rafmacalaba/datause-encoder-data huggingface dataset:
In cross-country comparison of the trends in investment rate, we note that the Indian experience differs from that of other emerging markets in that the investment rate increased far more rapidly in India prior to the GFC than in other countries, and the decline after the crisis is steeper too. This cycle is evident both in public and private investment rates, as both increased in the few years prior to the global financial crisis and declined thereafter. While private investment has continued to remain depressed in recent years, public investment rates have increased.<sup>39</sup>

# **Figure 20: A comparative analysis of the rate of investment in India and other developing countries**

<!-- Start of picture text --> 20 A: GFCF (% GDP)<br>GFCF (%GDP)<br>40 IND EMDE (median)<br>EMDE (25-75%)<br>35<br>30<br>25<br>20<br>15<br>10<br>20 C: Public investment (% GDP)<br>Public investment (%GDP)<br>12 IND EMDE (median)<br>EMDE (25-75%)<br>10<br>8<br>6<br>4<br>2<br>0<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016<br><!-- End of picture text --> <!-- Start of picture text --> 20 B: GFCF growth rates<br>GFCF growth rates<br>30<br>IND EMDE (median)<br>EMDE (25-75%)<br>20<br>10<br>0<br>-10<br>-20<br>20 D: Public investment growth rates<br>Public investment growth rates<br>40 IND EMDE (median)<br>30 EMDE (25-75%)<br>20<br>10<br>0<br>-10<br>-20<br>-30<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016<br><!-- End of picture text --> 20 E: Private investment (% GDP) 20 F: Private investment growth rates

> 39 The level of investment in India has been large, despite recent correction.

36
16

# Data

Equations 3a (or 3b), 5, and 7 were estimated separately for Argentina, Chile, and Colombia using annual data for 1960-85, and for Uruguay using data for 1965-85. Most of the labor market data used are original, constructed from primary aata sources in each country.S' Wages in the informal sector were proxied b) the average observed hourly income of self-employed workers with less than eight years of schooling.101 Wages of skilled labor were measured by the average wage of salaried blue-collar and white-collar workers in firms employing more than ten workers. Official statistics were used for the minimum wage and size of the labor force. The inverse real exchange rate (p) was defined as the ratio between the price index of nontradables and tradables. The price series were constructed using corresponding price deflators from national accounts. Agriculture, mining, and manufacturing were considered tradables; services, construction, and utilities were considered nontradables. The variable _e_ is not directly observable and thus we are forced to use an empirical proxy. We use the level of observed real nonwage labor costs per worker in firms with 10 or more workers (costs associated with payroll taxes including bonuses, fringe benefits, and other contributions to shared funds).

Nonwage labor costs were used as a proxy for labor market distortions other than the minimum wage because both government regulations and labor unions in the countries being considered have emphasized nonwage benefits as a prime target. For instance, in Argentina and Colombia, periods of more active union or government intervention have been characterized by higher non-wage payments;

- 9/ See appendix for a documentation of the data set and for a description of the historical experience of the four countries considered.

- 10/ This proxy is a suitable one in considering that the informal sector in LDCs consists fundamentally of very small firms, usually of the familytype, employing relatively unskilled labor.
to such a shock. Of course, the opposite would be true if the shock originated abroad. The magnitude of the shock, and relevant time horizon also matter greatly for resilience. For example, the 2011 earthquake in Japan caused severe disruptions in the global automobile industry that lasted for several months after the earthquake. But beyond the automobile industry, the global economy was not much affected, and even the automobile industry eventually recovered. Based on the criterion of “bend but not break”, the global economy proved incredibly resilient to this shock.

Returning to the COVID pandemic, it is plausibly the largest shock the global economy has faced since World War II. It represented both a demand and a supply shock, as unemployment rose and income dropped sharply during lockdowns, while at the same time, production and commerce ceased in many countries. It affected all sectors of the economy, but some more than others (e.g., hospitality services versus finance or tech). It affected most countries on the globe, but not at the same time, as the infection waves were not synchronized across countries in different continents. In some sectors, such as food and medical products, delivery delays and shortages are critical (i.e., life threatening), while in others, such as autos or semiconductor chips, they are simply inconvenient.

How would one judge the resilience of GVCs and the global economy against this background? While, as noted earlier, we lack an agreed-upon benchmark against which resilience can be measured, we can look at how global trade, both in aggregate terms and in some key products, evolved during the pandemic years, keeping of course in mind that the pandemic is not completely over yet.

Figure 4 shows world trade flows since 2018 up to 2021, when the most recent COMTRADE data are available. Values are reported as a multiple of the 2018 world import value in constant prices. There was a negligible drop in all goods trade during the pandemic during 2020, and a full recovery in 2021, as seen already in Figure 1. Imports of face masks, shown in Panel A of Figure 4, exhibit an interesting pattern: these imports increased sharply in 2020 reflecting the increasing use of face masks from China and Korea to meet the surge in domestic demand. Not only did international trade not let down the economy in this case, without imports, domestic demand for face masks would not have been met by domestic producers. Growth in use of imports to supply electric car batteries and semiconductor chips grew in real terms during the pandemic, with imports of batteries accelerating. Use of imports to 20
Table 5 (cont.)

|Independent|Sec|tor of Employ|ment|
|---|---|---|---|
|variables|Wage<br>Private|Wage<br> <br>Public|NonWage|
|Annual non-labor|-0.00006|<br>-0.00002|* -0.00001*|
|household income|(0.97)|(2.19)|(1.87)|
|Age|0.320*<br>(9.31)|0.483*<br>(8.56)|0.346*<br>(12.74)|
|Age squared|-0.0041*|<br>-0.0058*|-0.0038*|
||(9.31)|(8.56)|(12.74)|
|Number of househo|ld members|||
|#0-4 years old|-0.099|0.093|0.169*|
||(1.44)|(0.99)|(3.19)|
|# 5-11 years old|-0.031<br>(0.62)|0.011<br>(0.16)|0.046<br>(1.13)|
|#12-19 years old|<br>0.030<br>(0.68)|0.187*<br>(3.20)|0.040<br>(1.11)|
|# females 20-50|0.017|-0.118|-0.186*|
|years old|(0.32)|(1.40)|(3.35)|
|#males 20-50|-0.103*|-0.205*|-0.143*|
|years old|(1.72)|(2.20)|(2.62)|
|#50 years old|-0.066|-0.011|-0.274*|
|and over|(0.99)|(0.12)|(4.16)|
|Spouse's age|-0.030*|-0.026*|-0.007*|
||(5.13)|(3.26)|(2.47)|
|Spouse's years of|<br>0.056*|0.088*|0.014|
|schooling|(2.66)|(3.28)|(0.98)|
|Spouse's annual<br>compensation|-0.00002<br>(3.45)|*<br>-0.00003<br>(4.60)|* -0.00001*<br>(4.34)|
|Residence in Lima|0.532*|0.297*|-0.235*|
||(4.80)|(1.92)|(2.55)|
|Log Likelihood||3,504.2||
|X2||1,637.6||
|No. observations|<br>543|266|898|

Notes: * Statistically significant at the 10 percent level or better. a/ Marginal effects not shown.

- 28 -
24 1994. For the economy as a whole the differences in the entries of the transition probability matrices for the two periods are not statistically significant (G6ra and Lehmann, 1995). Without much loss of generality the period May 1992 to May 1993, when slightly a larger number of transitions occurred, can be chosen for the analysis.

Before discussing the transition probability estimates, some stock variables of table 6, namely participation and unemployment rates as well as LTU shares for men and women by age, will be briefly analysed for the data points May 1992 and May 1993. The different experience for men and women in the Polish labour market can be seen by the unemployment dynamics and the May 1993 entries where with all age groups unemployment rates are higher for women than for men and where for all but one age group LTU shares are much higher for women. A larger LTU share means, of course, lower outflow rates from unemployment; women have, therefore, less opportunities to regain employmen **t** han men. In fact, job accession rates which can be computed from transition probability matrices24 (the sum of the flows UE and NE) are with all age groups larger for men than for women. The female LTU shares would be reduced by increased labour force exit of the long-term unemployed women. That women in Poland do not exit the labour force more than they enter it in the reported period can be seen from the fact that women's participation rates do not decrease over time; with the exception of one age category between May 1992 and May 1993 these rates _stay constant or increase._ The age groups under 55 years of age have participation rates which are high relative to most OECD countries, as long as women have similar unemployment inflow rates like men and lower unemployment outflow rates these participation rates contribute to higher female unemployment rates and LTU shares.

While the higher unemployment incidence among Polish women is a well known fact which has been discussed _in extenso_ the much higher unemployment rate among the young (< 25 years of age) for both genders needs further discussion. At first glance this age group seems to be the "problem group" among the unemployed. Compared to the next three age groups its unemployment rate is between 90% and 270% higher if we take all four data points into consideration. Using Polish register data, Schmidt (1994) has shown that these differences disappear when participation rates among the young are assumed to be similar to those in Sweden and Germany. Schmidt moots that because of a large shadow economy in Poland where especially the young have gainful employment opportunities low participation rates among the 24These matrices are not presented here but available upon request.
|**Table 13b. Impact of Full Foreig**<br>|**n Direct In**<br>Overall<br>_change from b_|**vestment Lib**<br>_ase year, %_|**eralization**|**in Services**|||||
|---|---|---|---|---|---|---|---|---|
||average|Northwest|Central|South|Volga|Urals|Siberia|Far East|
|**Social Welfare Indices (ev/GDP)**|||||||||
|sigma=0|2.9|3.7|3.2|3.1|3.6|1.9|3.1|3.8|
|sigma=1|3.7|4.3|3.6|3.6|4.3|2.3|3.4|4.2|
|sigma=4|4.0|4.7|3.8|3.8|4.7|2.7|3.7|4.5|
|sigma=+inf|4.0|4.8|3.9|3.9|4.9|2.9|3.8|4.6|
|ev/C|7.3|9.1|6.2|7.1|9.1|7.1|6.5|9.1|
|**Aggregate trade**|||||||||
|Regional terms of trade (% change)|2.0|2.5|1.8|1.4|2.2|1.4|1.3|2.3|
|Regional exports (% change)|1.8|2.0|1.9|1.6|1.5|1.6|1.9|2.3|
|Real exchange rate (% change)|1.2|1.5|1.0|1.1|1.4|0.9|1.1|1.6|
|International exports (% change)|2.6|3.8|3.8|1.4|3.6|1.6|1.9|3.4|
|**Return to primary factors**(% change)|||||||||
|Unskilled labor|4.1|5.2|3.7|3.8|5.3|2.4|3.6|5.5|
|Skilled labor|2.7|4.1|2.3|2.5|4.0|1.4|2.3|4.0|
|Aggregate capital earnings|1.9|2.3|1.8|1.8|2.2|1.7|1.9|2.3|
|Regional mobile capital|4.6|6.1|5.2|4.6|5.4|2.9|3.7|5.5|
|Energy sector resources|3.2|4.6|7.8|3.4|4.2|2.5|6.4|6.6|
|Specific capital in domestic firms|-21.6|-20.3|-22.6|-23.6|-20.7|-22.3|-19.9|-21.6|
|Specific capital in multinational firms|88.5|60.3|64.5|102.9|128.1|153.7|138.1|103.7|
|**Factor adjustments**|||||||||
|Unskilled labor (% changing sectors)|1.3|1.6|1.2|1.1|1.4|1.3|1.6|1.9|
|Skilled labor(% changingsector)<br>Source: Authors' calculations.|1.9|2.2|1.8|1.5|2.0|1.8|2.1|2.6|

49
larger literature showing that misallocation can hurt productivity and growth (Restuccia and Rogerson, 2013; Hopenhayn, 2014).

One caveat is that our estimates present partial equilibrium effects. While we cannot formally test the magnitude of displacement effects, it is noteworthy that many formal sector employers, including in our study area, have open vacancies, often resulting from high turnover rates (Moon et al., 2022). In tight labor markets where employers struggle to fill positions, displacement effects tend to be less pronounced (Cr ́epon et al., 2013). With this caveat in mind, our results suggest that a one-time temporary wage incentive can be a relatively cost-effective intervention with lasting benefits.

A final question is why one would need a wage incentive rather than simpler and cheaper interventions such as providing job seekers with information, which could nudge job seekers to lower their reservation wages. While more evidence is needed to conclusively test for these mechanisms, our results suggest that only providing information about wage growth is not sufficient if youths heavily discount these future benefits. In addition, participants may not fully update their beliefs either because they pay limited attention to information or do not find it credible or relevant.<sup>36</sup> In these contexts, programs that offer temporary, immediate benefits may be more effective.

# **References**

- Abebe, G., A. S. Caria, M. Fafchamps, P. Falco, S. Franklin, and S. Quinn (2021a): “Anonymity or distance? Job search and labour market exclusion in a growing African city,” _The Review of Economic Studies_ , 88, 1279–1310.

- Abebe, G., A. S. Caria, and E. Ortiz-Ospina (2021b): “The selection of talent: Experimental and structural evidence from ethiopia,” _American Economic Review_ , 111, 1757–1806.

- Abebe, G. T., S. Caria, M. Fafchamps, P. Falco, S. Franklin, S. Quinn, and F. Shilpi (2017): “Job fairs: Matching firms and workers in a field experiment in Ethiopia,” _World Bank Policy Research Working Paper_ .

- Abel, M., R. Burger, E. Carranza, and P. Piraino (2019): “Bridging the intentionbehavior gap? The effect of plan-making prompts on job search and employment,” _American Economic Journal: Applied Economics_ , 11, 284–301.

> 36Indeed, other studies show that students do not change beliefs when presented with information about wages (Conlon, 2021).

39
# [FIGURE 1 HERE]

Following Grossman and Helpman (1994) and DGH (1997), we assume that the government’s objective function is a convex combination (with weight x) of the contributions it receives and of a social welfare function. This can either be interpreted as allowing for some ’benevolence’ on the part of the government agent, or simply to capture the fact that actual political processes are not limited to the interaction of pressure groups, and may also include more ”democratic” channels, which make the government care about the welfare of the people.

To allow for different organization (or coordination) capacities across the two groups of agents, without explicitly modeling their formation, we attach weights to each group’s contribution in the government’s utility function, representing a given group’s relative ease of organization - and thus greater lobbying effectiveness - by a greater weight. In fact, the usual argument in that discussion states that smaller and less disperse groups have a higher probability of actually being formed, due to transaction costs and to the problem of free-riding, and this could be represented by letting these weights depend on each group’s size. Within the present context, where the group of the poor (rich) has size p (1 − p), we can define these weights as λP (p) and λR(p), where λ0P (p) < 0 and λ0R(p) > 0, in order to capture this idea.<sup>17</sup> All these features may be expressed, drawing upon Grossman and Helpman (1994), by modeling a government that maximizes the following objective function: where Cj(g) is the political contribution from an individual member of group j as a function of the composition of government expenditures,<sup>18</sup> which we shall assume to be continuously differentiable, and x ∈ [0, 1] is the weight attached to contributions vis-à-vis social welfare (considering for simplicity a Benthamite welfare function in which every individual has the same weight<sup>19</sup> ). The individual’s utility - which is identical to its post-production disposable income - is written in (2), with a slight abuse of notation, as Ψj(g, Cj(g)), since kj = (1 − τ )wj − Cj(g).

Assuming perfect information, the problem is therefore written exactly as a generalized common agency game - where pressure groups are the principals, and the government is the agent - and its solution may be obtained as such.

> 17 This an admittedly very reduced-form attempt to allow for differentiated coordination capacities across different coalitions, which may impact on their effectiveness as pressure groups. The classic reference is Olson (1965), and Becker (1983) is another instance of application of this insight.

> 18 To write Cj (g), we use the fact that the government’s budget constraint allows us to write s as a function of g.

> 19 It should be pointed out here that such assumption implies that there is no social inequality-aversion.

10
|**Table A9: Impacts on Different Componen**|**ts of Capital St**<br>Mhi|**ock**<br>Oth|||Oth||Ld &|
|---|---|---|---|---|---|---|---|
||acnery|er|||er||an|
||& Equipment|Tools|Vehicles|Furniture|assets|Stock|Buildings|
|**Panel A: Pooled Sample**||||||||
|Assigned to Personal Initiative|901***|148***|435**|100***|1|331|831*|
||(228)|(51)|(169)|(36)|(13)|(396)|(428)|
|Assigned to Traditional Training|495**|3|237|89**|2|196|818*|
||(212)|(45)|(176)|(35)|(13)|(403)|(483)|
|Sample Size|1184|1184|1188|1188|1188|1188|1188|
|Control Mean|688|157|380|152|30|1668|885|
|P-value: PI = Trad|0.047|0.004|0.278|0.742|0.937|0.722|0.976|
|**Panel B: Impacts by Gender**||||||||
|Assigned to PersonaI Initiative * Male|1343***|177*|670**|94*|-5|656|757|
||(423)|(91)|(302)|(57)|(23)|(607)|(713)|
|Assigned to PersonaI Initiative * Female|462***|118**|206|115***|7|62|923*|
||(168)|(49)|(158)|(44)|(13)|(514)|(497)|
|Assigned to Traditional Training * Male|760*|18|361|156***|9|849|941|
||(390)|(86)|(303)|(58)|(24)|(659)|(790)|
|Assigned to Traditional Training * Female|222|-13|129|28|-7|-386|711|
||(173)|(36)|(192)|(38)|(11)|(478)|(584)|
|Sample Size|1184|1184|1188|1188|1188|1188|1188|
|Control Mean Men|1223|272|588|201|48|1526|1357|
|Control Mean Women|223|56|198|110|14|1793|471|
|p-value: PI Men=Women|0.054|0.573|0.173|0.767|0.648|0.456|0.848|
|p-value: Traditional Men=Women|0.208|0.741|0.518|0.067|0.557|0.129|0.815|
|Notes:<br>Regressions include randomization strata a<br>interaction between female and baseline c<br>significance at the 10, 5, and 1 percent leve<br>winsorized at the 1st and 99th percentiles,<br>components of the overall capital stock agg<br>from the overall capital measure given its h|nd baseline cap<br>apital stock. Ro<br>ls respectively.<br>and coded as 0<br>regate used in<br>ighly skewed di|ital stock<br>bust stand<br>Capital st<br>for firms<br>Table 2. T<br>stribution|. Panel B al<br>ard errors<br>ock expres<br>that are cl<br>he last col<br>and possi|so includes<br>in parenth<br>sed in real S<br>osed. The fir<br>umn of land<br>ble intertwi|a control<br>eses. *, **<br>eptember<br>st six colu<br>and build<br>ning with|for female<br>, *** deno<br>2021 USD<br>mns show<br>ings is exc<br>household|, and an<br>te<br>,<br>different<br>luded<br>assets.|

37
**Table 5. Parent Matchmaking and Wife Schooling and Earning**

|||**Wife Schoolin**<br>|**g**<br>||**Ln(Wife Inco**<br>|**me)**<br>|
|---|---|---|---|---|---|---|
||OLS|OLS|2SLS|OLS|OLS|2SLS|
|rural|0.447|0.272|1.944***|0.223|0.165|0.531|
||(0.284)|(0.321)|(0.675)|(0.253)|(0.261)|(0.389)|
|Parent matched|-0.374***|-0.360***|2.383|-0.072**|-0.065**|-5.517***|
||(0.116)|(0.115)|(1.717)|(0.029)|(0.029)|(1.273)|
|Parent Matched * Rural|-0.146|-0.141|-7.087***|-0.018|-0.017|2.102|
||(0.155)|(0.151)|(2.114)|(0.050)|(0.051)|(1.303)|
|Other controls as in column (1) of<br>Table 5|Yes|Yes|Yes|Yes|Yes|Yes|
|Religion and ethnic dummies|No|Yes|Yes|No|Yes|Yes|
|Number of observations|6,334|6,334|6,334|6,154|6,154|6,154|
|Adjusted R2|0.537|0.538|0.344|0.390|0.394|-4.175|
|Cragg-Donald Wald F stat|||29.8|||22.9|
|Kleibergen-PaapWalk rk F stat|||16.9|||14.2|

Note. *, **, and ***: statistically significant at the 10, 5, and 1 percent level. White-corrected standard errors clustered at the township level in parentheses.

40
€190,501 - 254,000 3% 4% €254,001 - 317,500 3.75% 5% €317,501 - 381,000 4.50% 6% €381,001 - 635,000 7.50% 7.50% €635,000 or more 9% 9% **Commercial Stamp Duty Rates:** The rates of stamp duty applicable to commercial property are different than those applied to residential property and the current rates are as set out below: Rate of Aggregate Consideration Duty Up to €10,000 Exempt €10,001 - €20,000 1% €20,001 - €30,000 2% €30,001 - €40,000 3% €40,001 - €70,000 4% €70,001 - €80,000 5% €80,001 - €100,000 6% €100,001 - €120,000 7% €120,001 - €150,000 8% Over €150,000 9% (Source : 2)

**Israel Stamp Tax Rates: Document Type: A. When the value is stipulated in the documents:**

- Insurance contracts for ships or airplanes= 0.1%

- Contract, memorandum, promissory note, lien and any non-transferable guarantee or obligation= 0.4%

- Issuing of nominee shares, nominee bonds and deeds of trust = 1%

- Insurance policies = 1.5%

- Transferable guarantees or obligations = 2%

- Sea freight insurance = 3%

- **B. Where the value is not stipulated in the document  = $1.20**

**Ehud Menirav (2000):** It is difficult to collect the stamp tax and most individuals do not pay it at all. Furthermore, the collection pf the tax is carried out inefficiently without a computerized database and with a low level of service to the taxpayer. The distortions to the economy include the increased cost of issuing securities on the stock exchange and the reduction of alternatives for saving and borrowing.

Ehud Menirav, Stamp Tax in Israel, Institute for Advanced Strategic and Political Studies, Policy Studies No. 44, January 2000.

42