rafmacalaba/datause-encoder-data huggingface dataset:
Table 3: ITT Effects on Business Outcomes: Intensive Margin

||Main Business<br>Sales|Main Business<br>Profts|Other Businesses<br>Profts|All Businesses<br>Profts||
|---|---|---|---|---|---|
||(1)<br>Levels|(2)<br>Levels|(3)<br>Levels|(4)<br>Levels|(5)<br>Index|
|||_Panel A: Midlin_|_e (6 months)_|||
|ITT|4087.60|16949.96|7015.56|27589.10<sup>_∗∗_</sup>|0.18<sup>_∗∗_</sup>|
||(6477.32)|(10892.99)|(5518.12)|(13570.82)|(0.09)|
||[0.545]|[0.327]|[0.327]|[0.188]|[0.188]|
|Observations|532|525|547|547|547|
|Control Mean|37714.29|69693.20|18589.11|88718.68|-0.05|
|Adj. R<sup>2</sup>|0.153|0.186|0.080|0.214|0.078|
||_P_|_anel B: Endline (_|_18–24 Months)_|||
|ITT|9380.31|8214.84|8606.68|18640.44|0.14<sup>_∗∗_</sup>|
||(7649.73)|(10581.50)|(5223.41)|(12726.53)|(0.06)|
||[0.337]|[0.475]|[0.248]|[0.248]|[0.079]|
|Observations|538|530|545|545|545|
|Control Mean|45476.26|76934.12|17251.55|94589.53|0.06|
|Adj. R<sup>2</sup>|0.146|0.113|0.058|0.126|0.058|

_Note:_ We winsorize all sales and profit measures at the 99th percentile. Coefficients are ANCOVA estimates that control for the outcome at baseline, the respondent’s location, marital status, household size, number of children, age, network size and level of education. ITT shows traditional intent to treat effects. Column (1) presents the reported sales for the 3 days prior the survey, columns (2) to (4) present the self-reported profits for the last month for either the main business, other businesses, or all businesses. Values in columns marked with heading _Levels_ are values in UGX. We report White robust standard errors in parentheses. Index is an Anderson index of all outcomes using the swindex command in Stata. Romano-Wolf multiple hypothesis test q-values are presented in brackets. Q-values include the same controls as above and are computed within pre-specified families of outcomes.

25
column, although they do predict the extensive margin to some extent. The number of years the migrant has spent abroad is never significantly associated with any measure of remittances conditional on these other covariates.

The analysis in Table 3 pools together (i) nationally representative surveys of recent immigrants (NIS, LSIA and DREES), (ii) a nationally representative survey of all immigrants (the ENI), and (iii) specific surveys of certain groups or of remitters only. To test the sensitivity of the associations seen in Table 3 to this pooling of data, we therefore also re-estimated Table 3 for each of the groupings (i), (ii) and (iii) separately. The results are quite robust to the choice of sample, with the sign, approximate magnitude, and statistical significance of the main individual characteristics generally not varying much over the sub-samples.<sup>7</sup>

# **_4.1 Remittance patterns by gender_**

The regressions in Table 3 show the linear association of particular variables with remittances, conditional on other variables. We now turn to exploring four key variables of interest to policymakers more closely. The first is gender. The percentage of respondents who are female varies from only 12 percent in the Italian NiDi survey, to 49 percent in the United States New Immigrant Survey and 51 percent in the French DREES survey. Table 4 summarizes the patterns of remitting by gender. In all surveys, women report remitting less money on average than men in the past year, and this difference is statistically significant in two of the surveys. In five of the surveys women are significantly less likely to remit than men. Overall, men remit an average of $568 per person than women (although this difference is not statistically significant). In the pooled sample, only 26% of female respondents remit, which is statistically different than the 42% of male respondents who remit. These differences appear to be largely driven by the differences in the observable characteristics of male and female migrants controlled for in Table 3, since after controlling for these characteristics males only remit $71 more and are only 2.8 percentage points more likely to remit, neither difference being statistically significant.

> 7 Results available upon request.

16
expect that GDP over-reporting would be higher in regions with worse institutional environment.

We thus divide the sample cities into two groups based on the median level of institutional quality in Table 5. Two proxies of institutional quality are chosen. One is measured by the provincial share of entertainment and traveling costs (ETC) over sales, which has been found to be a good proxy of corruption (Cai, Fang, and Xu 2011), and is widely used for capturing corruption in China. The other is the marketization index from the NERI report (Fan, Wang, and Zhu 2010). Lower ETC and a higher marketization index indicate better institutional environment.

As expected, we find the effect of political cycles on GDP manipulation is only limited to the regions with more severe corruption and less developed market economy. Judging by the contrast of the coefficients for the high and the low areas in both indices, the importance of marketization seems to be stronger than that of corruption.

# **5.4 Bureaucratic experience and performance manipulation**

Next, we explore the heterogeneity among officials by considering whether the PS has any prior experience as the city secretary or the mayor, as more experienced PSs could better understand and use the rules, and could more effectively manipulate GDP figures without causing problems.

In columns 5 and 6 of Table 5, the political cycle effect on GDP manipulation is indeed more relevant for experienced PSs. In contrast, reported growth from inexperienced secretaries is immune to this political cycle, with the estimated coefficients for both the linear and the quadratic terms small and statistically insignificant. Relevant political experience of the PS thus indeed facilitates performance manipulation.

# **5.5 Target and performance manipulation**

Cities differ in their ability to fulfill their growth targets. The difference could stem from bureaucrat capacity, or simply inherent growth potential. Whatever the cause, however, falling behind the target increases the appeal to inflate GDP to reach the target. We thus compare cities that achieve the growth target in the annual _Report on the Work of the Government_ with those that fail. We expect that for the PS truthfully accomplishing the plan in the sense that the predicted true growth surpasses the target, he would be less likely to exaggerate the reported figure.

Our results from the last two columns of Table 5 support the hypothesis that falling behind the target increases performance manipulation. The political cycle effect is observed and pronounced in cities whose true performance failed to reach the target. In contrast, those whose targets have been met truthfully do not exhibit the same pattern in over-reporting GDP figures.

22
depend on either year. In any case, the results discussed further below are not significantly affected by this choice of denominator.

In a nutshell, the decomposition exercise separates the contributions to annual export growth of incumbent, entering and exiting firms. The contribution of incumbent firms is simply the product of the share of exports of incumbent firms times their export growth. This contribution appears in the first term inside brackets in equation (1).

The contribution of entering firms has two components, both appearing inside the brackets of the second term on the right-hand side of (1). The first is simply the number of entering firms as a share of average number of total firms in t-1 and t. In (1), this is written as the number of entrants times the average exports per firm in t-1. The second component concerns the deviation of the average exports of entering firms from the average exports of incumbent firms, the latter being equal to the number of entering firms times the average exports per firm in the previous year. The calculation of the contribution of exiting firms is analogous to that of the contribution of entrants. The results from these decompositions of annual export growth rates are presented in the following section and Table 5.

As discussed below, it turns out that the annual growth rates are dominated by the contribution of incumbent firms. Thus, it is worthwhile to explore the contribution of new and exiting products and export destinations by incumbent firms. Tables 6 and 7 contain the results for these decomposition exercises.

# **_B. Short-run results_**

The first column of Table 5 shows the annual growth rate of real exports observed in the cleaned PROCOMER data. The second column shows the share in total exports in the previous year due to incumbent firms, and the third column contains the export growth of these incumbent firms. The evidence clearly shows that incumbent firms dominate export growth from year to year, as over 95 percent of total exports in the previous year were due to firms that remained as exporters in the following year. Consequently the export growth rate of the incumbent firms closely tracks the annual growth of total exports.

Interestingly, the rate of turnover of exporting firms is large. The number of entrants is higher than 27 percent of the number of incumbent firms in every year – see column 4. Similarly, the rate of exit is higher than 25 percent every year – see column 6. Furthermore, 9
|**_Fuel-Content Re_**<br>|**_gulations and T_**<br>|**_ailpipe Emis_**<br>|**_sion Standards_**<br>||which ould increase traffic<br>safety risks<br>|
|---|---|---|---|---|---|
|Auffhammer &<br>Kellogg (2011)|United States|1989-2003|Ambient air concentrations<br>of ozone, weather data|Regression<br>discontinuity and<br>Difference-in-<br>differences|The US federal gasoline<br>content regulation did not<br>improve air quality<br>because refiners lacked<br>incentives to reduce the<br>emission components that<br>are most closely related to<br>ozone formation|
|**_New technologie_**<br>|**_s (ride-sharing)_**<br>|||||
|Cohen et al<br>(2016)|United States|2015|Surge price, Uber-defined<br>geographic region, time and<br>date, anonymized rider id,<br>Uber’s prediction of<br>expected wait time, and the<br>ultimate decision of the<br>rider whether to request a<br>car|Identify price elasticity<br>and estimate demand<br>curve|Ridesharing could increase<br>consumer surplus|
|Leard & Xing<br>(2020)|United States|2016-2017|2017 National Household<br>Travel Survey|Discrete Choice Model<br>and Simulation|Availability of ridesharing<br>has led to modest increases<br>in total vehicle miles<br>traveled and greenhouse<br>gas emissions|
|Tirazhini &<br>Gomez – Lobo<br>(2017)|Chile|January<br>11-20,<br>2017|Survey results on Uber|Monte Carlo Simulation|Ridesharing is like to<br>increase vehicle km<br>traveled|
|Hall et al<br>(2018)|United States|2004-2015|Transit ridership, Uber entry<br>and exit data|Difference-in-<br>differences|Uber complements public<br>transit by solving the last-<br>mile problem of public<br>transit|

54
**Table 5:** Effects of Turnovers on Alignment with Citizens’ Preferences

|||**Bureaucr**|**at survey**||**Villag**|**e head survey**|
|---|---|---|---|---|---|---|
||**Offcials/c**|**itizens agree on:**|**Complain**|**ts received about:**|**Village he**|**ad takes action on:**|
|**Village Services:**|Priority|Worst-quality|Priority|Worst-quality|Priority|Worst-quality|
||(1)|(2)|(3)|(4)|(5)|(6)|
|New village head|0.105|0.236<sup>_∗∗_</sup>|0.162<sup>_∗∗∗_</sup>|0.174<sup>_∗∗_</sup>|0.127<sup>_∗_</sup>|0.213<sup>_∗∗_</sup>|
||(0.102)|(0.103)|(0.059)|(0.067)|(0.065)|(0.087)|
||[0.116]|[0.117]|[0.068]|[0.079]|[0.079]|[0.103]|
|Observations|1067|1067|1067|1067|443|443|
|Control mean|0.75|0.32|0.87|0.71|0.94|0.88|
|Robust p-value|0.204|0.015|0.006|0.028|0.073|0.019|
|<br>MSE-opt. bandwidth|17.5|17.0|18.4|25.7|22.7|26.4|
|Effective obs.|467|457|484|606|223|253|

_Notes_ : This table reports RD estimates of _γ_ in equation (1) obtained via the non-parametric method from Calonico et al. (2014). Units of observation are bureaucrats in columns 1–4 and village heads in columns 5–6. In column 1, the dependent variable is an indicator equal to 1 if the bureaucrat names as priority for future development spending a public service which village citizens identify as a top-3 priority. In column 2, the dependent variable is an indicator equal to 1 if the bureaucrat names as priority for future development spending a service which citizens rank as a bottom-3 quality public service. In column 3, the dependent variable is an indicator equal to 1 if the bureaucrat reports receiving complaints about at least one public service the majority of village citizens identify as a top-3 priority. In column 4, the dependent variable is an indicator equal to 1 if the bureaucrat reports receiving complaints about at least one public service the majority of village citizens believe is a bottom-3 quality public service. In column 5, the dependent variable is an indicator equal to 1 if the village head took action on a public service which village citizens identify as a top-3 priority. In column 6, the dependent variable is an indicator equal to 1 if the village head took action on at least one public service the majority of village citizens believe is a bottom-3 quality public service. See Section 4 for details. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Robust bias-corrected standard errors clustered by village in parentheses.

37
||Global<br>(1)|Factor<br>Advanced<br>(2)|Emerging<br>infows<br>(3)|Global<br>(4)|Factor<br>Advanced<br>(5)|Emerging<br>infows<br>(6)|
|---|---|---|---|---|---|---|
|Lagged global|0.615<sup>_∗∗∗_</sup>|-0.0916|-0.339<sup>_∗_</sup>|0.379<sup>_∗∗∗_</sup>|-0.217|-0.217|
|factor|(0.126)|(0.161)|(0.200)|(0.134)|(0.191)|(0.239)|
|Lagged advanced|-0.0348|0.216<sup>_∗∗_</sup>|-0.0495|-0.0463|0.209<sup>_∗∗_</sup>|-0.0516|
|factor|(0.0768)|(0.0979)|(0.122)|(0.0687)|(0.0982)|(0.123)|
|Lagged emerging|-0.195<sup>_∗∗∗_</sup>|-0.0961|0.663<sup>_∗∗∗_</sup>|-0.0321|-0.0328|0.610<sup>_∗∗∗_</sup>|
|infows factor|(0.0561)|(0.0715)|(0.0888)|(0.0698)|(0.0999)|(0.125)|
|BAA spread|-0.253<sup>_∗∗∗_</sup>|-0.470<sup>_∗∗∗_</sup>|-0.129|-0.196<sup>_∗∗∗_</sup>|-0.444<sup>_∗∗∗_</sup>|-0.153|
||(0.0631)|(0.0805)|(0.0999)|(0.0585)|(0.0838)|(0.105)|
|US real|0.163<sup>_∗∗_</sup>|0.190<sup>_∗∗_</sup>|-0.195<sup>_∗_</sup>|0.174<sup>_∗∗∗_</sup>|0.191<sup>_∗∗_</sup>|-0.201<sup>_∗_</sup>|
|exchange rate (log)|(0.0652)|(0.0831)|(0.103)|(0.0581)|(0.0831)|(0.104)|
|Financial openness|4.442<sup>_∗∗∗_</sup>|2.499<sup>_∗_</sup>|3.370<sup>_∗∗_</sup>|4.490<sup>_∗∗∗_</sup>|2.600<sup>_∗_</sup>|3.165<sup>_∗_</sup>|
|average|(1.082)|(1.379)|(1.713)|(0.953)|(1.363)|(1.706)|
|Yield curve|-0.0602|-0.353<sup>_∗∗∗_</sup>|-0.260<sup>_∗∗_</sup>|-0.0302|-0.342<sup>_∗∗∗_</sup>|-0.274<sup>_∗∗_</sup>|
|slope|(0.0667)|(0.0850)|(0.106)|(0.0598)|(0.0856)|(0.107)|
|Commodity price|0.245<sup>_∗∗_</sup>|-0.381<sup>_∗∗∗_</sup>|0.257||||
|<br>(non-energy, log)|(0.109)|(0.140)|(0.173)||||
|Agriculture price||||-0.210|-0.437<sup>_∗∗_</sup>|0.317|
|(log)||||(0.139)|(0.198)|(0.248)|
|Metals&minerals||||0.470<sup>_∗∗∗_</sup>|0.0258|-0.0607|
|price (log)||||(0.123)|(0.177)|(0.221)|
|_cons|-2.688<sup>_∗∗∗_</sup>|-1.537<sup>_∗_</sup>|-2.076<sup>_∗∗_</sup>|-2.737<sup>_∗∗∗_</sup>|-1.608<sup>_∗_</sup>|-1.942<sup>_∗_</sup>|
||(0.668)|(0.851)|(1.057)|(0.588)|(0.841)|(1.053)|
|_N_|36|36|36|36|36|36|
|_R_<sup>2</sup>|0.926|0.883|0.815|0.942|0.885|0.816|

Standard errors in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01 Table 8: VARX (in levels) with one lag of the endogenous variables. The Financial Openness measure is a 3-year moving average of the Chinn-Ito capital account openness index averaged over advanced and emerging countries.

44
10 In the case of rail, we show a quasi stagnation in km of tracks per capita in developing countries. This is not particularly surprising as rail construction has largely stopped in the last 20 years and given the fact that in most countries, privatization of the railroads has brought with it the abandonment of unprofitable lines. In high income countries we also see a small decline - note that this is in per capita terms and need not therefore imply an absolute decline; Overall, the implication for the rail sector is not that no new investment will be taking place, but rather that it is more likely to take the shape of upgrading and rehabilitation rather than of an actual expansion of the network. This is indeed what has been happening in a number of countries (Mexico, Brazil) already.

# **_Implications for investment*_**

From our projections for infrastructure stocks in 2010, we can derive the associated flow of required new investment. To do so we simply look at the predicted increase in stock, and price it using best practice prices taking into account associated network costs. This is important notably in the case of power, where generating capacity is only a share of total infrastructure cost.<sup>6</sup>

**Table 6. Unit costs for infrastructure investment**

|Sector|$|Unit|
|---|---|---|
|Electricity|$1,900|per kilowattofgeneratingcapacity, includingassociated<br>networkcost.|
|Roads|$410,000|per kilometeroftwo lanepavedroad|
|Railway|$900,000|perkilometersorrail,including associatedrollingstock|
|Sanitation|$700|perconnected household|
|Water|$400|perconnected household|
|Mainlines|$400(from2000onward)|perline|
|Mobile<br>$70|0in2000and$580from 2005 on|per subscriber|

Source: Mobile, Pyramid Research, World Bank specialists; Mainlines: Ruzzier, Kennet, Benitez, and Estache (2000). Water, Sanitation, Roads, Electricity : World Bank specialists.

It would be misleading however, to only look at investment needs, in the sense that this seriously underestimates the flow of resources needed to maintain or improve access to services. Thus we also look at maintenance needs. These are calculated, in rough estimates, to be 2% of the replacement cost of the capital stock for electricity generation, rail and road; 3% for water and sanitation, and 8% for mobile and mainline. These numbers are not meant to represent an optimum for maintenance expenditures but are broadly seen as being the minimum annual average expenditure on maintenance, below which the network's functionality will be threatened.

An argument can be made that since many developing countries have substantially underinvested in maintenance, we should also include an estimate for rehabilitation. Unfortunately, the data is simply not available to make such an estimate. Nevertheless, it

> **6** Depending on the choice of technology and the population density, the proportion will vary, but a decent rule of thumb could be that 60% of the investment cost is for generation, 30% for distribution, and 10% for transmission.
17

**Table 9. Shares of Local Governments in Total Consolidated Regional Revenues**

**and in Select Consolidated Regional Tax Revenues,** %

|**Summary**<br>**Statistics /Year**|**1992**|**1993**|**1994**|**1995**|**1996**|
|---|---|---|---|---|---|
|1. Share in Total Consolidated Regional Revenues*||||||
|1.1. Mean|66.59|N/A|62.37|67.83|70.25|
||(1.44)|N/A|(1.18)|(1.27)|(1.43)|
|1.2. Standarddeviation|13.24|N/A|10.73|11.7|13.02|
|1.3. Coefflicient<br>ofvariation|0.20|N/A|0.17|0.17|0.19|
|1.4. Minimum vakhe<br>|15.73|N/A|26.81|22.76|11.96|
|1.5. Maximum value|98.02|N/A|85.35|97.76|96.69|
|1.6. Vafid Number of Observations|85|N/A|83|85|83|
|2. Share in Profit Tax revenues||||||
|2.1. Mean|62.51|56.48|57.40|55.79|55.77|
||(2.13)|(2.02)|(2.16)|(2.23)|<br>(2.25)|
|2.2. Standard deviation|19.68|18.63|19.90|20.58|20.66|
|2.3. Coefficient ofvariation|0.31|0.33|0.35|0.37|0.37|
|2.4. Mininum value|22.82|21.08|21.59|19.70|<br>7.28|
|2.5. Maximum value|99.99|100|100|100|<br>100|
|2.6. Vaid Number of Observations|85|85|85|85|84|
|3. Share in VAT revenues||||||
|3.1. Mean|53.82|51.75|49.06|_58.15_|55.71|
||(3.32)|(2.96)|(2.85)|(3.21)|(2.82)|
|3.2. Standard deviation|30.46|27.13|26.00|27.40|26.00|
|3.3. Coefficient ofvariation|0.57|0.52|0.53|0.47|0.47|
|3.4. Minimum value|0|0|0|0|0|
|3.5. Maximumvalue|100|100|100|100|<br>100|
|3.6. Valid number ofobservations|84|84|83|73|85|
|4. Share in Personal Income Tax revenues||||||
|4.1. Mean<br>.|85.00|88.33|87.37|87.76|88.68|
||(1.9)|(1.74)|(1.8)|(1.81)|(1.74)|
|4.2. Standard deviation|17.20|15.99|16.63|16.42|15.93|
|4.3. Coefficientofvariation|0.20|0.18|0.19|0.19|0.18|
|4.4. Minimm vakle|40.75|46.61|40.10|40.70|42.20|
|4.5. Maximurnvahle|100|100|100|100|<br>100|
|4.6. Valid numnber<br>of observations|82|84|85|82|<br>84|
|_5._Share in Excise Tax revenues||||||
|5.1. Mean|32.95|36.25|41.78|45.5|46.19|
||(3.96)|(3.83)|(3.99)|(3.95)|(4.16)|
|5.2. Standard deviation|36.08|34.88|35.94|35.8|37.64|
|5.3. Coefticientofvariation|1.09|0.96|0.86|0.79|<br>0.81|
|5.4. Minimum value|0|0|0|0|0|
|5.5. Maxinumvalue|100|100|100|<br>100|<br>100|
|5.6. Valid number ofobservations|83|83|81|82|82|

# Standard error of the mean in parentheses.

The cities of Moscow and St. Petersburg, Chechnya and Ingushetia excluded from the sample.

* Calculated as a ratio of the sum of the total revenues of all tiers of local government minus the revenues from transfers from one local governmen **t** o another and the sum of the total revenues of local and regional governments minus the transfers from regions to local governments and from local governments to regions.
16 oilseeds and livestock tend to have more welfare-reducing policies in place. Grains, which comprise a mixture of tradable and nontradable products, had highlydistortionary policies in place in the 1960s on average, but these have been reduced over time.

Figure 6 gives a snapshot of the diversity across commodity markets in the regional TRI and WRI for 2000–04. Sugar and cotton markets continue to have highly distorted policies in terms of both the trade and welfare effects of policies. Soybean, by contrast, has trade-expanding policies in aggregate, but the policies are nevertheless welfare reducing.

# **Conclusions, caveats and areas for further research**

Reform of agricultural policy in Africa is topical at present. Recently announced international investment programs, domestic policy reforms, and the negotiation of international and regional trade agreements are on the agenda. To assess each of these policy initiatives, measurement of intervention levels is required. Certainly, economywide models can measure the welfare and trade (and other) effects of policy in a particular country or market. But such models require reliable data on the structure of the economy, and econometric estimates of myriad parameters, neither of which can be easily found for the poorer countries of Africa. Even where economy-wide models are available, they may be calibrated to a particular year and so incapable of providing a long time-series of estimates of the regional effect of distortional policies over time.

Scalar index measures, by contrast, can meaningfully summarize the welfare and trade effects of policy intervention in agriculture in poorer countries. As demonstrated above, these indexes can be estimated using already available price, quantity and distortions data, and so are relatively inexpensive to generate.

The estimates in the paper reveal several important findings. The national level TRIs and WRIs indicate that although there has been policy reform in African agriculture over the past 50 years, the overall trade- and welfare-reducing effects of current policies remain significant. Some individual commodity markets in Africa are more distorted than others, sugar and cotton being two of the most distorted.

The scalar index numbers reported in this paper provide a better measure of policy intervention than widely-used NRA-type measures because they correctly