rafmacalaba/data-use-mentions-extended huggingface dataset:
can be said to lead to more private sector jobs; if however, firms with more sales employ fewer workers and are not adding jobs at pace, it can be a sign of underemployment and job-less growth.

This paper takes advantage of firm-level data (the World Bank Enterprise Surveys) collected in 2013 on registered — i.e., formal — firms operating in the Ghanaian industrial and service sectors, to examine the characteristics of job-creating firms and analyze the relative quality of these jobs in terms of productivity and firms’ average wage bills. The analysis looks at the relationship between job creation (using recall data) and labor productivity by focusing on firms’ main characteristics such as age, size, sector, location, exporting status, foreign participation in ownership, managerial capacity, and workforce education. The aim is to provide evidence on whether factor and product markets are working efficiently by allocating jobs towards more productive firms, and whether workers are benefiting from productivity gains in their sectors in terms of higher wages.

Results from the analysis show that larger and older firms account for the majority of workers and that formal job density is highest in the Accra region, including the Accra Metropolitan Area (AMA) and Tema. In addition, large firms pay higher wages on average, are more productive, and account for most of the aggregate net formal job creation between 2010 and 2012. In developed countries, there is evidence that businesses start small and if they survive they grow as they age (Hsieh and Klenow, 2014). In emerging economies, though, this research has found that plants grow at a much lower rate compared to the United States (especially at younger ages); yet older, formal manufacturing plants are larger than their younger counterparts. Growing evidence has suggested that compared to developed countries, firms in developing countries tend to grow much slower over time due to poor marketing functioning that constrains firms to invest and expand. A large portion of this differential, however, is largely explained due to the firm size
value of consumption flow of durable goods.<sup>22</sup> While monetary poverty can measure temporal resource holdings, multidimensional poverty, as a more comprehensive measure, includes chronic and exacerbating sources of poverty. This difference explains the existence of mismatches between individuals identified as monetary versus MPI poor, which are often more prominent in poorer countries (Evans et al 2020). This section examines these differences in the contexts of displacement.

_Table 9. Percentage of the sample in each poverty category: Rows sum to 100%_

||Non-poor by both<br>measures|Only<br>Monetary Poor|Only<br>Multidimensional<br>Poor|Monetary and<br>multidimensional<br>poor|
|---|---|---|---|---|
|Ethiopia|38%|23%|12%|27%|
|N.E Nigeria|13%|69%|4%|15%|
|Somalia|20%|32%|14%|34%|
|Sudan|33%|47%|4%|17%|

Source: Authors’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). This table presents the distribution of households in each of the categories in the columns. Thus, each row adds up to 100%. South Sudan is excluded from this analysis as monetary data is not available for the country.

Given that monetary and multidimensional poverty identify different populations, a household can be classified as: non-poor according to both measures; poor only by the monetary measure; poor only by the multidimensional measure; or poor by both measures (see Table 9). The results confirm the expectation that monetary and multidimensional poverty measures do not always capture the same people. Ethiopia has the largest overlap between the two measures,
functions from which productivity measures are defined. In the context of country panels, these studies most often entail decomposing changes in total factor productivity, via a Malmquist index fitted to measured inputs and outputs.<sup>6</sup> In some instances, a second-stage regression is used to explain variations in calculated productivity in terms of a set of determining state variables – for example, past research expenditures or the strength of political institutions.

Still, while the approaches applied to the problem of estimated agricultural productivity have become more varied, the underlying measures have not, and the alternative methods often build on the same data and the same proxies for capital as earlier studies. See, for example, Lusigi and Thirtle (1997), Arnade (1998), Coelli and Rao (2005), Fleming (2007), Fuglie (2008), Alene (2010), Headey, Alauddin, and Rao (2010), Nin-Pratt and Yu (2010), and Fulginiti (2010).<sup>7</sup>

# **Data set on agricultural capital stock**

The series of agricultural capital stock presented here consists of three components: fixed capital, livestock, and treestock. In this sense, we distinguish between stores of capital that arise through investments and privately held productive natural endowments including land and water resources. National accounts report fixed capital investment, which does not wholly include livestock and treestock; therefore, we compute each component separately. Data sources along with the computer program used to calculate the capital series are documented in Crego et al. (1998).<sup>8</sup> We construct the fixed-capital series based on national account investment data, using a modification of the perpetual inventory method. The method requires integration of the investment data to obtain capital stocks. For livestock the initial data are the number of animals. We need only calculate the values of the individual herds and then aggregate these values to obtain the total for the full stock of animals. For
_<u>Table 4: Poverty and Conflict Debt at Sub-national Regions in Africa: UCDP and ACLED data.</u>_

||(1)<br>Poverty rate|(2)<br>Poverty rate|(3)<br>Poverty rate|(4)<br>Poverty rate|
|---|---|---|---|---|
|Conflict debt|14.13***|5.705*|9.974***|5.295**|
||(3.670)|(3.055)|(3.182)|(2.140)|
|Violence data|UCDP|UCDP|ACLED|ACLED|
|Country fixed effects|No|Yes|No|Yes|
|Population weight|Yes|Yes|Yes|Yes|
|Adjusted R-squared|0.0425|0.602|0.0322|0.604|
|Observations|618|618|618|618|

Notes: Robust standard errors in parentheses. * p<0.1, ** p<0.05, *** p<0.01. The dependent variable is poverty rate (in percent). Conflict debt is the discounted history of conflict years where a conflict year is defined as by more than 0.08 fatalities per 1,000 population. The regressions are on the cross-sectional data of sub-country regions in Africa between 2005-2018 whenever sub-regional poverty rates are available.

# Evidence from within Uganda

In order to drill into the dynamics at the regional level, we look at conflict history and poverty rates in a case study with comparable regional poverty data and more than one time-dimension: Uganda. We have access to poverty data for 2012 and 2016. This is a period a few years after the most intense fighting took place in Uganda and, in fact, we need to use the more detailed ACLED
of disruptions.<sup>6</sup> Being a country-level variable, it well reflects the systemic nature of an economy’s transport system and is not an ordinal perception index. This means it allows level comparison across firms and meaningful interpretation of marginal effects in the model. However, as a country-level variable, the LPI does not allow us to distinguish firm-level and regional variation (which is necessary for distinguishing direct and indirect effects). The results for this section are presented in Annex A.4.

Transport reliability is shown to have a positive and highly significant impact on firms’ utilization rates. Increasing the Logistics Performance Index on _Timeliness_ by 1 point – which, for instance, corresponds to the difference between Tanzania and South Korea – raises firms’ utilization rates by 5.8 percentage points on average (Annex A.4, model II). Introducing other country-level controls barely moves the point estimate, however, it inflates the standard error due to collinearity with the country-level LPI variable which also changes slowly across years. Among infrastructure disruption variables, transport is most potent to explain dependent’s variation.

Finally, governance quality as measured by the World Governance Indicators strongly improves firms’ utilization rates. While Table 5 shows results for the sub-indicator “Governance Effectiveness”, any other World Governance Indicator – such as “Control of Corruption” – yields very similar estimation results (not shown). Specifically, a one-step increase – for example the difference between Japanese and South African government effectiveness – would raise average utilization by roughly 7 percentage points (Table 5, model III). Not only is the marginal effect large, but also the variance explained, as measured by standardized beta coefficients (not shown). For further details refer to Kornejew, Rentschler, and Hallegatte (2019).

Note that using a fractional response regression model with a logit or probit link function – which in principle better suits the bounded dependent variable – has virtually no impact on these conclusions. We therefore present standard OLS coefficients
Data from HIES revealed that between 2010 and 2016 (comparable rounds), the mean per capita consumption increased by 13 percent in real terms. Concurrently, the World Development Indicators (WDI) Database from the World Bank shows that the real GDP per capita growth during this period was 35 percent, resulting in a pass-through rate of 0.37.<sup>16</sup> Utilizing the 2022 consumption distribution and real GDP per capita values for 2010, 2016, and 2022, the backcasting exercise estimated poverty rates at 25.3 percent for 2016 and 31.3 percent for 2010. The backcasted extreme poverty rates were calculated at 8.9 percent for 2016 and 12.2 percent for 2010.

Interestingly, except for the 2010 poverty rate, these backcasting results align well with the findings of this study. The 2010 poverty rate obtained through this alternative approach closely matches that derived from the 2010 HIES using the official welfare aggregate. However, if this consistency holds true,

> 16 Mahler, Castañeda Aguilar & Newhouse (2022) suggest, as a general guideline for international comparability, using a passthrough rate of 0.7. Applying this rate, estimated poverty and extreme poverty rates for 2016 would be 33.1% and 13.1%, respectively, and for 2010, 47.5% and 22.8%. These results show a decreasing trend, but the poverty and extreme poverty levels differ significantly from other robustness checks. For this reason, the chosen pass-through rate of 0.37, specific to Bangladesh, is used in this paper due to its sensitivity to the rate's value.

24
the case in Mumbai). For the slum households whose increased income allowed them to move out of a slum, it is likely that they have moved to the older part of the housing stock, many in the old city, which is cheaper and often more centrally located than the newer apartments in the more distant suburban areas.

> 8 It should be noted, as discussed later in the text, that the share of the population living in slums/challis was calculated independently using our mapping of slum areas combined with data on density per hectares from detailed surveys of representative slums and on average household size. Those computations provide us with the number of households living in slums/challis. The results are shown in Figure III. 2
Policy Research Working Paper 9435

# **Abstract**

Severe weather shocks recurrently hit Malawi, and they adversely affect the incomes of many farm households as well as small businesses. With climate change, the frequency of extreme weather events is expected to increase further. A clear understanding of households’ vulnerability to shock-induced poverty is critical for disaster risk management and the design of scalable social safety net programs. Standard poverty measures rely on static snapshots that are suitable for quantifying structural poverty but not for assessing the vulnerability of non-poor households to fall below the poverty line when they experience shocks. This study uses a nationally representative household survey and exogenously measured weather shocks to assess households’ vulnerability to poverty in Malawi. To accurately estimate the impacts of shocks on consumption and vulnerability, the study excludes any kind of assistance (aid and food or cash transfers) that households might have received after major disasters. The key findings of the study are as follows: (1) drought during the growing season decreases non-assistance consumption per capita by 5–12 percent, depending on its intensity; (2) excess rainfall at the onset of the growing season reduces food consumption by 1.8 percent, while excess rainfall later in the growing season appears to increase consumption; (3) vulnerability to poverty is generally higher than static poverty, especially compared to static poverty measured during a good weather year; and (4) in years of extreme droughts, such as 2016, recorded poverty rates are higher than vulnerability, which indicates that the magnitude of drought in 2016 was so large that the chance of falling below the poverty line as a result of an even higher magnitude shock was low. These results suggest that identifying vulnerable households is key in designing adaptive social safety net programs that can be scaled up to cover those who become eligible for such programs after experiencing shocks.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make
and control subdistricts should have been the same. This assumption is plausible due to the clusterlevel randomization, and we provide evidence to support it. By grouping observations in different time periods, we can depict the differences in malaria incidence between treated and control localities over time. The result, shown in Figure 5, allow us to detect potentially diverging trends before the campaign (i.e., between April and August 2020). Importantly, the figure supports the parallel trend assumption, as trends in incidence rates across treated and control subdistricts are comparable before the campaign, providing reassurance on the identification strategy underlying the analysis of administrative data. The figure also confirms that the campaign significantly impacted urban malaria incidence while having negligible effects on rural cases.

# **5.4 Placebo Regressions and Robustness Checks**

In this section, we present a series of placebo regressions and robustness checks for our results. As demonstrated, our findings remain consistent and reliable across these various checks.

For our survey-based findings, we conducted the following specification checks and placebo exercises. First, we assessed the sensitivity of our results to the inclusion of individual- and districtlevel covariates, and reported them in the tables above (and in the Appendix). The coefficients showed minimal changes, and the effects remained stable. Second, we estimated logistic regressions for all the binary outcomes presented above. The results are shown in Annex Tables D12-D14. The estimated marginal effects for the treatment indicator reported at the bottom of the tables are identical to the OLS estimates presented so far, validating the use of a Linear Probability Model in this context. Third, since we measured bed net usage at baseline (i.e., before the campaign started), we can conduct placebo regressions to further validate the randomization. Annex Table D15 shows the results of this exercise, where the dependent variable measured in July and August 2020 is regressed on the treatment indicator. The table provides strong evidence in favor of a correct randomization: all coefficients are small and statistically
Policy Research Working Paper 7918

# **Abstract**

This paper assesses two research questions: has the presence of foreign firms contributed to productivity increases in Turkey, and how could Turkey increase foreign direct investment inflows? First, the analysis applies dynamic regressions in differences over an AMADEUS firm-level data set. Similar to the results for other emerging countries, Turkish firms that received foreign direct investment will see an increase in productivity after the fourth year. The paper finds evidence of negative but small competition spillovers over domestic firms in the same sector of the multinational, as well as positive and large knowledge spillovers to domestic firms in broader two-digit sectors. This finding constitutes a case for foreign direct investment attraction policies in Turkey. Second, based on the findings of the cross-country regressions, the paper argues that Turkey could increase its attractiveness to foreign investors by strengthening institutional quality, in particular the rule of law, and mitigating exchange rate volatility.

This paper is a product of the Macroeconomics and Fiscal Management Global Practice 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 msanchezmartin@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
7.4 percent in 2018. As Malaysia pursues further development toward becoming a high-income and developed nation but faces a slowdown in both structural change and productivity growth, questions have been raised whether the reallocation of production factors can be a driver of the country’s future economic growth.<sup>1</sup> Against this backdrop, this study analyzes the process of structural transformation in Malaysia between the years 1987 and 2018 (the time horizon for which granular comparable data are available). It describes the patterns of output, employment, and labor productivity in each of the three decades. In addition, it decomposes the growth of output per capita into contributions from within- and between-sector labor productivity growth, employment growth, and changes in the demographic structure at both the aggregate and the sectoral level and investigates the contributions of static and dynamic reallocation of labor to overall labor productivity growth. These decompositions provide insight into the medium-term drivers of Malaysia’s economic growth, including the factors underlying the relative stagnation of growth in output per capita since the early 2000s.<sup>2</sup> The study confirms that Malaysia has undergone a very rapid structural transformation from an agriculturecentric to a services-driven economy. This transformation has included both a period of rapid industrialization and a period of relative deindustrialization. From the mid-1980s through the early 2000s Malaysia’s manufacturing sector grew at a fast pace, both in the terms of the share of employment and the share of output. Since then, while the manufacturing sector’s output has continued to grow in absolute terms, its growth has been at a slower pace and its share of overall output has stagnated. Coupled with a declining share of the

> 1 In this paper, structural change refers to labor reallocation to more productive sectors, while structural transformation refers to the overarching process of the reallocation of resources across the broad sectors of agriculture, manufacturing, and services over time.

> 2 In addition to the factors analyzed here, international trade can also influence the reallocation
Table (E5) Impacts of the Program on Transfers

|**Outcomes**|**Treatment**|**(se)**|**FDR**<br>**q-values**|**Control**<br>**mean**|**(sd)**|**N**|
|---|---|---|---|---|---|---|
|# Senders|0.02|(0.04)|[0.94]|0.06|(0.25)|1,422|
|Total Received Transfers (Annual, USD PPP)|22.08|(97.98)|[0.94]|75.73|(562.57)|1,421|
|# Receipients|-0.03|(0.04)|[0.94]|0.06|(0.25)|1,422|
|Total Transfers Sent (Annual, USD PPP)|51.94|(55.52)|[0.94]|25.61|(219.89)|1,422|

**_Notes_** :

- The table shows the regression results on pre-specified transfers outcomes using the 2021 in-person data. Each row is its own dependent variable.

- The outcomes that require definitions are: # Senders reports the number of households who have sent the focus respondent a cash or in-kind transfer in the past 12 months. Total received transfers indicates the total cash value of transfers received from these households. # Recipients reports the number of households to whom the focus respondent has sent a cash or in-kind transfer in the past 12 months. Total transfers sent indicates the total cash value of transfers received by these households. Total values are adjusted for 2020 PPP (2021 is not yet available
(Gbadamosi (1967), Hiskett (1975)). Yet Hailey observed that the education of girls was relatively more advanced in West Africa though the increase is more evident in respect of the total numbers and not in the proportion of girls to boys.

For example in 1954, of the 494,334 children in primary schools, 150,118 were girls; of the 8,602 pupils in Government secondary schools, 1,296 were girls. In that year 785 boys and 121 girls passed the examination for the Cambridge School Certificate (Hailey (1957: 1187)).

For instance, in Ghana, one of the few countries for which we have education statistics and records over the colonial period, enrollment rates were abysmally low and exhibited a strong gender bias. While primary enrollment rates for boys averaged 0.52% of the total population in 1901, girls‘ enrollment rates were even lower, less than 0.11%. And over the first decades of the 20<sup>th</sup> century, the gender gaps in primary enrollment widened, with more boys attending schools (0.84%) than girls (0.18%). Interestingly, that gender gap inherited in the colonial era has persisted over time as illustrated by Figure 1 below.

**Figure 1: Enrollment in Primary and Middle Schools in Ghana**

<!-- Start of picture text --> 500000<br>450000<br>400000<br>350000<br>Boys Girls<br>300000<br>250000<br>200000<br>150000<br>100000<br>50000<br>0<br>1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915-9 1920-4 1925-9 1930-4 1935-9 1940-4 1945-7 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960<br><!-- End of picture text --> Source: Kay (1972: 407-8).

French Africa would