rafmacalaba/datause-encoder-data huggingface dataset:
ments to competition law defines the market affected at the 4-digit ISIC level and the year for which the correction of competition was enforced. Most cases did not specify a particular region, which suggests that the anti-competitive behavior was not limited to a narrow location, so each case is assumed to have national impact. Moreover, the limited number of observations per region in the firm database reduced the possibility of studying the few cases that had a region-specific scope. When a resolution applied to a sub-region rather than the whole national market, our coefficients would be picking up a diluted, underestimated effect that would represent a lower bound of what the actual effect is.

We assume that competition enforcement is exogenous to firm performance. Osiptel and Indecopi are independent and autonomous bodies that receive cases initiated externally by complaint from any physical or moral persons and internally based on monitoring and control organs. This ensures that competition enforcement is applied nationwide regardless of firm or regional idiosyncrasies. Violation of competition law needs to be legally confirmed, following pre-defined requirements, for a case to be accepted. Rulings can be appealed to independent courts. All these elements support the assumption that interventions are exogenous.

The information about _de jure_ and _de facto_ regulations was collected from the digital legislative archive from OSIPTEL and INDECOPI, following the criteria that the degree or resolution had to be relevant (i.e. pertain to at least one of the ICT branches of hardware, software, telecom or ICT infrastructure), timely (i.e. had to be published and implemented in the 2007-2017 period) and had to be related to competition (e.g., encouraging firm entry, reducing misallocation and administrative barriers that hinder innovation).

# **4.2 Empirical Approach**

The methodology described above requires that productivity and markups be identified consistently if the economic impacts of regulation and enforcement are to be accurately characterized. A widely used methodology to estimate productivity is Ackerberg et al. (2015) and a popular extension for estimating markups is De Loecker et al. (2020). Recent critiques of both methodologies require a change in assumptions or a very specific data set. As an example, Bond et al. (2020) show that it is theoretically impossible to recover markups if the firm-level data do not contain separate entries for quantities and prices. Another possible problem arises in the presence of demand shifters, which may render estimates biased (Doraszelski & Jaumandreu (2019)).

On these terms, the data sets used here would be criticized; we do not observe price and quantity data separately (only price times quantity) and we need to account for several demand shifters. Sampi et al. (2021) recently addressed some of these criticisms and produced unbiased policy estimates in the presence of demand shifters as well as missing price data. The methodology, which entails several deviations from Ackerberg et al. (2015) 15
FEWS NET. 2018. “Yemen Food Security Alert.” Famine Early Warning System, Sana’a, Yemen, June.

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that the evidence for this has been mixed in existing studies. We examine this channel in columns 1 and 2 of Table 7. We see that although businesses are more likely to regularly use business records to know if sales of a particular product are increasing or decreasing, this does not translate into any impact on reducing fluctuations in profits in bad months relative to usual months.

Columns 3, 4, and 5 of Table 7 examine the inventory management and purchasing outcomes of the firms. We see no significant impacts of treatment on the fraction of stock lost to spoilage, the receipt of bulk discounts in purchasing, or in the cost of producing the firm’s most profitable item. The latter is measured with considerable noise, so the insignificant point estimate admits a wide confidence interval.

Better business practices may enable firms to obtain more financing. This may work through either the demand or supply side for formal finance. On the demand side, individuals who have gone through training may have more plans for expansion or feel more confident approaching banks. On the supply side, banks may be more willing to lend to firms which keep better records, or which have higher profitability and sales. However, column 6 of Table 7 shows that access to finance is limited in our sample, with only 17 percent of the pure control group receiving a loan from a bank or microfinance organization in the past year, and that treatment does not have a significant impact on this. This is consistent with the qualitative work, which found a number of challenges on both the demand and supply side for formal finance: women were often afraid of what might happen if they fell behind on payments, found the loan application process cumbersome and timeconsuming, and also faced challenges in terms of ability to provide documentation and/or collateral in some cases (ICRW, 2015).

We do find firms to be 5.8 percentage points more likely to be using business bank accounts (column 7). Columns 8 and 9 then look at whether firms have grown in size according to their inventory levels and capital stock. By year three both the inventory levels and capital stocks are higher in the treatment group, which is consistent with them having more to sell, and with building up the size of the firm. The inventory impact is larger after mentoring than after training alone. Since firms did not receive more finance, this build-up in inventories and capital is likely to reflect re-investment of profits. This may also explain why impacts are stronger after three years than one year, since it takes time for firm owners to slowly reinvest profits into more merchandise to sell.

# **6.4 Impacts on Owner Hours, Attitudes, and Social Capital**

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- Tim Ohlenburg. Machine learning for proxy means testing in Indonesia. Unpublished technical report. Technical report, World Bank, 2020.

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34
as the Breusch-Pagan test can be used to measure the strength of this instrument. In practice, Z can be a subset of X as long as Z is clearly exogenous.

We thus use the distance to family planning center as Z and provide estimation results in row 10 in Table 7. The Breusch-Pagan χ<sup>2</sup> (1) test values (not shown) hover around 270 and strongly reject the null of no heteroskedasticity. Interestingly, while the estimated coefficients on tutoring variables show both weaker and stronger statistical significance than the standard IV estimates—with tutoring attendance and expenditure losing their statistical significance while tutoring hours becomes marginally significant at the 10 percent level—two general education measures enrolment and completed years of schooling become strongly statistically significant at the 5 percent and 1 percent level respectively. The magnitudes for the estimates are much smaller than those for the standard IV estimates and are closer to OLS estimates (which is similar to the finding by Sabia (2007) using the National Longitudinal Study of Adolescent Health for the US).

As suggested by Lewbel (2012), these heteroskedasticity-based IV estimates rely on higher moments of the error terms and are less reliable than the standard IV estimates. But it is reassuring to see the qualitatively similar result that family size has negative impacts on household investment on their children’s education.

# **5.4. Further/ Heterogeneity analysis**

Estimation results thus far support the negative relationship between family size and household investment in their children’s education, in particular tutoring classes. This subsection delves deeper into this result, to investigate whether it holds up with different definitions of family size, inclusion of birth order and school quality variables, and a focus on younger cohorts, as well as whether the result might apply broadly or to only a subset of the population.

29
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**Table 9: Manufacturing growth with household credit as a share of total private nonfinancial credit**

|**Dep. Var:**|OLS|OLS|OLS|GMM|GMM|GMM|
|---|---|---|---|---|---|---|
|**growth_**|||||||
|**manuf.**|(1)|(2)|(3)|(6)|(7)|(8)|
|hhcredit/|||||||
|pnfscredit|-0.426***|0.7354|-0.418***|-0.635***|-0.546**|-0.751**|
||(0.000)|(0.177)|(0.000)|(0.004)|(0.014)|(0.049)|
|(hhcredit/|||||||
|pnfscredit)<sup>2</sup>||-0.0165**|||||
|||(0.029)|||||
|fdi|-0.2064|-0.1667|-0.1110|-0.5124|-0.0277|-0.7049|
||(0.362)|(0.437)|(0.587)|(0.140)|(0.928)|(0.130)|
|enrolment|0.1855*|0.1439|0.1537|0.2769**|0.3292**|0.2029|
||(0.063)|(0.163)|(0.128)|(0.023)|(0.013)|(0.145)|
|initialgdppc|-13.31***|-13.61***|-11.51***|-15.54***|-12.50***|-14.1504***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|financialcrisis|||-18.95***||-22.57***||
||||(0.000)||(0.000)||
|inflation||||||-0.4110|
|||||||(0.212)|
|Constant|144.5***|133.5***|132.8***|165.3***|128.2***|166.49***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|# of Obs.|147|147|145|147|145|142|
|R-squared|0.358|0.378|0.420||||
|Prob > F|0.000|0.000|0.000|0.000|0.000|0.000|
|# of countries||||37|36|36|

Robust p-values are presented in parentheses and significance at 1% (***), 5% (**), and 10%(*) levels are indicated.

**Table 10: Manufacturing growth with nonfinancial corporation credit as a share of total private nonfinancial credit**

|**Dep. Var:**|OLS|OLS|OLS|GMM|GMM|GMM|
|---|---|---|---|---|---|---|
|**growth_**|(1)|(2)|(3)|(4)|(5)|(6)|

26
land appraisal value.<sup>16</sup> The primary target of the program was rural land property, which accounted for most of the untitled land. However, Allocated Land was also included in the titling program. Figure 2 describes the timing of events related to the land titling reform.

Although the reform started by the end of 2009, the land titling policy was not implementable until the middle of 2012.<sup>17</sup> The first draft of the reform did not have the necessary details about the regulatory authority and qualifications for titling. It also lacked explicit procedures on dispute resolution when multiple users lay claim to the same piece of land. The problem was gradually resolved by the policy documents released in the subsequent two years, especially the two guidelines on implementation released in 2012.

Several measures were taken to ensure that newly entitled land had better property rights protections. The Shenzhen government established a registration system that ensured the exclusive rights for its users. The registration bureau created a new file with user rights and a history of transactions (including leasing) and related collateralization records to ensure exclusivity. The new 30-year tenure was endorsed by a legal contract between the government and land users rather than by a fiat order in the case of the Allocated land. The contract made it more difficult to expropriate a piece of titled land before its tenure expires, and in case of land appropriation, a large amount of compensation based on the market price of the land was required. These measures greatly alleviated land users’ concerns regarding expropriation. With less concern for the expropriation of the land of the firm, the firm with Allocated land should have more confidence that their investment would have higher returns in the future, and the firm would therefore increase their investment rate. We thus propose _the property rights_

> 16 Some requirements must be met by the land user to obtain the approval of the government. These requirements include the following: first, the land owner must submit a proposal for the renovation of the buildings above ground, which must be approved by the government; and second, approximately 15% of the total area of the land should be given to the government for public use.

> 17 For instance, a report by Shenzhen government suggests that the documents released in the middle of 2012 greatly accelerate the urban land titling program. More details are in <u>http://zwgk.gz.gov.cn/550590033/5.0/201701/28ab055ec59f42f4aa8274b2bd30d4c7.shtml.</u>

15
# 1. **Introduction**

Intellectual property rights (IPRs) affect international trade flows when knowledge-intensive goods move across national boundaries. The importance of IPRs for trade has gained more significance as the share of knowledge-intensive or high technology products in total world trade has doubled between 1980 and 1994 from 12% to 24%.1 At the international level, IPRs have traditionally been governed by several conventions - most prominently the Paris Convention for patents and trademarks and the Berne Convention for copyright - which are administered by the World Intellectual Property Organization (WIPO). In the 1980s, mounting disputes over IPRs lead to the inclusion of trade-related IPRs on the agenda of the GATT/WTO Uruguay round and the resulting "Trade Related Intellectual Property Rights Agreement, including Trade in Counterfeit Goods" (TRIPs) of 1994 represents the most far-reaching multilateral agreement towards global harmonization of IPRs.<sup>2</sup> Several studies have attempted to estimate the extent to which IPRs are trade-related. Maskus and Penubarti (1995) use an augmented version of the Helpman-Krugman model of monopolistic competition to estimate the effects of patent protection on international trade flows. Their results indicate that higher levels of protection have a positive impact on bilateral manufacturing imports into both small and large developing economies. These results are confirmed by Primo Braga and Fink (1997) where we estimated a similar model and found the same positive link between patent protection and trade flows.

This study provides new evidence regarding the effects of patent protection on international trade. It employs a gravity model of bilateral trade flows and estimates the effects of increased protection on a cross-section of 89x88 countries. It improves on previous studies in two respects. First, we estimate the gravity model for two different kind of aggregates: total non-fuel trade and high technology trade. Second, it addresses the problem of zero trade flows between countries by adopting a bivariate distributed probit regression model. Second, to measure the strength of IPRs regimes, we make use of a fine tuned index on national IPRs systems developed by Park and Ginarte (1996). Our results confirm previous findings suggesting a positive link between IPRs protection and trade flows for the total non-fuel trade aggregate. However, IPRs are not found to be significant for high technology trade flows.

> ' These estimates are based on trade data from the UN Comtrade database. For the definition of high technology products, see Table 1.

> 2 For a detailed review of the TRIPs Agreement and its economic implications, see Primo Braga (1996).

1
demonstrating that the control variables included help to explain achievement variation but are not driving our results. Moreover, we estimated models with quadratic time trends, allowing for non-linear trends in achievement, and with different years dropped from the analyses. In both cases the results for mathematics did not change substantially. Finally, we re-estimated results with original survey weights representing the target populations of 15-year-olds in every country. These results show learning loss estimates for the global population represented by the PISA sample rather than for the average across countries. Again, the results were almost identical to those presented above using senate weights that give equal weight to every country (see Annex Table A3). For brevity, we present the main estimates only and descriptions of the regression model applied. Full results are available upon request from the authors.

# **5. Global Economic Impact and the Need for Learning Recovery**

Our model predicts that every week of full school shutdown will result in a 0.44-point achievement decrease. Using the UNESCO database, we estimate that the average learning loss across all educational systems is approximately 9 points. The literature offers several estimates on how PISA scores translate to economic growth. Although the range of estimates available in the literature vary depending on the set of countries, estimation methods, and control variables, we used the coefficient of 1.74 (Hanushek and Woessmann 2010). The average loss of 9 points (9 percent of a SD) for 199 educations systems (recognized by the UNESCO school closure database) translates to an average of 0.15 percentage points of GDP growth losses, ranging from zero (if schools remained open) up to 0.57 points. This enormous GDP loss in nominal values using World Bank GDP 2021 data equals $17 trillion of economic loss. These estimates are similar to other studies (see, for example, Azevedo et al. 2021; Doty et al. 2022; Psacharopoulos et al. 2021).

To avert some of these losses, then learning recovery is needed. There is some evidence of recovery from some countries, but the evidence is mixed (Jack et al. 2023; Singh et al. 2022), 19