rafmacalaba/data-use-mentions-extended huggingface dataset:
The evidence presented in this section thus departs from previous studies, which use a similar empirical approach but document a negative local labor market impact of robots in manufacturing. Acemoglu and Restrepo (2017) and Dauth et al. (2017) find evidence of a negative impact on both employment and wages in the United States and Germany, respectively. Giuntella and Wang (2019) and Artuc et al. (2019) provide similar evidence for developing economies in China and Mexico, respectively. We discuss a possible explanation behind this difference in Section 6.

# **5 Distributional Impact of Automation**

Automation seems to deliver unambiguous benefits to manufacturing plants and even to benefit workers on average. However it is important to examine how these benefits are distributed across production factors, including capital, skilled and unskilled labor. This is a relevant policy question as inequalities within countries are rising and recent research identifies automation as one of the key drivers behind this rise (Hemous and Olsen (2014); Prettner and Strulik (2020)).

We look first at the impact of automation on the labor share in value added and the profit rate (measured as gross surplus over revenue). The results - presented in Table 14 - suggest that automation reduces the share of labor in value added (column 1). Consistent with this result, column (2) shows that automation is associated with a higher profitability (column 2), although the coefficient is not statistically significant at conventional levels (p-value = 0.12). These results are consistent with the idea that most of the gains of automation are captured by the capital owners. This is also consistent with the empirical evidence based on firm-level analysis in the US (Acemoglu et al. (2020)).

Next we test for the impact of automation across workers by skills’ level. To that end we estimate the following Mincer-type regression by matching IFR data and two cross sections of labor force survey data for 2008 and 2015: 36
fully admitting to IPV experiences and that the empirical results are capturing only a subset of the changes that are actually occurring.

# **Section 8. Mechanisms for the Decline in IPV**

We also investigate a potential mechanism for the observed changes. In the Introduction, we noted that disasters have the potential to substantially change the tasks needed to be performed by the entire household, which has the potential to change the roles and responsibilities of each household member. Thus, although there are reasons that negative economic shocks and increased stress might worsen IPV attitudes and prevalence, these added roles and responsibilities that women might support might actually improve their importance and standing in the household.

Although most of the intrahousehold roles and responsibilities are difficult to observe in a household survey, we do find evidence of an expanded role for women inside the household. The survey asks women who in the household is primarily responsible for four different decisions- how to spend a husband’s income, health decisions for the respondent, large household purchases, and for decisions about visiting other family members. For all four decisions, we re-estimate the baseline specification using indicators equaling one if the husband alone was responsible for the decision and indicators equaling one if the wife was alone responsible for the decision.

Figure 10 illustrates that there was a reduction in the instances in which the husband was solely responsible for each decision. The larger decline in the shares in disaster-affected regions varied between 3 and 16 percentage points, which represents between 9 and 34 percent of the pre-disaster shares in disaster-affected regions; and two of the four estimates are precisely estimated and are statistically significant at conventional significance levels (p-values of 0.035 and 0.051). Furthermore, the estimates for men being solely responsible for decisions are accompanied by an increase in the share of women who are alone responsible for each decision.

Although we are unable to list all the potential reasons for why IPV attitudes and IPV prevalence declined following the disaster, the results suggest a key pathway- an expanded role
below the 2019–21 average could indicate a strong conflict effect, although a more modest decline of the summer crop area in 2022 (by 0.74 million ha from the pre-war average) and an increase (by 0.33 million ha) in 2023 suggest that, before drawing conclusions, other factors must be considered and controlled for.

Table 3 illustrates the need for difference-in-differences analysis of conflict effects by presenting the area cultivated for summer crops (panel A), winter crops (panel B), and fallow land (panel C) overall and separately for VCs that were or were not affected by the conflict, based on official data, or that experienced crop damage, based on satellite imagery from the 2023 or 2022 planting and growing seasons. While the first line in each panel replicates the national figures, the subsequent lines point toward clear differences across villages by conflict incidence. In VCs with war-related field damage based on remotely sensed data, the summer crop area was 880 ha (31%) or 871 ha (26%) below the pre-war mean in 2022 or 2023, respectively (panel A). In VCs without field damage, the summer crop area in 2022 and 2023 was 16.6 ha (or 1.0%) below or 108.2 ha (or 7.0%) above the pre-war mean, respectively. Differences for VCs where conflict was measured using open-source data rather than satellite imagery are of similar magnitude.

Panel B in table 3 points toward similarly large effects for winter crop area, with a reduction of 1,028 ha (or 45%) for VCs where crops were damaged, based on remotely sensed data in 2023 (788 ha for VCs with conflict based on open-source data), compared to 120-125 ha for those not affected by the conflict. As 2022 winter crops were planted before the Russian invasion in February 2022, it is not surprising that even for VCs that were affected by the conflict during the growing season for 2022 winter crops, the
our results, although given that most VFs did provide data, and clearly some of those that did not are functioning nonetheless, we believe that this problem is manageable.

In our view, the more important point is that the quantity and nominal value of VF loans have been maintained over time; moreover, this information comes from the SES surveys of households, who have no incentive to pad the numbers. The implication is that the VF model has proven, in the aggregate, to be sustainable. This is noteworthy, especially as the loans flow disproportionately to the poorest two-fifths of the population. The institutional arrangements work because VF committees apparently desire to stay relevant, and so work to maintain their capital resources and credit ratings – pitching interest rates low enough to be attractive, but high enough to replenish loans that are not repaid, and using their local knowledge to direct credit to bankable borrowers. Most VF committees appear to do their work conscientiously; we find little evidence of elite capture, and the honoraria that committee members pay themselves do not generally appear to be excessive.

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further limit our analysis to countries with a 2010 population of 1 million or more. Within each country, we limit our samples to the working age population, 15-65 years old. The countries that we use in our analysis, and the year each survey was conducted, are listed in table 1. We report results using data sets from 98 countries: 74 of which are low and middle income countries (by the World Bank definitions). The countries for which we have data represent 63% of the population of all low and middle income countries, and 46% of the population of high income countries. Unfortunately, the data base does not include a data set from the most populous country in the world, China, but the countries in our data represent 83% of the nonChinese population of low and middle income countries. All of the results presented in this paper are weighted by the sample frequency weights in each survey. Summary statistics for the regional and income group aggregations are weighted by the number of 15-65 year old workers in each country.<sup>3</sup>

# **IV. Characteristics of employers, own account workers, wage and salary employees, and nonpaid employees**

_Proportion of workers in each employment category_ Table 2 presents the distribution of workers between wage and salaried employment, non-paid employees, employers and own account workers, by region of the world and level of per capita GNI. We use the World Bank definition and divide countries into low income (less than 1006 U.S. 2010 PPP dollars), lower middle income countries (1,006-3,975 dollars), upper middle income countries (3,976-12,275 dollars) and high income countries (greater than 12,275 dollars).

Table 2 shows that self-employment is very common in developing countries. In low and middle income countries fewer than half of all workers are wage and salary employees, compared to over 85% in high income countries. As the GNI per capita of the country increases
-4-

critical level than the rate is devalued. The current and capital accounts are fully endogenous in our model, and we will be able to derive cettain analytic conclusions concerning whether or not a particular program af public expenditure cuts lead& :o unsustainable losses in foreign reserves. The model simultaneously incorporates both public infrastructure as well as public crowding out of the private sector. Government debt financing affects the domestic interest rate, and since private investment is debt-financed and interest sensitive, it can be crowded out by increased public spending.

The next section will give a brief review of background literature as well as provide an intuitive explanation of our model. Section III will formally derive the analytics of the model, while section IV will sketch a proof of the existence of an intertemporal equilibrium. Section V will give some policy simulations using Mexican data, while Section VI will be a conclusion.

TabL_I1l: Mcxico: Maero-Variables, 1973-85

||1973|1974|1975|1976|1977|1978|1979|1980|1981|1982|1983|1984|1985|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Real**GDPc/|8.4|<br>6.1|<br>5.5|<br>4.3|3.4|<br>8.2|9.2|<br>8.3|<br>7.9|-0.5|-5.3|3.7|<br>2.7|
|Publie sector<br>revenues k/|20.0|21.0|23.0|23.6|24.2|25.5|25.2|28.
Herrera Dappe and Lebrand (2019) and Herrera Dappe et al. (2021) for Bangladesh and India. We assume that large corridor investments will significantly improve driving speed. Major upgrades of regional roads are expected to improve road quality, reduce some traffic constraints (slow vehicles, density along the roads, congestion around gateways, etc), and therefore increase driving speed. The current driving speed is assumed to be low along most West African roads (Teravaninthorn and Raballand, 2009). The paper does not speak to the issues related to inefficiencies in the trucking sector (high mark-ups, poor fleet quality, intermediaries). Finally, the use of a structural model is motivated by missing data on domestic and cross-border trade in the region. Little is known about internal trade, formal trade per land border, and informal cross-border trade while informal trade is known to be ubiquitous in the region. The model generates trade linkages between districts and provinces that come from gravity properties between population centers. Given the geographic, cultural, ethnic, and language proximity of most countries, we can assume that these trade linkages are potentially significant, while not fully observed in national and international trade data.

3
|18,402,756|16,570,727|
|**Sample**<br>**Total**||**106,497,082**|**92,292,200**||
||**Total Change**|**from 2009 to 2012**||**(14,204,882)**|
||**Percent chang**|**e from 2009 to 201 **|**2**|**(13.3%)**|

The following five considerations would facilitate such future work:

1. If the American Express data set were to include both the distance between the points traveled, and the IATA equipment code, the entire global scheduling data set would unnecessary, thus simplifying the data preparation work.

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an interaction between the proportion of households with piped water and distance to the sewer network. In the case of Harare, it suggests that closer to the sewer network (at 0km), the risk of cholera is slightly higher, the higher the proportion of households who have piped water. However, this relationship switches direction in areas >2km from the sewer network, where the higher the proportion of households with piped water, the lower the risk of cholera. This implies that there is a higher risk of having a piped water connection for households which are proximate to the sewer network in Harare, which we believe to be due to the high rate of sewer bursts in the city at the time of the outbreak. The most accurate way of testing whether this is the case would be through water quality testing data at different points along the network. Although water quality testing data was provided for this analysis, there were insufficient data points to be able to make such a comparison. In the absence of such data, this analysis can still provide some useful insights into investments that can reduce sewer bursts particularly in areas which are closer to the piped water network, and thereby offer the greatest health benefits in terms of reducing cholera risk.

We identify that spatially, the risk of sewer bursts on this occasion was concentrated in five wards in the south west of the city. On the assumption that sewer bursts do not occur at random, but are spatially correlated due to underlying weaknesses in the network in those areas, targeting these five wards promises to reduce cases across the city by up to 70%. Using the data from the bursts reported and analyzed, there appeared to be limited benefits (in terms of reducing cholera risk) in investing in repairing sewer bursts throughout the city as it would have led to a .87 reduction in 14
# **VII. Conclusion**

Large-scale infrastructure investment can facilitate structural change of an economy, stimulating economic growth and diversification. The literature is generally supportive of this. However, the real impact remains open to argument. One of the most important empirical difficulties comes from potential endogeneity between economic outcomes and infrastructure investment.

The paper examined the impacts of massive road investment under the Nurly Zhol program in Kazakhstan. Methodologically, the paper exploited detailed micro shipping data to estimate how the transport connectivity changed over the last 10 years. On average the transport costs along the major regional corridors are estimated to have been halved, while average travel speed has not been changed much, if not, only slightly increased.

However, realized benefits from improved corridors vary depending on location. The domestic market accessibility was particularly improved along the major regional corridors connecting Astana to Almaty, Astana to Pavlodar and Oskemen, and Almaty to Taraz and Shymkent. The regional connectivity has been improved in several regions, such as East Kazakhstan, West Kazakhstan, Zhamby and Almaty City, but other inland areas are still left far from the external market.

Improved transport infrastructure stimulated the demand for regional and national freight transport, creating more enterprises and jobs in the trucking industry. With intensified competition, the price cost margins are considered to have been narrowed. Average transport costs declined from 10 U.S. cents per ton-km in the early 2010s to 6 U.S. cents in the late 2010s.

More formally, the translog cost function was estimated by the dynamic panel data regression. It was shown that local connectivity is the most important factor to boost firm production. The elasticity was estimated at 0.24 in absolute terms. Inventory is also found to be a major cost factor to firms.
# **1. Introduction**

Excessive bureaucratic complexity in interactions with governments can be a significant obstacle for citizens accessing basic services and exercising their civil rights. Excessive bureaucracy can also retard progress toward the goals of eliminating poverty and promoting shared prosperity. For example, the compliance burden generated by administrative rules has the effect of restricting access to political rights. Voter registration issues account for a large percentage of nonvoters during elections (Alvarez et al. 2008). Administrative barriers reduce enrollment in social programs and access to health care (Currie 2004; Moynihan and Herd 2010). Legal issues can prevent households from connecting to public utilities such as water and electricity (Banerjee et al. 2008). These are just a few of the issues that can arise from inefficient bureaucratic procedures.

Governments are organized differently. Some impose cumbersome bureaucratic procedures, while others aim to streamline and simplify the burden on citizens to meet their responsibilities and to access the goods and services to which they are entitled. However, there are currently little to no comparable, cross-country data available to assess the extent of regulatory burdens or to generate a stimulus for reform.

While the effect of bureaucratic burden on the private business sector is measured and constantly monitored by governments who regularly consult the World Bank’s _Doing Business_ database, to date little attention has been paid to the effects of bureaucracy on the lives of private citizens. Anecdotal evidence suggests that the cost (either monetary or cognitive) of key interactions such as voting, obtaining identification, registering a birth, or accessing health care have the potential to limit a citizen’s access to basic rights and freedoms that should be universally guaranteed. The most vulnerable groups in the population are most at risk for infringement on access and rights. Drawing upon 78 participatory poverty assessments (PPAs) in different countries, the 2000 report _Voices of the Poor_ remarked that “poor people’s interactions with representatives of the state leave them feeling powerless, unheard, and silenced” (Narayan et al. 2000,
national population survey data also show that 16 percent of rural youth at the age of 16– 17 have participated in the labor market.

- **Regional and provincial gap.** The sample size in the 1 percent population data may not be large enough to derive meaningful provincial trends. However, provincial labor statistics indicate that variation of the provincial-level average years of schooling of employed labor force is large. Beijing (13.3 years) and Shanghai (12.5 years) have the highest education attainment, while Tibet (5.8 years), Guizhou (8.2 years), and Yunnan (8.4 years) are among the provinces with the lowest education attainment of their labor force (Figure 3.9).

Figure 3.9 Average years of schooling of employed labor force, 2015 <!-- Start of picture text --> 13.3<br>14.0<br>12.5<br>11.8<br>12.0 10.610.5<br>10.0 10.1 10.5 10.0 10.4 9.9 10.2 9.99.610.09.89.99.9 10.5 9.6 9.99.8 10.1<br>10.0 9.0 9.0 9.19.1 9.610.4<br>8.2 8.4<br>8.0<br>5.8<br>6.0<br>4.0<br>2.0<br>0.0<br>Years<br>National Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Tibet Shaanxi Gansu Qinghai Ningxia Xinjiang<br><!-- End of picture text --> _Source:_ National Bureau of Statistics of China 2016a.
*Average versus marginal productivity.** The country authors of this book compare gaps in sectoral productivities using measures of average labor productivity, as is done in McMillan and Rodrik (2011) and Gollin, Lagakos, and Waugh (2014). It is well known that efficiency in well-functioning markets is characterized by an equalization of productivities at the margin. Under a Cobb-Douglas production function specification, the marginal productivity of labor is the average productivity of labor multiplied by the labor share. Thus, if labor shares differ greatly across sectors, comparing average labor productivities can be misleading. However, the existing evidence suggests that labor shares do not vary widely across sectors, except in a few activities (like public utilities) that typically do not absorb lots of labor (Mundlak, Butzer, and Larson 2012; Gollin, Lagakos, and Waugh 2014).

**Quality of African statistics.** Recently, concerns about the quality of Africa’s national accounts data have been raised by a number of researchers, including

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raw materials and/or to have rapid access to the services and resources necessary for production (INS 2019, 2016, 2013).

- _Number of regional development zones (ZDR):_ To attract foreign investors and promote the industrial development of certain regions, the government provides companies that choose to establish their businesses in these zones with specific incentives in the form of tax and financial incentives such as: tax exemption, investment premium, or payment of the employer's social security contribution (FIPA, 2019).

- _Governance, participatory approach and institutional quality (GOV):_ This variable is measured by an index derived from qualitative and quantitative surveys carried out by the Arab Institute of Heads of Companies in Tunisia (IACE). Several indicators measuring different aspects of good governance are taken into account, such as: the quality of municipal regulations and administrative services, the quality of
with cross-sectional data some unobservable household or village characteristics correlated with the program placement and program outcome could introduce bias into the estimates of project impact.<sup>27</sup> If, for example, the program were placed in a village because a food shortage was anticipated there, the evaluation procedure would underestimate the effectiveness in the program. Given the available data, we can only say that there is nothing to indicate that the program has had an impact.

> 26 To measure the difference in nutritional outcomes between the children from the villages with and without the program, we use the standard estimator of the average treatment on the treated defined as: _E(z1 –z0| s=1),_ where _z1_ is a particular health outcome (z-scores) for the child in the treatment and z0 is a z-score for a child in the control group, and _s_ is the binary indicator equals to 1 if the child resides in the village with the program and 0 otherwise. We can also define the average effect conditional on some set of child’s characteristics **x** as: _E(z1 – z0| s=1,_ **x** _)._ Matching estimators require that conditional on a vector of observed characteristics, **x,** ( _z1_ and _z0_ ) are independent of _s_ .

Children for whom a match could not be found were dropped from the analysis in order to avoid the primary cause of a bias in a matching estimator (Heckman, Ichimura and Todd 1997, 1998).

27 This problem is thought to be severe for the programs in poor areas if the deficient state of children’s health in the initial period not only attracts the program, but also reduces future growth (Jalan and Ravallion 2003).

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