rafmacalaba/data-use-mentions-extended huggingface dataset:
tax reform. As shown in table 4, the quantitative effects of such a tax reform on all three variables of main interest is substantial: the before-tax wage increases by 0.68%, the labor-turnover rate decreases by 2.63% (of its initial value of .02448), and the formal-sector employment level rises by 0.91% (of its original level of 0.637).

Figure 1 shows the effect of the tax reduction program as shifts in the labor demand and efficiency wage curve. The labor demand curve shifts to the right: for given before-tax wage, w, 2 00f course, the value of 0.96 is derived by considering US data.

21
of mobility among sectors across the business cycle. It also explicitly explicitly tests for queuing to enter formal sector employment. Overall, the second "entrepreneurial" view emerges as the better first approximation. The last section offers preliminary evidence on the incidence of subcontracting and its impact on worker welfare by tracking the composition of the labor force after trade liberalization in a more comprehensive way than has been possible to date.

# **IL** Data

The Mexican case is well-suited to the study of dualism, informality, and subcontracting. First, roughly 40% of the urban labor force works without labor protections. Second, an archaic and onerous system of labor regulation, dating back over half a century, grants little flexibility and drives up labor costs. The Constitution conceives of the employment relationship as a lifetime contract and workers may only be fired under extreme circumstances and at great cost. For their part, workers lose generous severance pay and may lose their pensions if they quit, and enjoy no unemployment insurance should they be laid off. Taken at face value, the labor code thus discourages the mobility necessary to ensure an efficient allocation of workers and jobs, and constrains employers in their management of personnel. Legally mandated non-wage compensation drives a wedge of 30-60%, substantially above those for other OECD countries. Third, the radical trade reforms beginning in 1986 and the subsequent pursuit of regional inl:egration through NAFTA converted a relative closed economy to one competing aggressively in both import and export markets. The resulting adjustment of its labor market can offer some preliminary observations on the issue of globalization and subcontracting.

The National Urban Employment Survey (NUES) conducts extensive quarterly household 3
and 54 percent, respectively, also face significant challenges in this regard (Roberts, Shyam, and Rastogi, 2006).

Evidence on the development impact of rural roads in Asia is quite mixed. While the improvements in road access appear to positively contribute to socio-economic outcomes in several developing Asian nations (Emran and Hou, 2013; Khandker et al, 2009; Muralidharan and Prakash, 2017), the effects are not always found to be substantial (Asher and Novosad, 2020; Jacoby, 1999; Li et al, 2019). The available literature for Latin America is much more limited, but highlights the significant positive contribution of rural road investments to creating job opportunities in remote areas (e.g., Blankespoor et al, 2017) as well as stimulating the growth of firms through lower-cost access to markets (e.g., Martincus et al, 2017).

Emran and Hou (2013) present evidence on the effects of access to domestic and international markets on the per capita consumption of rural households in China. The results, based on a household survey in 1995, show that better access to both domestic and international markets has positive effects on per capita income, with the domestic market effect being the larger one. A 10-kilometer reduction in the distance to a domestic (international) market increases per capita consumption by 42.10 (29.68) yuan when the household is initially located 150 kilometers away from both the domestic and international markets, which is equivalent to 8 (6) percent of the rural poverty line income. The authors also find evidence of complementarity between access to domestic and international markets, with a reduction in the distance to one market yielding a higher benefit for a household when it is located closer to the other market.

Khandker et al (2009) find that the overall effect of road improvement on household per capita annual consumption was about 8 –10 percent across affected project areas in Bangladesh. In addition, road improvements have led to a poverty reduction of 3
018)|
|Constant|10.40***|10.41***|
||(0.0288)|(0.0289)|
|R-squared|0.962|0.962|
|Marginal Efect +1 SD in Index|-0.015|-0.065|
|p-val.|0.080|0.591|
|_Yh_ = Labor Costs to Sales Ratio (log)(Obs. 15,021)|||
|Pol. Infuence|0.002***|-0.019|
||(0.000)|(0.019)|
|Constant|-2.062***|-2.111***|
||(0.025)|(0.0425)|
|R-squared|0.928|0.928|
|Marginal Efect +1 SD in Index|0.013|-0.125|
|p-val.|0.000|0.336|
|**_X_** co-variates|✓||
|**_X_** interacted w/Pol. Infuence||✓|
|Location FEs|by Sector|by Sector|
|Sector FEs|by Location|by Location|

* _p <_ 0 _._ 10 ; ** _p <_ 0 _._ 05 ; *** _p <_ 0 _._ 01

OLS estimates. Robust standard errors, with two-way clustering at the 2-digit ISIC and region level in parentheses, per Cameron et al. (2011). Co-variates (not shown) include the log of age, the years of the manager’s experience, dummy variables for exporters, foreign-owned firms, and those part of a larger firm.

These results match the specifications in Cols. 4 and 5 from Table 5. In those main specifications, we find that (relative to firm _i_ ’s industry-region, an increase in Political Influence is
a lower extent, the Philippines and the convergence is only marginally improving since late 1990s. Moreover, since 2010, there is a noticeable slowdown of TFP growth in many countries (e.g. China, Malaysia, Thailand).

**Figure 2 Total factor productivity relative to the U.S. in selected developing EAP countries**

<!-- Start of picture text --> 1990 1995 2000 2005 2010 2015<br>Year<br>China Indonesia<br>Malaysia Thailand<br>Philippines Vietnam<br>.7<br>.6<br>.5<br>.4<br>TFP (US TFP=1)<br>.3<br>.2<br><!-- End of picture text --> Source: Penn World Table 9.0 and authors’ calculation. TFP series are calculated by the Penn World Table, except for Vietnam and Indonesia. Vietnam’s labor share is missing in the PWT 9.0. We assume labor share of Vietnam is 2/3 (consistent with Vietnam 2035 report) to calculate Vietnam’s TFP. TFP series for Indonesia between 2011-2014 is recalculated based on extrapolated years of schooling for the country.

Conversely, output per worker is converging towards the U.S. level at higher and consistent speed across China and ASEAN-5 (Figure 3 **Error! Reference source not found.** ). Again, labor productivity is expressed relative to the U.S. level. Malaysia shows the highest labor productivity, while China has the highest speed of convergence.

**Figure 3 Labor productivity relative to the U.S. in selected developing EAP countries**

5
third countries. This classification helps in determining the “natural” groupings (i.e. “cluster”) of observations in our data set based on a transparent statistical approach. In order to gauge the contribution of different policy areas in the outcome of the clustering, we characterize each group of agreements in terms of provisions coverage by policy domain. Controlling for the development level of participating countries, and for time periods, we estimate the marginal effect of the 18 policy areas on the probability of an agreement being in one of the two extreme clusters with respect to the medium one.

In a second step we estimate an explicit bilateral trade function taking stock of the clustering of agreements. Here again, we let the data speak and estimate what is the mean impact on trade of belonging to a PTA positioned in a certain cluster. Note that a large part of the literature estimating the impact of PTAs on trade is flawed because it does not control for the right benchmark in terms of trade cost, namely domestic trade. Importantly, we take the gravity equation seriously and embark internal trade flows in our estimation of the trade effects of the different types of PTAs. We compile the largest database for which internal and international trade is available in a panel, based on the last release of UNIDO data.

The last step of the analysis illustrates how to perform counterfactual exercises. We firstly move PTAs of interest away from their cluster towards a more ambitious one (i.e. a PTA cluster associated to higher trade, as estimated in the second step) and examine the effects of such policy reform of preferential agreements in general equilibrium. Since we are not changing the network of trade agreements but only their content, this exercise captures a variation in the intensive margin of PTAs. We then simulate the economic impacts of a policy at the extensive margin, consisting in signing new agreements (with different content and, hence, at different levels of ambition). To provide a quantitative illustration, we concentrate on the Latin America
<!-- Start of picture text --> Distribution of Task Team Leader Quality Measures<br>(Based on Project Sample of Approval FY95-09)<br>— Preparation TTL<br>04,<br>— _1st-half Supervision TTL<br>— _ 2nd-half Supervision TTL<br>0.3<br>ay<br>2<br>© 0.2<br>a<br>“<br>0.1<br>)<br>-4 -2 0 2 4<br>Value-added Measures of TTL Quality<br><!-- End of picture text --> <!-- Start of picture text --> Distribution of Practice Manager & Country Director Quality Measures<br>(Based on Project Sample of Approval FY95-09)<br>0.6<br>———_ Practice Manager<br>———-_ Country Director<br>0.4<br>ren<br>2<br>oO<br>S<br>“<br>0.2<br>0<br>-4 -2 0 2 4<br>Value-added Measure of PM/CD Quality<br><!-- End of picture text -->
a rectangle around them with his mouse. Based on the points selected by the official, a computer algorithm calculates the boundary and number of days for the resulting closure. The computer algorithm is not publicly available, but government officials have described it in workshops and personal conversations. Closure area increases in the total tons caught by the group of sets (Instituto Humboldt and SNP, 2017; Instituto Humboldt, SNP and CeDePesca, 2018). Closures last three, four, or five days.<sup>11</sup> Higher percentage juvenile values result in longer closures. The official can make manual adjustments to the closure generated by the computer algorithm, for example to ensure the closure covers all the sets selected by the official (Instituto Humboldt and SNP, 2017; Instituto Humboldt, SNP and CeDePesca, 2018).

Vessels are not allowed to fish inside active closures (inside closed areas during the threeto-five-day closure periods). To the extent that vessels conceal fishing inside active closures, this detection avoidance would accentuate my main result that the policy backfires.<sup>12</sup> The regulator announces closures on their website and sends emails and WhatsApp messages to firms (PRODUCE, 2020 _b_ ). Firms communicate closures to vessels at sea using radio, and vessels enter the coordinates of closed areas into their onboard navigation systems. Some fishing companies also monitor the locations of their vessels in relation to active closed areas and call vessels on the radio when they are near an active closure. 43% of closure announcements create multiple closures; the average announcement creates 1.58 closures.

I downloaded all temporary spatial closures announcements from the regulator’s website. The regulator declared 410 closures in the North-Central zone during my study period (Figure

> 11I estimate the effects of closures declared by the regulator, PRODUCE. The scientific agency, Instituto del Mar del Per ́u (IMARPE), can declare closures of up to 10 days. These closures can apply to all fishing grounds (i.e.,
with audited financial statements are more likely to apply for loans than others and that female ownership of firms was associated with a higher probability of loan rejection.

When we include those variables in our financial constraints regressions, neither is significant (Table 8, models 3 and 4). Their inclusion does not, however, change our main conclusions regarding firm size and foreign bank participation. We were unable to include two additional variables used in Brown et al. (2011) in this part of the analysis, the share of firm earnings received through a bank account and a general index of the severity of business obstacles.<sup>32</sup> As noted above, the share of firm income received through a bank account is not available in the 2008 BEEPS survey.<sup>33</sup> Because our dependent variable is based on perceptions of business obstacles, it did not make sense to include another measure of the perceived severity of business obstacles as an explanatory variable. In addition, changes in question format made it impossible to construct the business obstacles variable that Brown et al. (2011) use for all BEEPS surveys. In all, however, our results are robust to the inclusion of the explanatory variables used in Brown et al. (2011) that it was feasible to include.

_Fixed Exchange Rates, Current Account Deficits._ As a final robustness check, we attempt to distinguish the rapid, perhaps de-stabilizing increase in some types of foreign lending from the steady, gradual growth in lending through the subsidiaries and branches of foreign banks. Relative to local lending, direct cross-border flows to ECA had been growing much more quickly than those to other regions. When foreign claims are separated into direct cross-border flows (including overseas lending by the parents of multinational banks) versus local lending in host markets by the subsidiaries and branches of foreign-owned banks, it becomes clear that the decline in 2008 was attributable to reductions in cross-border flows.<sup>34</sup> Figure 2 shows that
14 and 2004-2011.<sup>5</sup> Sample countries include 29 high-income economies and 11 emerging countries. The data for trade in value added come from the World Input-Output Database (Timmer et al, 2015) and are processed based on the methodology described by Wang, Wei, Zhu (2015), using an algorithm developed in Quast and Kummritz (2015).

We present the regression results in a synthetic way, reporting in the table below the average of the country-specific elasticities for high-income economies and for developing economies. The first two rows in Table 2 report the (average) elasticity of exports to global demand (ex), where exports are measured in gross terms and in value-added terms, respectively. The upshot is that this elasticity has decreased over time for both the average high-income economy and the average developing economy when trade is measured in gross terms. For instance, a 1 percent increase in world GDP was associated with a 2.5 (2.7) percent increase in gross exports for the average high-income (developing) economy in the 1990s and with a 1.9 percent increase in the 2000s. The elasticity of value added exports to gross trade, however, has remained fairly stable across the two periods for both high-income and developing economies.

> 5 Note that, with an abuse of notation, we refer to the two periods in the analysis as the 1990s and the 2000s, even though as just discussed the actual coverage for gross and value-added export data is in part different.
<mark>surveys are cheaper to conduct compared to phone surveys.</mark><sup>3</sup> <mark>Traditional face-to-face surveys typically employ carefully designed probabilistic samples and are therefore representative. Another major advantage of in-person interviews is the ability to obtain high-quality and deeply insightful answers from these surveys, while Internet surveys are poorly suited for collecting answers to complex questions. However, face-to-face surveys are costly and are therefore difficult to implement frequently. They can also be difficult to field in conflict-affected environments due to security concerns.</mark> <mark>How important is the lack of representativeness of online surveys and to what extent can it be corrected using statistical reweighting techniques? To investigate these questions, we utilized data from online surveys to track labor market indicators during the COVID-19 pandemic. The surveys were administered over three data collection waves in Brazil, the Arab Republic of Egypt, Indonesia, Kenya, Sri Lanka, and Türkiye, between December 2020 and August 2021.</mark> <mark>Participants were recruited using Random Domain Intercept Technology (RDIT), a method that randomly selects internet users that mistype a web address or click on a broken link. The RDIT was developed by the RIWI corporation, and it works in the following way. RIWI takes ownership or temporary control of unused/abandoned websites or expired domains. Usually, users receive a “this page does not exist” message while they click on such websites while browsing the internet. However, because of RIWI’s ownership, users would now be led to a link to take up an RIWI survey. RIWI, by autodetecting the IP address of the survey participant, offers the survey in the local language. Participants are anonymized and do not get any incentives to take up the survey. The survey can reach countries where there could be potentially government restrictions because no entity has a list of RIWI-controlled domains, and RIWI often changes the list of domains it owns.</mark><sup>4
each cell.

To this data we add longitude and latitude information expressed in decimal degrees, which we normalize to have mean zero and unit variance. A higher value of longitude means more to the West; a higher value of latitude means more to the North. Population density in Ghana is in general higher in the humid South than in the drier North, largely due to the higher agricultural potential and better soils in the South. But there is a lot of local variation, something we aim to capture with our set of agro-ecological and geographical variables.

We also have information about the presence on a cell of a land border, sea border, or large body of water. We drop from the _yit_ database of all the cells outside Ghana, as well as those that are entirely covered by water, since they cannot be built on. This includes lake Volta, a large

> 5The agro-ecological information is missing for some of the cells located at the edges of the map.

7
- Shonkoff, J.P., D.A. Phillips, and National Research Council. 2000. _The developing brain._ National Academies Press (US): In From neurons to neighborhoods: The science of early childhood development. .

- Spelke, E., and K. Shutts. forthcoming. "Chapter 1: Learning in the Early Years." In _Quality Early Learning: Nurturing Children's Potential_ , edited by A. Devercelli and M Bendini.

- Spier, E., K. Kamto, A. Molotsky, A. Rahman, N. Hossain, Z. Nahar, and H. and Khondker. 2020. "Bangladesh Early Years Preschool Program Impact Evaluation."

- Tamis-LeMonda, C.S, K. E Adolph, S. A. Lobo, L. B. Karasik, S.. Ishak, and K. A. Dimitropoulou. 2008. "When infants take mothers' advice: 18-month-olds integrate perceptual and social information to guide motor action." _Developmental Psychology_ 44 (3): 734.

- Tanner‐Smith, E.E, and E. Tipton. 2014. "Robust variance estimation with dependent effect sizes: Practical considerations including a software tutorial in Stata and SPSS." _Research Synthesis Methods_ 5 (1): 13-30.

- Tanner-Smith, E.E., E. Tipton, and J.R. Polanin. 2016. "Handling complex meta-analytic data structures using robust variance estimates: A tutorial in R." _Journal of Developmental and Life-Course Criminology_ 2 (1): 85-112.

- Tipton, E. 2015. "Small sample adjustments for robust variance estimation with meta-regression." _Psychological Methods_ 20 (3): 375.

- Tipton, E., J.E. Pustejovsky, and H. Ahmadi. 2019. "Current practices in meta‐regression in psychology, education, and medicine." _Research Synthesis Methods,_ 10 (2): 180-194.

- Tomasello, M., M. Carpenter
, resulted in different types of informality. Although cultivated fields generally comprise several cadastral parcels, the land corresponding to individual owners’ shares was, after privatization in the early 2000s, mapped by donorfunded projects. These projects had varying levels of quality control, implying that parcels may have been left out of the cadaster by mistake or because they had a dispute pending at the time of cadastral mapping.

Figure 1, taken from Martyn _et al._ (2022), illustrates this for an irrigated area in the country’s South: each of the circles corresponds to a field, the boundaries of which are defined by the reach of the associated center-pivot irrigation system. Simple visual inspection shows that fields comprise many cadastral parcels and that there are cultivated areas that are not mapped in the cadaster, a situation we refer to as informality of type I. In 2013, a new digital registry of rights was created but documentation on rights that had been created before was not transferred. Even for parcels that are mapped in the cadaster, rights may thus not be visible in the registry either because owners did not register them or did so before 2013 so that registration information is still contained in the old (analogue) database in the cadaster. Consulting registry data allows to identify this type of informality, which we refer to as informality of

> 3 A small part of land for personal use includes non-transferable use rights to ‘reserve land’ that had not been cultivated before that village councils could give to individuals.

6
rebounded rapidly, as exports surged and tourist numbers recovered quickly, and in 2010 exceeded previous records.

While this paper has focused on the case of Thailand, there are some themes that are more broadly relevant. A simple simulation exercise based on the slowdown of consumption or GDP growth would have been incomplete; by ignoring the changes in relative prices that occurred at the same time, and perhaps other effects that are not easily observed, it would not have correctly identified the losers and winners. In order to identify who is really being hurt, it is important to have actual data. Thailand is a leader in this respect with good-quality monthly household-level data that can help the government target its response more effectively. It would be nice if one could simulate the effects of economic shocks on different social groups using readily-available data series, but our experiments in this suggest that such an approach is not realistic.

Economic structure matters, and that is the second lesson. The effects of the great recession on Thailand reflect the country’s dependence on export-oriented manufacturing, rice cultivation, and tourism. These effects are likely to be quite different from those experienced elsewhere, even in neighboring countries such as Myanmar or Malaysia. This makes it difficult to generalize productively about the effects of recession and the appropriate policies needed to soften the social impact. Country-specific policy analysis, rooted in timely local evidence, remains essential.

Losers and Winners in Recession: Thailand 2008-09 28