take a step further. With a battery of robustness checks at both firm and sector level, we argue that GVC participation plays a key role in shaping the effects of FDI. Specifically, a country-sector with lower GVC participation benefits more from FDI. To establish causality, we adopt a shift-share instrumental variable (IV) for sector FDI. In the IV regression, we find that doubling the FDI into a non-tradable country sector will increase the labor productivity of that sector by 15 percent; for a country-sector with an average GVC participation level (= 0.24), doubling the FDI will increase the labor productivity by 8 percent.

To understand the mechanism through which the spillovers take place, we further argue that lower GVC participation may result in higher exposure to domestic competition generated by FDI and thus higher propensity to do research and development (R&D). Our firm-level analysis supports this argument. We find that firms participating less in the GVC have higher propensity to engage in R&D in response to FDI in their sector. We discuss more about this mechanism in Section 5.3 and 5.4.

Moreover, by looking at cross-border FDI, we provide evidence for the existence of positive FDI spillovers through both domestic and foreign backward linkages. That is, FDI into the downstream sector of a home or foreign country also has positive effects on a domestic upstream sector through input-output linkages.

This paper contributes to the literature on the effects of FDI at least in three ways. First, by taking GVC participation into account, this paper reconciles the contradicting effects of FDI in different countries documented in the previous literature. The country variation of the extent of GVC participation explains why the effects of the FDI vary across countries. Second, we identify the existence of cross-border FDI spillovers. To
2 maternal health results from this development cycle in myriad ways. For example, female education tends to lower fertility "by delaying marriage, by increasing the effectiveness of contraceptive use, and by giving women ideas and opportunities beyond child bearing alone" (World Bank, 1984). Maternal health and nutrition improve directly as a result of lowered fertility, and indirectly as the development cycle gains another notch, which makes possible further advances in the living conditions of communities and individuals. While we will not dwell on these relationships, the contribution of family planning to maternal health that operates through overall development, clearly is of critical importance.

# I. DIRECT EFFECTS OF FAMILY PLANNING ON MATERNAL HEALTI

Here we examine the direct effect of family planning in preventing maternal mortality through fewer births, fewer high risk pregnancies and fewer crudely performed abortions.

# Fewer Births

The direct effect of family planning is most obvious anA dramatic in relation to the reduction of fertility. If a pregnancy do-, '.t occur, obviously its health risks are averted. Fortney (1985) il tes the impact with data from Egypt and Bali. There were 190 mater-. eaths per 100,000 live births in Menoufia, Egypt, in the early 1980s, cvmnared to 618 per 100,000 live births in Bali. This is the so-called maternal mortality rate (actually a ratio) which measures a woman's risk of dying once she is pregnant. The picture changes radically when we look at the true maternal mortality rate, i.e. the number of maternal deaths per 100,000 women of reproductive age, which measures both the rate at which women become pregnant and the risk of death once they are pregnant. This rate turns out to be 46 per 100,000 women in Egypt and 70 per 100,000 women in Bali. Bali still has the higher rate but the difference is much smaller. The difference between the two measures of maternal mortality reflects differences in the number of pregnancies in the two populations, which
35 As Fig. 2.17 below shows, when moving into a new building, practically all households (95.8%) managed to improve their housing conditions, while when moving into secondary market housing, only 69.8% did so.

Fig. 2.17. Change of housing conditions when moving into new and previously occupied housing (% of mover households in 1993-1995) <!-- Start of picture text --> Newly constructed Previously occupied<br>1.7% 4.G% 14.6%<br>2.5% H improved<br>E no change<br>I5.8% l691.8%I became worse<br><!-- End of picture text --> Source: The Moscow Longitudinal Household Survey, January 1996 As for the methods of obtaining housing (Fig. 2.18), its free allocation through the waiting list or purchase was generally accompanied by an improvement in the living conditions. In the case of apartment exchanges, however, 17% of the households reported moving into inferior apartments.

**Fig. 2.18.** Change in mover household living conditions in Moscow by methods of obtaining housing (% of mover households in 1993-1995; same legend as Fig. 2.17.)

<!-- Start of picture text --> Waiting list Purchase Exchange<br>0.0% 7.0% 55% 17.0%<br>_ ~~3.4% 1|- 8.2%<br>96.6% ~<br>86.5% 64.8%<br><!-- End of picture text -->

Source: The Moscow Longitudinal Household Survey, January 1996.

This can be explained, in part, by the social composition of the mover households. Those households who resort to housing exchanges (and, to a lesser degree, purchases) include quite a
# **1 Introduction**

All over the world, firms exert political influence. While this influence varies in its intensity, form, and effectiveness, it is widely understood to be in competition with the interests of other firms, groups, and the public at large, whether those interests are complementary or adversarial (Becker, 1983). Some of the ways in which firms try to manifest this influence are well established and tested in the economics and political science literature. In particular, the leverage of a connection to political leadership has drawn substantial attention, with most analyses showing large gains to well-connected firms (Fisman, 2001; Faccio, 2006; Malesky and Taussig, 2009; Earle and Gehlbach, 2015). Such connections are not the only means available to firms to exert influence, of course. Firms have many possible interactions with political decision-makers, and each presents a chance for firms to seek gains, but also they give political actors the ability to extract rents from those firms (Shleifer and Vishny, 1994; Harstad and Svensson, 2011; Bertrand et al., 2018; Haber et al., 2003; Markus, 2015; Frye, 2017). That is, while political connections may result in influence, that influence may be dampened or enhanced by particular combinations of political interactions, including through legal channels, such as business associations, or through generally illegal ones, like bribery. Just the same, firms can certainly realize influence without having a political connection. In this paper, we provide an original measure of firm-level political influence for 41 economies, increasing our knowledge about how a diverse array of state-business interactions affect firm performance.

Political influence is a social construct, and so it cannot be directly observed; as such, we treat political influence as a latent concept. This paper uses an original, firm-level data set from 41 diverse economies (mainly in Europe, Central Asia, the Middle East, and North Africa) to understand how firms engage in interactions with public officials to gain
good health and the means to adapt to a labor market with rapidly changing skills needs – elements that determine the ability to work into old age and influence worker productivity.<sup>16</sup>

# The nature of work among older adults

**Old-age labor force participation is generally lower, the higher a country’s average income level.** In the average LIC, more than half of the 65+ population is active in the labor market (Figure 2). Moving up through country income groups, participation rates fall systematically. In lower middle-income countries (LMICs), 33 percent of the 65+ population is active, and in upper middle-income countries (UMICs) and HICs, this rate falls to 19 and 13 percent, respectively. Across income groups, European countries are outliers, with significantly lower participation rates than other regions. High labor force participation in poor countries largely reflects a need to work. In Thailand, for example, 31 percent of people above age 60 report that labor income is their main source of income, which is much higher than the share reporting pensions (5 percent) or elderly allowance (17 percent).<sup>17</sup>

**Figure 2: Labor force participation for older workers falls with a country’s income**

<!-- Start of picture text --> labor force participation rate (%), age 65+, by<br>country income group<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>LICs LMICs UMICs HICs LMIC UMIC HIC<br>All regions Europe only<br><!-- End of picture text --> Source: Estimates based on Staudinger et al. 2016. Refers to 2013 data.

**The pattern of participation over the life cycle is similar across country income levels, despite very**

**different levels of participation*
In 2015, with presidential elections approaching, the economy stagnated, inflation remained high, and foreign exchange pressures continued. Fiscal policy remained without a clear medium-term framework and without quantitative fiscal rules at the federal level. Political budget cycles re-emerged. Monetary policy had multiple and expansive objectives and central bank financing of the government was institutionalized. The broader framework, or direction, for macroeconomic policy making was unclear, other than general political statements on “not repeating the mistakes of the 1990s.” Even understanding the size of fiscal and monetary problems was difficult, given doubts about macro statistics, and increasing use of off-budget fiscal mechanisms. In terms of the definition of credibility, and the characteristics of credible policy, outlined in the introduction, the path forward on credible macroeconomic policy could be challenging, to say the least.

# **IV. Microeconomic policy**

In this section, a brief historical survey will be organized by sector, or policy area, rather than chronologically. The areas covered are: trade and foreign investment; business climate; labor markets; financial markets; public utilities; and the new “supply law.” Social sector policies have a critical role in both economic performance and socioeconomic outcomes, as well as the political economy of reform; however, they are beyond the scope of this paper.

# _Trade and foreign investment_

Argentina is a member of the Mercosur customs union, which started functioning in 1994, and Argentina is a member of the World Trade Organization (WTO) since its inception in 1995. There continue to be long lists of exempted goods within Mercosur, so that it does not function as a true customs union. Common tariffs on manufactured goods remain high, compared to other emerging market economies. External trading partners also note high costs associated with customs clearance.<sup>44</sup> Argentine non-tariff measures have increased in recent years, as noted above, in the macroeconomic policy section. More specifically, the current dispute before the WTO concerns:

- <mark>The
use of standard survey instruments in majority of innovation data collection in OECD countries has mitigated the likelihood of a different impact of cognitive issues like ordering or wording used on the quality of the data collected. However, methodological issues related to the respondent’s understanding of innovation or to the type of survey instrument used have yet to be solved. The analysis of innovation and core economic indicators for EU countries, for instance, pointed out the existence of what is called the “Norwegian puzzle”, which shows relatively lower innovation rates for Norwegian firms than what would be expected when comparing the Norwegian economy to that of other countries. The results of an experiment conducted by Statistics Norway suggest that this is partially explained by the survey instrument used. The experiment shows that innovation rates are significantly different when captured by a stand‐alone innovation survey versus a combined R&D and innovation survey (Wilhelmsen, 2014). Similarly, an experiment conducted in Flanders region of Belgium shows that innovation rates are considerably and systematically higher when measured with a short questionnaire than when using a long questionnaire (Hoskens, 2015). While results are not clear as to which survey instrument is most accurate with respect to measured innovation rates, they clearly suggest that survey methodology can greatly impact the results.

In relation to context, cultural differences and social desirability there are several factors that can play a relevant role in how respondents understand and respond to innovation questions. Respondents are likely to have different views on what is considered as innovation depending on the context in which they live and operate. People in the USA, for example, are more likely to identify innovation with something new or unique and not to consider improvements as innovation. In Europe, on the contrary, people are more inclined to indicate both novelty and improvement as elements of innovation (Galindo‐Rueda and Van Cruysen, 2015). Furthermore, self‐reported innovation may be biased by social desirability that, in turn, might have a differential effect on respondents from different cultural backgrounds (
degree of selection present in school-based testing. However, current household-based data is limited and not yet comparable across a significant number of countries.

A second limitation regards data availability. While this is the largest learning outcomes database to date, data are still sparse for some countries. This introduces bias if data availability is correlated with education quality or progress. For example, if countries that perform worse have data only in later years (because they were later to introduce assessments), their average score will be likely biased upwards, as the test scores will reflect more recent testing, not stronger performance. Since we provide year-by-year scores this can be accounted for.

Relatedly, when averaging data across subjects, levels and over time, there is a possibility that averages reflect the availability of data rather than learning gains. For example, let’s examine a case where a country has a score of 500 in 2000 in math and jumps to 550 in 2005. If this country added reading in 2005 and scored 450, the average score across subjects in 2005 would be 500, reflecting no learning progress since average scores would be 500 in both years. However, an apples-to-apples comparison in math shows learning grains from 500 to 550. To address this issue, we construct disaggregated measures by subject and schooling levels as well as aggregated ones. This enables analyses at each level considering the trade-offs.

A point of emphasis is that while learning measures human capital better than prior proxies, such as enrollment, learning does not capture the concept of human capital in its totality. Moreover, assessments do not capture only cognitive skills. For example, recent evidence suggests test scores pick up as differential effort as well as cognitive ability (Gneezy, List, Livingston, Sadoff, Qin, and Xu 2017). We use learning outcomes in this paper with these caveats in mind.

# II. DESCRIPTIVE STATISTICS SUPPLEMENT

Several statistics demonstrate the coverage and detail of the database. Supplement Table III presents country-subject-level observations by year. The data are spread over time
NGO<br>and<br>Survey/HH|The Rule of Law Index is a hybrid of expert assessments and<br>household survey data. The expert assessments are scored by<br>locally recruited subject mater experts following a detailed<br>questonnaire subject to centralized review. The surveys<br>natonally representatve household surveys completed for a<br>rotatng sample of countries. A complete descripton of the<br>methodology, including expert and survey questonnaires, is<br>availablehere. The data arepubliclyavailablehere.|
|WCY|Insttute for<br>Management<br>Development|World<br>Compettveness<br>Report Survey|67 economies covering<br>a global sample.|Annual. Used in<br>the WGI since<br>1996.|Survey/Firm|We use data from the executve opinion survey conducted by<br>the World Compettveness Report. Full dataset is available by<br>subscripton. Averages of questons as assigned to six WGI<br>indicators arepubliclyavailable on the WGI website.|
|WMO|S&P Global|Country Risk<br>Service|209 economies<br>covering a global<br>sample.|Quarterly, with<br>real-tme<br>updates.|Expert/CBIP|Expert assessment of multple dimensions of business and<br>regulatory risks scored by S&P global analysts, ofen located in<br>the countries they assess. Data are constructed following a<br>structured methodology and detailed scoring criteria subject<br>to centralized review. An overview of the methodology is<br>availablehere. We use data from the fourth quarter of each<br>calendaryear in the WGI.|
|* Coun<br>reporte<br>Expert<br>househ|try coverage and fr<br>d in Table 1 which<br>assessment conduc<br>old survey;Survey/|equency refer to data<br
table 2 shows, the age profiles of one of our key dependent variables, women working off the family farm, are comparable across castes and in treated versus untreated districts.

# _Program Placement_

In addition to network formation and the program participation decision, another potential source of endogeneity might be that _Mahila Samakhya_ was systematically placed in villages where women have a relatively high level of autonomy and are more likely to respond favorably to the treatment. Using data from the Indian censuses of 1991 and 2001 on village-level female autonomy matched to the year that _Mahila Samakhya_ entered the smallest administrative unit (block), we compare the levels of autonomy in clusters that received _Mahila Samakhya_ with those that did not. The results for t-tests of equality, which are presented in table A1 in the appendix, are not significantly different for the overall sex ratio, 0-6 sex ratio, or the ratios for scheduled castes or tribes. Similarly, the male-to-female literacy ratio and sex composition of the labor force do not vary between treated and untreated clusters.

Another threat to our internal validity may come about if untreated districts do not represent statewide trends and women in these districts are less (or more) empowered than average, implying that program placement was targeted. However, the nationally representative National Family Health Survey-3 [IIPS and ORC Macro, 2007] and District Level Household Survey-3 [Ministry of Health and Family Welfare and International Institute for Population Studies, 2010] show that women in untreated districts in our sample do not differ significantly from those in the rest of the state. For instance, the average age at marriage for Uttarakhandi women is 17
Celade printout 2/87|
|Peru|Jul 81|17005 F|Bank projection from census,|1980-85, Bank assessment;|1980-85, Bank assessment;|
||||adjusted<br>for 4.1X undercount|1985-90, Bank projection|1985-90, Bank projection|
|Philippines|May 80|48098J|Bank projection<br>from census,<br>adjusted<br>for<br>1.9X uidercount|Based on 1986 Contraceptive<br>Prevalence<br>Survey|Based on 1983 National<br>Demographic Survey|
|Pitcairn<br>Island|Dec 85|b F|UNPVSR 4/87|||
|Poland|Dec 78|<br>35061 J|Official est., Statistical<br>Year Book|Based on INED 10/87|Based on UNPVSR 4/87|
|Portugal|Mar 81|98B33 F|Eurostat 1987|Eurostat 1987|Eurostat 1987|
|Puerto Rico|Apr 80|3197 F|UNPVSR 1/88 (official est.)|1980-85, UNPVSR 1984<br>Special Supplement;|1980-85, UNPVSR 1984<br>Special Supplement;|
|||||1985-90, Bank projection|1985-90, Bank projection|
|atar|Mar 86|369 F|U.N. 1988 revision<br>(prelim.)|U.N. 1988 revision<br>(pretim.)|Based on U.N. 1988 revision<br>(prelim.)|
|eunion|Mar 82|516 J|U.N. 1988 revision (prelim.)|U.N. 1988 revision (prelim.)|<br>U.N. 1988 revision (prelim.)|
|omania|Jan 77|<br>21560 F|Bank projection from official<br>1984 est., UNPVSR 4/87|Based on UNPVSR 4/87|Based on UNPVSR 4/87|
# **1 Introduction**

Determining the optimal level and composition of public education spending is a key policy question in many low-income countries. Many education advocates believe that low-income countries will fall short of achieving enrollment and learning goals without substantial increases in public spending on education (UNESCO, 2014); others argue that public sector inefficiencies leave considerable room for improvement within existing education budgets, and that fiscal constraints make it imperative to improve the efficiency of public expenditure (World Bank, 2010). However, the data to assess the relative importance of these contentions remains sparse, in part, due to the difficulty in detecting and measuring inefficiencies in public spending.

In this paper, we study one striking measure of public sector inefficiency - teacher absences - with panel data collected 7 years apart in India at a time of sharp increases in education spending. A large portion of this increase was accounted for by the salary cost of hiring teachers to reduce the student-teacher ratio in public schools. As a policy alternative to hiring more teachers, we show that reducing teacher absences by increasing school monitoring would be ten times more cost effective at reducing _effective_ student-teacher ratio (net of teacher absence). We also find that hiring more teachers _increases_ the teacher absence rate, which further increases the costs of teacher hiring once behavioral responses are adequately accounted for. Thus, while the default approach to improving education in low-income countries is input-augmentation, our results suggest that investing in reducing inefficiencies may yield much greater returns.

India presents a particularly salient setting for our analysis. It has the largest primary education system in the world, catering to over 200 million children. Further, over the past decade, the Government of India has invested heavily in primary education under the Sarva Shiksha Abhiyan (SSA) or “Universal Education Campaign.” Partly financed by a special education tax, this national program sought to correct historical inattention to primary education and led to a substantial increase in annual spending on primary education across several major categories of inputs including school infrastructure, teacher