rafmacalaba/data-use-mentions-extended huggingface dataset:
it both limits local communities’ access to the resources within protected areas and fails to provide local communities with alternative sources of economic opportunity, similar to the local effects of mining concessions [71].

Accordingly, we use the Armed Conflict Location and Event Database [72] to measure the presence of three forms of conflict in and around the protected areas in our sample: violence against civilians (“civilian targeting”), battles, and protests and riots. We define our spatial unit of observation as the area within a protected area’s boundaries plus a 25-kilometer buffer around the protected area’s boundaries. We include these buffer zones in our analysis to capture possible spillover in AP management’s effect on conflict. We use relatively small buffers—in comparison to research investigating the spillover effects of climatic shocks on conflict [73]—because we focus on local security conditions. Given the scale of the mechanisms described above, we hesitate to attribute distant changes in conflict to AP management. SI A.6.2 discusses this decision in greater detail and reports a robustness check where we recompile our results using smaller and larger buffers (Figure S18).

We find suggestive and concerning evidence that AP makes civilian targeting more likely in and around the protected areas it comes to manage (Figure 3, Row 6). The probability of any civilian targeting occurring in AP-managed protected areas increases by 7.7 percentage points post-transference. This estimate represents a 47.2% increase in the presence of civilian targeting relative to the control mean, and in the 10
- [18] Harding, D. and A. R. Pagan (2002). “Dissecting the Cycle: A Methodological Investigation,” Journal of Monetary Economics, 49, 365-381.

- [19] Huidrom, R., A. Kose, and F. Ohnsorge (2016). “Having Space and Using It: Fiscal Policy Challenges in Developing Economies,”CEPR Working Paper forthcoming.

- [20] Ilzetzki, E. O., E. G. Mendoza, and C. A. Vegh (2013). “How Big (Small?) Are Fiscal Multipliers?”Journal of Monetary Economics, 60, 239-254.

- [21] Kraay, A. C. (2012). “How Large Is the Government Spending Multiplier? Evidence from World Bank Lending,”Quarterly Journal of Economics, 127, 829-887.

- [22] Mineshima, A., M. Poplawski-Ribeiro, and A. Weber (2014). “Size of Fiscal Multipliers,” in Post-Crisis Fiscal Policy, ed. by C. Cottarelli, P. Gerson, and A. Senjadji, Cambridge, MA: MIT Press, 315-372.

- [23] Nickel, C. and A. Tudyka (2014). “Fiscal Stimulus in Times of High Debt: Reconsidering Multipliers and Twin Deficits,”Journal of Money, Credit, and Banking, 46(7), 1313-1344.

- [24] Owyang, M. T., V. A. Ramey, and S. Zubairy (2013). “Are Government Spending Multipliers Greater during Periods of Slack? Evidence from Twentieth-Century Historical Data,” American Economic Review, 103, 129-134.

- [25] Perotti, R. (1999). “Fiscal Policy in Good Times and Bad,”Quarterly Journal of Economics, 114, 1399-1436.

- [26] – – —(2005). “Estimating the Effects of Fiscal Policy in OECD Countries,”CEPR Discussion Paper 4842.

- [27
We construct the prices at quarterly-IRI market-store-barcode level by summing the total value of products sold in a particular quarter in a particular IRI market in that store and then dividing that by the total quantity sold of that product for that barcode/UPC in that store. Table OA1 in the appendix shows that the average IRI market sold about 13,255 unique UPCs per quarter across 52 stores, and the average unit value of the UPC was about 5 USD.

> 3Also see Greenstone and Moretti (2003) for the first use of this identification methodology. Greenstone et al. (2010), and Kim (2020) follow the same methods.

> 4For example, we have number of deals ranging from 19-36 depending on the outcome under study. For the retail inflation, we have data on 19 deals. We had to drop some deals for which we have missing information on either the winning or losing county or for which we do not observe the IRI market in the IRI data. The IRI market is a collection of geographically close counties. So, for example, a mix of counties in Maryland, DC, and Virginia all fall under the “BALTIMORE/WASHNGTN” IRI market. Overall, it covers a total of 968 counties, aggregated at 47-48 IRI markets.

> 5IRI set its market definitions in 1987 to achieve a representative sample of U.S. consumers. The data is shared by IRI at this market level in order to protect store identity. These broader geographic units are also able to account for the fact that consumers would sometimes shop in nearby counties outside their own counties.

18
ECCAS’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Figure 2.5. Time required to import goods by road through alternate gateways***

# **a. Total time (hours)**

<!-- Start of picture text --> 2000<br>1500<br>1000<br>500<br>0<br>Port Transport Border Administrative<br>b. By step (% of total time)<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Douala-Ndjamena Douala-Bangui Pointe Noire to<br>Brazzaville to Bangui<br>Port Transport Border Administrative<br>Share of time for importing goods<br>Bangui<br>Time (hours) for importing goods<br>Douala-Ndjamena Douala-Bangui Pointe Noire to Brazzaville to<br><!-- End of picture text -->

# **b. By step (% of total time)**

_Source:_ Data collected from World Bank 2008; AICD ports database; Teravaninthorn and Raballand 2009. _Note:_ Ports data are based on indicators from 2006–07.

16
# **Table A9. Descriptive Statistics for Programs in Ecuador** **_Using Program-Level Data from the WBSCPS and Administrative Sources_**

|**Variable**|**Progr**<br>**match**<br>**individ**<br>**Mean**<br>**(1)**|**ams not**<br>**ed with**<br>**ual data**<br>**Std.Dev.**<br>**(2)**|**Program**<br>**with in**<br>**d**<br>**Mean**<br>**(3)**|**s matched**<br>**dividual**<br>**ata**<br>**Std.Dev.**<br>**(4)**|**P-value**<br>**diff.**<br>**(1) – (3)**<br>**(5)**|
|---|---|---|---|---|---|
|**Panel A. Programquality determinants**||||||
|_Infrastructure_||||||
|Program offers at least one online class|0.37|0.48|0.35|0.48|0.78|
|>30% classes can be taken online|0.25|0.44|0.32|0.47|0.27|
|Number of workshops or labs available for practice|3.14|2.64|3.99|3.63|**0.03*
6 some of these differences (see Oster, 2005), the dominant view is that this unusual discrepancy reflects son-preference in these societies, implemented through selective abortion and post-natal care (see Sen,1990, and Klasen and Wink, 2003).

Opportunities continue to depend on morally irrelevant, predetermined circumstances even if you survive your first year. Access to basic health care, such as immunization services, is strongly correlated with parental wealth (Figure 2). Even a child’s cognitive skills seem to develop at different rates depending on family background, for children as young as 3-5 years old. Figure 3, drawn from Paxson and Schady (2004), shows the evolution of vocabulary recognition test scores (TVIP) for two groups of children from Ecuador: those whose parents have 0-5 years of schooling, and those whose parents have 12 or more years of schooling. By the time these children enter primary school, at age 6, they have markedly different learning abilities, shaped in large part by differential family backgrounds.

**Figure 2 Access to childhood immunization services depends on parent’s economic status**

<!-- Start of picture text --> 70<br>60<br>50<br>Poorest<br>40<br>30<br>20<br>10<br>Wealthiest<br>0<br>EgyptJordan (*)ColombiaRwandaSouth AfricaPeru KenyaMalawiZambia (*)BrazilVietnamTurkeyGuatemalaTanzaniaIndonesisiaTurkmenitan (*)MoroccoGhanaPhilippinasBeninBangladeshComorosBoliviaKazakhstan (*)ParaguayBurkina FasoYemenCameroonUgandaMauritaniaIndiaCentral African RepublicHaitiTogoEthiopiaMadagascarMozambiqueGuineaCambodiaMaliPakistanEritreaNigerChad<br>Percentage not covered<br><!-- End of picture text --> _Source:_ World Development Report 2006, from Demographic Health Survey (DHS) data. _Note:_ The continuous dark line represents the percentage of children without access to a basic immunization package in each country, while the endpoints of the whiskers indicate the percentages for the top and the bottom quintile of the asset ownership distribution.

* Indicates that the poorest quintile have higher access to childhood immunization services than the wealthiest
if yes, would like title|0.543|0.495|0.713|0.825|0.602|0.590|0.282|0.272|0.568|0.484|
|... if yes, mean WTP (ZMK)|754.01|666.53|968.14|949.21|756.39|521.01|556.61|464.37|803.84|613.83|
|... if yes, median WTP (ZMK)|350|250|500|500|150|200|350|100|350|200|
|Tenure perceived insecure|0.428|0.450|0.372|0.426|0.428|0.713|0.209|0.110|0.450|0.377|
|Tenure perceived very insecure|0.262|0.280|0.219|0.241|0.302|0.486|0.080|0.056|0.278|0.227|
|No. of sample households|4,888|3,703|1185|491|525|761|491|566|3,455|1,433|
|Population size|1,709,593|1,214,953|494,640|184,147|167,765|327,931|150,558|251,872|1,186,526|523,067|

_Source_ : Own computation from 2017/18 Quarterly Labor Force Survey conducted by Zambia’s Central Statistical Office. Sampling weights adjusted with non-response probabilities are used to estimate the mean values.

_Note_ : Questions on area and gender-differentiated transfer rights were administered only in quarters 3 and 4 (to a total of 2,637 households).

*The sum does not add up to one as households own
A recent comprehensive case study of the effects of Intel’s investment in Costa Rica by Larrain et. al. (2000) finds evidence that local suppliers benefited substantially from Intel’s investment. Similar evidence exists for other sectors and countries and such evidence is discussed in great detail in Moran (1998 and 2001). For example, in the electronics sector, Moran (2001) notes that in Malaysia, foreign investors helped their local subcontractors keep pace with modern technologies by assigning technicians to the suppliers' plants to help set up and supervise large-volume automated production and testing procedures. In a broader study, Batra and Tan (2002) use data from Malaysia’s manufacturing sector to study the effect of multinationals on inter-firm linkages and productivity growth during 1985-1995. Their results show that not only are foreign firms more involved in inter-firm linkages than domestic firms but also that such linkages are associated with technology transfer to local suppliers. Such technology transfers were found to have occurred through worker training and the transmission of knowledge that helped local suppliers improve the quality and timeliness of supply.<sup>13</sup> Javorcik (2004) examines backward linkages and technology spillovers using data from the Lithuanian manufacturing sector during the period 1996-2000. She finds that firm productivity is positively affected by a sector’s intensity of contacts with multinational customers but not by the presence of multinationals in the same industry. Thus, her results support vertical spillovers from FDI but not horizontal ones. Furthermore, she finds that vertical spillovers are realized only when the technological gap between domestic and foreign firms is moderate. Blalock (2001) uses a panel dataset from Indonesian manufacturing establishments to check for the same effects. He finds strong evidence of a positive impact of FDI on productivity growth of local suppliers showing that technology transfer from multinationals indeed takes place. He also plausibly suggests that since multinationals tend to source inputs that require simple technologies relative to the final products they produce, local firms that manufacture such intermediates may be in a better position to learn from
# **Are There Jobs for Everyone?**

# **An Analysis of the Relationship between the Employment of Older and Younger Persons in Indonesia**

# Amanina Abdur Rahman<sup>♦</sup>

_JEL Classification: J14, J21, J26_ _Keywords: aging, employment, youth employment_

# **1 Introduction**

**As a result of improvements in life expectancy and a reduction in fertility rates, Indonesia has passed the threshold of an aging society by international standards.** Based on the United Nations World Population Prospects 2022, in 2023, 7 percent of Indonesia’s population is estimated to be aged 65 or older (see Figure 1). This makes Indonesia an aging society by international standards (World Bank 2016).<sup>1,2</sup> By 2050, 15 percent of Indonesia’s population is estimated to be aged 65 or older, thus making it an aged society by international standards. At the same time, Indonesia’s life expectancy has been gradually rising in recent decades. In 1990, life expectancy at age 65 was 12.7

>  The author thanks Sara Giannozzi, Anastasiya Denisova, Robert Palacios, and Abror Tegar Pradana for critical inputs.

> 1 An ageing society is one with 7 to 14 percent of people aged 65 and older as a share of the total population. An aged society is one with 15 to 20 percent of people aged 65 and older as a share of the population. 2 Indonesia defines an older person as one who is aged 60 or older.

1
.

Unsurprisingly, few non-OECD countries make it into the top-25. Taiwan (China) is the highest-ranking non-OECD territory, the next being China and S Africa. Table 10 shows the top 25 institutions on the _h_ -index from non-OECD countries. U Cape Town comes out top on publications, but the National Taiwan U comes out top on total citations, the _h_ -index and the _I_<sup>3</sup> index with  =0.5. Taiwan (China) and S Africa both have four institutions in the top-25, while China and India both have three, two of China‘s being in Hong Kong SAR, China.

Table 11 shows the topical distribution of the top four countries‘ top-50 publications, along with the (normalized) Herfindahl index computed as in Table 8. The United Kingdom is the most specialized country, focusing heavily on the topic ̳Economic evaluation‘. The
all models; full results are in the Appendix.

Contrary to expectations, I find that female household share is not significantly correlated with personal attitudes. That is, respondents who report living in homes where women are responsible for a higher share of household tasks do not report more conservative personal attitudes, on average, than those living in households where women are responsible for a lower share of tasks. However, FHS is negatively correlated with the pooled perceived norms score ( _p <_ 0 _._ 05). Respondents in homes where women are responsible for a higher share of household tasks perceive other Moroccans’ attitudes as more conservative, compared to respondents where women are responsible for a lower share of tasks. This relationship is driven by respondent perceptions of men’s attitudes, which are negatively correlated with FHS ( _p <_ 0 _._ 01). FHS is not associated with respondents’ perceptions of women’s attitudes or their neighbors’ attitudes. The same relationship holds when the future perceived norms scores are used to predict current FHS (see Appendix 17
mechanisms as indiscriminate protection tools, since their application has affected less than 1% of the country’s total imports (see Table 12 below).

**Table 12 – Estimation of Mexican Total Trade Affected by Antidumping or Safeguard Measures, 2002**

|**Countries**|**Total imports per**<br>**country of origin**<br>**(USD million)**|**Imports of items**<br>**subject to**<br>**antidumping duties**<br>**(USD million)**|**Percentage of imports**<br>**from the country of**<br>**origin**|
|---|---|---|---|
|1. China|6,274|600.44|9.5697%|
|2. Ukraine|83|1.68|2.0078%|
|3. HongKong|509|7.74|1.5201%|
|4. Russia|236|2.18|0.9239%|
|5. United States|106,557|890.25|0.8355%|
|6. Denmark|177|0.74|0.4155%|
|7. Taiwan|4,250|8.07|0.1899%|
|8. Korea|3,910|0.97|0.0248%|
|9. Brazil|2,565|0.05|0.0018%|
|10. Japan|9,349|0.12|0.0013%|
|+ 17 investigated countries||No duties in en 2002|-|
|Mexico’s total imports|168,679|1.512|0.8965%|

Source: World Trade Atlas and 2002 Antidumping Agreement.
application process, large firms are more likely to be able to take advantage of these benefits, whereas they can be too confusing or complex for smaller firm to apply.<sup>52</sup> They can also introduce discretionary layers and loopholes into the tax system as a whole, and increase government administrative costs.<sup>53</sup> In low‐income countries, tax incentives generally rank low in investment climate surveys, and are often found to be redundant, meaning that the investment or activity would have been undertaken even without them.<sup>5455</sup>

# **Design considerations**

Schemes involving tax incentives should consider:

- **Incentivizing firms’ behavior or activities when lack of action is** **_not_ primarily due to immediate financial constraints** . If failure to hire or promote women is simply due to short‐term financial constraints (e.g. if firms are not able to financially invest in policies conducive to female employment, such as childcare and parental leave), grants or subsidies may offer a more responsive solution.

- **Adopting performance‐based incentives** that directly tie the gender‐related objective to the provision of the incentive (e.g. by providing allowances based on female employment levels) rather than using blanket exemptions or reductions.

- **Designing for maximum clarity and transparency** to promote accountability and reduce opportunities for rent seeking and corruption. Tax incentives should be transparent from a legal (i.e. having a foundation in tax laws), economic (i.e. costs and benefits clearly laid out), and administrative (i.e. criteria should be clear, simple, and objective) standpoint.

- **Setting up time‐limited programs** . It is important that the incentives are sustainable and reflect market conditions. Applying sunset provisions creates the need to regularly evaluate whether the incentive should be continued or not. Making incentives time‐limited also
22 For the MICs, the teacher survey from McKinsey (2020) reports reach _×_ effectiveness rate = 54% for China. Given an effective closure rate of 43.5%, the loss is about 20%. Likewise, children were 1.4 months behind by November 2020, also a loss of 20%. In India, another large MIC, the ASER (2020, 2021) surveys suggest students were about 50% behind in 2020. Given an effective closure rate of 77%, reach _×_ effectiveness = 35%. Since India is likely more representative of the MIC group’s ability to provide effective remote learning than China, and since no improvement has been observed based on descriptive evidence, we assume reach*effectiveness = 35% for MICs in both years.

Finally, for the LICs we have reach*effectiveness = 25% based on the ministries’ data. Since we use 40-45% for HICs, 35% for MICs, we arbitrarily use 20% for LICs. **Computing the loss** _ν_ **.** The disruption to school was greatest in the MICs (33% of two years of education), due to their higher effective school closure rate (see Table 2). Schools were then slightly more open in the LICs, but reach*effectiveness was much higher in HICs, making the shock less severe in HICs (21% vs. 28% for LICs). **Employment Shock** The ILO (2020) reports that the peak loss in total hours worked (from unemployment, inactivity and reduced hours) across the HICs, MICs and LICs are 15 _._ 8, 20 _._ 9 and 12 _._ 4 percent respectively. However, the peak is not indicative of the impact of the shock over the entire year. Instead, we assume a loss equal to the average over 2020, i.e. 8 _._ 3, 9 _._ 5 and 6
accumulation. Monticone (2010) uses data from Italy to show that wealth has a small but positive effect on financial literacy. Behrman et al. (2010) use an instrumental variables regression analysis to show that financial literacy also has a causal effect on wealth accumulation in the U.S. Jappelli and Padula (2011) estimate an intertemporal model of investment in financial literacy, which it is shown that financial literacy and wealth are jointly determined and correlated over the life cycle. Meanwhile, Hastings and Mitchell (2011) provide experimental evidence from Chile to show that financial literacy is correlated with wealth, but that measures of impatience might be a more important determinant. Behrman et al. (2010) focuses on the relationship between financial literacy, education, and household wealth accumulation in Chile. Using a nationally representative data set, they find that financial literacy has even stronger effect than educational attainment on wealth.

One cross-country study of note which addresses the effects of both education and income on financial literacy is Jappelli (2010), which analyzes data from a survey of 55 countries. The study shows that economic and financial literacy (as perceived by each country’s business leaders) is positively associated with human capital indicators (math and science test scores and college attendance), but negatively associated with living in a country with a generous social security system. The study hypothesizes that this is because the relative lack of opportunity for private capital accumulation decreases incentives to acquire financial literacy.

# **_Geographic and racial/ethnic disparities in financial literacy are common_**

Most country surveys indicate strong regional disparities in financial literacy, as measured by awareness of financial terms and institutions, particularly between urban and rural areas. This likely mirrors the differences in access to finance, and is especially prominent in developing countries. For instance, in Ghana, 52 percent of urban adults have commercial bank accounts, versus just 21 percent of rural adults. While rural adults are more likely to use informal financial products, this usage only partially close the
support convergence (Ministry of Finance 2017). Unlike previous research, a recent study using the intensity of nighttime light to proxy for income found evidence of convergence across Indian states, districts, and cities (Tewari and Godfrey 2016).

In this paper, we take a fresh look into the Indian convergence debate building on both the growth and the urban economics literatures. But we depart from previous studies in three important ways:

- We assess growth patterns across districts and across places below the district level. Some Indian states are bigger than all but a few countries in the world. Taking the state as the spatial unit of analysis can hide substantial heterogeneity in local growth patterns.

- We rely on household consumption expenditure per capita to measure living standards. This indicator, estimated on the basis of household expenditure surveys, provides greater spatial granularity than GDP per capita, while still being highly correlated with it.

- We use a Bayesian model averaging approach to identify the best predictors of local economic growth. Compared to the classical econometric approach, this allows us to better address the uncertainty on the true model of the economy resulting from the many potential predictors.

Our results reveal that there is growth divergence at the state level, but strong convergence when districts or places below district level are considered. This finding runs against the growth divergence consensus but is in line with more indirect evidence provided by nonmonetary indicators of well‐being and by nighttime light intensity. Growth is fastest in the midrange between purely rural places and major urban centers. But the best‐performing locations do equally well in high‐ and low‐income states. It is just that there are not many top performers among the latter.