rafmacalaba/data-use-mentions-extended huggingface dataset:
surveys (averaging over the various surveys when is there are more than one) and as reported in the World Bank's World Development Indicators (WDI) (World Bank, 1999) database which is based on UNESCO data (averaging over all available data between 1990 and 1999).'3 Overall the two data sources tell a similar story. The primary net enrollment rate for girls averaged across all the countries is very similar from the two sources: 58.6 percent based on the DHS surveys and 58.3 based on the WDI statistics (when restricting the sample to countries that have number from both surveys - the average over all DHS surveys is 63.4 percent). The average male/female ratio is similar when using the two sources as well (1.14 from DHS and 1.22 from WDI). Other characteristics of the distribution (standard deviation, maximum, minimum and median) are very similar as well.

The overall similarity, however, masks some large discrepancies at the country level. The difference between the enrollment rate based on the two sources ranges from -22 percentage points (Turkey where the DHS implies a rate of 71 percent and the WDI 93 percent) to 47 percentage points (Haiti where the DHS implies a rate of 70 percent and the WDI 23 percent). After Haiti, the next largest discrepancy is 19 percentage points (Mozambique where the DHS implies a rate of 54 percent and the WDI a rate of 35 percent).

In most countries, the two datasets tell a sirnilar story with respect to gender differences as well. The main difference occurs in the Western and Central African countries where (except for Cote d'Ivoire and Senegal) the WDI numbers imply a male/female ratio that is substantially larger than what the DHS show. For example, in Chad the WDI imply a male/female ratio of 1.80 whereas.the DHS imply a ratio of 1.53. Relying on the WDI one would overstate the male "
with immediate regional neighbors in the Economic Community of West African States (ECOWAS).

Several methodological issues should be borne in mind. First, because of the cross-country nature of data collection, a time lag is inevitable. The period covered by the AICD runs from 2001 to 2006. Most technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Where possible, data have been collected for the period 2007 to 2009 with a view to measuring progress made relative to the baseline. Second, in order to make comparisons across countries, we had to standardize the indicators and analysis so that everything was done on a consistent basis. This means that some of the indicators presented here may be slightly different from those that are routinely reported and discussed at the country level.

# Why infrastructure matters

As on the rest of the continent, West Africa’s growth performance improved markedly in the 2000s. The overall improvement in per capita growth rates has been estimated at <u>around 2 percent, of which 1.1</u> percent is attributable to better structural policies and 0.9 percent to improved infrastructure. During the five years from 2003 to 2007, Ghana’s economy grew at an average annual rate of 5.6 percent, which 2
the effects. To measure employment rates, we use the data on employment-to-population ratios from Pennings (2022), with the share of workers across different levels of schooling from national estimates in ILOSTAT (for those MENA countries where the data is available).

**Results.** We find gains in output per capita from closing gender employment gaps that are large for most countries in MENA, but (i) are very heterogeneous across countries, (ii) are not as large in the short-to-medium term when physical capital is fixed or the speed at which gaps themselves close is incomplete, (iii) are generally smaller with capital-skill complementarities and an unchanged skill distribution, though higher if share of skilled female workers also increases.

Quantitatively, the increase in long-run GDP per capita in MENA from closing gender employment gaps averages 54% using a Cobb-Douglas production function and the GEGI (median 49%), which is very similar to that in the Arab Republic of Egypt (56%), which we use as a typical country. However, the average gains halve in the short-run if capital remains fixed (they also halve in Egypt). Using the long term growth model (LTGM) at 2050, where we incorporate the structure of the economy in our parameter values and model a transition path, gains lie between what we call the short run and the long run values, averaging 31% (median 34%), with Egypt increasing 3
perspective these studies put forward the neo‐Shumpeterian growth theory in the sense that both innovation and imitation activities also have a separate bearing on growth and productivity. This approach has found empirical underpinnings in studies such as Nicoletti and Scarpetti (2003), Greffith _et al_ . (2004), Aghion _et al_ . (2004; 2005) and more recently by focusing on EU countries using industry‐level data by McMorrow _et al_ . (2010). The bottom line of these studies is that certain economic or policy characteristics such as factor endowments or regulatory policies have a different effect depending on the fact in which type of industry firms are placed, i.e. an industry that is characterized with many firms relatively close or relatively far away from the technology frontier.

Since we have firm‐level data we calculate the technology gap as developed in Aghion _et al_ . (2005) so as to derive a within‐industry specific measure which is the proportional distance of a firm relative to the technology frontier in terms of TFP as defined by the following equation: in which the technology gap is measured for each firm within an industry by country over time. In this equation firm _F_ is the firm with the highest TFP while all other firms which are non‐frontier firms are indicated with the usual _i_ . As in Aghion _et al_ . (2005), the technology gap for the frontier firm is set at zero. Similarly, as in their work this measure of firm‐specific technology gap is then transformed into an average over each industry and services sector for each country and year. Hence, industries showing a low value of the technology gap are those industries which are characterized by a “neck‐and‐neck” situation in which firm share equal technology levels whereas a higher value of the gap reflects so‐called “unleveled” sectors in which sectors are marked by many followers with unequal technology levels compared to the leader firm. Table A16 in the annex sets out the value of the technology gap for
17

# _3.5. Enabling Environment and Other Variables_

The focus of the 4x2 matrix—and the Global Financial Development Database—is on financial system characteristics. It does not explicitly include variables capturing financial sector policy, such as features of financial sector regulation and supervision. The conceptual reason for focusing on financial system characteristics is that those indicators bridge the gap between policy measures and final objectives. They function as ―intermediate‖ indicators and targets. To some extent, this is an analogy with monetary policy, where intermediate targets have a relatively clear link to the policy variable (e.g., central bank‘s interest rate) and an impact on the policy target (e.g., future inflation rate).

One group of the ―other variables‖ relates to the microstructure of the financial sector. The 4x2 framework (Table 1) captures the overall broad structure of the financial sector, in particular the relative sizes of financial institutions and markets, and the relative sizes of the key segments (total assets of banks and nonbanks; outstanding volumes of stocks and bonds). Also relevant are indicators of structure within the individual financial segments, such as the concentration ratios (Herfindahl index, shares of various types of financial institutions in total assets and in GDP, shares of individual markets in total market capitalization). Some of these measures (e.g., the percent of assets of three or five largest financial institutions in GDP), are important for the stability dimension, because they provide a rough approximation for the potential for impact in the case of a major financial disruption (e.g., Čihák and Schaeck 2010). More generally, recent research suggests that structure has an impact on performance, so it is important to include these indicators as part of the ̳enabling environment‘.

Another relevant group of indicators relates to the degree of international integration. This includes indicators such as consolidated foreign claims of BIS-Reporting Banks to GDP or measures cross-border banking interconnectedness, such as the degree
_Journal of Comparative Economics_ , 51(1): 372–402.

- **Gavoille, Nicolas, and Anna Zasova.** 2023 _b_ . “What We Pay in the Shadows: Labor Tax Evasion, Minimum Wage Hike and Employment.” _Journal of Public Economics_ , 228: 105027.

- **Gerard, Fran ̧cois, and Gustavo Gonzaga.** 2021. “Informal Labor and the Efficiency Cost of Social Programs: Evidence from Unemployment Insurance in Brazil.” _American Economic Journal: Economic Policy_ , 13(3): 167–206.

- **Ginja, Rita, Arizo Karimi, and Pengpeng Xiao.** 2023. “Employer Responses to Family Leave Programs.” _American Economic Journal: Applied Economics_ , 15(1): 107–135.

- **Jascisens, Vitalijs, and Anna Zasova.** 2021. “Million Dollar Baby: Should Parental Benefits Depend on Wages When the Payroll Tax Evasion is Present?” SSE Riga/BICEPS Research Paper 9.

- **Kleven, Henrik.** 2022. “The Geography of Child Penalties and Gender Norms: A Pseudo-Event Study Approach.” National Bureau of Economic Research Working Paper 30176.

- **Kleven, Henrik, Camille Landais, and Gabriel Leite-Mariante.** 2024 _a_ . “The Child Penalty Atlas.” _Review of Economic Studies_ , Forthcoming.

13
covariates include household characteristics and characteristics at the PSU, municipal and state level. Two models are selected: i) a model that includes household characteristics and characteristics at the PSU, municipal, and state levels and ii) another model that only includes characteristics at the PSU, municipal and state levels. The second model is used for the unit-context approach. All household level characteristics that are included also as aggregates at the PSU, municipality, and state levels have been previously standardized to ensure that these have mean 0 and standard deviation 1 for each PSU, municipality and state respectively. Note that aggregated covariates have been obtained from the “census,” thus, the aggregated covariates will be the same within the sample and the census. The lasso model selection process applied here ignores the error structure presented in (1) and (2) and does not ensure that selected covariates will be significant under the assumed models. Consequently, after the initial lasso model selection, model (1) is fit using Henderson’s Method III (with sampling weights) and random effects specified at the municipality level. Because the resulting model may

> 31The design weight for each household is simply the inverse of the inclusion probability. In a typical survey, the design weights would be further adjusted for nonresponse and calibrated to known population characteristics. However, since the sampling is only a simulation exercise, there is no nonresponse and thus no nonresponse adjustment is required. Calibration or post-stratification could be performed but was not implemented to simplify the process.

> 32In any given sample, roughly 11 observations have an income of 0, these are assigned an income of 1 prior to transformation.

> 33For more details on transformations, see Tzavidis et al. (2018).

> 34Figures make use of sample from a two-stage clustered design.

> 35Model is selected using 20 fold cross validation and shrinkage parameter ( _λ_ ) that is within 1 standard error of the one that minimizes the cross validated MSE.

18
are controlled for, and different trajectories across cities are allowed.

# 2.3 Data

This paper combines various data sources to explore whether FDI catalyzes local structural transformation and human capital accumulation.

## **Population census**

This paper uses population census data in 1990, 2000 and mini-census data in 2005 to estimate city level employment share in the three broad economic sectors and high school/college enrollment rate. The population census data also provides rich information on individuals’ age, gender, migration history, employment status, industry, and ethnicity. We aggregate such information at the city level as control variables. In addition, the paper uses the initial industry employment composition from population census to construct the Bartik shock IV for city level FDI inflows.

11
households with the infrequent household diary. Clearly the characteristics of the study population will be a critical factor in this decision – if the population contains a large proportion of urban households, the need for extra resources to implement a personal diary may well be justified.

From among the recall surveys, all far less expensive than diaries, certain recommendations are clear. For one, the savings in survey time from a reduced number of consumption categories in the recall list through aggregation (the collapsed list module 4) is minimal compared with a substantial cost in terms of loss in accuracy. We do not recommend this module type. On the other hand, the other short commodity list design (the subset list module 3), when scaled up based on reference data, performs very close to the longer list form and may be a suitable substitute to longer list recall modules. The gain from such a reduction in list length, however, is slight – less than 10 minutes of interview time – and researchers would have to decide whether the loss of additional detail in consumption information is worth the moderate reductions in interview time. The hypothetical “usual” month recall almost doubles the interview time while reducing the accuracy of measured consumption, leaving no practical basis to recommend this approach.

These considerations would lead to a recommendation of a long-list recall module with a reference period of 1 or 2 weeks. Both modules take the same amount of time to implement. The 7- day recall results in mean consumption closest to the benchmark as well as summary inequality measures only slightly higher and not significantly different from the benchmark. The 14-day recall yields significantly lower mean consumption and higher inequality (thus much higher poverty estimates as in Figure 1). By process of elimination, if one recall module must be chosen, the 7-day full list (module 1) presents the fewest complications. Nevertheless, researchers need be aware of the underlying fact that this module is still likely subject to recall and telescoping errors of varying degrees as well as presumably the inability to
area enrolled in each zone type under the present scheme. We then exploit our community-level median WTA estimates as best guesses of the payments that would induce participation of land submitted by the communities that we inter-

> 16Since deforestation tends to occur in small increments in Mexico it is difficult to predict precisely where it will be located. While another approach to assessing environmental effectiveness would be to use the exact predicted deforestation rate for each parcel (i.e., multiplying our risk index by the area enrolled from each community), we feel that this is likely to be inaccurate and somewhat misleading, since the areas at lowest risk are highly unlikely to contribute any avoided deforestation at all.

35
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6 developing countries than industrial countries. For the industrial countries, it is well known that the extensive literature on the impact of exchange rate volatility on trade is inconclusive, possibly because of its likely small impact (for a survey of the literature, see McKenzie 1999). Recent research does show that the impact of exchange rate volatility on trade is negative but small. Dell'Ariccia (1998), for example, shows in a gravity model (in which the volume of trade between two countries increases with the product of GDPs and decreases with distance) that exchange rate volatility has a negative, though small, effect on bilateral trade flows among 14 member countries of the European Union during the period 1975-94 (see also Gagnon 1993). All in all, at least for industrial countries, exchange rate volatility is not usually considered to have a negative impact on trade.

For developing countries, however, the negative effect of exchange rate volatility on trade appears to be more pronounced, although the scope of existing empirical work is somewhat limited (Calvo and Reinhart 2000a; McKenzie 1999). For example, Kumar and Dhawan's (1991) work on Pakistan's exports to Germany, Japan, and the United States for 1974-85 suggests that exports were significantly adversely affected by variability in nominal bilateral exchange rates. Looking at the effect of real exchange rate variability on the exports of Chile, Colombia, Peru, the Philippines, Thailand and Turkey, Caballero and Corbo (1989) have obtained the clear evidence of generally significantly negative and substantial impact. Arize, Osang and Slottje (2000) have used quarterly data for 13 emerging market economies (including Indonesia, Korea, Malaysia, the Philippines, Taiwan Province of China [POC], and Thailand) during 1973-96 to confirm the significantly negative effect of real effective exchange rate volatility on exports. In addition, Calvo and Reinhart (2000a) and McKenzie (1999) cite additional studies that indicate the evidence of significantly negative effects of exchange rate volatility on trade, including
Sixty-one percent of migrants currently residing in cities and towns came from other urban areas, while 47.8 percent of migrants living in rural areas came from the countryside (see Table 2).

Table 2 – Direction of Migration - Urban/Rural (classified according to destination)

|Previous Location|Current l<br>Urban||ocation<br>Rural|Total|
|---|---|---|---|---|
|Urban|61.0||52.2|59.6|
|Rural|39.0||47.8|40.4|
|Total|100||100|100|

Source: Authors’ calculations using Labor Force Survey

# **Migration by Region**

We considered seven regions in Egypt: 1) Cairo governorate; 2) urban Lower Egypt, including three metropolitan governorates (Alexandria, Port Said and Suez); 3) rural Lower Egypt; 4) urban Upper Egypt including urban Giza; 5) rural Upper Egypt (including rural Giza); 6) urban Frontier governorates; and 7) rural Frontier governorates.

Lower Egypt is the preferred destination for migrants (64 percent), followed by Cairo (17 percent), as shown in Table 3. The majority of migrants from Cairo (70 percent) chose Lower Egypt as a destination, as did 46.5 percent of people migrating from Upper Egypt. Additionally 74.9 percent of migrants from Lower Egypt moved to different localities in the same region.

# **Direction of Migration**

Table 3 – Direction of Migration- Region and Urban/Rural (classified according to origin)

|Previous Region|Cairo|Lower<br>Urban|Lower<br>Rural|Curre<br>Upper<br>Urban|nt Region<br>Upper<br>Rural|Frontier<br>Urban|Frontier<br>Rural|Total|
|---|-
consider these trade-offs when we discuss the critical importance of program coverage.

Finally, targeting is ultimately a policy issue, and it is not possible to make optimal choices in terms of targeting strategy without defining the goals and the criteria for assessing its performance. “What is the target?” is thus an important public policy question, but it is rarely clearly defined _ex-ante_ (Stoeffler et al., 2020; Schnitzer, 2019). Cash transfers in particular address a wide variety of distinct, competing objectives (e.g., improving nutrition, health, and education for children, and generating investments in productive assets and agriculture). In the absence of clearly defined goals, targeting studies do test performance along several welfare metrics such as food security, per-capita consumption, an asset index, a health index, or poverty self-assessment, (Alatas et al. 2012; Schnitzer 2018; Premand & Schnitzer 2020; Stoeffler et al. 2016; Çubukçu & Stoeffler 2019). Several of these studies (although not all) have found that one targeting

> 6 An additional challenge is a logistical one in low administrative capacity environments. Providing cash transfers in the Sahel is non-trivial in terms of uniquely identifying and registering beneficiaries, delivering transfers, and administering the program (among others) and requires a strong infrastructure in addition to technical capacities. Both universal and targeted programs would share this difficulty if they were implemented without prior geographic targeting (see below).

> 7 For instance, Briggs (2017b) and Briggs (2018) show that foreign aid tends to target wealthier regions and households in Africa. Burgess et al. (2015) show that road construction in Kenya is politically targeted based on the leader’s ethnicity during non-democratic periods, which is costly in terms of economic growth.

6
the household level. This point is detailed in the Supplementary Appendix S1. The comparison between the inequality and poverty assessments reached when using one or the other measure of consumption is central for the analysis presented in this paper.

# 2.2. Implications for poverty and inequality measurement

In this section we examine how the specific design of the PSF survey may impact the measurement of consumption compared to that of regular surveys, and how it may change inequality and poverty estimates based on household consumption levels.

Collecting data at the cell level implies that several members of the household are contributing information to the consumption survey. Since the household head doesn’t directly observe all individual expenditures, in particular in a context where individual resources are not public knowledge within the household (see Boltz, Marazyan and Villar, 2015, Ziparo, 2014, Baland, Guirkinger and Mali, 2011), interviewing other household members allows to record expenditures that might otherwise have gone unnoticed. Proxy reporting as a potential source of underestimation was mentioned by Deaton (2005). Furthermore, the sheer fact that consumption is recorded in a more disaggregated manner might allow for a better recall (see Deaton 1997; Beegle _et al_ . 2012). However, adding more respondents could also increase the amount of noise. If noise dominates, that should not introduce any systematic difference with regular household consumption surveys. Otherwise, increasing the number of respondents should lead to a more exhaustive recollection of consumption, and the consumption measured in PSF should be greater than in regular surveys, the more so for more complex households. One then expects the differences to increase with the number of cells.

We use data from the _Enquête Suivi de la Pauvreté au Sénégal_ (ESPS) collected in 2005 to explore this conjecture. Since this survey is the one used by the Senegalese Government and 11