rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2. Growth and technology, OLS and IV regressions**

||||Relative dec<br>|adal growth<br>|||
|---|---|---|---|---|---|---|
||(1)<br>OLS|(2)<br>IV|(3)<br>OLS|(4)<br>IV|(5)<br>OLS|(6)<br>IV|
|Relative income|-0.0525***|-0.0555***|-0.0558***|-0.0544***|-0.0654***|-0.0862***|
||(0.00297)|(0.00366)|(0.00266)|(0.00323)|(0.00656)|(0.00721)|
|Average technology<br>readiness (-)|0.000383***|0.000413***|0.000407***|0.000389***|||
||(0.0000262)|(0.0000347)|(0.0000234)|(0.0000305)|||
|Relative income x<br>Average technology<br>readiness (-)|0.000691***|0.000582***|0.000662***|0.000524***|0.00275***|0.00202***|
||(0.0000756)|(0.0000874)|(0.0000672)|(0.0000770)|(0.000174)|(0.000205)|
|Observations|2794|2794|2794|2794|2794|2794|
|Year fixed effect|no|no|yes|yes|yes|yes|
|Country fixed effect|no|no|no|no|yes|yes|
|R-square|0.201|0.200|0.382|0.380|0.555|0.552|
|First-stage F-stat||249.5||255.1||1009.8|
|First-stage Sargan-stat||70.56||95.94||107.0|

- Notes: Coefficient estimates from ordinary least squares regressions at the country year level. Standard errors are given in parentheses. * p<0.1, ** p<0.05, *** p<0.01. The dependent variable is the relative annualized decadal growth of real GDP per capita, compared to the growth in the US. Relative income is the relative real GDP per capita from the initial year of the decade (US’s real GDP per capita at the same year equals 1). Average technology readiness in the regression represents the average ranking for technology readiness. A higher number means a better ranking, and higher technology readiness. This variable is instrumented in column (2) (4) (6), by 6-dimensions of country specific attitudes. First stage F-stat and Sargan test for over-identification are both reported. Regressions in all columns have the same sample to ease comparison. The coefficient estimates on constant are not reported to save space. Column (1) and (2) has no fixed effects, while column (3) and (4) is added with year fixed effects. In column (5) and (6), we added country fixed effect to replace the linear term of average technology readiness, in order to capture country specific characteristics in addition to technology readiness. The main variable of interest in all columns are the technology readiness, interacted with relative income from the initial year. This coefficient has been significant through all columns. Countries involved this regression are listed below. The relationship between technology readiness and 6 dimensions of attitude are graphed in Figure 5. First stage regressions of column (4) is provided in Appendix Table A3. We have also conducted regressions in Table 2 with quadratic term of relative income, and confirmed the relationship between decadal growth and initial income to be negative in the segment of interest.

11
Trade and FDI affect the incentives to migrate in multiple ways through increased job creation.<sup>16</sup> First, as discussed, better jobs increase incomes for many, which makes migration less appealing. This flow of capital is widely considered to be a _solution_ to the problem that workers cannot (or do not) move quickly and flexibly enough. FDI can, therefore, reduce migration in the short term (Kugler and Rapoport 2007), particularly among the people for whom FDI creates jobs. In the long term and/or among other groups of workers, FDI and migration may complement each other.

**Figure 4 Economic integration through trade increases with development**

_Sources_ : Original calculations based on trade data from World Bank 2022 and GDP data from the Penn World Tables (PWT) 10.0 (Feenstra, Inklaar, and Timmer 2015).

_Note_ : The figures show the relationship between trade (as a share of GDP) with GDP per capita. Each line on the background traces the evolution of trade and GDP for each country between 1960 and 2020 (with trade shares capped at 300 percent of GDP). The solid lines in the foreground plot the smoothened locally linear estimate pooling all countries and years between 1960 and 2020. CI = confidence interval.

But trade and FDI also reduce other barriers to migration, which can encourage migration. Trade and FDI result in a strong network of firms across countries that facilitate better information flows. Through these linkages, firms get better information about the ability of specific workers or groups of workers and have a credible way to recruit them. More generally, the advances in and adoption of communication technologies due to globalization has lowered search costs for employers and workers. The reduced information friction in the labor market can facilitate migration. More broadly, globalization has facilitated increased information flow about life abroad—including lifestyles, cultures, norms, and specific amenities and services—that could help lower

> 16 Trade creates more jobs due to expansion of market size and the scale of production. FDI brings in additional capital, improved business processes, and advanced technology, which increases worker productivity. However, recent literature also finds that trade reduces incomes for some, and affects migration incentives in the opposite way. Majlesi and Narciso (2018) find that Mexican migration to the United States has fallen following the job losses induced by import competition from China.

12
Observers generally agree that except for the reconstruction per.od during 1949-57, industrial TFP during 1957-78 was stagnant.<sup>3</sup> 23 By expanding the profit motive, managerial autonomy, and market exposure, the following reforms have potentially contributed to greater industrial efficiency and productivity growth:' Profit retention: Limited opportunities for profit retention were introduced in the late 1970s and early 1980s. In one sample of 20 state enterprises, the retertion rate, zero d ring 1975-77, grew to an average rate of 13.9 percent during 1980-82. Within our sample of 226 large and medium-size state enterprises, this rate rose from 36.6 percent in 1986 to 44.2 percent in 1989.

Product markers: A key element of China's industrial reforms has been the introduction of the dual pricing system, in which enterprises self-market above quota output at flexible prices while, over time, the share of within plan production declined. Enterprises that face market prices at the margin are making production decisions that are more reflective of underlying scarcities.' In 1989, within our sample of 226 state enterprises, 149 enterprises sold all or a portion of their output on the market.

Labor and wage reforms: 'arious measures to enhance incentives and flexible labor allocation have been introduced to scate industry. Among the more notable reforms have been the provision of bonuses out of retained earnings, the introduction of contract labor to replace permanent workers,' and the optimal labor reorganization program which has enabled managers to reassign workers within the enterprise and encourage redundant workers to find new employment outside th: enterprise.<sup>7</sup> Enterprise data also show substantial increases in inter-

- 3 See Chen et al (1988) and Perkins (1988).

- 4 For informative discussions of China's industrial reform program, see Gene Tidrick and Jiyuan Chen, eds. C0hina's Industrial Reform (1987) Oxford University Press; Bruce L Reynolds, ed. Reform in China: Challenges and Choices (1987) M.E. Sharpe, Inc.; and Dwight H. Perkins, 'Reforming China's Economic System," Journal of Economic Literature- 26,2 (June 1988): 601-645.

- s A survey of 429 state-owned industrial enterprises showed that market sales of these enterprises reached 32 percent of total sales in 1984, rising to 44 percent during the first half of 1985. See Diao (1987).

- **6** For an evaluation of the positive incentive effects of raising the share of bonuses in the total wage bill and the share of contract workers among total employees, see Groves, McMillan and Naughton, 1991.

- 7 For an analysis of the beneficial impact on labor productivity of enterprises adopting the optimal labor reorganization program, see Jefferson and Xu, 1991.

**_Asng Gain_** _in_ **_Efficknt Prodution_** _Among_ **_Chia's_** _Indusil_ **_Entsprises,_** by **Gary** H. Jeffenon and Xu Wcnyi

3
Finally, responses of local-level officials (typically local-level housing & communal services officials) indicate potholes on the territory of small municipal agglomerations, e.g., housing cooperatives.

Table 3 shows that although more than 50 percent of reported complaints are at the city-and district-level, there is no economically significant difference across the response complaints by the responding officer's rank. The median time of response varies between 7 and 9 days and is consistent with the Moscow city Resolution 762-PP. The complaints responded at the municipallevel take a bit longer to address (only 47.5 percent are answered within 8 days’ time), but more than 80 percent of these complaints are handled within two weeks. The complaints responded at the community-level have a relatively lower completion rate within the two weeks’ time (78.2 percent versus more than 80 percent at the municipality-, and city- and district- level), but the difference is not economically significant.

Table 3. Pothole Complaints by the Type of Responding Official, 2012-2019.

|Responding|Total|Median Time|% of Complaints|% of|
|---|---|---|---|---|
|Official|Number of<br>Complaints|between the<br>Complaint and<br>Resolution (days)|resolved in 8<br>days’ time|Complaints<br>resolved in 15<br>days’ time|
|City- and District-<br>level|90,881|7|59.1|80.5|
|Municipality-level|30,545|9|47.2|80.9|
|Community-level|46,477|8|56.0|78.2|
|Total|167,903|8|56.1|80.0|

Note. City- and district-level responses are by the heads of city and district prefectures, roads inspectorates, and budgetary institutions “ _Avtomobil’nye Dorogi,” “Gormost,” “Mosvodostok,”_ and _“Mosvodokanal.”_ Municipality-level responses are by heads of municipal administration ( _<u>uprava</u>_ <u>). Community-level responses are by heads of municipal budgetary</u> institutions for communal and housing services _“Zhilischnik.”_ Source: authors’ calculations based on scraped data from _gorod.mos.ru._ Among 175,386 scraped data points, the rank of the responder was not stated in 7,483 cases.

Even though Table 3 suggests no significant differences across responding officials' rank, we perform an additional robustness check by estimating our empirical specification based on sub21
(panel b), referred to as the regional intersectoral productivity gap. These gaps are all negative, which means that average productivity is lower in the informal sector than in the formal sector in every region. However, the productivity gap is statistically significant at the 5 percent level in five regions (Upper East, Northern, Eastern, Western, and Greater Accra). The intersectoral productivity gap averaged across the five richest regions (154.9 thousand cedis per worker) is three times larger than that for the five poorest regions (51.1 thousand cedis per worker).

Greater Accra has the largest productivity gap (at 177 thousand cedis per worker). It is more than four times larger than the productivity gap averaged across the other nine regions (which comes to 42 thousand cedis per worker). Greater Accra’s large productivity gap is due mainly to the substantially higher average productivity in its formal sector compared to the formal sectors in other regions.

The ratio of average productivity between the informal and formal sectors ranges between 0.08 and 0.83 across regions. The average ratio across all regions, weighted by the regional shares of total value-added, is 0.26. These numbers are in the ballpark of informalformal productivity gaps estimated by other studies using microdata. For example, Busso et al. (2012) estimate a ratio of 0.38 using data for Mexico for the period 1998–2008.

As a sensitivity check, we also estimate regional intersectoral productivity gaps controlling for characteristics other than the formality status of the enterprise that potentially influence enterprise productivity. See Table A1 for average values for these characteristics by the formality status of the enterprise (including differences in average values in these characteristics between informal and formal enterprises). And see Table A2 for results from region-specific, enterprise-level OLS regressions of log labor productivity on formality status, controlling for these other characteristics. These regressions provide estimates of regional log conditional intersectoral productivity gaps.

The conditional intersectoral productivity gap is statistically significant at the 5 percent level in six regions (Upper West, Upper East, Northern, Eastern, Ashanti, and Western) and at the 10 percent level in Greater Accra. Figure 9 plots the conditional productivity gap alongside the unconditional productivity gap for each region. The regional pattern in productivity gaps is qualitatively similar across the two types of gaps.

# **IV. Regional productivity losses from intersectoral misallocation**

A. _Model_ 10
_HOTTER PLANET, HOTTER FACTORIES_ _15_

# **IV. Empirical Strategy**

We estimate a non-linear regression of the log of sales per worker and other measures of firm performance on area-level deviations in temperatures. Our identification strategy exploits the variation in area-level temperatures near the establishment. In a robustness exercise, we narrow the 30km radius of our main mode, to _≈_ 27.5km, which is the minimum grid size for both the temperatures and relative humidity data. The variation in area-level temperatures is plausibly exogenous as they are not the result of the firms’ activities. However, there are various sources of bias that we need to address, the main source arising from potentially omitted variables. In such cases, the deviations in area-level temperatures could be picking up variations in other observed and unobserved area-level factors, which are likely to be correlated with both temperatures and firm productivity. These include area-level sub-national patterns in economic activities, transportation activities, population growth, environmental degradation or restoration such as deforestation or afforestation, and so forth. Some of these factors are time-varying and potentially correlated with temperatures. The variation in these factors could also differ by administrative levels in that some could vary at the city level, while others may vary at the sub-national, country, or continental level. We control for these factors using country, sub-national, and World Bank global region-level fixed effects. We also interact the geographic fixed effects with year-fixed effects.

The basic linear regression model that estimates log sales per worker on deviation in WBT controlling for firm-level characteristics and sub-national, country, and regional factors is given by: where _yijkc,t_ denotes firm-level productivity measured by the log of sales per worker of firm _i_ in area _j_ of sub-national region _k_ , country _c_ , and year _t_ . ∆<sup>_WBT_</sup> _jk,t_ is the deviation in average annual near-surface WBT from the long-term average within a radius of 30km around the establishment (that is, area _j_ ) and sub-national region _k_ , country _c_ and year _t_ . _Xijkc,t_ is a vector of firm-level characteristics, _θkc_ is the sub-national region fixed effect, _ρt_ is year fixed effect, _θkc · ρt_ is the interaction term for sub-national and year fixed effects, _εijkc,t_ is the independent and identically distributed error term, and _β_ and _γ_ are vectors of the coefficients to be estimated. All regressions are weighted using WBES weights.

The specification in Equation 3 assumes that the effects on productivity are linear. However, depending on the climate zone, and WBT categories, temperature increases could have heterogeneous effects on firm productivity. Kolstad and
with the recent aggregation by the World Bank of country level data from 45 countries (with different time coverage), which provides a real international comparison base for various elements of export dynamics (Cebeci, et al. 2012).

This paper complements the empirical literature by exploring a rich firm-level dataset of customs transactions from the Lao PDR to identify determinants of export survival. Such detailed analysis for a small, developing and landlocked economy, which, furthermore, relies heavily on exports of raw materials, can be of considerable help to policymakers and economists interested in export-led growth and diversification, and there are currently very few other examples available (Volpe and Carballo 2008). It continues work already carried out with this dataset (Stirbat, Record and Nghardsaysone 2011) which has provided novel descriptive statistics of the Lao exporter population, in particular the dominance of the intensive margin for export growth and the great deal of churning and experimentation happening in exports. That article, looking at the chances of an export unit (a firm exporting an HS 6-figure product to a particular destination) to survive past the first year (in a cross-section setup), has identified positive externalities from agglomeration and a positive impact of product- and destination-related experience.

In contrast to this previous work, the current analysis of the determinants of exporter survival is carried out in a dynamic context, using the full panel structure of the data. It has also been slightly extended to cover five full years. The aggregation of international trade data by years as well as a general shortage of detailed firm-level data means that yearly reporting is the general norm in the literature. In this case, however, the richness of the dataset allows for an analysis of month-level evolutions instead of just yearly ones. There are, however, several exceptions (Tovar and Martinez 2011), including one of the first instances of survival analysis of exporter data (Sabuhoro, Larue and Gervais 2006). This kind of analysis is expected be more relevant in light of the short export spells and significant levels of experimentation encountered in developing countries.

The main analysis is carried out with a discrete-time survival model that takes into account unobserved heterogeneity among exporters. The choice of survival analysis method follows a recent push to break away from the basic (and extended) Cox survival analyses that have generally been used in the literature. Instead of relying on the rather simplistic assumptions of continuous-time, proportional hazards and no unobserved heterogeneity (all three of which can be shown to be fallacious) the case has been made for using binary outcome models with random effects (Hess and Persson 2011a).

Following Besedes and Prusa (2011), the thrust of the argument hinges on the belief that what matters for trade deepening and the export growth of developing economies is the survival of new exports and not just entry into foreign markets. The survival of specific trade links is a further refinement of exporter survival and allows for 3
The proportion of firms with industry coverage below one is an important indicator of the proportion of firms that are below the threshold of interest coverage that is considered safe. As a conservative estimate, the threshold of 0.75 is used, following Jones and Karasulu (2006). The transportation sector stands out with the highest proportion of firms with low interest coverage – at 30%, closely followed by industry, at 25%. The better performers on this measure are mining and tourism and hotels (close to 10%) and utilities and energy (only 4 firms in that category and not one has interest coverage below 0.75).

However, the proportion of firms that falls below the threshold for ICR does not take into account different firm size, or more precisely the amount borrowed by different firms. For example, take two industries in which 20% of all firms have ICR below the threshold of 0.75. However, in one industry these 20% are some of the smallest firms in the industry, with small amount of total debt, which in the other industry these 20% of affected firms are some of the largest firms in the industry. The different debt exposure needs to be weighed in in the assessment of sectoral vulnerability. This problem is addressed by the metric referred to as the uncovered debt ratio, discussed below.

The uncovered debt ratios (UDR) shows a proportion of industry-debt for which earnings (EBTDA) do not adequately cover the interest payments. It is measured by ratio of total debt of firms with interest coverage below 0.75 to total debt of all firms in the industry. Thus, it takes into account the relative sizes of debt. The UDR measure provides a useful proxy for underlying credit quality in the corporate sector.<sup>6</sup> Based on this metric, construction is the most vulnerable sector, with a UDR of 27%, followed by th financial sector with a ratio of 24%. Trade and industry have UDR’s close to 20%. utilities and energy and mining report UDR’s close to zero.

Finally, Altman’s Z is an aggregate measure of vulnerability, which takes into account several key ratios used to predict corporate default in the US and other countries. According to this measure, the transportation and mining sectors score well (with scores of 9 and 8

> 6 The UDR is not reported for Worldscope analysis because it was zero for Jordan since no firm fell below the threshold for interest coverage.

9
**Figure 6.** Concentration coefficient by public program. Uruguay, 2017.

<!-- Start of picture text --> Food transfers -0.770<br>Plan de equidad -0.689<br>Non-contributive pensions -0.541<br>Child care -0.457<br>Primary education -0.445<br>Preschool education -0.412<br>Direct transfers -0.407<br>Low secondary education -0.405<br>Education -0.231<br>Upper secondary education -0.165<br>Other direct transfers -0.123<br>Health 0.010<br>Tertiary education 0.316<br>Adjusted income 0.380<br>Disposable income 0.426<br>Pre-fiscal income 0.461<br>Net market income 0.466<br>-1.000 -0.800 -0.600 -0.400 -0.200 0.000 0.200 0.400 0.600<br><!-- End of picture text --> Source: Authors’ estimates based on ECH (2017), INE and Fiscal Accounts and CEQ Methodology. Note: the concentration coefficient is calculated after ordering population according Pre-fiscal income.

# **D. Comparing findings from 2009 and 2017**

This is not the first study perform an incidence analysis in Uruguay. In fact, Bucheli et al. (2013) benefit from the availability of data in 2009 and look into several policies. Specifically, they consider direct taxes and transfers, in-kind education and health, and indirect taxes. The authors conclude that direct taxes are progressive, indirect taxes are neutral, and the rest of the policies are progressive (except for spending in tertiary education). We revisit the 2009 data with the updated CEQ methodology in an effort to produce estimates for income concepts that are as comparable as possible and to the extent allowed by the data available. A notable difference with respect to the methodology used in previous analyses is that our approach scales the in-kind transfers _down_ instead of scaling-up income and monetary interventions (as was suggested in previous versions of the methodology) (CEQ 2018). We present results comparing the two years, but raise caution about potential limitations to the comparability with earlier results.

Official data report that inequality and poverty decreased between 2009 and 2017. As we have access to estimations for all defined income concepts for 2009, in this section we explore whether there is a role of on fiscal interventions in explaining the observed evolution of well-being indicators.

23