rafmacalaba/datause-encoder-data huggingface dataset:
**Table A13: Firm-level statistics - current assets (in 1000s)**

|**Size**|**N**|**Mean**|**Std. dev.**|**p10**|**p25**|**p50**|**p75**|**p90**|
|---|---|---|---|---|---|---|---|---|
|**Micro**|199,242|201780|4164167|696.0193|4258.19|18245.55|60785.59|172630.1|
|**Small**|202,450|860909.3|1.38E+07|17474.07|65299.63|199388.3|539761.1|1293593|
|**Medium**|40,217|2890061|1.01E+07|209437.4|563468.2|1276528|2713552|5648698|
|**Large **|42,068|1.80E+07|1.81E+08|742631.3|1719325|4249895|1.07E+07|2.86E+07|

Source: Author’s calculations using RUSLANA

**TableA14: Firm-level statistics - value added per worker (in 1000s)**

|**Size**|**N**|**Mean**|**Std. dev**|**p10**|**p25**|**p50**|**p75**|**p90**|
|---|---|---|---|---|---|---|---|---|
|**Micro**|199,242|3370.888|35761.82|-469.968|89.83334|848.6792|3300.341|8142.333|
|**Small**|202,450|3859.066|2.22E+04|6.107754|322.6116|1569.344|4351.495|9116.152|
|**Medium**|40,217|3821.288|6.61E+03|-11.8224|563.0723|2154.832|5117.769|9690.881|
|**Large **|42,068|3.20E+03|4.92E+03|4.85822|569.4197|1932.481|4.36E+03|7.86E+03|

Source: Author’s calculations using RUSLANA

**Table A15: Firm-level statistics - age**

|**Size**|**N**|**Mean**|**Std. dev**|**p10**|**p25**|**p50**|**p75**|**p90**|
|---|---|---|---|---|---|---|---|---|
|**Micro**|199,242|5.4|5.2|0|1|4|9|12|
|**Small**|202,450|7.9|7.0|2|3|6|11|15|
|**Medium**|40,217|10.8|12.5|2|5|9|13|17|
|**Large **|42,068|21.5|30.0|4|8|13|17|57|

Source: Author’s calculations using RUSLANA

**Table A16: Sector-level statistics - price cost margin**

|**Industry Subsection**|**N**|**Mean**|**Std. dev**|**p10**|**p25**|**p50**|**p75**|**p90**|
|---|---|---|---|---|---|---|---|---|
|**Food products, beverages**|117,624|0.09|0.05|0.03|0.05|0.08|0.11|0.16|
|**Textiles and textile products**|50,947|0.07|0.04|0.04|0.05|0.07|0.09|0.12|
|**Leather and leather products**|47,792|0.07|0.03|0.04|0.05|0.07|0.09|0.10|
|**Pulp, paper and paper products**|44,142|0.08|0.05|0.06|0.07|0.08|0.10|0.11|
|**Coke, refined petroleum**|4,049|0.10|0.04|0.06|0.08|0.10|0.12|0.12|
|**Chemicals, chemical products**|35,305|0.14|0.06|0.08|0.10|0.14|0.17|0.24|
|**Rubber and plastic products**|39,747|0.09|0.02|0.06|0.08|0.09|0.10|0.12|
|**Other non-metallic minerals**|50,464|0.09|0.04|0.05|0.06|0.08|0.11|0.14|
|**Basic metals and fabrications**|77,807|0.09|0.03|0.06|0.07|0.09|0.11|0.12|
|**Machinery and equipment**|94,175|0.10|0.03|0.05|0.07|0.09|0.12|0.14|
|**Electrical and optical equipment**|74,309|0.11|0.05|0.07|0.09|0.11|0.14|0.16|
|**Food products, beverages**|21,632|0.09|0.03|0.06|0.07|0.08|0.10|0.11|
|**Textiles and textile products**|36,497|0.11|0.04|0.07|0.08|0.10|0.14|0.15|

Source: Author’s calculations using RUSLANA 31
9 Figure 5: U.S. financial flows: Composition and partners (A) FDI inflows from the United States (B) Portfolio inflows from the United States <!-- Start of picture text --> Percent Percent of GDP Percent Percent Percent of GDP Percent<br>14 Percent of total (RHS) 80 25 Percent of total (RHS) 80<br>12 70 70<br>20<br>10 60 60<br>50 15 50<br>8<br>40 40<br>6 30 10 30<br>4 20 5 20<br>2 10 10<br>0 0 0 0<br>(C) Cross-border bank claims of U.S. (D) Remittance inflows from the<br>banks on selected EMDEs United States<br>Percent Percent of GDP Percent Percent Percent of GDP Percent<br>10 Percent of total (RHS) 40 5 Percent of total (RHS) 120<br>35<br>8 30 4 100<br>6 25 3 80<br>20 60<br>4 15 2<br>40<br>10<br>2<br>5 1 20<br>0 0<br>0 0<br>Chile Mexico Kazakhstan Hungary Malaysia Thailand Brazil Philippines South Africa South Africa Malaysia Mexico Chile Hungary Philippines Thailand Indonesia Ukraine<br>Mexico Chile India Brazil Turkey India<br>Malaysia Hungary Philippines Mexico Nigeria Pakistan Ethiopia Colombia Ukraine Thailand<br>South Africa Philippines<br>Poland India<br>Nepal<br>Colombia<br><!-- End of picture text --> Sources: Bank for International Settlements, International Monetary Fund, World Bank. A. Share of FDI inflows from United States in total FDI inflows into (and in percent of GDP of) each EMDE, average of 2010-2014.

B. Share of portfolio investment from United States in total portfolio inflows into (and in percent of GDP of) each EMDE, average of 2010-2015.

C. Share of consolidated U.S.-headquartered BIS-reporting banks’ claims on each EMDE in total consolidated BIS-reporting banks’ claims on (and in percent of GDP of) each EMDE, average of 2010-2015. D. Share of remittances inflows from United States in total remittances inflows into (and in percent of GDP of) each EMDE, average of 2010-2015.
_Table 5: Comparing Basic Characteristics of the DTS and RP graduates_

||A|ll||RP||DTS|Difference|
|---|---|---|---|---|---|---|---|
|Variables|N|Mean|N|Mean(a)|N|Mean(b)|(a)−(b)|
|Age|833|26.6|471|28.6|362|23.9|4.7***|
|Gender(% female)|833|34%|471|22%|362|50%|−28%***|
|Formal education(no. ofyears)|833|12.1|471|12.0|362|12.3|−0.2*|
|General averagegrade at HSgraduation|833|82.6|471|82.1|362|83.3|−1.2***|
|TVET(no. ofyears)|833|0.7|471|0.3|362|1.4|−1.1***|
|College enrollment - before TVET(%)|833|17%|471|25%|362|8%|17%***|
|College completion - before TVET(%)|833|24%|471|35%|362|11%|24%***|
|College enrollment - after TVET(%)|833|9%|471|12%|362|5%|8%***|
|College completion - after TVET(%)|833|1%|471|1%|362|1%|0%|
|Work experience(no. ofyears)|833|9.4|471|11.6|362|6.6|5.0***|
|Years sincegraduatingTVET|833|3.3|471|3.5|362|2.9|0.6***|

Source: _DTS Tracking Survey Data, 2015, World Bank._ Note: _*** Statistically significant at 1% level; * Statistically significant at 10% level._ 30
# **2.2.2 FDI presence**

The data I use include ownership information for 2012, 2015 and 2016. Given the timing of the export data, I focus on ownership in 2012 and 2015. I define a firm as foreign owned if the Global Ultimate Owner (GUO) reported by ORBIS is foreign.<sup>11</sup> If the GUO is reported to be domestic or the GUO nationality is missing, I use information on shareholders of a given firm reported in ORBIS. I define a firm as foreign it has at least one foreign shareholder with more than 10% equity share.<sup>12</sup> I define industry-level FDI presence based on the standard formula used in the literature: where _i_ denotes firm, _s_ 3-digit NACE sector and _t_ year. In addition, _fjt_ is an indicator variable for foreign owned firms and _Yjt_ is the operating revenue. Given the limited availability of the ownership information, I measure Own FDI at two points in time, namely in 2012 and 2015.

To capture vertical spillovers from FDI in the input-sourcing sectors, I rely on the as– sumption that a domestic firm is more likely to supply foreign affiliates and benefit from – vertical spillovers if foreign affiliates account for a larger share of downstream industries’ output. As in Javorcik (2004), I do not include inputs’ sales within the firm’s own sector, as this effect is captured by the Own FDI variable. Thus, this proxy will be capturing a lower bound on the effect of FDI in the input-sourcing industries. I define the downstream FDI measure as

> 11A company’s GUO is the individual or entity at the top of the corporate ownership structure, controlling at least 25.01% of that company.

> 12I exclude capital from ‘tax havens’ when defining foreign ownership. I treat the following countries as ‘tax havens’: Anguilla, Aruba, Bahamas, Barbados, Belize, Bermuda, British Virgin Islands, Cayman Islands, Cook Islands, Curacao, Cyprus, Dominica, Gibraltar, Guernsey, Guyana, Hong Kong SAR China, Isle of Man, Jersey, Liberia, Lichtenstein, Luxembourg, Macao, Malta, Marshall Islands, Mauritius, Monaco, Montserrat, Nauru, Samoa, San Marino, Seychelles, Singapore, St. Kitts & Nevis, St. Vincent & Grenadines, Taiwan China, Turks & Caicos Islands, US Virgin Islands.

7
the _Trabajar_ program in Jalan and Ravallion (2003), who estimated foregone income to be 100 pesos per month — about half the _Trabajar_ wage. However, it is unsurprising that _Jefes_ would have lower foregone income given the general decline in real wages due to the crisis (World Bank, 2003); the opportunity cost of participation in workfare would undoubtedly have been lower in the wake of this crisis. While we cannot reject the null hypothesis of zero foregone income for the double-difference estimate of the impact on household income, this is attributable to the considerably greater noise in this estimator.

# **6. Conclusions**

We have studied a large social protection operation, _Plan Jefes y Jefas_ , undertaken by the Government of Argentina, with support from the World Bank, in response to the 2002 crisis. In identifying impact we have used applicants who have not yet been accepted into the program as the source of a comparison group. To help clean out remaining sources of selection bias we have used propensity score matching methods and longitudinal observations following a (pre-crisis) baseline survey.

The _Jefes_ program provided a basic cash transfer to all households satisfying certain eligibility criteria and for about 80% of participants the transfer payment came with a work requirement. However, it is clear from our results that the program’s eligibility criteria were not rigorously enforced. We find that about one third of those receiving the program do not satisfy the eligibility criteria (and about three fourths of those adult individuals who are eligible are not receiving the program). The aim of only targeting unemployed heads of households with dependents was clearly not realized; indeed, our results suggest that a large share of participants were women who would have not otherwise have been in the labor force. About half of the employment gain due to the program came from unemployment and half from inactivity.

24
<!-- Start of picture text --> 1400000 7000000<br>1200000 6000000<br>1000000 Algerie Telecom 5000000<br>Orascom Telecom Algeria<br>Total subscribers<br>800000 4000000 Meditel<br>IAM<br>Total<br>600000 3000000<br>2000000<br>400000<br>1000000<br>200000<br>0<br>0 Sep- Dec- Mar- Jun- Sep- Dec- Mar- Jun- Sep- Dec- Mar- Jun- Sep- Dec- Jan- Feb- Mar-<br>1/1/2002 31/12/2002 16/08/2003 22/09/2003 99 99 00 00 00 00 01 01 01 01 02 02 02 02 03 03 03<br>Fig-7- GSM subscribers evolution in Algeria - Source: ARPT<br>Fig. 7 bis – GSM subscribers growth in Morocco. – Source :<br>ANRT (2003)<br><!-- End of picture text --> Fig-7- GSM subscribers evolution in Algeria - Source: ARPT (2003) In fact, OTA’s entry in the Algerian market was facilitated by AT’s lack of preparation. As already discussed, AT’s transition from a government department into a commercial firm went through a long, complex, and inefficient process. For instance, its mobile network was congested and plans to expand its capacity were stalled in a highly politicized procurement process. A public tender to procure 500,000 lines was launched in November 2001, awarded in April 2002, and declared unsuccessful in November 2002 because of allegations of corruption. In contrast to Morocco, where the public incumbent (Maroc Telecom or IAM) had strong technical and managerial capacity to compete (see Fig. 7bis), AT was not ready to sustain a fierce competition against OTA. Despite the lack of fierce competition in the mobile market, Algeria witnessed 1,525% increase of the number of mobile subscribers from 1999 to September 2003. The total number of mobile subscribers jumped from 72,000 in 1999 to more than a million in September 2003. The increase in the number of fixed lines was only 25%, from 1,600,000 in 1999 to 2,000,000 by the end of 2002. Additionally, 32<sup>13</sup> out of 48 wilaya, as well as most national highways are covered mobile networks (ARPT, 2003a, b). Further, the expected entry of a third GSM operator in 2004 has already led to price reduction for connection fees which have been divided by 3 for post – paid subscribers<sup>14</sup> , and by 2 for pre-paid subscribers<sup>15</sup> . Therefore, the government was right in favoring the implementation of market liberalization policies as opposed to corporate restructuring.

> 13 - Wilaya stands for provincial government units.

> 14 - The average connection fee went down from DH22,000 in 2002 to DH8,054 in 2003.

> 15 - The average connection fee went down from DH8,900 in 2002 to DH3,999 in 2003

14
# **Table 23a.  Piecemeal Sensitivity Analysis -- Welfare Impacts as a percent of GDP by Region from WTO Accession (percentage change from base year)**

||**Overall**||**Far**||||||**St.**|||
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Parameter being changed**|**average**|**Central**|**East**|**North**|**Siberia**|**South**|**Urals**|**Moscow**|**Peters.**|**Tumen**|**Northwest**|
|**Central Results for reference**|4.0|4.1|4.8|4.4|3.9|4.4|3.2|4.2|5.2|2.9|5.6|
|**esubc = 0.5**|3.6|3.7|4.4|4.1|3.6|4.0|3.0|3.9|4.3|2.7|5.2|
|**esubc = 1.5**|4.1|4.2|4.9|4.6|4.0|4.5|3.3|4.4|5.4|2.9|5.7|
|**esub = 2**|3.9|4.1|5.1|4.6|3.7|4.3|2.9|4.2|5.8|2.7|6.1|
|**esub = 4**|3.8|3.9|4.5|4.2|3.8|4.2|3.2|4.1|4.8|2.8|5.2|
|**esubs = 0.5**|3.1|3.2|3.7|3.4|3.1|3.5|2.5|3.4|4.1|2.2|4.4|
|**esubs = 2.0**|5.3|5.6|6.5|6.1|5.3|5.8|4.3|5.6|6.2|4.0|7.6|
|**esubva = 0.5**|4.0|3.9|4.9|4.2|3.9|4.4|3.1|4.3|5.1|2.9|5.3|
|**esubva = 1.5**|4.0|4.1|4.7|4.4|3.9|4.3|3.2|4.2|5.2|2.9|5.7|
|**etad = 10**|4.1|4.1|5.1|4.6|3.9|4.5|3.2|4.5|5.5|2.8|5.7|
|**etad = 5**|3.8|4.1|4.4|4.2|3.9|4.2|3.2|3.8|4.8|2.9|5.5|
|**etadx = 3**|3.9|4.0|4.7|4.1|3.9|4.3|3.2|4.1|5.1|3.0|5.5|
|**etadx = 7**|4.0|4.1|4.8|4.4|3.9|4.4|3.2|4.3|5.3|2.8|5.6|
|**etaf = 12.5**|3.4|3.5|4.3|3.9|3.3|3.8|2.7|3.7|4.7|2.4|5.0|
|**etaf = 17.5**|4.5|4.6|5.2|4.9|4.4|4.9|3.7|4.7|5.6|3.3|6.1|
|**sigmadm = 4**|3.9|4.0|4.6|4.3|3.9|4.2|3.2|4.1|4.9|2.9|5.3|
|**sigmadm = 2**|3.9|4.0|5.0|4.5|3.7|4.3|2.9|4.1|5.6|2.6|5.9|
|**Source: Authors' calculations.**||||||||||||
|**Notes:**<br>b. Hicksian equivalent variation as apercen<br>**Parameter**<br>esubc<br>a. Thepiecemeal sensitivityanalysis empl|t of the val<br>**Central**<br>**value**<br>1.25<br>oys central|ue of consump<br>**Definitions of**<br>**for moreprec**<br>Elasticityof su<br>values for allp|tion in the<br>**theparame**<br>**ise elastici**<br>bstitution in<br>arameters|benchmark eq<br>**ter(see figur**<br>**ty structure).**<br>consumer de<br>(see below)o|uilibrium.<br>**es, especiall**<br>mand<br>ther than the|**y figure 5)**<br>testedpara|meter.|||||

|as apercen<br>**Parameter**|t of the val<br>**Central**|ue of consumption in the benchmark equilibrium.<br>**Definitions of theparameter(see figures, especially figure 5)**|
|---|---|---|
||**value**|**for moreprecise elasticity structure).**|
|esubc|1.25|Elasticityof substitution in consumer demand|
|esubs|1.25|Elasticityof substitution between value-added and business services|
|sigmadm|3|"Armington" elasticityof substitution between imports and domesticgoods|
|etaf|15|Elasticityof multinational service firm supplywith respect toprice of output|
|etad|7.5|Elasticityof Russian service firm supplywith respect toprice of output|
|esub|3|Elasticityof substitution between firm varieties in imperfectlycompetitive sectors|
|esubva|1|Elasticityof substitution betweenprimaryfactors ofproduction in value added|
|etadx|5|Elasticityof transformation(domestic output versus exports)|

67
stable than large improvements. One possible reason which we discuss further below is that bundles of management practices are complementary, so that adopting only parts of them may be less stable than adopting all of them. Of course, given the small sample sizes in this experiment this could also reflect sampling noise - something that should be remembered when evaluating all our results from this experiment.

<u>Non-experimental plants: Third, the non-experimental plants in the treatment firms showed</u> no net change, with their management practice adoption rates remaining constant at 0.47. Indeed, by 2017 their management scores were very similar overall to the treatment experimental plants (indeed slightly higher, although not significantly so). Similarly, in the control firms the nonexperimental plants also converged with the experimental plants (again slightly higher but not significantly). This suggests (as we discuss further below) that the practice improvements in the experimental plants spilled over to the non-experimental plants during the eight years after the experiment.

<u>Expectations on durability of the intervention: Finally, before we re-contacted the firms in</u> December 2016, each member of the consulting team from the original intervention and the academic team provided predictions for the management scores we expected to find on revisiting the firms in 2017.<sup>5</sup> These expectations were informed by the contrasting views of management improvements noted in the introduction: under the “Toyota way” of continuous improvement we would expect the management practices to not only persist, but to continue to improve in treatment plants so that the gap with the control plants would widen; whereas under the “inappropriate technology” view, we would expect many practices to be dropped and the treatment group to converge back to the control group. The average values of the estimates of the seven team members are shown for the treatment experimental, treatment non-experimental, control experimental plants and control non-experimental plants with the symbols TE, TN, CE and CN respectively on the graph.<sup>6</sup> These predicted values are all below the actual outcomes, indicating that the project team

> 5 Other examples of getting experts to provide ex ante predictions of the results of an experiment can be found in Hirschleifer et al. (2016), Groh et al. (2016) and Dellavigna and Pope (2017).

> 6 The predictions of the individual consultant and academic team members were made independently – Bloom estimated first and then the other team members individually e-mailed

10
household as determined by computed distances. This procedure implicitly assumes that households have perfect information about their health care market. The same implicit assumption is also used in Leonard et al. (2002), where from the registry records of a sample of 42 facilities, the authors draw a sample of users. Using patients’ information recorded by the facility, which in addition to age, gender and symptoms, include the village of origin, the authors compute distances to health centers.

Contrary to Akin and Hutchinson (1999) and Leonard et al. (2002) this paper defines bypassing using the information set of the patients. Each patient was asked whether she could have used other facilities (formal or informal) than the one she just visited. Then, for each such facility and the facility she was in, the patient were asked to provide information on the distance and travel time from the facility to her household. The set of facilities the patient identified is labeled her “local health market”. Moreover, all facilities which are farther from the patient’s household, according to her own estimated distance, than the facility she ultimately visited are “bypassed” facilities.

This measure of bypassing seems more adequate than previous attempts to measure the phenomenon for at least two reasons. First, the patient’s knowledge of her ‘theoretical’ health care market is in all likelihood imperfect, therefore using estimated distances a patient may be considered to have bypassed a given facility which she “in fact” does not know about. Second, individuals do not forcefully have a good sense of distance and their estimated distance may be different from the “objective” distance as computed from maps. Our definition of bypassing, however, is based on the patient’s own judgment of distance to available facilities. It is the richness of our data set that affords us the availability of -what we contend is- a more accurate measure of bypassing.

# **6.2 The Importance of Bypassing**

The extent of bypassing of the formal and informal sectors, measured using patients’ information, is presented in Table 8. More than a third (35.6%) of the patients had at least two providers (formal or informal) available to them, cherry-picked their preferred provider on the basis of either cost or quality, and knowingly travelled a higher distance to seek care. While about 1/4 (24.6%) of patients bypass the modern 13
findings are representative of smaller cities. However, the disparity among the forty cities analyzed here is a large component of the spatial inequality in industrial activity in India at the moment, and is probably what many people have largely in mind when the issue of regional (as opposed to local) inequality is raised in the policy literature.

# **References:**

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- Dollar, D., I. Giuseppe, and T. Mengistae, 2002, “Investment Climate and Economic Performance: Some Firm Level Evidence from India” Working Paper No. 143, Center for Economic Research and Policy Reform, Stanford University.

- Dollar, D., M. Hallward-Driemeier, and T. Mengistae, 2004, “Investment Climate and Firm Performance: in Developing Economies,” Processed, World Bank, February

- Dubin, M., and D. McFadden, 1984. “An Econometric Analysis of Residential Electric Appliances Holdings and Consumption,” Econometrica, vol. 52, pp. 345-362.

- Eifert, B., A. Gelb, and R. Ramachandran. 2005. “Business Environment and Comparative Advantage in Africa: Evidence from the Investment Climate Data,’ mimeo, World Bank Group, Washington DC, January.

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