rafmacalaba/datause-encoder-data huggingface dataset:
# **2. Data**

To document worldwide patterns of capital flows by domestic and foreign agents, we assemble a comprehensive dataset on aggregate gross capital flows, including not only capital inflows and outflows but also their subcomponents, reflecting the different flow types. The data come from the analytic presentation of the IMF’s Balance of Payments Statistics Yearbooks (BOP).<sup>12</sup> The IMF’s BOP dataset provides country-level data, on an annual basis from 1970 until 2009, on different types of capital inflows measured in U.S. dollars. Fundamental to our goal, this dataset allows us to disentangle, respectively, capital outflows by domestic agents ( _COD_ ) and capital inflows by foreigners ( _CIF_ ), which are reported as flows related to the reporting country’s assets and liabilities vis-à-vis non-residents. In other words, _CIF_ is recorded as capital inflows to the reporting economy by foreign agents, indicating an increase in foreigners’ holdings of domestic assets. Analogously, _COD_ is reported as flows from the reporting economy, where positive values correspond to an increasing of the holdings of foreign assets by domestic agents.<sup>13</sup> Hence a positive _COD_ should be interpreted as capital outflows by domestic agents whereas a negative _COD_ means capital inflows.

Our dataset also allows us to analyze the behavior of the different types of capital flows. Flows are classified as: direct investments (also known as FDI), portfolio flows, other investments (mostly bank flows and trade credit), and international reserves.<sup>14</sup> Portfolio flows are further divided into equity and debt flows. Both private and public flows are included in our

> 12 Debt refinancing and rescheduling entries that involve changes in existing debt contracts or replacement by new ones, generally with extended debt service payments are excluded from our dataset. In the analytic presentation of the IMF’s BOP, these flows (credit and debt entries that account for the new contracts) are computed within a country's financial account as exceptional financing items. Therefore, our analysis excludes items derived from the rescheduling or refinancing of existing debt contracts as they generally do not involve new capital inflows to the reporting country.

> 13 These measures however do not capture increases in foreigners’ (domestic agents’) holdings of domestic (foreign) assets that are due to valuation effects.

> 14 Due to their relatively small size and the scarcity of data, we exclude flows in financial derivatives from our analysis.

8
not necessarily substantiated by strong _ex-ante_ evidence and hence it cannot be ruled out that a more traditional development financing approach (without explicit incentives) could be more effective, as recent evidence from the health sector demonstrates (Kandpal et al., 2020).

# **6 Conclusion**

Going forward, we recommend, first, that projects targeted at building state capacity invest more resources in learning through policy experimentation within the project, given the lack of established knowledge about how state capacity comes and the existing critiques of donor approaches to transplant formal institutions (Bourguignon & Gunning, 2018; Andrews et al., 2017; World Bank, 2017). Second, we recommend more research to understand forces of change in countries that may be strengthening incentives of local governments to deliver services. From reviews of research available so far, it seems that greater political contestation and demands from citizens for improved governance and service delivery are behind these improving incentives, but nevertheless with several risks and pitfalls (Olken & Pande, 2013; World Bank, 2016c; Dal Bo & Finan, 2016; Bardhan & Mookherjee, 2016; Khemani, 2019). A deeper analysis of administrative data can be an essential asset for both of these recommendations.<sup>28</sup> Project designs going forward can use the recommendations from these reviews to strengthen political incentives for service delivery, and thus enable the emergence of state capacity along similar lines as how such capacity emerged in today’s rich countries (Besley & Persson, 2009; Fukuyama, 2004; World Bank, 2016c; Khemani, 2019). For example, performance assessments could focus on rigorously measuring service delivery performance—e.g., road connectivity, garbage collection, coverage of drainage and sewage systems—rather than on primarily reviewing documents and protocols as is currently being done through the APAs. Project design could focus on the communication of, and deliberation around, these performance assessments, with citizens, especially through mass media whose role in strengthening institutions has been recently emphasized in research (World Bank, 2016c; La Ferrara, 2016). Investing in communication and deliberation is not a soft option but rather one that could be applied more scientifically through dedicated projects aimed at building state capacity,

# **References**

- Abadie, A. (2005). Semiparametric Difference-in-Differences Estimators. _The Review of Economic Studies_ , _72_ (1), 1–19. 67

Acemoglu, D. (2005). Politics and Economics in Weak and Strong States. _Journal of Monetary Economics_ , _52_ (7), 1199–1226. 1, 4, 6

> allocated to the capacity building component, with the rest going to the financing of local infrastructure. 28See a recent discussion on this in Legovini & Jones (2020).

28
|Social Fund for Development Project|Qaiser M. Khan|
|---|---|
|Social Fund for Development Project|Qaiser M. Khan|
|Social Fund for Development Project|Qaiser M. Khan|
|Social Fund for Development Project|Qaiser M. Khan|
|BASIC EDUCATION IMPROVEMENT PROJECT|Mahmoud Gamal El Din|
|BASIC EDUCATION IMPROVEMENT PROJECT|Mahmoud Gamal El Din|
|BASIC EDUCATION IMPROVEMENT PROJECT|Mahmoud Gamal El Din|
|BASIC EDUCATION IMPROVEMENT PROJECT|Mahmoud Gamal El Din|
|BASIC EDUCATION IMPROVEMENT PROJECT|Mahmoud Gamal El Din|
|BASIC EDUCATION IMPROVEMENT PROJECT|Mahmoud Gamal El Din|
|BASIC EDUCATION IMPROVEMENT PROJECT|Mahmoud Gamal El Din|
|BASIC EDUCATION IMPROVEMENT PROJECT|Mahmoud Gamal El Din|
|BASIC EDUCATION IMPROVEMENT PROJECT|Mahmoud Gamal El Din|
|BASIC EDUCATION IMPROVEMENT PROJECT|Mahmoud Gamal El Din|
|BASIC EDUCATION IMPROVEMENT PROJECT|Mahmoud Gamal El Din|
|BASIC EDUCATION IMPROVEMENT PROJECT|Mahmoud Gamal El Din|
|Egypt Population Project|Alaa Mahmoud Hamed Abdel-Hamid|
|Egypt Population Project|Alaa Mahmoud Hamed Abdel-Hamid|
|Egypt Population Project|Alaa Mahmoud Hamed Abdel-Hamid|
|Egypt Population Project|Alaa Mahmoud Hamed Abdel-Hamid|
|Egypt Population Project|Alaa Mahmoud Hamed Abdel-Hamid|
|Egypt Population Project|Alaa Mahmoud Hamed Abdel-Hamid|
|Egypt Population Project|Alaa Mahmoud Hamed Abdel-Hamid|
|Egypt Population Project|Alaa Mahmoud Hamed Abdel-Hamid|
|Egypt Population Project|Alaa Mahmoud Hamed Abdel-Hamid|
|Egypt Population Project|Alaa Mahmoud Hamed Abdel-Hamid|
|Technical Assistance Project for Privatization and Enterprise and Ba|nRedha Behbehani|
|Technical Assistance Project for Privatization and Enterprise and Ba|nRedha Behbehani|
|Technical Assistance Project for Privatization and Enterprise and Ba|nRedha Behbehani|
|Technical Assistance Project for Privatization and Enterprise and Ba|nRedha Behbehani|
|Technical Assistance Project for Privatization and Enterprise and Ba|nRedha Behbehani|
|Technical Assistance Project for Privatization and Enterprise and Ba|nRedha Behbehani|
|Private Sector Tourism Infrastructure and Environmental Managem|James A. Reichert|
|Private Sector Tourism Infrastructure and Environmental Managem|James A. Reichert|
|Private Sector Tourism Infrastructure and Environmental Managem|James A. Reichert|
|Private Sector Tourism Infrastructure and Environmental Managem|James A. Reichert|
|Private Sector Tourism Infrastructure and Environmental Managem|James A. Reichert|
|Private Sector Tourism Infrastructure and Environmental Managem|James A. Reichert|
|Private Sector Tourism Infrastructure and Environmental Managem|James A. Reichert|
|Private Sector Tourism Infrastructure and Environmental Managem|James A. Reichert|
|Private Sector Tourism Infrastructure and Environmental Managem|James A. Reichert|
|Private Sector Tourism Infrastructure and Environmental Managem|James A. Reichert|
|Private Sector Tourism Infrastructure and Environmental Managem|James A. Reichert|
|Egypt Education Enhancement Project|Michel J. Welmond|
|Egypt Education Enhancement Project|Michel J. Welmond|
|Egypt Education Enhancement Project|Michel J. Welmond|
|Egypt Education Enhancement Project|Michel J. Welmond|
# TABLE 5. Correlates of Dyadic Differences: Contractual Practices

|||Eth|iopia|||Su|dan||
|---|---|---|---|---|---|---|---|---|
||[1]<br>Any direct<br>imports of<br>inputs?<br>|_yi_ −_yj_||[2]<br>Do you sell on<br>credit?<br>|_yi_ −_yj_||[3]<br>Does firm<br>subcontract<br>production?<br>|_yi_ −_yj_||[4]<br>First principal<br>component<br>|_yi_ −_yj_||[5]<br>Any direct<br>imports of<br>inputs?<br>|_yi_ −_yj_||[6]<br>Do you sell on<br>credit?<br>|_yi_ −_yj_||[7]<br>Does firm<br>subcontract<br>production?<br>|_yi_ −_yj_||[8]<br>First principal<br>component<br>|_yi_ −_yj_||
|_i_&_j_trade with each other|0.0245|−0.0122|0.110|0.114|−0.423**|−0.436**|0.0488|−0.813**|
||(0.117)|(0.105)|(0.119)|(0.252)|(0.174)|(0.177)|(0.331)|(0.380)|
|_i_&_j_have common supplier|−0.0186|−0.00130|0.0755**|−0.116|−0.145*|0.0150|0.0479|0.0304|
||(0.0501)|(0.0404)|(0.0366)|(0.0751)|(0.0762)|(0.0789)|(0.0812)|(0.135)|
|_i_&_j_have common client|0.0838|−0.0139|0.0765|0.184|−0.0457|−0.00759|−0.0310|−0.0390|
||(0.0572)|(0.0524)|(0.0702)|(0.124)|(0.0422)|(0.0430)|(0.0651)|(0.0998)|
|log distance btw_i_&_j_|−0.0123**|−0.000254|0.00530|−0.00906|0.00814***|0.00915**|−0.00867**|0.00854|
||(0.00572)|(0.00143)|(0.00671)|(0.00796)|(0.00314)|(0.00422)|(0.00417)|(0.00923)|
|_i_&_j_belong to same sector|−0.0397**|−0.0171|−0.00192|−0.0368|−0.0299*|−0.00602|−0.00572|−0.0459|
||(0.0160)|(0.0155)|(0.00908)|(0.0286)|(0.0157)|(0.0137)|(0.0131)|(0.0279)|
|Abs diff firm age|−0.000424|0.000480|−0.00207***|−0.00417***|0.000105|−8.22e−05|−0.000668|0.000303|
||(0.000804)|(0.000556)|(0.000636)|(0.00145)|(0.000240)|(0.000582)|(0.000563)|(0.00107)|
|Abs diff managers’ education|0.0299|0.000668|−0.0204***|0.0373|0.0215*|0.00583|0.00164|0.0278|
||(0.0199)|(0.00500)|(0.00748)|(0.0279)|(0.0112)|(0.00657)|(0.00477)|(0.0173)|
|Abs diff managers’ experience|−0.00152*|0.000485|−0.00178|0.00456**|−9.80e−05|−0.000212|−0.000397|−0.000631|
||(0.000779)|(0.000642)|(0.00120)|(0.00210)|(0.000305)|(0.000728)|(0.000633)|(0.00121)|
|Owners’ gender differ|0.0457|0.00391|0.0235|0.0402|0.00257|−0.0164|0.0109|−0.00210|
||(0.0297)|(0.00948)|(0.0306)|(0.0420)|(0.00677)|(0.0126)|(0.0382)|(0.0392)|
|Abs diff log employment|0.131***|0.00259|0.0150|0.138***|0.0659***|0.00494|−0.00398|0.0977***|
||(0.0150)|(0.00473)|(0.0132)|(0.0312)|(0.0145)|(0.00781)|(0.0123)|(0.0264)|

_Source:_ Authors’ computations based on data described in the text.

_Note_ : The table shows ordinary least squares results. A constant is included in all specifications. The numbers in parentheses are bootstrapped standard errors that are robust with respect to heteroskedasticity and cross-observation correlation in the error terms involving the same firms. Statistical significance at the 10 percent, 5 percent, and 1 percent levels is indicated by *,**, and ***, respectively.

41
of HWoMsb MOWS by RS0wn 41 Tom AM OWth

|Regkon|0.|10-1|4<br>15.19|<br>2024|2544|45054|6644|Ove64|
|---|---|---|---|---|---|---|---|---|
|Aehahr|245<br>(4.04)|114<br>(33.02)|71<br>(15.36)|46<br>9.57)|1S<br>(6.20)|47<br>(21.43)|30<br>(6.33)|30<br>(4.04)|
|&WV<br>Ahdo<br>Brong|<br>as<br>66"0|~<br>34<br>(33.33)|22<br>(16.43)|21<br>(10.63)|42<br>(10.14)|10<br>(20.29)|3<br>(4.83)|6<br>(1.45)|
|CAnI"|154<br>(3.06)|61<br>(33.19)|44<br>(13.16)|36<br>(9.46)|104<br>(6.19)|26<br>(22.41)|20<br>(5.60)|17<br>(4.31)|
|EansWn|162<br>(SS7)|s<br>(32.21)|49<br>(13.52)|43<br>(9.74)|104<br>6.56|33<br>(20.66)|16<br>(6.56)|26<br>(3.18)|
|OrseWAccx|29|10<br>(34.94)|10<br>(12.06)|7<br>(12.05)|15<br>(.43)|6<br>(16.07)|4<br>(7.23)|2<br>(4.82)|
|Noitin|254<br>(1.47)|105<br>(34.00)|as<br>(14.06)|40<br>(984)|192<br>(1.16)|0<br>(25.70)|21<br>(7.76)|11<br>(2.81)|
|Upper<br>Eas|63|23<br>(33.16)|12<br>(12.11)|14<br>(6,35|45<br>(7.37)|22<br>(23.08)|6<br>(11.58)|a<br>(3.16)|
|upperWeN|71<br>(1.70)|26<br>(30.21)|30<br>(11.91)|24<br>(12.77)|49<br>(10.21)|16<br>(20.66)|11<br>(766)|4<br>(4.68)|
|Vom|201|76<br>(37.71)|47<br>(14.26)|40<br>(5.62|114<br>(7.50)|22<br>(21.39)|21<br>(4.13)|12<br>(3.94)|
|_Western_|136<br>(3. 2)|57<br>(33.33)|39<br>(13.77)|37<br>(9.42)|102<br>(6.54)|15<br>(24.64)|I1<br>(3.62)|15<br>(2.66)|
|Ghao.a<br>(Survey)|1366<br>(33.06)|576<br>(13.99)|**360**<br>(9.47)|310<br>(7.2)|(22.49)|251<br>(6.24)|143<br>(3.47)|130<br>(3.16)|
|T<br>iw<br>Ghws|4_<br>(3.|**12**<br>(11.71)|**1161**<br>(9.10|**lOMs**<br>_(YA_|<br>**UN**<br>OL14)|**734**<br>5|430<br>(33|472<br>(3.64)|

_S/ Figw is_ pwrmnUes deams prcen of ON ib region.

* EsUnaftd _pOPulnIOn_ (nVd.yer 1966) by BSte11i SIF5ios. Amr. Febuwy 1967.

# Land

The average **lnd** of a houshod an hoW the _WA_ Is used Is presented **hI** Table .1.3. On the average, the **Ounalan** farme **has abOut** 10 **acrs** of land at **his** disposal. Although there Is more **land pr** capa the North than In the South, the average
# **Table 2:  South Africa – Species in top 3 protected areas**

## **2a. Group 1**

|**Vertebrates**|**Plants**|**Arthropods**|**Other Species**|
|---|---|---|---|
|_Anchichoerops natalensis_|_Crassulafloribunda_|_Aedes demeilloni_|_Achnanthes rupestris_|
|_Bathygobius niger_|_Eriosema superpositum_|_Aedes hansfordi_|_Bullia mozambicensis_|
|_Canthigasterpunctata_|_Gerrardanthus_<br>_tomentosus_|_Anoba disjuncta_|_Chalcocystis burnupi_|
|_Halichoeres zulu_|_Gymnosporia woodii_|_Bandusia rubicunda_|_Diodora crucifera_|
|_Pempheris eatoni_|_Phymaspermum_<br>_pinnatifidum_|_Brachyplatys_<br>_testudonigra_|_Echinolittorina natalensis_|
|_Thamnaconus arenaceus_|_Senecio exuberans_|_Cabomina hilariformis_|_Gomphonema entolejum_|
||_Syncolostemon latidens_|_Callioratis abraxas_|_Halgerda dichromis_|
|||_Callyna decora_|_Kerkophorus corneus_|
|||_Carbula recurva_|_Kerkophorus melvilli_|
|||_Cassidaguttipennis_|_Medusafissurella dubia_|
|||_Catamonus melancholicus_||
|||_Catephia amplificans_||
|||_Chiasmia natalensis_||
|||_Cicynethus decoratus_||
|||_Drosophila vulcana_||
|||_Glenea arida_||
|||_Helcita wahlbergi_||
|||_Hortipes mesembrinus_||
|||_Hyblaea occidentalium_||
|||_Hypenapalpitralis_||
|||_Leptopholcusgracilis_||
|||_Monoleptajacksoni_||
|||_Negera natalensis_||
|||_Phonoctonusfasciatus_||
|||_Plecopterapoderis_||
|||_Plusiopalpa dichora_||
|||_Pseudandriasa mutata_||
|||_Pseudochromatosia_<br>_nigrapex_||
|||_Raceloma natalensis_||
|||_Rhynocoris venustus_||
|||_Stagira microcephala_||
|||_Syllepte orbiferalis_||
|||_Synema diana_||
|||_Syngamiafervidalis_||
|||_Thysanoplusia spoliata_||
|||_Trichoplusia roseofasciata_||
|||_Veniliodes inflammata_||
|||_Xosopharaguttata_||
|||_Zerenopsisgeometrina_||

19
9 same per capita income levels reporting poverty rates in a range from zero to 65%. Latent country-level heterogeneity may well be confounding the ability to detect the true relationship; we will return to this point. However, as we will see in the next section, this heterogeneity in poverty levels conditional on mean incomes has a lot to do with between-country differences in the level of inequality.

To look at poverty trends over time, we resort to a longer time series than the one presented in Table 1. Chen and Ravallion (2007) compile poverty indicators for 560 surveys from 100 countries (essentially the same sample of countries used by _PovcalNet_ ) _._ Since poverty incidence at the $1-a-day threshold is effectively zero in high-income economies (which account for the main differences between the _PovcalNet_ dataset and that presented in Table 1) we restrict our attention to the Chen-Ravallion sample of countries.

Tables 2 and 3 present the world and regional average poverty levels, both as incidence ( _H_ ) and in absolute numbers of the poor for selected reference years spanning 1981-2004. Table 2 uses the $1-a-day poverty line, while Table 3 uses the $2-a-day line. There is clear evidence of a decline in absolute poverty in the developing world over the last quarter century. The incidence of $1-a-day poverty, as a proportion of the developing world’s population, fell from 40% in 1981 to 18% in 2004. By 2004, the developing world as a whole was only four percentage points short of attaining MDG1 (a poverty rate of 14.3% by 2015). The corresponding proportions for the total population of the world are 34% and 15%, assuming that nobody lives below $1 a day in the high-income countries. Although the rapid reduction of poverty in China (from 63% to 10%) accounts for much of this global decline, there has clearly been progress elsewhere too: global poverty incidence excluding China falls from 31% to 21% over 1981-2004.

The rates of poverty reduction have been quite disparate in different countries. If one partitions the country sample into the broad regions defined by the World Bank, we see clear heterogeneity in poverty reduction across regions (Table 2). The most pronounced decline was registered in East Asia (from 58% to 9%). South Asia came second, with a fall from 50% to 31%. At the other end of the spectrum, poverty incidence actually rose in ECA during the period of transition from socialism to market economies, though showing encouraging signs of progress since the late 1990s. In Sub-Saharan Africa, poverty was essentially the same in 2004 and 1981, having first grown during the 1980s, and then declined slowly since the late 1990s. Such a small decline in poverty rates, combined with a growing population, translates into a rise in the
28 significant stocks of raw materials and intermediate products," they had accumulated stocks of convertible currencies under the export earnings retention scheme (used in Bulgaria and Poland), and they had easy access to credits and subsidies. Under these circumstances, the restraints on money supply affected the liquidity position of households and private firmse5 but not that of SOEs cushioned by the "old" system. Another consideration is that the dwindling domestic demand might have pushed some SOEs to engage in discounting prices below full production costs. These 'distress sales," also experienced by private firms in market economies during an economic downturn, may have contributed to the weaker sensitivity of SOEs to the exchange rate policy.

It is impossible to link specific policy variables with export performance in the circumstances peculiar to each country. There are too many variables, and they were Ph"mging too rapidly throughou **t** he 1990-91 period. Yet two broad sets of circumstances were shared bv exporters in the CEE-3. First, the shift to a demand-constrained economy was accompanied by a fall in aggregate output and domestic demand. We have no reliable data on the developments in domestic absorption, but the downturn in domestic demand as proxied by the Net Material Product (NMP) and industrial output was quite significant. During the first year of the transformation program in the FCSK, the NMP fell by 20 percent and industrial output by 23 percent. In Hungary, the NMP was 11 percent lower and in Poland 23 percent lower in 1 than in 1989, while industrial output was 23 percent and 33 percent lower, respectively.' It is interesting to note the correlation between the contraction in domestic aggregate activity and the growth of exports to OECD markets (measured by the increase in the share of OECD imports). The relationship is somewhat obscured by the Czechoslovak performance: the FCSK's increase in OECD import share in 1991 was smaller than Poland's, but the fatl in industrial output and national income during the first year of the transition program (by 16 percent and 22 percent, respectively) was slightly larger then in Poland (13 percent and 23 percent, respectively). This slight difference between the Polish and Czechoslovak cases might have been due to several factors: the above-mentioned alleged macroeconomic overkill at the outset of the transformation program in the FCSK; greater dependence of the FCSK on trade with the FSU; and " Because of the unreliability of supplies and persistent excess demand for their products, the SOEs tended to maintain high inventories of inputs rather than of finished products. For an extensive analysis, see Kaminsi (l991:Ch.2). 45 A comparison of real changes in households' and SOEs' deposits suggests that this was the case in Poland in 1990. It may be worth adding that this situation continued in 1991 (for more discussion on this point, see Winiecki, 1990:765-790).

46 See Tables 3.2.3 and 3.2.6 in UN, ECE 1992.
physical output. It is possible to invert a constant elasticity of substitution (CES) demand function and recover from revenues, up to a constant, a counterfactual measure of TFPQ _j_ equal to the value of industry productivity if marginal products for all factors were equalized across establishments. This approach requires only an assumption about demand and profit maximization and does not assume anything about the structure of factor markets.<sup>18</sup> Columns (5) and (6) show this measure of TFPQ _j_ for alternative values of the elasticity of substitution parameter. The effects of financial sanctions are positive and statistically significant in both columns, indicating that in addition to raising average TFPR, sanctions increase industry-wide TFPQ, with _τ_ 1<sup>_TFPR_</sup> = 0 _._ 076(0 _._ 030) in Column (5) which assumes a relatively low substitution parameter _σ_ = 3, and _τ_ 1<sup>_TFPR_</sup> = 0 _._ 108(0 _._ 061) in Column (6) which assumes _σ_ = 5. These results confirm that the mechanism behind the effects of antitrust on aggregate productivity is largely improvements in TFPQ at all firms rather than a reduction in the dispersion of TFPR. Further they suggest that in the absence of misallocation, effects of antitrust on productivity could be higher.

**Aggregation of Effects on Productivity** How much could the Commission’s contribution to aggregate productivity growth in the status quo be, without additional reforms to reduce misallocation? GDP is simply the sum of value added in the Economic Census, plus some adjustments, and GDP grows at the growth rate of total factor productivity in the steady state. Roughly, as shown in Figure 1 the Commission has investigated 40 percent of output over 25 years, implying it can investigate about 1.6 percent of output per year. In our sample, 40 percent of investigations lead to trial. Taking the estimate from Column (1) of Table 10 of the effect of sanctions on market share weighted average TFPR as the effect of these sanctions, this implies the contribution of the Commission to GDP growth is 0 _._ 016 _×_ 0 _._ 4 _×_ 0 _._ 009 = 0 _._ 0000576, which divided by 0 _._ 01, the long run average GDP per capita growth rate, is about 0.576 percent of aggregate productivity growth in the steady state. This number is small in relative terms, but economically significant. We can attribute about half a percent of GDP per capita growth per year to sanctions by the commission. The evidence on false negatives suggests this effect could be larger if the Commission had more resources with which to investigate more of the economy more frequently, and was also able to collect sufficient evidence to secure sanctions at trial.

> 18Another method to assess whether TFPR overstates TFPQ due to a markup would be to estimate the markup itself using alternative assumptions. We experimented with the markup estimator of De Loecker and Warzynski (2012), but the results were noisy and so inconclusive.

32