rafmacalaba/data-use-mentions-extended huggingface dataset:
timing stems from Indonesia’s democratic transition, as district mayors appointed in the final years of the Suharto era were allowed to complete their terms (Martinez-Bravo et al., 2017). The 2021–22 group serves as a placebo, as elections held after the 2021 _Podes_ survey should not affect service outcomes measured in 2021.

We find that improvements in service provision grow over time. Effects are nearly twice as large in villages that held elections in 2015–17 (column 2) compared to those with more recent elections (column 3), while, as expected, there is no evidence of improvements in villages where elections occurred after the _Podes_ wave (column 4). This pattern is consistent with the idea that the benefits of turnover take time to materialize, potentially reflecting short-run adjustment costs (Akhtari et al., 2022). While these comparisons are based on variation in election timing that is plausibly exogenous, they should be interpreted as suggestive given the possibility of residual variation across cohorts (e.g., differences in baseline institutional quality or geographic composition).

# **5.2 Perceived Service Provision and Village Government Actions in Survey Data**

**Main Results.** Our survey data provides further evidence that perceptions of service access and quality improved, and village governments were more likely to take action on public services when the village experienced an electoral turnover. We examine citizens’ views and reported village investments on the public goods that most closely correspond to those enumerated in _Podes_ , namely water access, garbage collection, roads, electricity (for street lighting), kindergartens, primary schools, and local healthcare delivery. Here, we look at service provision along both the extensive margin (whether the service is accessible in the village) and the intensive margin (reported service quality, based on citizens’ ratings of service provision for each type of service on a 5-point Likert scale). Table 7, columns 2–3 report the results for citizens’ perceptions, while column 4
countries. The decline in potential growth between 2000-10 and 2011-21 was almost as large in advanced economies (0.8 percentage point per year) as in EMDEs (1.0 percentage point per year).

The study also presented an application of the new database by studying the effects of recessions and other adverse events on potential growth. Recessions, on average, have been followed, even five years later, by a drop of 1.4 percentage points in potential growth. The magnitude of this estimated decline varies somewhat among the possible measures of potential growth, but it is virtually always statistically significant. This lasting effect of recessions operates through the many channels: Four to five years after recessions, investment growth, productivity growth, and employment growth all remained statistically significantly lower. In addition, this study compared the effects of recessions with those of other adverse events, such as banking crises and epidemics. The long-term effect of recessions was somewhat deeper than that of banking crises and more broadbased than that of epidemics.

Understanding the behavior of potential growth is of fundamental importance to shortand long-run macroeconomic analyses and policy formulation. The new database will facilitate future research on a number of topics related to potential growth.

17
) and their corresponding error measures. To this aim, we perform simulation experiments under the same setup as in MR (2010), but extended to consider also more realistic scenarios.

Thus, like in MR (2010), a census data set of _N_ = 20 _,_ 000 observations is created, where all observations are uniformly spread among _C_ = 80 areas, labeled from 1 to 80. This means that every area consists of _iid Nc_ = 250 observations. Location effects are simulated as _ηc ∼ N_ �0 _,_ 0 _._ 15<sup>2�</sup> ; note that every observation within a given area will have the same simulated effect. Then, values of two right-hand side binary variables are simulated. The first one, _x_ 1, takes the value 1 if a generated random uniform value between 0 and 1 is less than or equal to 0 _._ 3 + 0 _._ 5 80<sup>_<u>c</u>_.Thismeansthatobservationsinareaswithahigherlabel</sup> are more likely to get value 1. The next one, _x_ 2, is not tied to the area’s label. This variable takes the value 1 if a simulated random uniform value between 0 and 1 is less than or equal to 0.2. The census welfare vectors _yc_ = ( _yc,_ 1 _, . . . , yc,Nc_ )<sup>_T_</sup> for each area _c_ = 1 _, . . . , C_ , are then created as follows: ln( _ych_ ) = 3 + 0 _._ 03 _x_ 1 _,ch −_ 0 _._ 04 _x_ 2 _,ch_ + _ηc_ + _ech,_ _iid_ where household-level errors are generated under the homoscedastic setup, as _ech ∼ N_ �0 _,_ 0 _._ 5<sup>2�</sup> . The poverty line is fixed at _z_ = 12,
Current Market Prices (US$ mil.)|305504|308848|309760|
|Gross Disbursements (LT+IMF+ST) (US$ mil.)|7140|6682|9044|
|Net Disbursements (LT+IMF+ST) (US$ mil.)|220|37|2493|
|Interest Payments (LT+IMF+ST) (US$ mi.)|4605|4441|5514|
|Debt Outstanding (LT+IMF+ST) (US$ mil.)|94387|93435|94404|
|Index REER X-rate Index (1990=100), IMF|176|177|166|
|Official Capital Grants (US$ mil.)|345|410|379|
|PPG debt/GDP (current $US) (Ext. Debt)|26%|25%|26%|
|Total Govt. Domestic Debt (General Govt.) (curr. LCU mil.)|4962925|5734965|6698931|
|Net Domestic Credit to Govt. (current LC(U mil.)|2577780|2886200|3306190|
|Domestic Debt (current LCU mil.) (Public Finance)|8218824|9154199|10441148|
|Domestic Debt/GDP (current LCU mil.) (Public Finance)|67%|65%|67%|

_Source:_ World Bank Country Data and Author's Calculation
|0.000000|0.405465|

_basis: OECD and EU-KLEMS._ – Table 6: Regression analysis MFP and regulation

||OLS|robust<br>regression|
|---|---|---|
|discrimination against foreign frms|-0.3796***|-.3975 ***|
|domestic price regulation|-.3822 **|-.3735 *|
|intercept|4.9861***|4.9952***|
|summary stats|Rsq: 0.61||
||n:15|n:15|
||F(2,12)=18.93|F(2,12)=7.99|
||pr_>_F=.0002|pr_>_F=.0006|

_note: Regressions are based on 2007 MFP indexes. Indexes are rescaled OECD PMR sub-indexes. All variables are in logs. Significance levels: .01=***, .05=**, .1=*._ We assume that the coefficients capture, in a reduced form, the impacts of variations in openness and domestic regulation on MFP in the service sectors. On the basis of the coefficients above, we then construct an experiment wherein we move the Italian regulatory regime toward the North European norm, as proxied by the regulatory index values for the average for Northern European Members of 32The correlation of MFP against various OECD PMR sub-indexes is stronger for some than others. The correlation between MFP and openness (discriminatory procedures against foreign firms) is 0.6799. The correlation between MFP and domestic regulation (here represented by the price regulation index) is 0.4989.

30
# **Table B1: Data sources and summary statistics**

|Variable|Descriptions|Country<br>N|Obs. No.|Mean|S.D.|Min|Max|
|---|---|---|---|---|---|---|---|
|**National poverty rate (1**|**979–2019) (percent)**|o.||||||
|_Source: The World Bank_ (|https://datacatalog.worldbank.org/home)|||||||
|Poverty rate $1.90|Poverty Headcount Ratio at US$ 1.90 a day|134|464|7.288|14.814|0.000|78.841|
|Poverty rate $3.20|Poverty Headcount Ratio at US$ 3.20 a day|134|464|15.399|23.975|0.000|91.518|
|Poverty rate $5.50|Poverty Headcount Ratio at US$ 5.50 a day|134|464|26.593|32.088|0.051|97.485|
|**Subnational poverty rat**|**e (Global Subnational Atlas of Poverty – GSAP)**|||||||
|**(percent)**||||||||
|_Source: The World Bank_ (|https://datacatalog.worldbank.org/home)|||||||
|Poverty rate $1.90|Poverty Headcount Ratio at US$ 1.90 a day|134|4,972|10.061|19.389|0.000|98.010|
|Poverty rate $3.20|Poverty Headcount Ratio at US$ 3.20 a
in the region are riparian.

_East Africa’s road network is in relatively good condition, with prominent patches of poor-quality roads along some corridors and significant stretches of unpaved road._ There are three main corridors in the region. The northern corridor—which connects Kenya, Uganda, Rwanda, and Burundi, with a branch heading north to Sudan—is the main trading corridor. The central corridor competes directly with the northern corridor. It connects Tanzania, Burundi, Rwanda, and Uganda and extends into the northeastern part of the Democratic Republic of Congo. The third corridor connects Addis Ababa to Djibouti in the northern part of East Africa.

_Surface transport in East Africa is challenged by border crossings and trade logistics but faces fewer obstacles from the trucking industry which is comparatively mature and more competitive than in some other subregions._ In East Africa, road transport tariffs are on the order of $0.07 per tonne-kilometer, compared with $0.05 per tonne-kilometer in southern Africa and $0.04 per tonne-kilometer in much of the rest of the developing world. Nevertheless, in East Africa, trucking industry prices are determined by market forces rather than restrictive regulations. Recent surveys based on transporter perceptions (World Bank 2010b) indicate that logistics in East Africa are the worst in Africa, with bottlenecks exceeding those in West Africa. The primary reason for the challenges appears to be a steep increase in traffic and trade flows across borders without a corresponding improvement in infrastructure and in the policy environment and institutions associated with cross-border trade.
in this study, i.e., when the food crop yields are perfectly anticipated. Specifically, it shows three scenarios, where the value of the crop technology index corresponds to (i) expected values of the stochastic crop productivity index (deterministic-baseline scenario), (ii) the most pessimistic climate and bad technology states (deterministic-pessimistic scenario), and (iii) the most optimistic climate and good technology states (deterministic-optimistic scenario). The right-hand side of Figure 3 shows the di↵erence range between the 1,000 simulation paths based on di↵erent ex-ante realizations of the stochastic crop productivity index and the deterministic-baseline path. The 10%, 50% and 90% quantile lines represent 10%, 50% and 90% quantiles of 1,000 simulated values respectively at each time, and the average line (the “+” line) represent the average of 1,000 simulated values respectively at each time. The right-hand side of Figure 3 also shows two extreme cases of optimal land-use paths conditional on period _t_ realizations of crop productivity index states _At_ ( _J_ 1 _,t, J_ 2 _,t_ ). The realized crop productivity index always takes the highest possible value in a _stochasticoptimistic_ case (the line of squares), and the lowest possible value in a _stochastic-pessimistic_ case (the line of marks). As future realizations of the stochastic crop productivity index are uncertain, these extreme stochastic solutions are not the same as the corresponding deterministic solutions, where the values of future crop productivity index are known with certainty. In the stochastic-optimistic case, for example, potentially lower realizations of future crop productivity index result in larger current-period agricultural land allocation as compared to the deterministic-optimistic solution. For other model variables, due to resource limits (e.g., the total land area is unchanged over time) and other constraints, the impact of uncertainty is theoretically difficult to assess.

We start with the left-hand side of panels (a)-(e) of Figure 3 that shows the
28 percent. According to the estimates, services exports would fall by 62 percent, which implies a 14 and 46 percentage points drops with respect to the intermediate and “Norway” scenarios. Intermediates value added exports would decrease by about a quarter and foreign value added in the UK’s exports would decrease by one‐third.

To better understand these magnitudes, we use the 2011 data from WIOD to obtain the dollar equivalent of the losses for the three different scenarios. It should be noted that these back‐of‐the‐envelope calculations account for the partial effects and do not have general equilibrium or welfare implications since they do not account for possible re‐direction of exports to non‐EU countries (i.e. trade diversion and deflection), price adjustments or changes in wages. Although the largest percentage changes are for service exports, in terms of dollar equivalents we find that export of goods would decrease the most in all three scenarios. The “Norway” case, in which the UK signs an agreement similar to the EEA, suggests that export of goods would decrease by $27.447 billion, while services and value added exports would drop by $19.906 billion and $16.445 billion respectively. To put these numbers in perspective, the impact on total (goods and services) trade is equal to 1.83 percent of the UK’s GDP and 0.63 percent for value added exports. In the worst case scenario, we find that goods exports would decrease by $114.363 billion, which is equivalent to 4.41 percent of the UK’s GDP.<sup>11</sup> For services and value added exports, the values are lower, around $78 billion each, which accounts for 3 percent of GDP.

_Table 6:_ Changes in UK’s bilateral trade with the EU under different scenarios

||"Norway"<br>scenario<br>(36)|"average PTA"<
0.077]|**|[0.059]|[0.062] **|
|EU neighborhood country dummy x CAB|0.485|0.471||0.495|0.445|
||[0.135]|*** [0.130]|***|[0.178]|*** [0.129] ***|
|Gross capital formation (in percent of GDP, four-year averages)||||0.318<br>[0.068]|***|
|Gross national saving (in percent of GDP, four-year averages)|||||0.228<br>[0.066] ***|
|Number of observations|584|584||584|584|
|Number of countries|88|88||88|88|
|Number of instruments|45|40||49|49|
|Hansen test for over-identifying restrictions|0.204|0.204||0.188|0.062[B]|
|Arellano-Bond AR(1) test (p-value)|0.000|0.000<br>||0.000|0.000|
|Arellano-Bond AR(2)test(p-value)|0.769|0.725||0.680|0.937|

Notes:<sup>[A]</sup> GDP data are adjusted for international differences in purchasing power of the dollar.<sup>[B]</sup> When the p-value of the Hansen test is a borderline case, the instruments are further validated by the supplementary evidence from the Sargan test of the over-identifying restrictions. Robust standard errors in parentheses; asterisks indicate statistical significance at the ***1, **5, and *10 percent level. Data for the transition economies refer only from 1997 onwards.
- 6 -

their formal designation, would have to deal with other matters as well and hence their capacity to deal with WTO matters diluted. However, the use of the measure itself, counting heads, tends to introduce an offsetting, small country bias: At the limit, each country represented has no less than one representative. A very small country with a minute proportion of world trade would still have a representative - and an influence disproportionate to its trade share -- while a country like the US or a group of countries like that of the EC whose trade or GNP may be many hundreds or even thousands of times larger would not be expected to have Missions of proportionate size.<sup>3</sup> But of course, numbers and formal representation and chairmanships do not tell the whole story. Informal arrangements work along with and sometime supersede the formal committee structure and arrangements. Information on these informal processes of developing consensus is hard to document. In some cases, the groups involved and their membership is a matter of public record. In other cases, they are not, and the analysis presented here has had to rely on personal interviews with the information provided on condition of no attribution.

Finally, it was not possible to undertake a systematic analysis of institutional constraints and requirements for effective representation and participation in the WTO for all developing countries. The main new data presented on this issue are based on the "Trade Related Technical Assistance Needs Assessments" prepared by a number of Least Developed Countries in the context of the High Level Meeting on Integrated Initiatives for Least Developed Countries' Trade Development held in Geneva in October 1997.

3It was also thought that Mission size would also be affected by a country's geographical proximity to Geneva. Were this to be an important factor, it would tend to bias downward the Mission size of developed, European countries. It turns out, that if that bias exists, it does not show in the statistics, - as the European countries are substantially represented in
interacted with intl as part of the C ∗intlo term. Coefficient results for the control vectors of GRAV and C can be found in Annex Table A4.

A similar conclusion is reached in Table 6 when adding the OECD Digital STRI as a control variable instead of the DTRI. This indicator considers the sub-index that includes restrictions related to Infrastructure and connectivity. This area of the Digital STRI covers restrictions on cross-border data flows, as presented in Ferencz (2019), amongst various others. Note that this index does not cover any measures related to domestic data processing. In this case too, the results of the variables of interest remain unchanged. One notable change however is that the positive significance of the coefficient of the open model for the cross-border flows of data in column 2 is reinforced, as it is also the case of the negative significance of the limited model regarding the domestic processing of data, also in column 2. The outcome on the OECD Digital STRI variable has varying coefficient signs, which is probably due to the fact that it measures exports (see footnote 24).

# **5.2 Performing Panel Series**

Thus far this paper has dealt with a cross-section analysis. Even though informative, a one-year assessment doesn’t allow for any inferences over time, nor does it say anything about a causal relationship between the data models that countries opt for and trade in digital services. For instance, countries change their regulatory regime for data over time, which likely has an influence on trade patterns between them. Among the 116 countries analysed, a substantial number has changed their regulatory framework across the two components of cross-border data transfers and domestic data processing. Given that the default option is the open model, Table A4 shows the list of switching countries that since 2000 have changed their governing framework to either the conditional model or the limited model.

Although it’s hard to resolve any endogeneity concern that may arise due to the fact that countries with stronger bilateral trade ties may therefore choose a certain
services are more expensive in low income countries 47. This is important because services compose a large share of government consumption in low income countries. Finally, to sharpen further the point estimate of price elasticity, we revisited the Summers-Heston data and excluded countries that Summers and Heston had given a low quality rating (quality D) and re-estimated the size regression of _Table 2_ using the remaining data. As expected, this reduced the estimate of elasticity to -0.578 (OLS) and - 0.697 (IV).

Turning to the growth estimations _(Table 12),_ we can again largely replicate results. The growth regression that uses World Bank data has a lower R-squared (0.5602 versus 0.6161) than the regression with constant 1985 international prices . For the IV regression, we again plug in the prediction from the size regression of _Table 11._ Lagged five-year averages are used as instruments for the other endogenous variables. The state variables generally yield similar signs and point estimates. In the case of the initial income or convergence term, the coefficient when using the World Bank values is smaller in absolute value, indicating a slower conditional convergence rate of 0.3 percentage points per year as opposed to 1.8 percentage points . The government size variable is negative and significant in both estimations. The quality of bureaucracy variable

> **47The** correlation coefficient between relative prices of government consumption and real per capita GDP is 0.62 when using Summers Heston data and -0.1 when using World Bank data; both are significant at I **percent level.**

> The dependent variable is different, but it is known that per capita growth from the two data sets are closer to each other than the level of per capita GDP (see _Table 5_ and Barro and Sala-i-Martin (1995) p. 444). Hence, the comparison of the R-squared is valid and is in fact equivalent to a non-nested J- type hypothesis test carried out by Barro and
sup> Reduced transaction costs, greater liquidity, and increased credit worthiness are associated with mobile money activities.<sup>75</sup> Regulations have not caught up with the rapid development of mobile money activities.<sup>76</sup> In the Arab Republic of Egypt, Bangladesh, and Kazakhstan, laws prohibit nonbank businesses from providing mobile money services on their own. The traditional financial institutions that lobby heavily to remain the gateway of the digital payment system, as well as the uncertainty about the financial risks posed by mobile money service providers, dissuade governments from giving the green light to the new type of payment service providers.

That said, governments can take a few steps to avoid handicapping innovation or discouraging new entrants from entering the digital payment system, while ensuring that customers are protected. Standardizing the licensing of payment service providers ensures quality control of players in the market and helps streamline the government’s supervision and oversight of their activities. Security measures should be in place for all types of payment service providers. Most countries covered by the Digital Business Indicators project except Armenia, Honduras, Korea, and Pakistan require payment service providers to establish at least one 20
Table 1: Response Rate at Workshops

|Institution|Location|Year|Eligible<br>Attendees|Surveyed|Response<br>Rate|
|---|---|---|---|---|---|
|IDB|Washington, D.C.|2018|49|18 (18)|0.37 (0.37)|
|World Bank|Athens, Greece|2019|39|38|0.97|
|World Bank|Marrakesh, Morocco|2019|41|33|0.80|
|World Bank|Bangkok, Thailand|2023|30|27|0.90|
|World Bank|Dar es Salaam, Tanzania|2023|31|30|0.97|
|Total|||190|156|0.82|

The IDB rows include responses from the “pre” period and, in parentheses, the “post” period. The total is calculated using the total number of unique respondents across both rounds of the IDB survey (28). We exclude researchers from both the “eligible attendees” and “surveyed” counts, as too few researchers attended these workshops to be considered. Otherwise, all workshop participants are considered to have been eligible. Overall, response rates were very high.

## **2.2 IDB Sample**

Participants were also recruited from a workshop organized in May 2018 at the IDB headquarters in Washington, DC. Like the WB workshops, this workshop ran for approximately one week, was attended by policymakers and policy practitioners, and focused on impact evaluation methods. This workshop, however, did not include matching participants with researchers to design an impact evaluation as the World Bank workshops did.

Participants were emailed the survey link by the workshop organizers before the start of the workshop and again after the workshop (for a second, identical survey). We focus on the survey responses collected before the start of the