rafmacalaba/datause-encoder-data 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 measures is the increase in products’ safety and/or quality, this may benefit the productivity of producers using such goods as intermediate inputs. On the other hand, NTMs may also generate compliance costs for foreign producers thereby increasing the costs of sourcing imported goods. This may be particularly evident for those measures which violate one of the key principles of WTO

> 2 The UNCTAD classification can be found at UNCTAD’s web page.

> 3We observe large variation in the application of NTMs across products and over time. For instance, the average HS-10 digit product in Indonesia is subject to 2.7 changes in the application of NTMs over the period 2008-15.

3
The paper is structured as follows: Section 2 describes the collection of the data and the survey design, Section 3 gives some facts describing the patterns of bank and non-bank supervision around the world, and the related patterns of data availability for different elements from the survey, Section 4 describes the procedure for estimating the number of accounts world wide and the number of banked and unbanked individuals, Section 5 contains the results of the regressions with the correlates of deposit, loan, and bank branch penetration. Section 6 concludes.

# **2. Description of Data Collection Methodology**

The primary purpose of the Financial Access Survey was to assemble a dataset of measures of the breadth of usage of the most basic financial products including deposit accounts, loans, and payments. A secondary goal was to measure the pervasiveness of physical access points such as bank branches and ATMs which are necessary elements to facilitate broad inclusion. Finally, the survey attempts to collect information on some of the policies and practices in each country which might affect financial inclusion.

It is important to distinguish between usage of financial services, which is what we can collect data on, and the broader concept of access to finance. A widely cited definition of financial access is that outlined by Claessens (2006) who breaks the population into three parts, those who have access and use it (group A, included), those who have access but don’t use it (group B, voluntarily excluded), and those who don’t have access (group C, involuntarily excluded) where “access” is defined equal A + B. He contends that while data on the usage of financial products measures A, B is almost impossible to measure, which highlights the difficulty in finding true measures of “access to finance”, so defined. While in concept, not being able to measure the voluntarily excluded is a major issue, in reality, there are very few households anywhere which live in true voluntary financial autarky (though they may choose only informal financial options if formal ones are not readily available).<sup>6</sup> The real issue is that usage data is only widely

> 6 In _Portfolios of the Poor_ , by Collins, Morduch, Rutherford, and Ruthven (2009),  the authors show that _all_ 250 of the very poor slum residents they study have some form of debt and savings and none use fewer than 4 types of instruments (be they formal or informal) throughout the year. The true picture is one where poor households are 5
In the calculation of storm surge (wave heights or extreme sea levels), the method outlined by Nicholls (2008) was primarily followed. In our slightly modified version, surges (for the two storm surge scenarios – with and without climate change) were calculated as follows: Current storm surge = S100 Future storm surge = S100 + SLR + (UPLIFT * 100 yr ) / 1000 + SUB + S100 * x Where: S100 = 1 in 100 year surge height (m); SLR = 1 m; UPLIFT = continental uplift/subsidence in mm/yr; SUB = 0.5 m (applies to deltas only); x = 0.1, or increase of 10%, applied only in coastal areas currently prone to cyclones or hurricanes.

Surges were calculated using data associated with the coastline. Vector coastline masks were extracted from SRTM version 2. Coastline attributes were downloaded from DIVA GIS database. Attributes used in this analysis are:

1. S100: 1-in-100-year surge height based on tidal levels, barometric pressures, wind speeds, sea-bed slopes and storm surge levels from monitoring stations;

2. DELTAID: coastline segments associated with river deltas; 3. UPLIFT: estimates of continental uplift/subsidence in mm/yr from Peltier (2000). This parameter includes a measure of natural subsidence (2 mm/yr) for deltas.

Surge (wave height) associated with current and future storms were then compared to the elevation value of inland pixels with respect to a coastline to delineate a potential inundation area for storm surges.

6
tricts. BDs for log cash expenditures support the same conclusions, although they are lower on average (+0.41 to +0.70).

Although we discuss the channels driving these results in more detail in section IV, we need to assess the role of differences in price levels because discontinuities in “nominal” consumption may not match discontinuities in purchasing power. According to the World Bank (2012), in 1988, the price level of private consumption in Cˆote d’Ivoire was 0.98 that of Ghana, whereas according to the Penn World Tables 7.1 (Heston, Summers and Aten 2012), it was 1.09 at official 1988 exchange rates (i.e., 298 CFA francs and 202 cedis for one US dollar, respectively). After 1983, the Ghanaian government began a gradual process of devaluation of the cedi that ended with free float in 1990 and the elimination of the black market premium. In 1988, the parallel market exchange rate was estimated at 252 cedis/USD.<sup>8</sup> Using this parallel exchange rate instead of the official one would enhance the border discontinuities in consumption by a factor of 1.25 (=252/202). Thus, it is hardly plausible that the real exchange rate holding in border areas could reverse the conclusion of a large discontinuity in real consumption per capita.<sup>9</sup> Furthermore, the figures for the share of stunted children are consistent with the Cˆote d’Ivoire advantage in consumption. For children six months to four years of age, we find large and robust BDs, ranging between 19 and 44 percentage points, with only one exception (linear RD with 75 km bw and heads born in border districts). For the sub-sample of younger children (6 to 35 months), the point

> 8Data from Robert Bates, Karen Feree, James Habyarimana, Macartan Humphreys, and Smita Singh. 2001. “Economic Data (updated 2005)”, http://hdl.handle.net/1902.1/14978 UNF:5:8vHsoDT1Q8sUbiTZgDkctw== Murray Research Archive [Distributor] V1 [Version]. 9In a completely different context, Gopinath, Gourinchas, Hsieh, and Li (2011) find retail and wholesale markets to be segmented at the border between Canada and US. However, relative prices co-move with the nominal exchange rate, so there is little room for changes in the real exchange rate.

24
|Sustainable Land and Water Management|Neeta Hooda|
|---|---|
|Sustainable Land and Water Management|Neeta Hooda|
|Sustainable Land and Water Management|Neeta Hooda|
|Sustainable Land and Water Management|Neeta Hooda|
|Sustainable Land and Water Management|Neeta Hooda|
|Sustainable Land and Water Management|Neeta Hooda|
|Sustainable Land and Water Management|Neeta Hooda|
|Sustainable Land and Water Management|Neeta Hooda|
|Sustainable Land and Water Management|Neeta Hooda|
|Sustainable Land and Water Management|Neeta Hooda|
|Sustainable Land and Water Management|Neeta Hooda|
|Sustainable Land and Water Management|Neeta Hooda|
|Sustainable Land and Water Management|Neeta Hooda|
|Second Programmatic Development PolicyLoan|EvgenijNajdov|
|Second Programmatic Development PolicyLoan|EvgenijNajdov|
|Northern Negros Geothermal Power Project|Victor Dato|
|Republic of Congo - EmergencyReintegration Program|Sarah G. Michael|
|Republic of Congo - EmergencyReintegration Program|Sarah G. Michael|
|Republic of Congo - EmergencyReintegration Program|Sarah G. Michael|
|Republic of Congo - EmergencyReintegration Program|Sarah G. Michael|
|StrengtheningE-Procurement|Jesus Renzoli|
|StrengtheningE-Procurement|Jesus Renzoli|
|Colombia: San Nicolas Carbon Sequestration Project|Franka Braun|
|Colombia: San Nicolas Carbon Sequestration Project|Franka Braun|
|Colombia: San Nicolas Carbon Sequestration Project|Franka Braun|
|Colombia: San Nicolas Carbon Sequestration Project|Franka Braun|
|Colombia: San Nicolas Carbon Sequestration Project|Franka Braun|
|Colombia: San Nicolas Carbon Sequestration Project|Franka Braun|
|NG-Lagos Landfill Gas &Composting (FY06)|Andrew Mbayaki Makokha|
|NG-Lagos Landfill Gas &Composting (FY06)|Andrew Mbayaki Makokha|
|NG-Lagos Landfill Gas &Composting (FY06)|Andrew Mbayaki Makokha|
|NG-Lagos Landfill Gas &Composting (FY06)|Andrew Mbayaki Makokha|
|Core FiduciarySystems Support Project(TF056336)|Adenike Sherifat Oyeyiola|
|Core FiduciarySystems Support Project(TF056336)|Adenike Sherifat Oyeyiola|
|Core FiduciarySystems Support Project(TF056336)|Adenike Sherifat Oyeyiola|
|Core FiduciarySystems Support Project(TF056336)|Adenike Sherifat Oyeyiola|
|Core FiduciarySystems Support Project(TF056336)|Adenike Sherifat Oyeyiola|
|Core FiduciarySystems Support Project(TF056336)|Adenike Sherifat Oyeyiola|
|Core FiduciarySystems Support Project(TF056336)|Adenike Sherifat Oyeyiola|
|Core FiduciarySystems Support Project(TF056336)|Adenike Sherifat Oyeyiola|
|Core FiduciarySystems Support Project(TF056336)|Adenike Sherifat Oyeyiola|
|Core FiduciarySystems Support Project(TF056336)|Adenike Sherifat Oyeyiola|
|Thermal Power Efficiency|XimingPeng|
|Thermal Power Efficiency|XimingPeng|
|Thermal Power Efficiency|XimingPeng|
|Thermal Power Efficiency|XimingPeng|
|Thermal Power Efficiency|XimingPeng|
Many surveys ask for a reason for the item non-response. Both NIDS and Statistics South Africa household surveys allow “refusal” and “don’t know” answers to earnings questions. In the Statistics South Africa surveys, “don’t know” responses are overwhelmingly given by proxy respondents, who are answering on behalf of another household member. In some sense then, don’t know item nonresponse is similar to non-contact unit non-response, but it is a specific household member who cannot be contacted to obtain answers to some questions that another household member does not know the answer to.

Lepkowski (2005) and Si et al. (2023) highlight that item non-response for total individual or household income or wealth may be a combination of item non-response or response for several questions on each subject for the same person or household, making measurement of total income, consumption or wealth even harder. In South Africa wave one of the National Income Dynamics Study (NIDS) in 2008, 41% of households had at least one of a detailed set of consumption items missing (Finn et al. 2009). We discuss solutions to item non-response in the sections that follow.

# _Measurement error_

Even if individuals respond to a survey and to the questions on incomes, there is still a concern that the true income of household members is not accurately captured. The obvious example of measurement error when thinking about the top tail of the income distribution is under-reporting by respondents who do not want to reveal their true incomes, perhaps because of concerns that these will be reported to tax collection agencies (such data sharing is illegal in some countries). Other types of measurement error affecting the top tail could occur when survey questions ask about gross income, but respondents give after tax income or even after other deductions such as medical insurance or pension fund contributions (which could be important in the top tail even in low-income countries and more important in middle income countries).

It is hard to find direct evidence of under-reporting because this requires matched survey and administrative data. We noted above that Flachaire et al. (2022) obtained such data for Uruguay. The authors showed that under-reporting in the household survey they used is, on average, highest at the top of the tax income distribution, as might be expected, but that there is over-reporting at the bottom of the distribution. In the top five percentiles, the tax data values are, on average, around 2 to 2.5 times as large as the values reported in the surveys. This is novel evidence that under-reporting is indeed an important issue when measuring the top tail of the income distribution, although the data requirements are so exacting there are, to our knowledge, no other similar studies for LMICs. Bollinger 7
Table 6: Average treatment effects by 2-digit codes for only fully liberalized Sectors

||Realized FDI|Realized DDI|Planned FDI|
|---|---|---|---|
||ATET|ATET|ATET|
|Tobacco Processing Industry|0.146***|-0.0382|-0.125***|
||(0.0563)|(0.0349)|(0.0425)|
|Paper and paper products industry|1.102***<br>|0.186*<br>||
||(0.0453)|(0.0989)||
|Printing and reproduction media for recording media|-0.0845|-0.337***|0.505***|
||(0.0732)|(0.0855)|(0.108)|
|Base metal industry|0.528***|0.309***|0.335***|
||(0.0306)|(0.0566)|(0.0732)|
|Water management|-0.298**|-0.266*|0.566***|
||(0.139)|(0.140)|(0.0908)|
|Land transportation and transportation through pipelines|-0.757***|0.418***|0.0623|
||(0.0641)|(0.0588)|(0.0609)|
|Warehousing and transportation support activities|0.208***|-0.593***|-0.0997***|
||(0.0369)|(0.0454)|(0.0212)|
|Food and Beverage Supply|-0.00658|0.509***|0.284***|
||(0.0257)|(0.0400)|(0.0665)|
|Telecommunication|0.00140|0.385***|-0.281***|
||(0.0648)|(0.0713)|(0.0541)|
|Activities of fnancial services, not insurance and pension funds|-0.0804***<br>(0.0141)|-0.0518***<br>(0.0151)|0.0471***<br>(0.0100)|
|Supporting activities for fnancial services, insurance and pension funds|-0.162***<br>(0.0162)|0.312***<br>(0.0333)||
|Real Estate|-0.0817***|-0.214***|0.464***|
||(0.0141)|(0.0129)|(0.0258)|
|Science research and development|-0.234***||-0.332***|
||(0.0170)||(0.0214)|
|Advertising and market research|-0.0788***|-0.243***|-0.228***|
||(0.0132)|(0.0131)|(0.0524)|
|Other professional, scientifc and technical activities|-0.0939***|-0.255***|0.316***|
||(0.0138)|(0.0137)|(0.0268)|
|Employment activities|-0.0782***|-0.108***|0.0474***|
||(0.0134)|(0.0148)|(0.00977)|
|Security activities and investigations|-0.0799***<br>|-0.219***||
||(0.0136)|(0.0129)||
|Activities of service providers for buildings and parks|-0.0795***<br>(0.0134)|-0.203***<br>(0.0129)||
|Offce administration, offce support and other business support activities|-0.0716***|-0.337***|0.224***|
||(0.0133)|(0.0163)|(0.0310)|
|Education|-0.0726***|-0.272***|0.00617|
||(0.0133)|(0.0128)|(0.0356)|
|Libraries, archives, museums and other cultural activities|-0.0824***|-0.215***||
||(0.0132)|(0.0128)||
|Other sports and recreational activities|-0.259***|0.797***|0.0477***|
||(0.0371)|(0.0433)|(0.00955)|

Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 Note: The treatment corresponds to 1 while the control group is 0. ATET denotes the weighted average treatment effect on the treated. ATET estimates are adjusted for sector/panel effects and time effects. Each industry is estimates separately.

# **4.4 Robustness Checks**

## **Effect of Trade Policies on FDI**

In November 2021, the Indonesian government undertook more regulatory changes by addressing the trade regulatory regime. The three major regulations enacted were Ministry of Trade Regulations 18,19 and 20 of 2021 which removed some non-tariff measures and introduced other new procedures (Montfaucon et al. (2023)). Considering the main policy being analyzed in this 19
**Table 7: Discounted live years lost per year (in 000)**

||||**Lives**<br>|**Saved**<br>|**% of M**<br>**Redu**<br>|**ortality**<br>**ction**<br>|**L**<br>|**YS**<br>|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||Births|Deaths|Counter-<br>factual<br>I|Counter-<br>factual II|Counter-<br>factual I|Counter-<br>factual II|Counter-<br>factual I|Counter-<br>factual II|
|SSA Average|705|103|9|26|0.088|0.263|269|767|
|SSA Sum|16'931|2'476|215|613|||6'445|18'397|
|Other Average|3'224|255|14|50|0.054|0.183|419|1'505|
|Other Sum|45'130|3'573|196|702|||419|1'503|
|Total Average|1'633|159|11|35|0.073|0.228|324|1'039|
|Total Sum|62'060|6'049|410|1'316|||12'315|39'467|

_Notes:_ LYS: Life-year saved. Calculation of LYS based on 3 percent time discounting and uniform age weights and standard life expectancy at age 5 (Mathers, et al., 2001). No difference was made between males and females.

To provide a better sense of the magnitude of these potential benefits in terms of impact on child mortality, we construct two alternative policy counterfactuals (scenarios III and IV) where we focus on mothers’ education rather than on water and sanitation infrastructure. One of the most robust findings in the analysis of factors associated with child mortality risks is the large protective effect of maternal education (Ruthstein, 2000). The potential contribution of water and sanitation improvements to achieve MDG 4, as shown in Table 6, may be better appreciated when compared with an alternative policy focusing on maternal education.

Table 8 shows the results of this additional counterfactual exercise: for counterfactual III we assume that that all mothers attain at least some primary education, while for counterfactual IV we assume that all mothers receive at least some secondary education. Even though primary education of the mother is as protective as basic sanitation and water access combined, the aggregate child mortality reductions that would be feasible to achieve via investments in primary education appear small in comparison to the reductions achievable via investments in water and sanitation. The major reason is that many more children lack access to improved sanitation (about 60%), while the percentage of mothers without at least primary education has rapidly declined over the past decades (less than 40% in our sample).

In order to achieve improvements similar in magnitude to the expected improvements with a comprehensive coverage of piped water and flush toilets, all mothers in our sample would have to receive at least some secondary education. With only 27% of mothers currently having attained secondary or higher education, and only about 20% of households currently 22
# **Table 4: Contemporaneous and lagged effects of internet access on municipality-level worker outcomes by sector**

Municipality-year variation

|**Outcome/sector**|**InternetCoe**|**f. lag InternetCoef.**|**_N_**|**Adj. ****_R _**<sup>**_2_**</sup>|
|---|---|---|---|---|
|ln(wage)- All|-0.024***|-0.027***|74170|0.802|
|ln(wage) - Wholesale & Retail|-0.002|-0.004|67070|0.717|
|ln(wage) - Electric, gas, & water|-0.024***|-0.045***|32793|0.752|
|ln(wage) - Manufacturing|0.002|0.008|58354|0.797|
|ln(wage) - Public Administration|-0.036***|-0.040***|67663|0.835|
|ln(wage) - Real Estate|0.020**|0.010|50093|0.593|
|ln(wage) - Construction|-0.006|0.004|46894|0.440|
|ln(wage) - Agriculture|0.002|0.006|61649|0.756|
|ln(wage) - Transport & Storage|-0.012**|-0.007|65279|0.575|
|ln(wage) - Other Soc. Svcs.|-0.004|0.008|58806|0.653|
|ln(wage) - Health and Social Svcs.|0.005|0.001|42532|0.705|
|ln(wage) - Hospitality|0.013**|0.013**|47423|0.597|
|ln(wage) - Education|-0.001|0.003|35929|0.769|
|ln(wage) - Finance and Insurance|0.002|-0.001|50176|0.646|
|ln(wage) - Mining|-0.011|0.008|25084|0.738|
|ln(wage)- Fishing|-0.004|-0.010|11542|0.584|

Note: Estimates based on RAIS employee-employer records aggregated by municipality and year merged to internet access data from the School Census. Outcome measures listed in rows are in natural log. Interpretation of coefficients relate an increase in internet access from 0% to 100% in the municipality to the percent change in the outcome measure. Rows indicate outcome used in a common specification including unreported vectors of municipality and year fixed effects and a constant term. Standard errors clustered by municipality in parentheses. * p<.1, ** p<.05, *** p<.01.

# **_Employment and wages by skill_**

We segment employment into terciles of relative skill level based on variation in mean wages across 300 3-digit occupations. We then estimate the employment and wage outcomes in Table 2 separately by skill groupings to assess whether overall effects can be attributable to differential labor market responses across skill levels. Table 5 presents these results, with some stark patterns: increased internet access reduced job churn and overall employment in both low- and high-skill occupations. Employment falls economy-wide for the lowest-skill tercile. Average wages decreased among the higher-skill occupations in response to internet access, and average wage inequality grew among the lowest skill tercile occupations but declined in the highest skill occupations.

12
registry to collect information on nonpoor households is a first step toward making the RNU dynamic and enabling an expansion of the RNU to respond effectively in times of crisis.

What are the implications of accounting for vulnerability in allocating RNU eligibility quotas across communes? Poverty- and risk-induced vulnerability are not equally distributed across communes. Relative to urban communes, rural communes tend to present higher poverty rates. However, a considerable share of the population in urban areas is susceptible to falling into poverty. The revised quotas therefore increase the participation of urban households in the registry.

21