rafmacalaba/datause-encoder-data huggingface dataset:
the case for those countries farthest away from the technological frontier, where the effect on employment from generating new sales due to innovation is largest. On the other hand, for higher income countries, the additionality impact on employment mirrors that of lower income countries with low additionally given their greater ability to generate productivity gains in new products.

More work is, however, needed to better understand the short-run impact of innovation on the skill composition of the firm and the effect of the different types of innovation combined. While innovation is likely to be skill biased, we know very little about the extent of potential skill displacement within the firm, on the job learning and more generally what is the impact

# **References**

- AGHION, P., AND P. HOWITT (1992): “A Model of Growth through Creative Destruction,” _Econometrica_ , 60(2), 323–51.

- ARRIGHETTI, A., AND M. VIVARELLI (1999): “The Role of Innovation in the Postentry Performance of New Small Firms: Evidence from Italy,” _Southern Economic Journal_ , 65(4), 927–939.

- AUTOR, D. H., AND D. DORN (2013): “The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market,” _American Economic Review_ , 103(5), 1553–97.

- BENAVENTE, J. M., AND R. LAUTERBACH (2008): “Technological innovation and employment: complements or substitutes?,” _The European Journal of Development Research_ , 20(2), 318–329.

- BROWN, J., J. EARLE, AND D. LUP (2005): “What Makes Small Firms Grow? Finance, Human Capital, Technical Assistance, and the Business Environment in Romania,” _Economic Development and Cultural Change_ , 54(1), 33–70.

- BRYNJOLFSSON, E., AND A. MCAFEE (2012): _Race Against the Machine: How the Digital Revolution is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy_ . Digital Frontier Press.

- CASTILLO, V., A. MAFFIOLI, S. ROJO, AND R. STUCCHI (2014): “The effect of innovation policy on SMEs employment and wages in Argentina,” _Small Business Economics_ , 42(2), 387–406.

- DACHS, B., AND B. PETERS (2014): “Innovation, employment growth, and foreign ownership of firms,” _Research Policy_ , 43(1), 214–232.

23
Table 14: Heterogeneous effects of savings default on net deposits, within two weeks of transfer (+14 days)

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Panel A:|Male<br>HH head|Any previous<br>bank use|Close to<br>branch|A<br>Assets|bove median:<br>Patience|
|Savings Default|-426.248<br>(1460.488)|-690.794<br>(2951.815)|2929.975**<br>(1028.894)|1432.075<br>(2067.610)|1831.533**<br>(892.766)|
|Observations|312|153|234|227|191|
|Mean of dependent variable in control group|3429.71|5637.63|2399.13|3732.18|1583.10|
|R-squared|0.68|0.71|0.89|0.68|0.11|
|Panel B:|Female<br>HH head|No previous<br>bank use|Far from<br>branch|B<br>Assets|elow median:<br>Patience|
|Savings Default|4323.073***|2150.497**|631.954|1443.579**|1603.971|
||(1269.949)|(720.566)|(921.983)|(698.556)|(1673.170)|
|Observations|162|321|240|247|283|
|Mean of dependent variable in control group|1431.28|1252.17|2981.41|1640.43|3402.98|
|R-squared|0.18|0.07|0.68|0.11|0.67|
|P-value: equal efect of savings default|0.014|0.230|0.088|0.995|0.882|

The top panel includes respondents from female-headed households, those with above-median values for number of transactions at NBS in the 90 days preceding the transfer, those who live above the median distance from the bank branch, those with above-median values of a principle-index of asset ownership, and those with above-median patience, as measured at baseline by amount required to prefer a transfer in one month instead of immediately. P-values are from tests of equality of Savings Default coefficients in Panels A and B. All specifications include village and week-of-first-survey fixed effects, and the value of the outcome measured at survey 1. Exchange rate: MK 420 per USD. Robust standard errors in parentheses. * <u>p</u> _<_ 0.10, ** <u>p</u> _<_ 0.05, *** <u>p</u> _<_ 0.01 40
30 Table 8: Balancing tests with households in non-NCMS counties as ‘untreated’

||Before m|atching|After matchi|ng w/ PS|After matching|w/ kernel|
|---|---|---|---|---|---|---|
||Diff|t-stat|Diff|t-stat|Diff|t-stat|
|Head is rural wage laborer (omitted category = professional worker)|0.232|20.61|0.230|18.02|0.256|22.11|
|Head is farmer (omitted category = professional worker)|-0.534|-44.88|-0.537|-41.88|-0.524|-42.55|
|Head is retired (omitted category = professional worker)|0.162|13.89|0.189|17.65|0.171|14.63|
|Head is employee (omitted category = professional worker)|0.235|21.32|0.245|23.53|0.235|22.32|
|Head has primary education (omitted = illiterate or semi-literate)|-0.373|-28.90|-0.291|-18.61|-0.364|-24.38|
|Head attained junior high school (omitted = illiterate or semi-literate)|0.111|8.53|0.076|4.84|0.104|7.18|
|Head attained high or vocational school (omitted = illiterate or semi-literate)|0.242|18.83|0.202|13.54|0.221|15.99|
|Head has college education (omitted = illiterate or semi-literate)|0.104|8.08|0.100|6.68|0.095|6.68|
|Distance to closest facility category 2|0.250|19.14|0.179|11.56|0.230|16.90|
|Distance to closest facility category 3|-0.008|-0.65|-0.032|-2.05|-0.001|-0.10|
|Distance to closest facility category 4|-0.080|-6.23|-0.209|-11.05|-0.097|-7.18|
|Distance to closest facility category 5|-0.380|-26.63|-0.319|-18.80|-0.410|-22.81|
|Distance to closest facility category 6|-0.406|-31.54|-0.516|-27.13|-0.303|-23.11|
|Central China (omitted category is East)|-0.013|-1.04|0.046|3.17|-0.109|-7.05|
|Western China (omitted category is East)|-0.687|-55.47|-0.824|-58.46|-0.613|-43.84|
|Household per capita income|0.689|65.12|0.676|61.67|0.660|65.32|
|Head's gender|-0.082|-6.35|-0.123|-8.44|-0.100|-7.11|
|Share of household members chronically sick|0.187|14.77|0.210|15.08|0.210|15.32|
|Share of household members with health fair or bad|-0.048|-3.78|-0.043|-3.04|-0.042|-2.93|
|Household size|-0.358|-27.49|-0.515|-34.58|-0.217|-16.94|
|Ethnic minority household|0.689|52.68|0.840|44.90|0.558|39.32|
|Share of household members over 65|0.085|6.77|0.113|8.48|0.090|6.37|
|Share of household members under 10|-0.160|-12.27|-0.176|-11.25|-0.127|-8.47|
|_Dibao_household or classified as poor (_pinkun_)|-0.102|-7.63|-0.053|-3.28|-0.069|-4.66|
|Received safety net payments last year|-0.070|-7.11|-0.090|-8.42|-0.007|-0.98|
|Someone in household enrolled with GIS or LIS|0.092|7.44|0.152|11.75|0.090|6.44|
|GDP per capita in county|1.205|111.56|1.201|120.04|1.188|118.86|
|_Average (absolute difference)_|0.281||0.303||0.263||
- 65 -

Table 3 Bolivia Monthl Rate of Growth (Percentages)

|||Consumer<br>Free Market<br>Price Indexl/<br>ExchangeRate-|Moneta<br>y<br>**B-se**|
|---|---|---|---|
|September|1984|<br>37.33<br>94.44|15.12|
|October||59.13<br>11.11|27.14|
|November||31.56<br>21.47|25.52|
|December||60.88<br>32.74|124.22|
|January|1985|<br>68.76<br>197.84|36.63|
|February||182.77<br>93.25|39.50|
|March||24.94<br>-9.19|42.51|
|April||11.78<br>30.66|43.50|
|May||35.67<br>62.68|64.93|
|June||78.46<br>76.87|32.83|
|July||66.30<br>83.80|57.72|
|August||66.46<br>33.52|69.85|
|||Beginning of Stabilization<br>Program||
|September||56.51<br>-8.02|46.07|
|October||-1.86<br>3.01|15.04|
|November||3.20<br>22.01|-.36|
|December||16.80<br>25.55|48.39|
|January|1986|<br>32.96<br>30.56|-10.35|
|February||7.95<br>-14.43|11.67|
|March||.07<br>2.39|4.13|
|April||3.59<br>-1.76|8.57|
|May||.97<br>1.88|10.53|
|June||4.26<br>-.90|2.93|
|July||1.76<br>-.69|6.91|
|August||.66<br>.06|-.07|
|September||2.28<br>-.52|9.06|
|October||.58<br>.57|9.19|
|November||-.11<br>.14|10.36|
|December||.65<br>6.1|8.25|

# Source: Morales (1987.b)

/a Month to month changes of monthly average levels.

/b End of month to end of month changes.
# <u>3.1 Background about COVID-19 and vaccination rates in PNG</u>

PNG is the second largest fragile state in East Asia and the Pacific (after Myanmar) with a population of almost 10 million people, over a third of which live below the extreme poverty line (World Bank, 2021). The country is diverse, with over 800 languages spoken, and large pockets of the population are geographically isolated due to mountainous terrain and remote islands (Filer and Wood, 2021). On average, adults have completed less than five years of schooling, which is one of the reasons that PNG ranks in the bottom 20 percent of countries globally in terms of the United Nations Human Development Index (United Nations Development Programme, 2020).<sup>3</sup> Provision of public services, such as access to basic health care facilities, is poor (Howes et al., 2014). PNG also has among the lowest vaccination rates in the world for a range of diseases, which contributed to a polio outbreak in the country in 2018 (Howes and Mambon, 2021; Morgan et al., 2020). In addition, vaccination rates have fallen considerably in recent years (Howes and Mambon, 2021).

Since the start of 2021, the COVID-19 situation in PNG has dramatically deteriorated. The cumulative total of confirmed cases grew from 834 and 9 related deaths on January 1 to 17,775 confirmed cases and 192 COVID-19 related deaths by August 2. Most of these cases were reported between the end of February and start of June (see Figure 2). The actual case numbers are likely to have been much higher due to low overall testing, underreporting and limited contact tracing capacity (World Health Organization, 2021). PNG’s Prime Minister, James Marape, claimed that one in three or four Papua New Guineans may have been infected with COVID-19 in March (McGuirk, 2021). Although the epidemiological curve of confirmed COVID-19 cases began to flatten throughout July and August, it is unclear the extent to which this is simply due to limited testing (around a third of COVID-19 tests are positive), and it is believed that community transmission remains widespread in much of PNG (World Health Organization, 2021).

**Figure 2 – COVID-19 cases and Vaccinations in PNG in 2021**

<!-- Start of picture text --> 2500 350<br>300<br>2000<br>250<br>daily vaccines smoothed<br>1500<br>200<br>new cases smoothed<br>150<br>1000<br>100<br>500<br>50<br>0 0<br>01-Jan-21 01-Feb-21 01-Mar-21 01-Apr-21 01-May-21 01-Jun-21 01-Jul-21 01-Aug-21<br>daily confirmed cases<br>daily vaccines administered<br><!-- End of picture text -->

> 3 There were only four countries outside Sub-Saharan Africa with a lower Human Development Index ranking in 2018 than PNG (the Syrian Arab Republic, Afghanistan, the Republic of Yemen, and Haiti), three of which were undergoing severe conflict.

8
Figure A3: Average effects across chambers and on aggregate <!-- Start of picture text --> A. Duration of pre-trial hearings (in days) B. Likelihood of pre-trial completion in 4 months<br>Combined Combined<br>1st com 1st com<br>2nd com 2nd com<br>3rd com 3rd com<br>1st civ 1st civ<br>3rd civ 3rd civ<br>-150 -100 -50 0 50 0 .2 .4 .6 .8<br><!-- End of picture text --> Notes: 4<sup>_th_</sup> commercial chamber not displayed as it has too few post-adjustment period observations (see Figure A1).

49
heterogeneity at the individual level within disaggregated geographic areas, while assuming that the endogeneity problem between areas can be addressed through controlling for geographic observables influencing infrastructure placement.

We apply our method to nationally representative survey data for rural areas across several countries where water access and women's time burdens for collecting water have been highlighted as important policy issues. We examine impacts of water access on women’s off-farm work. In addition, we also look for signs of intra-household responses. As mentioned, easier access to water may result in a reallocation of domestic chores that allows children to attend school. We examine this question and then turn to the potential impacts on child health as measured by anthropometric outcomes. Although easier access to water does not ensure improved water quality, health may be indirectly affected. A reduction in the price of water should increase its consumption with potential beneficial effects. Moreover, women’s freed up time may be devoted to child nutrition and health needs, including visits to health centers.

The following section briefly reviews the literature. Section 3 proposes a simple model of time allocation that clarifies what the theory predicts. Our proposed approach to testing these issues is outlined in Section 4. Section 5 discusses the data and Section 6 our results. A final section concludes.

# **2. Literature review**

Resource constraints in rural areas, including household burdens for collecting water and fuel, have been longstanding policy concerns for developing countries. Women, in particular, often shoulder a large share of this burden, particularly for collecting water. Based on data for 18 African countries, a recent UN report shows that women are five times more likely than men to collect drinking water for the household (UNICEF and WHO, 2008). Studies also indicate that water collection is borne primarily by women in South Asia,<sup>1</sup> and in countries across North Africa and the Middle East, including Morocco

> 1 See World Bank (2005a), for evidence for Pakistan; Loughran and Pritchett (1997) for evidence for Nepal; and National Commission for Women, India (2005).

4
Global Investment Costs for Coastal Defense through the 21st Century Report to the World Bank 52 be useful to distinguish between the cost components of unit costs of sea dikes that are constant independent of the size of a dike increment, and the cost components that are not. Without this distinction, unit cost estimates may be of less use to inform coastal adaptation decisions, and they may reduce the accuracy of cost estimates at the global (or smaller) level.

# **A2.6 References**

Delcan Corporation 2009. Cost of adaptation - Sea dikes & alternative strategies. Final report EB3762. (British Columbia Ministry of Forests, Lands and Natural Resource Operations, Vancouver).

Dijkman, J., 2007. A Dutch perspective on coastal Louisiana: Flood risk reduction and landscape stabilization. (United States Army: International Research Office of the US Army, London).

Dronkers, J., Gilbert,J.T.E., Butler, L.W., Carey, J.J., Campbell, J., James, E., McKenzie, C. , Misdorp, R., Quin, N., Ries, K.L., Schroder, P.C., Spradley, J.R. , Titus, J.G., Vallianos, L., von Dadelszen, J., 1990. Strategies for adaptation to sea level rise. Report of the IPCC Coastal Zone Management Subgroup: Intergovernmental Panel on Climate Change. (Intergovernmental Panel on Climate Change, Geneva).

Eijgenraam, C., Brekelmans, R., den Hertog, D., Roos, K., 2016. Optimal strategies for flood prevention. Management Science.

Environment Agency 2008. Feasibility of upgrading fixed defences. Report and Appendices. (Environment Agency, TE2100 Project Thames Barrier, London).

Hoozemans, F.M.J., Marchand, M., Pennekamp, H.A., 1993. Sea level rise: A global vulnerability assessment. Vulnerability assessments for population, coastal wetlands and rice production on a global scale. (Delft Hydraulics / Rijkswaterstaat, The Hague).

Jonkman, S.N., Hillen, M.M., Nicholls, R.J., Kanning, W., van Ledden, M., 2013. Costs of Adapting Coastal Defences to Sea-Level Rise— New Estimates and Their Implications. Journal of Coastal Research 1212–1226. doi:10.2112/JCOASTRES-D-12-00230.1 Kok, M., Jonkman, B., Kanning, W., Rijcken, T., Stijnen, J., 2008. Toekomst voor het Nederlandse polder concept (in Dutch). (TU-Delft / HKV Lijn In Water / Royal Haskoning).

Lenk, S., Rybski, D., Heidrich, O., Dawson, R.J., Kropp, J.P., 2016. Costs of sea dikes – regressions and uncertainty estimates. Nat. Hazards Earth Syst. Sci. Discuss. 2016, 1-21.

Linham, M.M., Green, C.H., Nicholls, R.J., 2010. Costs of adaptation to the effects of climate in the world’s largest port cities. (AVOID programme).
- [29] Jones, B. and Olken, B. (2010). "Climate shocks and exports." American Economic Review, 99: 454-459.

- [30] O’Hare, M. and Kammen, D. (2008). "Memorandum on Land Use Change and the Global Food System.", UC Berkeley.

- [31] Paarlberg, P. L. and Abbott, P.C. (1986). "Oligopolistic Behavior by Public Agencies in International Trade: The World Wheat Market." American Journal of Agricultural Economics, 68: 528-42.

- [32] Pick, D.H. and Park, T.H. (1991). "The Competitive Structure of U.S. Agricultural Exports." American Journal of Agricultural Economics, 73: 133-141.

- [33] Sarris, A. H. and Freebairn, J. (1983). "Endogenous Price Policies and International Wheat Prices." American Journal of Agricultural Economics, 65: 214-24.

- [34] Smith, A. (1776). An Inquiry into the Nature and Causes of the Wealth of Nations. Methuen & Co., Ltd.

- [35] Spencer, B. and Brander, J. (1983). "International R&D rivalry and industrial strategy." Review of Economic Studies, 50: 707–22.

24
# **1 Introduction**

Regulations toward the liberalization of global capital flows have been debated, which reflects the concerns about the loss of national sovereign rights and other potentially negative consequences of free capital movements. Foreign direct investment (FDI), more than other types of capital flows, has historically sparked such concerns because it often involves a controlling stake by large multinational corporations over which domestic authorities, it is feared, have little control. For these reasons, governments have sometimes imposed restrictions on inward FDI. On the other hand, reforms that facilitate foreign direct investment entry and operations appear to have a significant and sizable impact on the level of inward FDI, whereas anti-competitive product market regulations are negatively associated with investment (Mistura and Roulet, 2019).

Before the reforms analyzed in this paper, Indonesia had the third highest restrictions on FDI among the high- and middle-income countries surveyed by the Organisation for Economic Cooperation and Development (OECD) and was the second most restrictive behind all countries in the East Asia region next to the Philippines (World Bank, 2018). Indonesia’s negative investment list ( _Daftar Negatif Investasi_ - DNI) included widespread foreign equity limits, sectoral reservations for MSMEs, and special licensing regimes. Until early 2021, the DNI applied at least one investment restriction in almost one-third of all economic sectors, and in 20 percent of them it either limited foreign equity participation or prohibited foreign investment altogether. These restrictions significantly reduced both foreign and domestic investments, reduced entry and performance, increased prices in the sectors to which they were applied (World Bank, 2018), and were associated with productivity losses (Genthner and Kis-Katos, 2022).

In 2021, the Government of Indonesia (GoI) implemented its most comprehensive investment reform program in decades. Most direct restrictions on investments were removed through a February 2021 presidential regulation (Perpres) 10/2021, later amended by Perpres 49/2021 on Investment in May 2021 (World Bank, 2022). The new presidential regulation on investment significantly reduced business activities subject to two main restrictions: (i) sectors that are reserved for MSMEs and Cooperatives, or open with requirements to partner/cooperate with MSMEs and 1
# **1. Introduction**

In this paper, we explore the influence of skills on foreign investment in a small newly industrialized country (NIC), Singapore. Singapore has been successful in attracting FDI during its period of rapid economic development (Shin, 2005). The country has also experienced significant deepening of its human capital stock during this period. It is possible that the rapid growth of its skill endowments has been a significant factor in attracting foreign investment, with causation running in both directions.

One framework within which to analyze the role of skill endowments on FDI location is the Knowledge Capital (KK) model. This model has been applied to a number of industrialized countries, including the United States (Carr, Markusen and Maskus 2001, hereafter CMM). The KK model has, however, not been tested for any of the NICs and has been applied to investment data for only one developing country, China (Gao, 2003).

At the time of independence in 1965, Singapore was a relatively unskilled-laborabundant and natural-resource-poor developing country with high levels of poverty and low economic growth. Shortly thereafter the Government of Singapore (GOS) adopted an industrialization strategy, including opening the country to foreign investment in most sectors. The GOS actively promotes cross-border investment and has removed almost all barriers to trade (Ramkishnen and Thangavelu, 2008). In addition, it has taken measures to facilitate trade and investment as well as inflows of skilled labor. Singapore is a member of several regional and international trade and investment forums (including ASEAN, APEC, WTO, etc) and has negotiated bilateral trade and investment pacts with many countries around the world.

Until the late 1970s, multinational enterprises (MNEs) were attracted by the country’s large low-skilled population and low wages (Low, 1999). By the early 1980s, Singapore’s 2
|Intercept|-1.699***|(0.38)|-0.552***|(0.15)|
|---|---|---|---|---|
|Wald chi2|90.94||||
|Prob > chi2|0.00||||
|Selectivity test|0.40||||
|Total N|7976||||
|Censored N|5004||||
|Uncensored N|2972||||

* p<0.10, ** p<0.05, *** p<0.010; SE are clustered at village level.

In short, we find generally little support for H1: only 8% of households used environmental extraction as their preferred coping strategy in response to a shock. This does not preclude a correlation between e.g. climate-induced shortfalls in agricultural income and increased environmental extraction, but the latter is not by households ranked is the most important response to the former. However, some minority groups were significantly more likely to go first to the forest to cope with shocks: large, ill-educated, land-poor households living in market-near villages with high regular forest incomes, especially in Asia.

# 5.2 Observed climate shocks and income

We now turn our attention to sector income composition in response to a climate anomaly. Where in the preceding section we had looked at households’ self-perceived shocks and self-stated responses, here we will turn to shifting income patterns observed in response to weather anomalies. The approach thus changes in two respects. First, the population of events is more directly weather-focused (observed climate variables) and more inclusive of subtle changes: as a direct correlation analysis reveals, many climate anomalies are not perceived as “shocks” by households, and may thus include more moderate, and possibly repetitive reallocations of production factors in response to variable weather. Second, where in the previous section we looked at what people said they did in response to the event based on their own recall, here we are modeling what they actually seem to have done – in the sense of depicting differences in income patterns in response to the observed anomaly, while trying to control for other factors of variation.<sup>13</sup> In Hypothesis 3, we expected that a decline in crop income would cause an overall decline in total income, but probably cushioned by some expansion in other income components to which households would reallocate production factors because they were less severely, or not at all hit by the weather impact. We also expected that, other things being equal, extraction from bio-diverse (forest and non-forest) natural environments would be more resilient to the anomaly than agricultural activities, yet with the qualification that activities based on perennial biomass accumulation (wood extraction from forests, livestock) would also be more resistant to the changed weather than annual biomass increments (crops, food extracted from non-forest wildlands).

> 13 Both approaches have their pros and cons; we would not want to rank one as universally more reliable than the other.

25