rafmacalaba/datause-encoder-data huggingface dataset:
endogenous regressor _LogDistGRs_ but independent from the error _εs_ . We can argue that old main roads would have been maintained to transport armies, and later on these roads would also have higher quality.

# **6 Results**

The results of the two separate datasets and methods are summarized in different subsections below.

## **6.1 ILCS**

First, I show the results from the analysis of the repeated cross-sectional data of Integrated Living Conditions Survey (ILCS). The survey has collected information on perception of respondents on road quality in the region and locally - within the village. The road quality indicators include “good”, “average”, and “poor” (“good” is a reference category in the following results tables).

Table 2 shows the marginal effects of the Probit regression. It shows the probability of a respondent being employed in non-agricultural sector (columns (1) and (2)), doing seasonal work (columns (3) and (4)), and whether there is anyone from the household working outside of village (column (5)). Since the distance variables were not included in the questionnaires in 2007 and 2008, the regression is done with and without the distance variables. The first column shows a clear negative association between the household living in a region with average or poor road quality and non-agricultural employment. Individuals reporting poor road quality are 5.4 percentage points less likely to be working in non-agricultural sector than the individuals reporting good quality roads in their regions. Controlling for the distance to the nearest market, kindergarten, and health center lowers the marginal effects, but still holds statistically significant 3.5 percentage points lower probability of working in non-agricultural sector (column (2)). As expected, average roads show lower marginal effects; individuals reporting quality of roads in their region leading to towns and markets as average, are 1.9 percentage points less likely to work in nonagricultural sector than the individuals reporting good quality roads in their regions. The marginal effect changes slightly to 1.4 percentage points (column (2)) when distance 17
# **Figure 3.  Interest Rate Simulations for Uncovered Debt Ratios**

<!-- Start of picture text --> Uncovered Debt Ratios Simulations<br>80.00%<br>70.00%<br>60.00% Baseline<br>50.00%<br>40.00% Interest rate shock 4%<br>30.00%<br>20.00% Interest rate shock 8%<br>10.00%<br>0.00%<br>5.<br>1. Mining 2. Industry Hotels Energy<br>8. Financial<br>3. General<br>Trade/Services 4. Construction Transportation 6. Tourism and 7. Utilities and<br><!-- End of picture text -->

# **4.2 Shocks to Earnings**

In addition to stressing the interest payments, corporate data allows for simulations and stress tests of shocks to corporate earnings. The EBITDA is the key ingredient in the calculation of interest coverage and uncovered debt ratios discussed above. Shocks to corporate earnings, i.e. to EBITDA, will be directly reflected in corporate ability to repay their debts.

While the interest rate shocks were the same as in bank stress testing scenarios, the earnings shocks are not directly comparable to macro economic bank stress testing scenarios. Therefore, we develop separate scenarios for earnings shocks based on historical volatility of earnings. Table 9 reports historical median growth rates in EBITDA by sector. The firm-level yearly EBITDA growth is calculated, then the sector-level median is reported for each year.<sup>11</sup> One observation from Table 9 is high volatility of EBITDA growth – with wide swings between periods of high growth, or high contraction.<sup>12</sup>

> 11 Table 4 reports nominal growth rates. Inflation in Jordan was ranging between 1.5-6% in the years in the sample. Thus, real growth rates would be adjusted by the inflation rates.

> 12 Note that the growth rates are calculated only using the balanced panel, to prevent differences in sample composition, i.e. the fact that not all firms have data for all years, from affecting the results.

20
**16**

> _Y;_ **_= a+_** _PFINANCE +Y'X; +e_ **(8)**

# 2. Cross-country estimation

In the pure cross-sectional analysis we use data averaged for **63** countries over **1960-95,** such that there is one observation per country.<sup>16</sup> The basic regression takes the form: where Y is either _GROWTH, CAPGROWTH, PRODI, PROD2, PROD3 or SAVING._ **_FINANCE_** equals PRIVATE CREDIT, and **LIQUID** LIABILITIES or COMMERCIAL-CENTRAL BANK in the robustness tests. Xrepresents a vector of conditioning information that controls for other factors associated with economic growth and F is the white noise error term.

To examine whether cross-country variations in the exogenous component of financial intermediary development explain cross-country variations in the rate of economic growth, the legal origin indicators are used as instrumental variables for FINANCE. Specifically, given the vector Z of instrumental variables and assuming that **E[s] = 0,** this results in a set of orthogonality conditions E[Z'e]=0. We can use standard GMM techniques to estimate our model, which produces instrumental variable estimators of the coefficient in **(8).** After computing these GMM estimates, the standard Lagrange-Multiplier test of the overidentifying restrictions assesses whether the instrumental variables are associated with growth beyond their ability to explain cross-country variation in financial sector development. Under the null-hypothesis that the instruments are not correlated with the error terms, the test is distributed X<sup>2</sup> with **_(J-K)_** degrees of freedom, where **_J_** is the number of instruments and _K_ the number of regressors. The estimates are robust to heteroskedasticity.

> 16The cross-country sample for private saving has **61** countries over the period **1971-95.** Due to the potential nonlinear relationship between economic growth and the assortment of economic indicators, we use natural logarithms of the regressors in the regressions of GROWTH, CAPGROWTH, PROD **1,** PROD2 and PROD3.
58 as resource transfers by political elites to buy the support of these groups even in nondemocratic settings. Among the most interesting work in this regard has been the effort to deduce the political logic of support coalition formation from analyses of government expenditures (see Ames 1987).

13. T.N. Srinivasan has suggested that society-centric explanations focus on the demand side of politics, while state centric approaches reflect supply side dynamics. The important point of distinction for political analysis, as with economic analysis, is the reversal of independent and dependent variables in these two perspectives. Demand side approaches (societycentric) assert that social groups initiate policy and politicians respond to pressure. In supply side perspectives (state-centric) politicians initiate policy and citizens respond with support. (Personal communication, Aug:ust 1989) 14. Riker argues that under simple majority rules, there is an ongoing process of the formation and dispersion of minimum winning coalitions around specific issues. "Each coalition gains by dispossessing the losers; the ultimate result is that everyone has victimized everyone else, and everyone is probably worse off" (Koford and Colander 1984:212).

15. For the policy analyst, cataloguing the organized interests that are affected by a particular policy reform--a standard procedure in much Western policy analysis--may be an unproductive exercise unless there is concomitant appreciation of the real capacity to exert influence and the real impact of informal processes of power in a given country. Findlay and Wellisz (1984) distinguish between a society-centric democratic lobbying model and a more state-centric authoritarian model in which "the prince" is driven to justify his rule by maximizing the output of his regime...." Even in democratic systems in developing countries, however, a purely society-centric model may misrepresent the dynamics of policy making and state leadership.

16. What the new political economy explains is not new to students of politics. Political scientists studying developing countries have long known of and dissected the political logic of clientelism, corruption, and policy choice in ways that did not expect economic rationality but as ways to achieve or ensure political stability, support, or power.

17. I am grateful to David Abernethy for this formulation of the limits of neoclassical analysis.

18. Many have pointed out that this clear policy advice has not been adopted historically by some of the most dynamic developers in the third world. Korea, for example, is a case of a highly interventionist state that through a highly centralized decision making system, effectively "orchestrated" policy in coordination with politically insulated state technocrats and a captive private business sector (see Haggard and Moon 1982).
<!-- Start of picture text --> 50<br>GeeCoverage ——Eligible households<br>40<br>30<br>3<br>°<br>J<br>5<br><!-- End of picture text --> <!-- Start of picture text --> Ss<br>=<br>oe<br>2<br>2<br>10<br>; eC L Bakes = 2 eS<br>Poorest 2 3 4 5 6 7 8 9 Richest<br>Market Income + Pensions decile<br>Source: Authors’ estimates based on LCMS 2015.<br><!-- End of picture text -->
**Figure 8: Breakdown of the ninis population aged 15 to 24 in LAC, 2010**

<!-- Start of picture text --> Ninis<br>(aged 15 - 24)<br>100%<br><!-- End of picture text --> <!-- Start of picture text --> Start new HH<br>without children<br>0.7%<br>Start new HH with<br>children<br>1%<br>Remain in HH<br>45.5%<br>Start new HH<br>without children<br>4.4%<br><!-- End of picture text --> <!-- Start of picture text --> Look for work<br>13.2%<br>Do not look for work<br>16.5%<br>Look for work<br>0.4%<br>Do not look for work<br>0.3%<br>Look for work<br>0.7%<br>Do not look for work<br>0.3%<br>Look for work<br>11%<br>Do not look for work<br>35.5%<br>Look for work<br>0.6%<br>Do not look for work<br>3.8%<br>Look for work<br>1.1%<br>Do not look for work<br>17.6%<br><!-- End of picture text --> Source: authors’ own computation with data from CEDLAS and The World Bank (2015) In the remainder of this analysis, we develop the cross-sectional breakdown from Figure 8 into a more detailed, longitudinal analysis to observe how the composition of ninis across these dimensions has changed over the past two decades. Here again, we analyze trends for the 15 to 18 and 19 to 24 age groups separately, to reflect the possibility that school dropout and labor market participation decisions differ in nature across these subgroups. From a policy perspective, being a nini between the ages of 15 and 18 is particularly worrisome, because at this stage in the life cycle many societies have affirmed that formal education is the most socially productive activity in which youth can be engaged. Youth under 18 years of age are still of school age and are still developing physically, mentally, and emotionally. During these critical years, being in school in a protected and constructive environment contributes to the development of cognitive and socio-emotional skills, and it can also be a mechanism for becoming integrated into the community, acquiring social values, and developing trust in institutions and the rule of law. In contrast, the story for 19 to 24 year-old ninis may be more one of youth unemployment; nevertheless, we present results for both subgroups both to understand the heterogeneity of ninis as a group, and for comparison purposes.

15
Policy Research Working Paper 7011

# **Abstract**

This study uses household models based on detailed expenditure and agricultural production data from 31 developing countries to assess the impacts of changes in global food prices on poverty in individual countries and for the world as a whole. The analysis finds that food price increases unrelated to productivity changes in developing countries raise poverty in the short run in all but a few countries with broadly-distributed agricultural resources. This result is primarily because the poor spend large shares of their incomes on food and many poor farmers are net buyers of food. In the longer run, two other important factors come into play: poor workers are likely to benefit from increases in wage rates for unskilled workers from higher food prices, and poor farmers are likely to benefit from higher agricultural profits as they raise their output. As a result, higher food prices appear to lower global poverty in the long run.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at wmartin1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
as much as a 25 percent reduction in employment growth over the ten-year period 1996-2006. For the broadest measure of connectedness, which includes firms that received investments from connected private equity funds, the relevant coefficient of the interaction term is still negative and of comparable magnitude, but not significant at conventional levels.

These findings provide direct evidence that the growth impact of entry of connected firms is more than offset by their adverse impact on the growth opportunities of the majority of unconnected firms that stop growing or exit. In the case of Egypt, such large exogenous cost advantages are granted by energy subsidies, trade protection, access to prime land, biased regulatory enforcement, and other policy advantages. Even though these privileges might help the few benefitting firms to grow and create jobs, we show that the aggregate employment impact is negative due to the adverse effects of such policies on competition and thus the growth opportunities of the large majority of unconnected firms.

**Table 9: Employment growth declines after politically connected firms enter initially unconnected sectors**

|||**Emplo**|**yment g**|**rowth 19**|**96-2006**||
|---|---|---|---|---|---|---|
||**CE**|**O**|**Ow**|**ner**|**Br**|**oad**|
|**Entry PC**|32.2*|36.1**|7.15|10.3|4.83|4.40|
||(1.95)|(2.09)|(0.84)|(1.24)|(0.99)|(0.77)|
|**Not connected before 1996**||-6.32||15.1||-10.5|
|||(-0.58)||(0.82)||(-0.67)|
|**(Entry PC) ***||-24.8**||-18.7**||-14.96|
|**(Not connected before 1996)**||(-2.17)||(-3.47)||(-0.97)|
|ln(empl)|-.418**|-.401**|-.420**|-.382**|-.420**|-.376**|
||(-2.44)|(-2.17)|(-2.37)|(-2.16)|(-2.34)|(-2.62)|
|Age|12.5|12.6|12.4|12.3|12.4|12.9|
||(1.57)|(1.56)|(1.51)|(1.53)|(1.51)|(1.55)|
|No. of sectors|224|224|224|224|224|224|
|R-squared|0.161|0.163|0.155|0.159|0.048|0.160|
|Sector dummies|Yes|Yes|Yes|Yes|Yes|Yes|

Source: Establishment census data all non-agriculture, non-government sectors in 1996 and 2006 and sample of politically connected firms. Note: PC indicates the number of politically connected firms varying at the 4-digit sector level. The three different types of connections (ordered by their restrictiveness) are defined in Table 1. The number of employees (size) and firm age vary at the firm level. All firm level regressions include 1-digit sector dummies. Standard errors are clustered at the sector level. *, ** denote significance at the 10%, 5% significance level, respectively, t-statistics are reported in parentheses.

# **Evidence supporting identification**

Our interpretation of the results in Table 9 rests on the plausibility of our identification assumption, that politically connected firms did not disproportionately enter into intrinsically low growth sectors. On its face, it seems implausible that connected firms would avoid high growth sectors – the contrary seems more likely. In fact, observers argue that several sectors entered by connected 28
Policy Research Working Paper 10860

# **Abstract**

This paper addresses the question of whether farm input subsidy programs should be targeted at non-poor farmers instead of poor farmers, using a two-wave, nationally representative panel data from Malawi. The question is addressed by estimating the net gain in maize yield for targeting nonpoor farmers instead of poor farmers after accounting for the difference in inorganic fertilizer use efficiency and the difference in crowding-out of commercial fertilizer by subsidized fertilizer between the poor and non-poor farmers. Consumption expenditure is used to classify households into consumption poor and non-poor households, and an asset-based wealth index is used to classify households into asset poor and non-poor households. The difference in inorganic fertilizer use efficiency is estimated with a multilevel model of maize yield, and the difference in crowding out is estimated with a double hurdle model of demand for commercial, inorganic fertilizer. The results indicate that non-poor farmers are significantly more efficient in the use of inorganic fertilizer but have significantly higher levels of crowding out, compared to poor farmers. This suggests that there is a trade-off between targeting non-poor farmers and targeting poor farmers. However, further analysis of the trade-off indicates that targeting non-poor farmers instead of poor farmers, even after accounting for the difference in crowding out, would result in an overall gain in yield of 3.14 to 4.33 kilograms of maize per kilogram of nitrogen distributed by the subsidy program. Therefore, the productivity enhancing objective of Malawi’s farm input subsidy program would be better served by targeting nonpoor farmers instead of poor farmers.

This paper is a product of the Agriculture and Food Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at fdarko@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table 27. Indonesian Exports, ICT use, and Log of Skills Measures Excluding Industries Involved in GVCs. All workers. 2005-2010

|Dependent variable:|L|abor force pa|rticipation ra|te||Employm|ent rate|||Unemplo|yment rate||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
|IDN exports per worker|-0.0179||-0.0159|-0.0518|-0.0334||-0.0325|-0.0805|0.0219||0.0232|0.0529|
||(0.0387)||(0.0372)|(0.0629)|(0.0387)||(0.0380)|(0.0623)|(0.0278)||(0.0279)|(0.0458)|
|Share of hhlds with computer||0.191|0.192|0.131||0.0818|0.0839|0.00281||0.129|0.127|0.177|
|||(0.0956)**|(0.0960)**|(0.112)||(0.0950)|(0.0958)|(0.108)||(0.0526)**|(0.0532)**|(0.0630)***|
|IDN exports per worker *||||0.313||||0.418||||-0.258|
|Share of hhlds with computer||||(0.284)||||(0.273)||||(0.195)|
|Observations|195|195|195|195|195|195|195|195|195|195|195|195|
|R-squared|0.940|0.941|0.941|0.941|0.961|0.962|0.961|0.961|0.934|0.935|0.935|0.936|

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010, 2015-2016 Philippines STEP survey, and 2011-2015, and UN Comtrade database 2005-2010.

Notes: IV regressions. IV: Global demand for foreign products excluding Indonesian products. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province controls include the age structure of the population, share of urban population, real GDP per capita, minimum wage level, gender and educational composition of employment. All models control for province and year fixed effects. Robust standard errors between parentheses clustered by province. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively.

44
For the UK, Javorcik et al. (2019) show it covers just above 85 percent of all (online and offline) job vacancies as measured by the UK Vacancy Survey. Bastos et al. (2023), exploiting the same dataset across 35 countries, find a close relationship between total job vacancy rates as measured by official statistical agencies and each country’s ratio of Lightcast job postings to total employment.

We complement the above evidence by comparing Lightcast job postings as a share of each country’s working age population, shown in Appendix Table A1. We find that Belgium, Switzerland and Luxembourg have the highest ratios of vacancies to working age population, while New Zealand, Norway and Italy have much lower shares. Another limitation of the data on online job postings is that they only measure stated but not necessarily realized demand. We therefore note that these measures of high-tech job postings serve as a proxy to track technology diffusion in the labor market, but future work expanding this with measures relating to the hires, separations, wages and internal training might provide a more complete picture. At this point,however, such consistent data is not available for such a wide sample of countries over time.

# **2.1.1 Identification of new technologies**

We use the raw text of the job postings to search for the 305 technology keywords from the classification of 29 technologies identified by Bloom et al. (2021).<sup>3</sup> We use Google translate to translate these keywords into all of the official languages of the 17 countries. We search for the keywords in both English and all official languages of the country. We then classify each job posting as being related to a specific technology if it includes at least one keyword for that technology.

This classification by Bloom et al. (2021) represents one classification of influential innovations that have been discussed prominently by investors and executives of publicly listed companies in the last two decades. Given that 53% of utility patents filed by USPTO are of foreign origin as of 2019 and that one-third of the earning calls were hosted by non US-based firms, these 29 technologies should also be suitable for studies examining diffusion outside the US.

Figure 1 lists the number of job postings mentioning each of the 29 technologies. Cloud Computing and Social Networking were mentioned the most, with more than 10 million job postings identified for each of these technologies, followed by Smart Devices then Machine Learning or AI and Online Streaming.

> 3For example, _“cloud environment”_ , _“cloud solution”_ , _“cloud storage”_ and _“cloud service”_ all belong to Cloud Computing technology.

5