rafmacalaba/data-use-mentions-extended huggingface dataset:
1990s due to survey changes.

One reason for the lack of studies that look at union effects in RCI is that the topic can only be satisfactorily analyzed by firm level data. This paper presents one of the first studies of employer-employee data, the so-called Regional Program on Enterprise Development (RPED) data set on Côte d’Ivoire from 1995 and 1996. This type of data represents a clear advantage to prior studies of the labor market, which rely on household surveys that are rather sparse on

> 1 Appendix E contains an overview of labor market studies in RCI that makes use of regression techniques, based on individual data, to analyze wage/income generation.

> 2 This drop is in line with market average wages which fell 60 percent in the 1985-95 period (Verner, 1999). 3 Rama (1998) quotes ILO numbers, estimating 300,000 individuals as being union members in 1995 out of a total labor force of 7,063,000, which is equivalent to 4.25 percent of the entire labor force.  These numbers are only estimates and highly uncertain.

2
suffer from. We estimate the production function separately for sectors and years with establishments’ real output as the output measure.<sup>20</sup> This approach allows us to extract the unbiased estimates of the establishment-level output elasticities required to determine an unbiased measure of establishments’ productivity (TFP) and markups that accounts for unobserved productivity shocks across establishments facing heterogeneous technologies and potentially imperfect competition. More specifically, the estimated output elasticities are computed using the estimated coefficients of the production function and the establishment-level information on labor, capital, and age—the output elasticity for labor under the translog production function, for instance, is given by Note that the use of the standard Cobb-Douglas production function implies that the output elasticity of labor is simply given by β<sup>�</sup> ��. We use the estimated unbiased output elasticities in (3) to compute establishments’ TFP and markups, whereby we compute the latter by multiplying the output elasticity to labor with the ratio of establishments’ sales over total labor costs.

# **5.2 Subnational regulatory reform impact**

The matched subnational regulatory reform and establishment census panel data allow us to estimate the effects of eliminating subnational barriers to market entry on establishments’ productivity in a difference-in-differences setting. The census data are available from 2008-15 so that we can track productivity and markups of establishments impacted by one of the subnational reforms for up to 5 years before and 2 years after the changes in the national legislation expanding the enforcement power of Peru’s competition authority over subnational jurisdictions. We thus consider the following generalized difference-in-differences specification: where _i_ stands for an establishment, _d_ for an establishment’s state ( _departamento_ ) or province municipality, _m_ for an establishment’s municipality (district or province), and _t_ for the year. Y���� measures TFP or markups as defined earlier. R��� is the regulatory reform indicator; it is a dummy variable which is equal to 1 in the years of and after
investments in digital solutions and product innovation persists (Figure 18, panels b and c).<sup>28</sup> Smaller firms possibly face larger barriers to invest in digital or product innovation due to the lack of complementary inputs (i.e., managerial skills) or access to finance.

These results suggest that the benefits of the digital acceleration and opportunities to pivot productmix may be accessible only to larger firms or selected groups which have both access to finance and the required managerial skills to take advantage of them. While this might widen the inequality across firms, in the long-run it may also lead to a possible reallocation of market shares towards more productive firms. This is not surprising because more productive firms have a higher tendency to adopt digital technologies and they also appropriate larger benefits from such adoption because they have complementary capabilities (e.g. technical, managerial, and organizational) (Sorbe et al., 2019; Gal et al., 2019).<sup>29</sup> BPS data also confirms that the probability of adopting digital technologies tends to increase with productivity, although the differences are slight and not significant (Figure C15). Thus, the COVID-19 shock may end up reallocating resources and market share from low productive firms to those with higher productivity.

> 28We show only wave 2 data for product innovation because it is not exactly comparable with wave 1. It should be noted that the gap between large and micro or small firms in the aftermath of the COVID-19 crisis, which ranges between 7 and 9 percentage points, is actually lower than the typical gap found in innovation surveys during normal times in the OECD countries where the average innovation (product, process or organizational) gap between large firms and SMEs is 24 percentage points (OEC, 2021).

> 29In the context of the COVID-19 crisis, Riom and Valero (2020) find path dependence in digitalization, that is, firms that had already invested in digital technologies were more likely to adopt more of
the indexes developed by Deaton and Tarozzi (2000). Thus, all wages in the paper are expressed in terms of the 1999 average of India’s rural prices.

Workers engaged in salaried occupations were asked about the type of enterprise where they carried out their main work activity. In the 50<sup>th</sup> round, all salaried workers were asked to choose from three categories: public, private, or semipublic, and we

> 4 These cross-sectional surveys use a two-stage stratified sampling design, and are representative of the national, state, and so-called “NSS region” level. The first-stage sampling units (FSUs) are census villages in the rural areas, and the NSS Urban Frame Survey (UFS) are blocks in the urban areas. The second stage units are the households in both urban and rural sectors. For more information on the survey and sample design see, e.g., NSSO, Ministry of Planning (1994, 2000).

> 5 In the 50th round, the sector of employment – public or private – is defined only for those engaged in regular salaried/wage work (in all industrial activities), while in the 55<sup>th</sup> round, it is defined for those who are employed in non-agricultural activities. In both rounds, wages are defined for everyone who reported being employed. In order to maintain consistency and comparability across the two rounds, the classification of the sector of employment was defined only for those with regular salaried/wage employment in non-agricultural industries. We also excluded casual workers in public works as well as individuals who were sick or did not work for other reasons.

10
Although its location on the edge of Lake Victoria enables a small fishing industry, Mara Region is primarily agricultural. The bulk of farming activity takes place over the main, long-rains season (the masika), which runs roughly from January to June. The two main crops cultivated in the villages in our study are maize and cassava. Maize has a fixed seasonal cycle of land preparation, planting, weeding, and harvesting, a cycle which is governed by the onset of the rains.<sup>11</sup> By contrast, cassava has no specific cultivation cycle and is grown throughout the year. Cassava harvesting occurs throughout the year, depending on household food needs. Households frequently diversify cultivation, intercropping the two staples with beans, sweet potatoes, and sorghum.

Before comparing labor reporting by survey design, we use the benchmark weekly visit data to provide some context. Households have an average of 6.4 members and are typically composed of about one-third children under 10, with membership 50/50 by gender. The average household cultivates 4.6 plots of about 1 acre each. These plots tend not to be located adjacent to the household’s dwelling, nor are they typically adjacent to each other. On average, households report their plots are located a 26-minute walk from the primary residence.<sup>12</sup> Most people aged 10 or above were engaged in household farm labor. Table 2 provides an overview of the activities of these household members in our sample according to the weekly visit data. Consistent with the agricultural character of the region, the most common activity was work on a household farm; 88 percent of people spent at least one day in this activity over the season. Paid work, whether agricultural or otherwise, was rare: only 16 percent of people engaged in any paid agricultural work for others, and 11 percent performed paid nonagricultural work. A large share of people spent at least some time collecting firewood and water. About a quarter spent at least one
effectiveness, and equity, though in this paper most of the discussion centers around using the quantified attributable benefits identified in impact evaluations as the numerator to establish efficiency with costs as the denominator. The ratio of Impact to Cost yields an estimate of efficiency, recognizing that there are many ways to arrive at estimates of each of those broad constructs.

There are times, of course, when institutions may act as both producers and consumers of both impact evaluations and Cost analyses. For example, a large agency (such as USAID) or multilateral bank (such as the World Bank) could potentially use the evidence from evaluations of vaccine programs that it funded to determine the most effective way of distributing vaccines in resource constrained countries.

We worked through a simple framework of the process by which evaluative evidence is created, disseminated, and applied. That is, we solicited views from impact evaluation 4
Several partnership agreements on illegal migration between host and source countries have been concluded or are being considered. For example, exchange of information on migrant smuggling is promoted by means of technical cooperation agreements between Japan and Korea, Japan and China, Hong-Kong, China and the US, the EU and some of its neighbors. Readmission agreements are being used to facilitate the return of illegal immigrants back to their countries of origin or transit, and are elements of the dialogue between a cluster of South Caucasus and Western European countries (OECD, 1999; page 74). Highlighting the importance of such agreements, a report by the Council of Europe (1997) noted that “the return of illegal migrants can be made effective only through the full co-operation of the authorities of the country of origin and, as appropriate, of those of the transit countries.” The report goes on to recommend that “readmission clauses, relating to both nationals and third country citizens, be inserted in general co-operation agreements with countries which are sources of irregular migration, such as agreements relating to economic or political cooperation.”<sup>2</sup> The Binational Study on Migration (1997) conducted jointly by the U.S. and Mexico has also emphasized the need for a joint effort by the two countries to resolve the problem of illegal migration. There is already evidence suggesting that Mexico can play an important role in this respect. For example, the government of the Mexican state Baja California Norte has taken the initiative of designating zonas de exclusion (off-limit areas) at especially treacherous border areas and stationed agents to prevent illegal crossings. Although these measures are limited in scope, they have signi fi cantly boosted United States’ efforts at securing part of its

> 2 Chinese officials have also accepted US demands to pursue smugglers more aggressively and to impose severe punishment on illegal immigrants (Djajic, 2001, page 150).

2
affiliation within the Bank (operations; central vice-presidential units related to research, knowledge management and dissemination; or others, primarily support units such as accounting, personnel, and finance)." Several additional factors that are thought to influence salary and promotion are not available to us. For example, while data on pay, grade, and departmental affiliation are routinely updated in computerized files, updated information on education completed, in-Bank and other training, language skills and so on, are self-reported and so contain gaps and are difficult to verify. Further, qualitative information about performance and annual performance merit ratings are strictly confidential and not available to us.

Omitted variables also include pre-entry or time-of-entry information. For example, we are not able to control for nationality and marital status at entry or for the first hiring unit within the Bank. For some individuals these attributes will have remained fixed, for others they will have changed. The data on pre-Bank work experience are also limited. The length and nature of one's previous work experience as well as previous salary are probably good indicators of one's alternative labor market. Although, as mentioned, we include years of previous work experience in our regressions, we do not have data on salary or location for the previous work. Oaxaca and Ransom (1993), who had data on the location of previous work experience, found only a marginally significant salary effect of experience outside the U.S. for Part I men (1.8 percent) and of multi-country experience only for Part II men (2.5 percent). Their finding would suggest that previous experience does not count for much in Bank initial salaries. Hence, it is probably

> 12 only indication of pre-Bank work experience is in the time elapsed between the completion of a staff member's last degree and his or her start date at the World Bank. Because some staff finalize their degree after joining our variable for pre-Bank work experience is sometimes negative. A dummy

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period. They validated the results using actual poverty rates from the HIES. The results were encouraging, with imputation-based poverty estimates not showing statistically significant differences from true poverty rates.

Douidich et al. (2013) used the method in Morocco, and Cuesta and Lara Ibarra (2017) simulated poverty in Tunisia using household budget and labor force surveys (LFSs). Using the latest available HBSs to impute consumption and calculate poverty using LFSs in each case, the authors found that the approach accurately reproduced actual official poverty statistics.

## <u>Data requirements and pitfalls</u>

Survey-to-survey imputation methods rely on several key assumptions, which may not necessarily hold— a fact that should be taken into consideration before applying them (Dang et al. 2014): First, the relationship between consumption and other household characteristics in the household consumption survey used in the base period is assumed to remain stable over time. This assumption is not likely to be accurate in countries experiencing structural changes, such as those that occur during conflict or due to a significant economic shock. Assuming stability of household characteristics over time

> 12 Here we focus on the methods which allow getting missing consumption at the household level, but there is another method to get missing poverty rate at the aggregate level by using forecasting techniques. This method is used by the World Bank to track the corporate goal of reducing extreme poverty. The method and its shortcomings are discussed in the annex.

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# **Introducing the Adequate Housing Index (AHI)**

## **_A New Approach to Estimate the Adequate Housing Deficit within and across Emerging Economies_**

**_Behr, Daniela M.; Chen, Lixue; Goel, Ankita; Haider, Khondoker Tanveer; Singh, Sandeep*; Zaman, Asad_**<sup>**_1_**</sup>

### **Keywords** : **Adequate Housing; Housing Deficit; Household Survey; Emerging Economies; SDG 11**

### **JEL classification** : **R21; O18**

> 1 The authors are part of the Sector Economics and Development Impact Department at the International Finance Corporation (IFC). *Corresponding author: <u>ssingh13@ifc.org. This paper is part of a larger research project on</u> affordable housing at IFC’s Sector Economics and Development Impact Department. The paper has vastly benefitted from thoughtful guidance and encouragement from Issa Faye, Dan Goldblum, Camilo Mondragon Velez, Zekebweliwai Geh, and participants of the IFC Sector Economics research sounding board. We want to thank our peer reviewers for their helpful suggestions and thoughtful comments: Catherine Lynch, Nicholas Johnston, Nora Libertun de Duren, Victor Mints, and Will Seitz. We also received very helpful feedback and comments throughout the process from Friedemann Roy, Henriette Kolb, Kudret Akgun, Lorenzo Ciari, Montserrat Ganuza, Nouhoum Traore, Philip Schellekens, Riadh Naouar, Valentina Saltane, and participants of the IFC Sector Economics BBL event. The methodology and data analysis have benefitted from discussions with Christoph Lakner, David Newhouse, Imam Setiawan, Minh Cong Nguyen, Nobuo Yoshida, and Sailesh Tiwari. Views expressed here are those of the authors, and all errors remain our own.
Using bank-level data, we first investigate the impact that larger exposure to the energy sector had on banks’ balance sheet outcomes and risk dynamics after the shock. We then use the loan-level data to examine separately the lending dynamics of firms in energy-related sectors and all other sectors. Focusing first on borrowers in energy-related sectors, we examine changes in the value and terms of their loans across banks after the shock. Our outcomes of interest include the total amount of credit borrowed by energy firms, as well as the amount borrowed to finance working capital and investment projects. Other outcomes we analyze include interest rates, collateral rates, and loan maturities. To investigate whether banks transmit this sector-specific shock to other sectors, we compare lending to non-energy firms by banks with different degrees of exposure to the energy sector before and after the energy price shock.

Since firms may switch to other financing sources to smooth bank credit shocks, we complement our loan-level data with firms’ yearly balance sheet information. The firm-level data allow us to identify whether shocks that affect the credit conditions of firms also affect their real outcomes. We construct a measure of exposure to the energy price shock at the municipality level. In addition, we investigate whether the states with more exposed banks had a sharper slowdown. We use quarterly gross domestic product (GDP) data for Mexican states and construct a state-level measure of exposure to the energy sector of the banks operating in the state.

We find that banks with greater ex-ante exposure to the energy sector significantly expanded their ex post lending to firms in the sector. This expansion took the form of larger loans for working capital at lower interest rates, suggesting that banks attempted to keep stressed firms afloat and their own capital ratios intact. For instance, an increase of one standard deviation in exante exposure to the energy sector (relative to the bank’s tier 1 capital) is associated with an increase of 1.2 percentage points in ex post exposure to the sector, an 18 percent increase in the size of loans to
;30.31]|[30.17;30.23]|[30.18;30.25]|[30.15;30.15]|[30.15;30.15]|[30.15;30.19]|[30,16;30,21]|
|Change in real<br>earnings, %||0.12|0.02|0.06|0.01|0.00|0.00|0.02|0.00|
|_Note: Kosovo_|_LFS 2018_|[0.06;0.18]<br>_is used. Wage_|[-0.06;0.08]<br>_imputation is_|[0.01;0.09]<br>_described in S_|[-0.05;0.05]<br>_ection 2. Short_|[-0.01;0.00]<br>_- (long-) run_|[-0.01;0.00]<br>_labor demand_|[-0.01;0.05]<br>_elasticity value_|[-0,03;0,04]<br>_is set at -_|

_Note: Kosovo LFS 2018 is used. Wage imputation is described in Section 2. Short- (long-) run labor demand elasticity value is set at - 0.5 (-0.8) for skilled labor and -0.7 (-1) for unskilled labor. Only employees with wages between the old and the proposed minimum wage are assumed to be affected. Aged 15-64. Confidence intervals are in brackets._ 31
This can be combined with data on all parcels owned by a subset of legal entities to test if concentration of land use at field or village level affects prices, as a potential indication of market power.<sup>12</sup>

# **2.3 Evolution of sales and lease market volume and prices**

Table 1 shows that, since January 2021, 1.97 million new leases and close to 390,000 sales of agricultural land were registered. While reporting of transaction prices was legally mandated, weak enforcement implies that there are 307,083 sale and 933,102 lease transactions for which prices are reported, with a median price of US$ 981 per hectare for sales and US$ 89 per hectare per year for leases as a basis for subsequent analysis (panel A). The data also suggest that the number of registered mortgages using agricultural or any other type of land as collateral remained minuscule.<sup>13</sup> Appendix table 1 illustrates the time profile of transactions. After opening of land markets for individuals in July 2021, monthly land sales quickly increased to 27,070 for 73,818 ha in December 2021 before, in February 2022, all land sales were stopped and leasing activity reduced significantly as a result of the war.<sup>14</sup> Median prices for sales and leases decreased by 14% from US$ 1,133 to US$ 989 and by 6% from US$ 90 to US$ 85, respectively, between 2021 and 2022 and remained close to this level thereafter.<sup>15</sup> Compared to individuals or FOPs who accounted for 5.7% and 8.4% of transactions and area,<sup>16</sup> legal entities dominated in the lease market, accounting for 94% of all transactions and 92% of the land transacted in the period (table 1 panel B). Of all the land parcels leased, 81.4%
|Maize(GS)|32%|29%|23%|36%|
|---|---|---|---|---|
|Highland(GS)|23%|27%|33%|29%|
|Roots and lowland(GS)|9%|7%|42%|23%|
|Agropastoral(RS)|35%|28%||28%|
|Pastoral(RS)|40%|36%||27%|

_Source:_ World Bank.

_Note:_ NDVI = Normalized Difference Vegetation Index; SPEI = Standardized Precipitation Evapotranspiration Index; GS = growing season; RS = rainy season.

To assess whether the extreme values of the hazards are within or outside the range of hazards experienced by households within the districts and years used, we assessed the percentage of households in a given livelihood zone that experienced a hazard value more than two standard deviations lower than the mean or more than three standard deviations lower than the mean. The results for soil moisture for maize, highland and roots and lowland livelihood zones and for NDVI for agropastorals and pastorals are shown in table 5.2. Assessing the common support between the range of possible hazards and those used in estimating coefficients 2 and 3 can help address concerns regarding the plausibility of the welfare impact predictions that use the fixed effects estimates. In sum, while moderate deviations in rainfall are well reflected in the data used to estimate 2 and 3 , more extreme deviations are less well reflected in the ββ̂ ββ̂ data. ββ̂ ββ̂

**Table 5.2. Share of households with an extreme hazard outcome for soil moisture**

|**Livelihood zone**|**Hazard z-score < -3**|**Hazard z-score < -2**|**Negative hazard z-score**|
|---|---|---|--
; deferrals of utility payments; and other supports for simplification of procedures for MSMEs. The Georgian government also implemented support policies, with some targeting of the tourism sector (loosely defined as hotels and restaurants) and small firms. The support measures in Georgia included fiscal relief; subsidies, guarantees, or other forms of assistance for loans; deferral of payments; and micro-grant programs for new business projects. Appendixes A.1. and A.2. provide details of government support policies during the

> 4 - The Map is available here: https://dataviz.worldbank.org/views/SME <u>COVID19/Overview?%3Aembed=y&%3AisGuestRedirectFromVizportal=y&%3Adisplay_count=n&%3AshowAp pBanner=false&%3Aorigin=viz_share_link&%3AshowVizHome=n.</u> See also the IMF policy tracker, <u>https://www.imf.org/en/Topics/imf-and-covid19/Policy-Responses-to-COVID-19.</u>

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