rafmacalaba/data-use-mentions-extended huggingface dataset:
road decreases the probability to work as a farmer by 30 percentage points (DHS sample). In locations along a main road, being 10 km closer to the grid decreases the probability to work as a farmer by 30 percentage points (DHS sample). The impact is smaller when using the LSMS sample. Proximity to roads complements proximity to the electric grid. Farming employment decreases more from being closer to roads for locations closer to the grid, and vice versa. The joint effect of both infrastructures is larger in the DHS than in the LSMS sample. Overall, the findings indicate that these infrastructures reinforce each other in decreasing employment in the agricultural sector.

In columns 3, 7 and 8, we analyze how employment in low-skilled occupations responds to road and electricity expansion. In general, we find large negative effects. Columns 3 and 8 show that the probability of employment in a low-skilled occupation decreases by nearly 5 percentage points in both the DHS and LSMS samples in response to being 10 km closer to a main road in areas along the electric grid. Being 10 km closer to an electric gird in areas along a main road leads the probability of employment in a low-skilled employment to decrease to a far greater degree: falling by 21 percentage points in the DHS sample and by 15 percentage points in the LSMS sample. Again, the two types of infrastructure have greater effects in combination. Our analysis shows that being both 10 km closer to a main road and 10 km closer to an electric grid further decreases the probability of being employed in a low-skilled occupation by an additional 0.6 percentage points in the DHS sample and 0.5 percentage points in the LSMS sample.

These estimates have similar signs as the OLS estimates (Table 11) in most cases.

Overall, the findings indicate that improved access to roads and electricity increases the probability to be employed (Table 4) in the Appendix and induce labor reallocation, with workers moving from farming and other low-skilled employment to skilled sectors. Finally the two types of
Le Barbanchon, T., D. Ubfal, and F. Araya (2023): “The Effects of Working While in School: Evidence from Employment Lotteries,” _American Economic Journal: Applied Economics_ , 15, 383– 410.

- Osman, A. and J. Speer (2022): “Are Soft Skills Enough? Experimental Evidence on Skill Complementary for College Graduates,” Working PAper. Available at SSRN: https://ssrn.com/abstract=417856.

- Rwandan National Institute of Statistics (2020-2022): “Quarterly Rwanda Labour Force Survey Reports,” Tech. rep., National Institute of Statistics of Rwanda.

- ——— (2021): “Rwanda demographic and health survey 2019-20.” Tech. rep., National Institute of Statistics of Rwanda - NISR, Ministry of Health - MOH, and ICF.

- ——— (2022): “Rwanda Labour Force Survey,” Tech. rep., National Institute of Statistics of Rwanda.

- Sandvik, J. J., R. E. Saouma, N. T. Seegert, and C. T. Stanton (2020): “Workplace Knowledge Flows,” _The Quarterly Journal of Economics_ , 135, 1635–1680.

- Ubfal, D., I. Arraiz, D. Beuermann, M. Frese, A. Maffioli, and D. Verch (2022): “The Impact of Soft-Skills Training for Entrepreneurs in Jamaica,” _World Developmet_ , 152, 105787.

17
are exposed to different levels of female labor-force participation than in their country of origin. This situation has strong predictive power for female labor-force participation in the United States and for men’s acceptance of this participation.

Cross-country analyses are challenging because comparable and meaningful measures of customs and institutions are difficult to find. Following the recent practice in cross-country studies (e.g., Branisa et al. 2009), we now describe customs and institutions that relate to gender inequality using the OECD gender-linked institution and development database.

Figure 3 illustrates considerable variation in “discriminatory practices” across the sample countries. Some of the components of this index, such as restrictions on freedom of movement, have clear implications for women’s participation in economic and political life. The correlation between the incidence of discriminatory practices and GDP is negative and precisely estimated, with the usual caveat that causality is impossible to establish. A recent paper by Field et al. (2010) provides some microlevel evidence for the effect of social norms on returns to human capital. The authors design and implement a field experiment in which a random selection of poor, self-employed women is provided with lessons on financial literacy and investments. In their sample, Muslim women face the most social restrictions on mobility, followed by Hindu upper classes and Hindu scheduled castes, although the difference between the first two is four times as large as the difference between the last two. They find evidence suggesting that social restrictions on mobility reduce returns to financial training because treated upper-caste Hindu women are most likely to take out loans (13 percentage points) and are significantly more likely to funnel loans into their own microenterprises and to discuss business plans with their families. Although this situation suggests that social norms slow women’s economic participation, the three groups differ along other characteristics that might correlate with business decisions. Additional studies on this subject are needed to consolidate the findings.

Next, we move from social norms to institutions. Figure 4 describes the variation in a composite index of various women’
- Duclos, Jean-Yves, Abdelkrim Araar and John Giles. 2010. “Chronic and Transient Poverty: Measurement and Estimation, with Evidence from China,” _Journal of Development Economics_ 91(2): 266-77.

- Du, Yang, Albert Park and Sangui Wang. 2005. “Migration and Rural Poverty in China,” _Journal of Comparative Economics_ , 33(4) (December 2005): 688-709.

- Flaherty, Joseph H. 2009. “Nursing Homes in China?” _Journal of the American Medical Directors Association_ , 10(7): 453-455.

- Flaherty, Joseph H. 2007. “China: The Aging Giant,” _Journal of the American Geriatrics Society_ , 55(8): 1295-1300.

- Giles, John. 2006. "Is Life More Risky in the Open? Household Risk-Coping and the Opening of China’s Labor Markets," _Journal of Development Economics_ 81(1) (October 2006): 25-60.

- Giles, John and Ren Mu. 2007. “Elderly Parent Health and the Migration Decision of Adult Children: Evidence from Rural China,” Demography 44(2) (May 2007): 265-288.

- Giles, John and Kyeongwon Yoo. 2007. "Precautionary Behavior, Migrant Networks and Household Consumption Decisions: An Empirical Analysis Using Household Panel Data from Rural China," _The Review of Economics and Statistics_ , 89(3) (August 2007): 534-551.

- Green, Samuel. 2010. “The Rural Life and Times of China’s Aging Population, Part III: Institutional Problems,” _Asia Healthcare Blog_ (January 27, 2010). (http://www.asiahealthcareblog.com/2010/01/27/the-rural-life-and-times-of-chinas-aging- <u>population-part-iii-institutional-problems/)</u>

- Jalan, Jyotsna and Martin Ravallion. 1999. “China’s Lagging Poor Areas,” _American Economic Review_ 89(2): 301-305.

Jalan, Jyotsna and Martin Ravallion. 2002. "Geographic Poverty Traps? A Micro
<mark>Despite these advantages, Internet surveys conducted with RDIT could be biased because of nonprobability-based sampling.</mark><sup>5</sup> <mark>To better understand these biases, we take two approaches to assess the quality of internet survey estimates. The first approach compares time-invariant measures, particularly demographic variables, calculated from the online surveys to nationally representative surveys collected prior to the crisis, contained in the World Bank’s Global Monitoring Database (GMD). The results indicate that online surveys represent a substantially different demographic group compared to the national picture, overrepresenting male, younger, and more educated members of the country’s population. Therefore, using the raw online survey results to track labor market indicators would be misleading.</mark> <mark>As a first quality check, we attempt to address the bias described above using a propensity score adjustment, based on the estimated probability of participation in the Internet survey. In particular, we estimate a logit model of selection into the online survey, as opposed to the GMD survey, in each country. The model is used to construct weights equal to the inverse of the predicted propensity scores. The results show that, for variables included in the propensity score calculation, the reweighting process produces a smaller deviation of the means of the internet survey from the GMD survey. The deviations between variables excluded from the propensity score model, however, remain relatively high. For example, the deviation for model variables was 32.0 percent in Brazil, but 56.8 percent for the non-model variables. In Indonesia, while the deviation for model variables was 15.3 percent, the deviation was 582.0 percent for non-model variables. This result highlights that it is problematic in this context to assume that selection bias becomes ignorable after conditioning on demographic characteristics such as age, gender, and education.</mark> <mark>The second quality check compares reweighted estimates of labor market indicators from the internet survey with other
finance, banks lacking money to lend, as well as the need for special connections in banking. In unreported results we also ran regressions of the high interest rate obstacle on individual financing obstacles. Specific financing obstacles are all individually correlated with high interest rates. When we consider all financing obstacles together, only collateral, paperwork, special connections, lack of money to lend, and access to long term loans are correlated with high interest rates, as in the DAG analysis.

# **IV. Conclusions and Policy Implications**

We use firm level survey data for 80 countries to investigate which features of the business environment are the most constraining for firm growth. Specifically, we examine factors such as taxes and regulations, judicial efficiency, infrastructure weaknesses, and financing issues that have attracted significant attention in the literature. Although firms report many obstacles to their growth, not all of them are equally constraining – in that they either affect firm growth only indirectly through their influence on other factors, or not at all. Using regressions as well as Directed Acyclic Graph (DAG) methodology, we find that only Finance, Crime and Policy Instability are binding constraints, which have a direct association with the growth rate of firms. Thus, while other obstacles we study in this paper are also associated with firm growth, through their impact on each other and on direct obstacles, maintaining policy stability, keeping crime under control, and undertaking financial sector reforms to relax financing constraints are likely to be the most effective routes to promote firm growth. We also find that the Financing obstacle’s impact on growth is robust to varying samples of countries while the Policy Instability and Crime results are less robust to the exclusion of 27
MONGOLIA'S INFORMAL SECTOR Size and Trends 16

|**Estimates**|**of the Size of the In**|**formal Sector**||
|---|---|---|---|
|Method|Source(s) of Data|Reference|Estimate|
|Estimate the ratio of informals who<br>are not counted as employed to those<br>that are counted as employed;<br>multiply this ratio by the number of<br>people responding that they work for<br>sole proprietorships during the 1996<br>labor force census.|Informal sector<br>surveys conducted<br>by the author;<br>Annual Labor Force<br>Balance|Ulaanbaatar|23-32% of the working<br>age population were<br>employed solely in the<br>informal sector at the end<br>of 1996 and beginning of<br>1997;<br>including moonlighters<br>and working pensioners,<br>30-37%|
|Use the percentage of households<br>with informal income and the<br>average number of working age<br>household members to infer the<br>percentage of the working age<br>population with informal income.|SSO Household<br>Survey data.|Ulaanbaatar|30-33% of the working<br>age population of<br>Ulaanbaatar in the last<br>quarter of 1996.|
|Estimate the aggregate percentage of<br>informal income in aggregate total<br>income using household survey data.|SSO Household<br>Survey data.|Ulaanbaatar|32% of aggregate<br>household income in<br>Ulaanbaatar in the last<br>quarter of 1996.|
|Attribute the increase in the ratio of<br>currency to M2 between 1990 and<br>1996 to an expansion in the<br>
An estimated 90% of the population of the population of northern Uganda was uprooted throughout the conflict, with the majority of IDPs living in squalid displacement camps, sometimes for as long as a decade (Brookings 2010). Our Uganda surveys thus capture perceptions at two key junctures in the displacement crisis in northern Uganda: during ongoing violence (2007) and as resettlement efforts began after the LRA fled to neighboring countries (2010).

Together, these three contexts are united in that they experienced large scale forced displacement due to ongoing on recently ended episodes of wide-scale political violence. The displacement in all contexts analyzed in this paper was overwhelmingly internal and driven by violence. As such, the theoretical and empirical scope of the paper does not include cross-border refugee flows or economic migration, which likely have distinct causes and consequences. We are instead able to examine the consequences in behaviors and perceptions of experiencing internal displacement across the cases.

Differences in the character and stage of violence that caused the displacement waves across contexts that may limit direct comparisons while also providing analytically useful variation to draw on. Each survey focuses on subnational geographic units: Mosul city in Iraq, Central Mindanao region in the Philippines, and the northern Acholi districts in Uganda. When we conducted our surveys, ISIS has recently been defeated and fled Mosul, so our Iraq sample is recently postviolence. Similarly, our 2007 sample for Uganda took place recently post-violence and during on-going peace talks. In contrast, the 2010 Uganda sample took place after the peace negotiations collapsed, but violence had not resumed after the Lord’s Resistance Army fled to neighboring South Sudan and Democratic Republic of the Congo. Our survey in the Philippines occurred as violence was ongoing in Central Mindanao.

The perpetrators of the violence also varied across the contexts in ways that may have implications for how willing or able an IDP to participate in their local community. In Iraq, the population we surveyed had recently lived under ISIS rule for 3 years and recently experienced the battle where government and coalition forces retook Mosul
Policy Research Working Paper 9018

# **Abstract**

The Phillips curve is central to discussions of inflation dynamics and monetary policy. In particular, the New Keynesian Phillips Curve is a valuable tool to describe how past inflation, expected future inflation, and real marginal cost or an output gap drive the current inflation rate. However, economists have had difficulty applying the New Keynesian Phillips Curve to real-world data due to empirical limitations. This paper overcomes these limitations by using an identification-robust estimation method called the Tikhonov Jackknife instrumental variables estimator. Data from Morocco are used to examine the ability of the New Keynesian Phillips Curve to explain Moroccan inflation dynamics. The analysis finds that by adding more information to the hybrid version of the New Keynesian Phillips Curve model by increasing the number of moment conditions, the inflation dynamics in Morocco can be well-described by the New Keynesian Phillips Curve. This framework suggests that the New Keynesian Phillips Curve would be a strong candidate for short-run inflation forecasting.

This paper is a product of the Macroeconomics, Trade and Investment 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 authors may be contacted at vtsounguibelinga@worldbank.org, and m.doukali@bkam.ma.

_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
are being provided with housing by others (e.g., employer, relatives, other arrangements) are asked what they would estimate the rent for their dwelling to be. We used this self-reported value in the consumption aggregate for these households.

The consumption aggregate is computed at the household level in _per capita_ terms. We consider as household members those people in the household who share expenses. As such, we exclude domestic workers, their relatives, lodgers, and nonrelatives who do not share expenses from the calculation of the welfare aggregate.<sup>13</sup> _Temporal and spatial price adjustment._ To make the consumption aggregate and average costs per calorie comparable across households, we adjust expenditure values temporally and spatially. POF was collected throughout a full year and its reference periods go back up to 12 months. Figure 2 shows the temporal variation of prices for different Brazilian states over the reference period of the survey. On average, prices increased by 26.8 percent over the reference period – a monthly average of 1 percent. IBGE (2020) provides temporal deflators in the publicly available data, which we use to deflate prices and the consumption aggregate to express all monetary values in January 2018 values.

For Brazil, among the largest countries in the world and covering an area of over 8.5 million square kilometers, spatial differences in prices should be a concern in the estimation of the poverty line and in welfare measurement. While there is no spatial price level index available for Brazil that one could use to adjust for these differences, research suggests that these differences exist and should be taken into account. Ferreira, Lanjouw, and Neri (2003) calculate spatial price indices for Brazil and find that they matter for ranking poverty across regions. More recently, Azzoni and Almeida (2016) show that overall price-level differences in Brazilian metro areas can diverge, with levels as low as −19 percent and as high as +14 percent from the national average. We hence use the data from POF
purpose (50 percent as compared to 36 percent for women). In contrast only 6 percent of loan proceeds were used for purchasing business equipment, but here women were 3 times more likely to report this use (12 percent compared to about 4 percent for men). For other uses, men and women look roughly similar, though women use loans more frequently for consumer durables (8 percent of loan proceeds on average versus about 3 percent for men), while men use loan proceeds more frequently for housing improvements (8 percent versus about 3 percent for women).

# **3. Data**

Baseline data collected in November 2006, prior to the business training and loan lottery orientations, included questions on the CO member, the member’s household and the business. Besides the usual set of variables, such as age, education, marital status etc, individual characteristics include measures of entrepreneurship, digit span recall, risk preferences and decision making autonomy across a range of household and business outcomes. Household characteristics include information on the income generating activities of all household members, total household assets including livestock and past and current borrowing and saving of household members. Business characteristics include age, location and type of business activity, as well as the scale of the business as measured by its assets, hired workers and monthly sales.

Summary statistics from the baseline survey are presented in Table 2, and variable definitions are provided in Appendix B. CO members are about 38 years old, have 4.1 years of education, own some 3.5 acres of land and have average household expenditures of about Rs 5,200 per month (roughly 61 USD). This places them significantly above the 11
inefficiency – as human capital cannot be allocated where it could yield the highest returns (Gatti et al., 2013 and World Bank, 2012).

Previous comparative analysis of labor regulation in the MENA region (see Angel-Urdinola and Kuddo, 2010 and World Bank, 2013) indicate that some features of labor regulation in Morocco, notably minimum wage policy and payroll taxes, may contribute to elevating the cost of formal labor in the country and to constrain the demand and mobility of formal labor, especially among youth (Betcherman 2012 and World Bank 2012).

In particular, the level of the non-agricultural minimum wage (SMIG) in Morocco is high relative to the average worker’s productivity and well enforced in the formal private sector, especially in urban areas (Benhayoun et al. 2001 and Agénor and El Aynaoui, 2003). Compared to international benchmarks, this ratio is the highest in the MENA region, even by OECD standards (AngelUrdinola and Kuddo, 2010).<sup>4</sup> Moreover, between 2007 and 2013, the SMIG increased by 27 percent while average consumer prices increased by only 13 percent.<sup>5</sup> Also, payroll taxes (paid by employers) and social security contributions (paid by employees) in Morocco approach 25 percent

> 2 Latest available office data from Morocco’s High Planning Commissariat (for year 2015) indicate that overall employment rates in the country (population 15-64) are at 42.8 percent (64.8% for men vs. 22.1% for women) (data are available at http://www.hcp.ma)

> 3 Latest available office data from Morocco’s High Planning Commissariat (for year 2015) indicates that the country unemployment rate was at 9.7% (20.8% for youth 15-24 and 10.5% among women). In urban area, the figures of unemployment are even worst for youth (39%) and for women (21.