rafmacalaba/datause-encoder-data huggingface dataset:
ally reluctant to hire new workers and why they rely<sup>mainly on personal</sup> relationships to do so, as shown by some available studies<sup>(CNJA(1995),</sup> Montmarquette et al. (1996)) and as noted earlier. Through<sup>personalcon-</sup> tacts, employers minimize the risk related to<sup>hiringbygatheringcomplete</sup> information on the worker. Knowing the candidate<sup>alsoincreasesthelikeli-</sup> hood that they will be able to exercise social<sup>pressurestonegotiatebetter</sup> terms for his/hers eventual firing. Globally,<sup>nonetheless,thislegalframe-</sup> work constrains the capacity of enterprises to adapt to<sup>changes ineconomic</sup> activity and relative price incentives, and encourages<sup>temporarycontracts.</sup> The very nature of these contracts prevents significant<sup>gainsinproductiv-</sup> ity, because they dissuade firms from investing<sup>in</sup> the<sup>humancapitalof their</sup> workforce.

# 2.3.3 **Institutional Job Matching Processes**

Formally, the job matching process is a public<sup>monopoly.Thecountryhas</sup> recently adopted a set of active labor market<sup>policiesaimedatimproving</sup> the job matching process for young skilled workers. This<sup>policyismainly</sup> based on regional public institutions in charge<sup>ofimprovingthematching</sup> process between labor demand and young skilled labor<sup>supply,andspecific</sup> labor tax deductions favoring their employment.<sup>AstudybyIbourkand</sup> Perelman (1999) shows that these institutions tend to<sup>improvethe efficiency</sup> of the job matching process, but that they are<sup>limitedin theiractionbythe</sup> gap between labor demand and labor supply.

## **2.3.4 Nonwage Labor Costs**

In addition to minimum wage regulation and<sup>firinglegislation,thereare</sup> numerous compulsory social contributions that<sup>also affecttheprivatelabor</sup> demand function. In the private formal industrial<sup>sector,besidesincome</sup> taxes,4 nonwage labor costs represent around<sup>24percentofthetotalcosts</sup> of the labor force. Hence, for a worker in the private<sup>formalsector,thereis</sup> a substantial wedge between the gross wage and<sup>thenetincome.According</sup> to the current regulatory framework, employers<sup>payaround18.6percentof</sup> the gross wage to the Social Security (CNSS)<sup>forpensioncontributionsand</sup> other social coverages. There is also a 1.6 percent<sup>compulsorytaxonthe</sup> 4Income tax rates range from 13 percent to 44 percent.<sup>Socialcontributionsarede-</sup> ductible from the tax base.

14
# References

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Abubakar U, Amir O, Rodríguez-Baño J (2022). Healthcare-associated infections in Africa: a systematic review and meta-analysis of point prevalence studies. J of Pharm Policy and Pract. 15(99).

Adams C, Peterson SR, Hall AJ, Parashar U, Lopman BA (2022). Associations of infection control measures and norovirus outbreak outcomes in healthcare settings: a systematic review and meta-analysis. Expert Rev Anti Infect Ther. 20(2): 279-290.

Aerts C, Leahy S, Mucasse H, Lala S, et al (2022). Quantifying the Acute Care Costs of Neonatal Bacterial Sepsis and Meningitis in Mozambique and South Africa. Clin Infect Dis. 20(74 Suppl 1): S64-S69.

Ahmed MI (2012). Prevalence of nosocomial wound infection among postoperative patients and antibiotics patterns at teaching hospital in Sudan. N Am J Med Sci. 4(1): 29-34.

Ahoyo TA, Bankolé HS, Adéoti FM, Gbohoun AA et al (2014). Prevalence of nosocomial infections and anti-infective therapy in Benin: results of the first nationwide survey in 2012. Antimicrob Resist Infect Control 3(1): 1–6.

Aiken AM, Karuri DM, Wanyoro AK, Macleod J (2012). Interventional studies for preventing surgical site infections in sub-Saharan Africa. A systematic review. Int J Surg. 10(5): 242-9.

Aiken AM, Mturi N, Njuguna P, et al (2011). Risk and causes of paediatric hospital acquired bacteraemia in Kilifi District Hospital, Kenya: a prospective cohort study. Lancet 378: 2021– 2027.

Akoko L, Mwanga AH, Fredrick F, Mbembati NM (2012). Risk factors of surgical site infection at Muhimbili national hospital, Dar es Salaam, Tanzania. East and Central African Journal of Surgery 17(3).

Alemu AY, Endalamaw A, Bayih WA (2020). The burden of healthcare-associated infection in Ethiopia: a systematic review and meta-analysis. Trop Med Health 48(77).

Ali S, Birhane M, Bekele S, Kibru G et al (2018). Healthcare associated infection and its risk factors among patients admitted to a tertiary hospital in Ethiopia: longitudinal study. Antimicrob Resist Infect Control 7(2).

Allegranzi B, Nejad SB, Combescure C, Graafmans W et al (2011). Burden of endemic healthcare-associated infection in developing countries: systematic review and meta-analysis. Lancet 377(9761): 228–41.

Amare B, Abdurrahman Z, Moges B, Ali J et al (2011). Postoperative Surgical Site Bacterial Infections and Drug Susceptibility Patterns at Gondar University Teaching Hospital, Northwest Ethiopia Journal of Bacteriology and Parasitology 2(8): 1000126.

American Thoracic Society; Infectious Diseases Society of America (2005). Guidelines for the management of adults with hospital-acquired, ventilator-associated, and healthcareassociated pneumonia. Am J Respir Crit Care Med. 171(4): 388-416.

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Table A4.3: Bound tariff rates (ad valorem equivalents) on agricultural products; Examples of differences among G-33 <u>countries</u>

|Dimension|Low<sup>a</sup>|Middle|High|
|---|---|---|---|
|Median rate|China, 13%<br>Cote d’Ivoire, 15%|Turkey, 58%|Zimbabwe, 150%<br>Nigeria, 150%|
|Maximum rate|Suriname, 20%<br>Mongolia, 40%|Venezuela, 135%|Korea Rep, 887%<br>Botswana, 597%|
|Percentage of tariff<br>lines with rate above<br>60 percent|8 countries, 0%|Botswana, 16%|9 countries, 100%|

Source: Tabulated from Jales 2005, Table 1, p. 8. Note: a The figures following the commas are ad valorem equivalent rates.

73
> <sup>18</sup>

development or initial per capita income in 1975. Both of these are highly correlated with initial values of the political variables.

**Table 1b:  Two stage least squares estimates of Table 1a**

||Competi<br>|tive Electio<br>|ns(1975)<br>|Check<br>|s and Balances<br>|(1975)<br>|
|---|---|---|---|---|---|---|
|Lending to the<br>private sector by<br>banks and non-<br>bank financial<br>institutions|Growth<br>75 – 00<br>(_IV_)|Level<br>2000<br>(_IV_)|Level<br>2000<br>(_IV_)|Growth<br>75 – 00<br>(_IV_)|Level 2000<br>(_IV_)|Level<br>2000<br>(_IV_)|
|Political variable|.02<br>(_0.0_)|.09<br>(_.07_)|.17<br>(_0.0_)|.033<br>(_.001_)|.27<br>(_.004_)|.30<br>(_0.0_)|
|Land (thousands<br>of square miles)|-.00002<br>(_.28_)|-.00008<br>(_.70_)|-.00007<br>(_.80_)|-.00002<br>(_.41_)|-.0002<br>(._60_)|-.0002<br>(_.53_)|
|Average|-.0005|.006|.003|-.00007|.004|.004|
|population<br>(millions)|(_.03_)|(_.25_)|(_.60_)|(_.01_)|(_.57_)|(_.61_)|
|Log real||.12|||.02||
|income/capita<br>(1975)||(_.09_)|||(_.76_)||
|Private sector<br>lending (1975)|-.10<br>(_.01_)|||-.03<br>(_.001_)|||
|_F_-statistic on<br>instruments (first<br>stage)|3.84|3.62|7.2|4.39|3.53|7.3|
|Hansen_J_-statistic<br>(Chi-squared_p_-<br>value)|.45|.06|.17|.55|.68|.83|
|_N_|78|91|103|78|91|103|

N.B. _p_ -values are reported in parentheses, based on robust standard errors. Instruments for the fourth and eighth regressions are three dummy variables indicating whether a country has British, French or Spanish colonial origins. Constants not reported.

Whether elections or checks and balances matter most in cementing the credibility of government decisions is unclear in the literature. North and Weingast (1989) argue for the importance of political checks and balances in the absence of competitive elections and a
for outcomes just before the harvest takes place.<sup>10</sup> The regression specification becomes _yiv_ = _α_ + _δ_ 1Lean _v_ + _δ_ 2Lean _v ∗_ Topup _i_ + _δ_ 3Harvest _v_ + _δ_ 4Harvest _v ∗_ Topup _i_ +Γ _d_ +Θ _t_ + _εiv_ (2) The sum _δ_ 1 + _δ_ 2 is the direct effect of having been offered the lean season program – including one 24-day work cycle at planting time and a second 24-day cycle during the lean season – on outcomes just before the harvest. The sum _δ_ 3 + _δ_ 4 captures the direct effect of the standard program timing, under which participants have experienced only one 24-day work cycle at planting season. The difference between the two sums is the marginal effect on household outcomes just before harvest of an additional 24 days of potential PWP work during the lean season.

The parameters we estimate are intent-to-treat, with identification derived from randomized village and household treatment status, rather than the endogenous participation status of households. Intent-to-treat parameters are policy relevant, in that the government can control the coverage rate and offer of PWP but not households’ decisions to take it up. The effect of the program on participating households is also of interest, and future research will use instrumental variables specifications to estimate treatment-on-the-treated parameters and will explore heterogeneous effects on village-chosen versus randomly-assigned beneficiaries.

# **4.1 Balance**

To explore the balance between treatment and control villages in terms of pre-treatment covariates and outcomes, we use the IHS3 data from 2010/2011. Although our first round of follow-up survey pre-dates the PWP implementation in all but three villages, the survey was conducted after the intervention was announced in treatment villages. Anticipation of the program may have affected household behaviors.

We first examine the subset of our 182 villages which were not included in the IHS3 national household survey in 2010/2011 but were included in our sample due to administrative errors. They were randomly assigned to our treatment/groups (six, six, three, three, and five villages to Groups 0-4 respectively). The differences between the IHS3 and non-IHS3 villages reflects a composition effect and has a bearing on the external validity of the results, but it is orthogonal to treatment assignment. Using our first round of follow-up data, we find that households in the non-IHS3 sample are better off than the IHS3 sample, with better educated household heads, smaller household sizes, and fewer children below the age of 14

> 10Anticipation effects due to the (announced) timing of Cycle 2 could affect survey round 2 outcomes, even though all villages with PWP programs had the same work opportunities as of that survey. In estimates available upon request, we show that the effects of the lean and harvest season variants were very similar in survey round 2, so we prefer the statistical efficiency of the pooled specification using equation (1).

11
pressures of small farm size and fragmentation. In the analysis, we include the number of plots in 2000 (and its squared term) as control variables.

The quantities of both machine investment and demand for machine services are measured as purchases of machines and payments for machine services in yuan at 2000 constant prices, respectively (Figure 5). Given their small farm size, it is not a surprise that smallholders are less likely to invest in machines. Among 905 rural households in the sample, about half did not invest in machines. About 15% invested less than 200 yuan (24 US$) in machines. These investments are typically in small tools such as pesticide sprayers, etc. The censored distribution of machine investments motivates us to use the Tobit model in Section 4.

The increased use of machine services could be found from the increased percentage of rural households who spent more on machine services (Figure 5). The percentage of rural households who rely on mechanical farm operation services increased from 49% in 2000 to 58% in 2008, with the average growth rate of 2%. Without adjusting for the price of machine services (yuan/mu), we found that the average expense increased from 217 yuan (26 US$) to 285 yuan (34 US$). This result is consistent with the national-level statistics that show the expansion of mechanical farm operations in plowing, planting and harvesting.

# **3.3 Labor Markets and Wages**

In this study, the key variables of labor supply and wage rates (agriculture and nonagriculture) are calculated at village level in order to mitigate the household-level endogeneity that jointly affects labor supply, wages and productivity. Labor supply to off-farm employment is proxied by the proportion of offfarm income in total income for the sampled households in a village. On average, the proportion of offfarm income increased by 20.4 percentage point from 55.7% in 2000 to 75.1% in 2008. Furthermore, labor shortage in agriculture, especially those in peak seasons, could be captured by the migration rate in a village, which is defined as the proportion of household members who lived away (migrants) out of total
# **Table 7**

# **What is the Effect of a High Labor Tax Wedge on Participation**

Dependent Variable: Participates in Employer-Based Pension (1= Yes, 0=No)

||(1)<br>(2)<br>Worker Type|(3)<br>Worker|(4)<br>Type|
|---|---|---|---|
||Local<br>Migrant|Local|Migrant|
|Estimated Labor Tax Wedge|-0.235***<br>0.026|-0.135***|0.018|
||(0.021)<br>(0.037)|(0.021)|(0.039)|
|_Alternate Controls_||||
|Industry|no<br>no|yes|yes|
|Work Unit Ownership|no<br>no|yes|yes|
|Observations|6565<br>3265|6565|3265|
|Average Participation|0.818<br>0.264|0.818|0.822|

Notes (for both panels): Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. The sample includes 9830 working age adults, aged 18-60, who are employed for a wage. The sample is drawn from the five cities common to the 2005 and 2010 CULS survey rounds (Fuzhou, Shanghai, Shenyang, Wuhan and Xian). All models include age, age-squared, years of schooling, post-secondary indicator, marital status, female indicator, dummy for 2010 survey round and city dummy variables.

34
of deterioration became evident half a decade later. In a different paper, Aschauer (23) offered several checks on the direction of causation, concluding that increases in public capital stock lead to higher total factor productivity, which is a proxy for per capita income.

(h) Using 1965 data from 47 less developed countries and 19 developed countries, Antle (24) demonstrated the importance of transportation and communication facilities for raising aggregate agricultural productivity.

(i) An analysis of the economic rates of return of World Bank financed projects in the period 1968 to 1984, carried out by Israel (25), indicates that transportation investments, particularly roads, are among the most productive.

Therefore, the notion that road infrastructure is a necessary element in the development process is supported by several pieces of research. However, many factors can influence the impact of roads on income. In particular, an exploration of the linkages between policy distortions and the actual outcome of infrastructure investments, carried out by kauffman (26), concluded that a distorted policy environment reduced significantly the ex-post return of the investments. A good example of policies that would probably increase the impact of road investments on productivity was given by Small, Winston and Evans (27). Their policy recommendations include a set of pavement-wear taxes for heavy trucks, a set of congestion taxes for all vehicles, and a program of optimal investments in road durability. Such policies are based on two economic principles: efficient pricing to regulate demand for highway services and efficient investment to minimize the total public and private cost of providing them (27).

# Conclusions

The analyses in this paper show that there is a statistically significant relationship between road infrastructure and economic development on a worldwide basis: cross-section analysis of data from 98 countries (circa 1988), and time-series analysis of U.S. data between 1950 and 1988 showed significant relationships between per capita gross national product (PGNP) and density (i.e., per capita length, LPR) of paved road network. Moreover, there is relatively good consistency between the regression equations from cross-section and time-series analyses, when compared according to their respective inference space. Because of the high correlation between the densities of paved and unpaved roads, LPR should be interpreted as a proxy for a country's road stock, both paved and unpaved.

The per capita stock of road infrastructure in high-income economies is dramatically greater than in middle and low-income economies. For instance, the average density of paved roads (kmn/million inhabitants) varies from 170 in lowincome economies to 1,660 (plus 876 percent) in middle and 10,110 in high-income economies, the latter being 5,800 percent higher than the low-income group. Road condition also seems to be associated with economic development: the average 10
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Figure 9: Regional Imports per Worker, and Exports per Worker - 1992-2010 Each point reflects an individual regression coefficient where the dependent variable is the change in log employment (from RAIS), change in imports per worker, or change in exports per worker, calculated from 1991 to the year listed on the x-axis (see text for details). For all years, the independent variable is the regional tariff change reflecting tariff changes from 1990-1995 (described in the text), with state fixed effects and pre-trend control. Positive (negative) estimates imply that regions facing larger tariff declines experienced larger declines (increases) in exports per worker, or imports per worker. Vertical bar indicates that liberalization was complete in 1995. Dashed lines show 95 percent confidence intervals. Standard errors adjusted for 112 mesoregion clusters. Regional imports and exports per worker calculated as in Autor et al. (2013) 48
to serve in Korea. de Walque (2007) also imposes this sample restriction.<sup>8</sup> Because we are interested in educational attainment, we limit our sample to those over age 25 at interview as most individuals have completed their schooling by this age. We further restrict our sample to those with valid measurements of height and weight because we use these to create BMI.<sup>9</sup> In certain analyses, we use a restricted-use version of the NHIS which provides us with data on state of birth (blinded),<sup>10</sup> whether and in which specific year an individual died if he or she died by 2011 (NHIS-Mortality Linked File), and his or her age of death. These mortality data were collected by matching NHIS participants annually to the National Death Index system death certificates by a combination of social security number, gender, first and last name, date of birth, or birth month and year.

# **3.2.Selective Service Reports**

We use annual (1955-1966) and semi-annual (1967-1975) reports of the Director of the Selective Service for induction eligibility, the number of monthly inductions, as well as additional institutional details and troop level data. These induction data are available by month, which we use to construct our measure of induction risk. We calculate risk based on the relevant laws governing both the pre-lottery years and the lottery years of the draft (see Appendix for more details on the history of the draft). We calculate induction risk as the number of inductions in a given 12 month period, using month and year of birth and monthly induction numbers from

> 8 We also perform analyses using 1930-1956 birth cohorts in sensitivity analyses reported in section 5.4. This sample would have been completely ineligible to serve in WWII, which is an important restriction as we only want to consider the post WWII induction system, created in 1948.

> 9 To be consistent across the years of NHIS, we drop individuals below 59 inches and above 76 inches, and those weighing less than 100 pounds and more than 285 pounds, the most restrictive data publishing policies used by NHIS over the years of our sample.

> 10 This measure allows us to determine individuals who were born in the same state, permitting us to estimate models including state fixed effects. However, this measure is only available for a subsample of our data.

9
ownership of banking assets (Esty 2004) and in countries with smaller cultural and geographical “distances” between the foreign bank headquarters and local branches (Mian 2006).

The empirical literature on state-owned banks in developing nations generally finds unfavorable performance. Individual state-owned institutions have relatively low efficiency and high nonperforming loans, and large market shares for state-owned banks are associated with reduced access to credit, diminished financial system development, and slow economic growth (e.g., La Porta, Lopez-de-Silanes, and Shleifer 2002, Barth, Caprio, and Levine 2004, Beck, Demirguc-Kunt, and Maksimovic 2004, Berger, Hasan, and Klapper 2004, Berger, Clarke, Cull, Klapper, and Udell 2005). In some cases, they have also been found to subsidize or direct credit for political purposes (e.g., Cole 2004, Sapienza 2004) or lend mostly to large firms (e.g., Francisco and Kumar 2004). The low efficiency and high nonperforming loans are not necessarily contrary to the objectives of state-owned banks that are mandated to subsidize some negative net present value projects. However, the generally reduced access to credit, diminished development, slow economic growth, and focus on large firms do not appear to be consistent with their mandates.

# **3. The Indian banking sector**

We distinguish among four categories of banking institutions identified by the Reserve Bank of India (RBI): _Foreign banks_ that mostly entered after 1990 and operate local branches, the state-owned _State Bank of India_ ( _SBI_ ) formed in 1955 and its associates; _nationalized banks_ that were formerly private large banks and became state-owned in two waves, 1969 and 1980, and _private Indian banks_ that were mostly created after 1990.

Following independence in 1945, the RBI was formed as the central bank and high priority was given to increasing credit to rural areas and small businesses. In 1955, the government took over the largest bank, the Imperial Bank of India, to form SBI. The State Bank of India Act in 1959 directed SBI to take over regional banks that were associated with local governments and make them subsidiaries of SBI, which were later named “associates.” SBI is now the largest commercial banking organization in the country – and one of the largest in the world. SBI and its seven regional associates have a substantial rural branching footprint – of about 14,000 branches of these banks, 74% are located in rural and semiurban areas (India Banking Yearbook 2003).

10