rafmacalaba/data-use-mentions-extended huggingface dataset:
to each product.<sup>2</sup> Third, we analyze changes in the application of NTMs year by year, which allows us to build a time-varying dataset and apply panel data methods.<sup>3</sup> A further advantage of using Indonesian data is the availability of price and quantity information, which allows us to disentangle the impact of NTMs on plants’ physical productivity and price markups.

This paper makes three main contributions. First, it examines the net impact of NTMs on plants’ performance and compares it with the impact of import tariffs. Our findings suggest that NTMs across all categories have on average a neutral impact on manufacturing plants. Hence NTMs in Indonesia appear to create less distortion than import tariffs, which consistently with the literature we find to reduce competition and productivity (e.g. Amiti and Konings (2007); De Loecker et al. (2016)). We interpret the neutral impact of NTMs as the result of two countervailing effects. One the one hand, some of the measures boost plants’ performance by increasing the quality of the goods on which NTMs are applied. On the other hand, NTMs can have a negative impact on performance due to an increase in trade compliance costs. The extent to which these two effects are at play differs significantly across NTM types.

To explore the heterogeneity of impact of NTMs, we go beyond their average net effects and study specific measures, which is the paper’s second main contribution. This is important as different types of NTMs are likely to have highly heterogeneous effects according to the objective (explicit or otherwise) they fulfill. On the one hand, NTMs’ _raison d’etre_ should be to increase domestic welfare by addressing market failures, such as negative externalities or information asymmetries. For instance, a country might impose sanitary standards on imported products, or require certain foreign goods to satisfy fabrication and performance requirements in order to be commercialized domestically. To the extent that the main outcome of such
Fletcher, K., C. Keller, P. K. Brooks, D. Lombardo, and A. Meier (2007), _Safe to save less? Assessing the recent decline in Turkey’s private saving rate_ , Selected Issues: Turkey, IMF.

- Graham, J. W. (1987), International differences in saving rates and the life cycle hypothesis, _European Economic Review_ , Vol. 31 (8): 1509-1529.

- Gregory, P., M. Mokhtari and W. Schrettl (1999), Do the Russians Really Save That MuchAlternative Estimates From the Russian longitudinal Monitoring Survey, _The Review of Economics and Statistics_ ; November, 81: 4: 694-703.

- Haque, N. U., M. H. Pesaran and S. Sharma (1999), _Neglected Heterogeneity and Dynamics in Crosscountry Savings Regressions_ , Cambridge Working Papers in Economics 9904, Faculty of Economics, University of Cambridge.

- Jensen, S. E. H. and O. H. Jorgensen (2008a), _Uncertain Demographics, Longevity Adjustment of the Retirement Age and Intra-Household Risk-Sharing_ , in S. J. Alho, S. E. H. Jensen, and J. Lassila (eds.), Uncertain Demographics and Fiscal Sustainability, pp. 239-257, Cambridge University Press.

- ________ (2008b), Low Fertility, Labour Supply, and Retirement in Europe, _The WDA-HSG Discussion Paper Series on Demographic Issues_ , World Demographic Association, No. 2008/3.

- ________  (2010), Reform and Backlash to Reform: Economic Effects of Ageing and Retirement Policy, _World Bank Policy Research Working Paper_ , No. 5470.

- Jorgensen, O. H. (2010a), Health, Demographic Transition, and Economic Growth, _World Bank Policy Research Working Paper_ , No. 5304.

- ________ (2010b), _Should Oil Finance Aging in Brazil? Efficiency and Equity Implications of Oil Windfalls and Population Aging_ , working paper (available from author).

- Jorgensen, O. H
maintenance resulting from limited state capacity and local presence undermining the value of titles for third party transactions. We also find that the pace of land acquisition peaked in the 5-year period just after the entry into force of the new land law in 1995 rather than in response to the 2007/08 global commodity price boom; that large farms rarely engage in land transfers and own on average 20 ha of which they cultivate less than half; and that there is high unmet demand and willingness to pay for title.

Farm survey data allow examining the impact of title on land and financial market participation, investment, and productivity. Instrumental-variable (IV) regressions, with possession of title instrumented by parcels’ tenure at acquisition and length of tenure suggest that, if anything, title reduces land market participation and has no significant impact on credit access, land-attached investment, and productivity of land use. While title seems associated with higher levels of perceived transferability and land values, this may point towards speculative land acquisition and rationing in title access, two factors that may, e.g., by impeding long-term rental of land acquired as an investment, hinder rather than help structural transformation.

In terms of policy, improving record quality and coverage while lowering cost of service delivery through elimination of rents are key to improve land markets (Arruñada 2012). By leveraging traditional authorities’ informational advantages (Basurto _et al._ 2020) and trust (Baldwin 2018), decentralization can possibly help to achieve this objective. Increased use of urban land taxes in Africa (Deininger _et al._ 2020) with positive effects (Weigel 2020) also suggests that a land tax on state land<sup>4</sup> could increase incentives for efficient land use and -if tax payment provides evidence of use and non-payment to be used to cancel legacy titles which would otherwise threaten to undermine tenure security- also clarify records over time and, if all or part of revenues are shared, generate resources for local authorities in an incentive compatible way
Figure 16 also demonstrates that direct taxes and direct transfers are more effective at reducing inequality than are, for example, subsidies, in-kind services, or indirect taxes. This is not uncommon—see Figure 17, which presents the range and mean of the Inequality Impact Effectiveness Indicator for different classes of instruments in 11 African countries between 2010 and 2015 as well as the value of the same indicator in Egypt in 2015. Egypt’s inequality impact effectiveness indicators for direct transfers and direct taxes are below the mean, but still within the range shown in figure 17. Subsidy spending is approximately at mean effectiveness, while indirect taxes (and also total taxes) are outside the range and actually contribute to an _increase in inequality_ . This is due to the steep excises on alcohol and, especially, tobacco.<sup>51</sup> Figure 17 also presents Egypt’s fiscal quandary: while fiscal policy (circa 2015) was indeed reducing inequality as well as poverty, most Egyptian expenditures are not targeted and are therefore _larger_ than they need to be in order to have positive welfare impacts. That in turn indicates that the 2015era profile of expenditures and revenue collections is likely not sustainable (see also tables 1 and 2).

# **Figure 17. Inequality Impact Effectiveness Indicator: Egypt (red square), Range (gray bars), and Mean (black square) and 11 Low- and Middle-Income African Countries**

<!-- Start of picture text --> Total benefits<br>Total taxes<br>Hospital (Inpatient Health)<br>Health<br>Tertiar y education<br>Education<br>Indirect taxes<br>Subsidie s<br>Direct transfers<br>Direct taxes<br>‐1.00 ‐0.75 ‐0.50 ‐0.25 0.00 0.25 0.50 0.75 1.00<br><!-- End of picture text --> _Source:
**Table 1: Estimates of the Sensitivity of Consumption to Income**

||**_All_**|**_Advanced_**|**_Developing_**|
|---|---|---|---|
||**_Countries_**|**_Economies_**|**_Economies_**|
|**Panel Regressions**||||
|[1] 1960-2011|0.801|0.753|0.806|
|[2] 1960-2011|0.795|0.758|0.796|
|[3] 1960-1971|0.879|0.877|0.881|
|[4] 2000-2011|0.743|0.683|0.759|
|**Cross-Section Regressions**||||
|[5] 1960-1971|0.895|0.867|0.890|
|[6] 2000-2011|0.786|0.711|0.791|

Notes: These statistics refer to estimates derived from a regression of consumption growth on output growth. The coefficients are statistically significant at 1% level. Sensitivity of consumption to income refers to the coefficient estimated from a regression of consumption growth on income growth. Row [1] shows estimates derived from the GMM Dynamic Panel estimates using lags 2-4 of consumption and income growth and lagged saving as instruments. Rows [2]-[4] show panel OLS estimates using country and year fixed effects. Rows [5]-[6] show the average of the cross-sectional estimates for the respective period.

18
descriptive evidence helps resurrect the fundamental question raised by Bowles and Gintis (1976), this time for low-income countries: What are the skills that children learn in schools that are then valued in the labor market?

# **I. SAMPLE**

# **I.1. SAMPLE SELECTION**

The data come from the Learning and Education Achievement in Punjab Schools (LEAPS) project, a longitudinal study of education in Pakistan (Andrabi, Das, and Khwaja 2022). In 2003, the LEAPS project randomly sampled 112 villages from three districts in the province of Punjab (from a list frame

> 6 Using the Young Lives data, Mitchell et al. (2020) find that individuals with higher task effectiveness skills are less likely to engage in risky behavior. Singh et al. (2018) show that psychosocial skills are positively correlated with progression through school. Das, Singh, and Yi Chang (2022) show that test scores at age 12 predict years of schooling at age 22 but still leave a substantial role for socioeconomic status.

7
the Euro<br>Area.|
|Smets and<br>Wouters (2004)|Euro Area. Many<br>macro variables|Forecasts the baseline using BVAR DSGE model.|Many macro variables including<br>GDP, consumption, investment,<br>employment, and inflation.|
|Reserve Bank of<br>India, Apr. 2016<br>(Banerjee and<br>Das 2011)|India. Inflation, real<br>GDP growth|Fan charts for inflation and GDP growth are constructed based<br>on historical forecast error variances.|Wholesale price index inflation<br>rate, real GDP growth,<br>international investment<br>position, real effective exchange<br>rate, M1 money aggregate.<br>External and domestic|
|Bank of Israel,<br>Monetary Policy<br>Report, H12015|Israel. Inflation,<br>interest rates|Uncertainty results from shocks to endogenous variables<br>whose distribution is based on their past developments.|developments: global demand,<br>commodity prices, financial<br>market index, real GDP growth,<br>inflation.|
|Bank of Japan,<br>Outlook for<br>Economic Activity<br>and Prices, Jan.<br>2016|Japan. Inflation,<br>real GDP growth|Distributions of forecasts are based on the Policy Board<br>members’ assessment of uncertainty and their judgement of<br>the balance of risk associated with their forecasts. The<br>distributions of Board members forecast are presented to<br>illustrate the extent of uncertainty and balance of risk<br>associated with the projections.|Inflation, real GDP growth.|

15
Policy Research Working Paper 10807

# **Abstract**

Preparing for—and responding to—disasters requires a people-centered approach and a strong understanding of households’ ability to cope with shocks. Relying on novel household survey data, this paper examines the ability of households in the Caribbean to cope with disasters caused by natural hazards. The analysis sheds light on disaster preparedness in five “data deprived” countries: Belize, Dominica, Haiti, Saint Lucia, and Suriname. The analysis points to a clear income gradient in possession of emergency supplies needed to cope with disasters. This gradient can be observed at both the country and household levels. In contrast, no such income gradient is observed for other key elements of preparation for disasters: community disaster management systems and discussion of risk mitigation strategies within households (both of which are common in the Caribbean hurricane belt). There is substantial variation in preparedness to cope with disasters across sociodemographic groups, as households with less educated heads, with children, and residing in rural areas are generally less able to handle disasters. All in all, a large share of households in all five countries indicates that they are not prepared to cope with a natural disaster. The COVID-19 pandemic had a negative impact on disaster risk preparedness, primarily due to households’ deteriorating financial circumstances.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at banglade@worldbank.org, mcucagna@worldbank.org, jdehoop@worldbank.org, and apaffhausen@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text -
# PART IV. A FORMULA-BASED EQUALIZATION TRANSFER SYSTEM FOR CHINA: MODEL AND SIMULATIONS

The current Chinese central-provincial fiscal transfer system mainly consists of three mechanisms.22 The first mechanism is based on the old contract system prevailing during 1988-1993. That is, after 1994, the localities (provinces and cities with independent planning status) continue to remit revenues to or receive transfers from the center according to their fiscal contracts in effect in 1993. The second type of transfer is "returned revenue" from the center according to a calculation that ensures each locality retains no less than what it did in 1993. These two types of transfers are general purpose transfers, or unconditional transfers. The third type of transfer includes various specific purpose grants, such as those for price subsidies, educational projects, environmental projects, disaster relief, and the development of poor regions.

The general purpose transfers from the center to the localities account for the major part of the total transfers. In 1994, the central government's net transfer to the local governments amounted to about 181 billion Yuan, of which two-thirds were general purpose grants. However, these general purpose transfers suffer from at least two major flaws. First, they were not designed to address the increasingly significant regional disparity issue; rather, they were largely designed to recognize the vested interests of the localities.23 Second, the criteria by which these transfers are allocated are rather ad hoc, that is, the transfer system lacks scientific measurements of fiscal capacities and fiscal needs. This can easily lead to an unjustified distribution and encourage the localities' bargaining activities.

T'his part presents an illustrative equalization transfer model and the simulation result using 1994 data on China. It provides the estirnates of fiscal capacities and fiscal needs of 30 Chinese provinces using the methods discussed in the paper. Based on a formula that aims to ensure that provinces with similar levels of tax effort be able to provide similar levels of public services, the calculation results in a set of hypothetical transfers from the center to the provinces
1.8 conclusion 43

preceding 5 years, from 2001 to 2005. The basic results not only stand, but the coefficient of FDI Arbitration increases substantially when the dependent variable uses FDI stock for the year 2006. A 10 percent increase in a country’s combined score in the Ease of Process and the Judicial Assistance indices increases FDI by over 5.6 percent.

Table 11 reports results obtained by estimating the model using data for 2007. Here, both the coefficients of FDI Arbitration and FDI Procedures fall to the extent that the FDI Arbitration coefficient is statistically significant only at the 10.6 percent level.<sup>33</sup> It is conjectured that host country regulations and characteristics matter less when there is a global glut in investment funds. Recall that the year 2007 recorded the highest levels of outward FDI ever in the world. Total FDI stock was valued at nearly US$18 trillion and FDI flows nearly reached the US$2 trillion mark Table 12 reports results obtained by estimating the model using data for 2008. The estimates are comparable to the main results, except for the coefficient of the quality of institutions. As in Table 5 when it was measured by the rule of law indicator, the coefficient is negative and significant, that is, an improved measure of the rule of law is associated with lower FDI. As stated earlier, this coefficient becomes insignificant when GDP per capita is dropped from the regression.

# 1.8 conclusion

Gravity models have long been a workhorse for explaining trade, investment, and migration flows between countries. They fit the data well, but until recently, they were seen as atheoretical (Anderson & Van Wincoop 2003). This paper uses a new empirical methodology to estimate the impact of FDI-specific institutions on FDI inflow in a theoretically derived gravity-like model.

> 33 It is significant at the 3 percent level if standard errors are clustered by source country, but I opt for the more conservative estimates.
However, before this willingness to save can be harnessed towards actual gains on the ground, we need to better understand the challenges that the poor might face in setting money aside as savings. Banerjee and Duflo (2007) have documented that a huge proportion of the poor still lack access to formal banking services. Chaia et al. (2009) estimate that 80 percent of the entire adult population in SSA was unbanked at the beginning of the 2000s. Expectedly, the penetration numbers are much worse for those living below the poverty line: Gaul (2011) calculates the absolute difference between the population living below the poverty line and the population with access to financial services, and finds that the numbers are as high as 80 million for Nigeria and 48 million for Congo. While this is a somewhat crude and imprecise way to measure financial inclusion, it does underline the fact that a vast majority of the very poor continue to be bypassed by financial institutions. Data from IMF statistics also reflects this lack of access to financial services. The number of deposit accounts as a percentage of population is a meager 19 percent on average (excluding South Africa) (Figure 5). Again, the caveat applies that these are supply-side data that do not control for multiple and dormant accounts and actual formal bank penetration is likely much lower.

The lack of access to formal financial institutions drives the poor to save in sub-optimal ways. For instance, the widely prevalent practice of saving through Rotating Savings and Credit Associations (ROSCAs) in Africa comes with a significant risk of the ROSCA itself collapsing before all the members have „won‟ the draw. Similarly, in an RCT based in rural Kenya, Dupas and Robinson (2011a) offered savings accounts to 163 microentrepreneurs. These accounts provided no interest and entailed a withdrawal fee, which effectively means that they had a negative interest rate. They find that 53 percent of those who were offered this account chose to save through it, implying that they could not access a better way to channel their
labor force participation rates are significantly larger than in most European countries.<sup>1</sup> In addition, the country registered increasing gender gaps over the last 20 years, mainly driven by declining female labor force participation rates, while the European Union (EU) on average reported decreasing gender gaps in labor force participation (World Bank 2023). In addition, female labor force participation is among the lowest in the EU. There are also signs of gender norms around childcare usage that could deteriorate female labor market participation. According to data from the World Value Survey (2018) 37.2 percent of Romanians thought that a preschool child with a working mother suffers.

While these trends already make for an interesting country setup, Romania is also an interesting case given its communist past and its long history of centralized public planning. In the pre-transition period, Romania registered one of the highest female labor force participation rates in the world, also driven by a wide range of childcare programs subsidized by the government (Fong and Lokshin 1999). The availability of public childcare services dropped significantly in the post-transition period and in 2020 the share of households with children below six years old that relied on these services was less than 1 percent (EU-SILC 2020).<sup>2</sup> Our setting provides a clean policy setup to study the underlying research question. We find no evidence of confounding policies or trends that affected maternal employment of six-year-olds differently from the one of three- to four-year-olds. While the government introduced several policies that potentially impacted employment decisions, it is unlikely that these policies resulted in different dynamics between mothers of six-year-olds and those of three to four-year-old children.

Our paper contributes to the economic literature studying drivers behind female labor force participation and employment. Next to the literature analyzing the impact of universal childcare, our work is closely related to a number of papers studying childcare subsidies

> 1In 2021, Romania’s female-to-male labor force participation ratio stood at 66 percent, compared to the EU average
# **4.2 Traffic, Weather and Air Pollution**

Our second exercise tests the impact of motor vehicle emissions on air pollution, using hourly data from the CAMS-3 monitor and a .01-degree square area whose centroid has the coordinates of the monitor (Figure 1). Within that area, we use the previously-described methodology to compute hourly average traffic speed, which is an inverse index of congestion: emitting vehicles occupying road space in that area. Among the weather variables, prior experimentation showed that only temperature, humidity and wind speed have consistent and highly-significant effects. We therefore exclude rainfall and pressure from the final estimation model. We specify it in log form to minimize outlier effects. (1) 0 + 1l 2l l 0<sup>l</sup> 0<sup>l</sup> tt tt tt<sup>+ ββ</sup> tt<sup>+ ββ</sup> tt<sup>+ εε</sup> tt where (measured at date/time t): PP = ββ ββ llSS + ββ<sup>ll</sup>

- P  =  Pollutant concentration measured by CAMS-3 [PM 2.5 (ug/m<sup>3</sup> ); NO2 (ppb)]

- S  =  Average traffic speed (km/hr) in the .01-degree cell surrounding CAMS-3

- H  =  Humidity (%)

- T  =  Temperature (C)

- W  =  Wind speed (km/hr)

- ε = Random error term

Table 4 reports our results for PM2.5 and NO2 readings from the CAMS-3 monitor. Our primary estimates use variable levels, but we also include estimates for time-differenced<sup>7</sup> variables to test the possible effect of serial correlation on the parameter estimates. The time-differenced version provides a weaker test of the weather variables, whose dominant variation is seasonal rather than hourly. Table 4 shows that all variables in the two levels equations are highly significant