rafmacalaba/datause-encoder-data huggingface dataset:
Involving men in this effort has been shown to be crucial. Engaging couples in joint planning around maledominated activities, such as export-crop farming, can lead to increased female engagement in those activities (Donald et al., 2022). Moreover, providing male mentors to women is a promising avenue: having a male role model is significantly associated with women crossing over into male-dominated sectors in pooled data from 97 countries (World Bank 2022).

# **4.2 Increase Women’s Access to Productive Inputs**

## _Financial Inclusion and Business Capital_

Increasing women’s financial inclusion and capital to run their firms and farms is an important policy priority to close gender gaps in Burkina Faso, though it is worth noting that the country is already ahead in terms of financial inclusion relative to its neighboring countries.<sup>14</sup> One way to do this is by encouraging the formation and expansion of Village Savings and Loan Associations, which have been shown to be effective across contexts (Bass et al. 2014). More broadly, providing women with secure savings mechanisms (including through digital/mobile technology) can help women in generating revenue and saving for working capital for future investment (Bastian et al., 2018). A study in Côte d’Ivoire, for example, found that offering a private commitment savings account to female agro-processing workers led to an 11% increase in their earnings, primarily by increasing their attendance at work (Carranza et al, 2022).

Furthermore, making it easier for women to obtain loans can increase women’s liquidity and investment. Blattman et al (2016) showed that providing microloans along with business skills training helped to increase the incomes of ultra-poor women. Since women may have lower creditworthiness using traditional scoring mechanisms (due to lower assets, profitability, and tenure), experimenting with new methods to assess creditworthiness is a promising avenue to increase women’s access to capital. For example, using psychometric credit scoring as a substitute for loan collateral was found to have positive impacts on women’s credit, and survival of their firms during the COVID-19 pandemic and conflict in Ethiopia (Alibhai et al., 2022). Bossuroy et al. (2022) also find that providing group savings promotion in the context of a multi-faceted ‘graduation’ program increased profits in women-owned businesses, along with their psycho-social well-being. Offering cash-flow based loans to procure machineries and other productive inputs, such as hired labor, is a further policy priority worth testing.

> 14 <u>https://www.worldbank.org/en/publication/globalfindex/Report.</u>

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**Figure 6:** Where is hidden debt the largest?

_Panel A:_ In borrower countries with weaker institutions <!-- Start of picture text --> 2.5%<br>2.0%<br>1.5%<br>1.0%<br>0.5%<br>0.0%<br>-0.5%<br>-1.0%<br>-1.5%<br>Lowest Second Third Highest<br>quartile quartile quartile quartile<br>Institutional strength<br>....<br>Panel B: In loans from bilateral and private creditors<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>-5%<br>-10%<br>-15%<br>Private of which: Bilateral Multilateral of which:<br>lending Bonds lending lending World Bank<br>Hidden debt<br>(in % of GDP)<br>Hidden debt<br>(in % of initially reported debt stock)<br><!-- End of picture text --> _Notes:_ This boxplot shows the distribution of hidden debt, as defined in equation (1), across debtor (Panel A) and creditor (Panel B) groups. Panel A splits the observations into quartiles of borrowing country institutional strength, as measured by average Polity V scores between 1970-2020 (Marshall and Gurr, 2020) . Panel B splits observations by creditor groups as defined by the World Bank. GDP data is taken from World Bank (2022) and is not subject to revisions.

17
the percentage of women commuting by bus in Tamil Nadu increased from 40% to 61% (Sundaram, 2022). This increment led the Government to increase the budget of the program from |12 million ($168 mn), allocated in the first year, to |15.2 million ($212.8 mn) in the second year (Durai, 2021; Sundaram, 2022).

# **E Data**

## **E.1 Consumer Pyramids Household Survey**

Our main data source is the Consumer Pyramids Household Survey (CPHS). It is a household-level longitudinal survey conducted by the Centre for Monitoring the Indian Economy (CMIE). Starting with the first wave in January-April 2014, the CMIE runs surveys three rounds yearly (January-April, May-August, and September-December). Each wave covers about 160,000 households from all major Indian states, maintaining a consistently high household response rate of over 80%. A multi-stage stratified survey design is deployed. The broadest level of stratification is a homogeneous region (HR), defined as a set of neighboring districts within a state that is comparable in the following characteristics: climate, urbanization, female literacy rate, and population. In Appendix Table D1, we list the two treated states of Punjab and Tamil Nadu—which implemented free bus ride schemes for women—and the control states, i.e., the states that are adjacent to the treated states and did not distribute free bus tickets to women.<sup>28</sup> Since the CPHS data is representative at the Hr level, our analysis includes only the adjacent HRs in the control states; Non-adjacent HRs in control states are excluded.

The CPHS has four sections: Consumption Pyramids (CP), People of India (PoI), Aspiration India (AsI), and Income Pyramids (InP). This study uses the CP, InP, and PoI sections. The CP is a household-level monthly survey reporting household expenditures on various kinds of goods and services, without a breakdown of individual members of multi-person households. Specifically, it asks households about their monthly expenditure on all types of transport, including expenses on a combined category of “buses, trains, and ferries” (BTF). Our study period for the CP is from November 2020 to September 2021. The InP is a monthly survey that tracks the income of each household member. We use the same study period as in the CP data. The PoI is an individual-level survey conducted every four months. There are three waves a year: January-April, May-August,

> 28The CMIE started to collect household members’ time usage information in the wave of SeptemberDecember 2019, while the government of Delhi started the _Pink Slip_ scheme in November 2019. We do not include Delhi in the analysis since we do not have pre-period information on time usage.

5
Customary Land Conversion and the Formation of the African City Pierre M. Picard<sup>_∗_</sup> Harris Selod<sup>_†_</sup> Keywords: Urbanization, land markets, property rights, market failure JEL codes: O43, R14, P14

> _∗_ CREA, University of Luxembourg, 6, rue Richard Coudenhove-Kalergi L-1359 Luxembourg. Email: pierre.picard@uni.lu.

> _†_ The World Bank, 1818 H Street NW, Washington, DC 20433. Email: hselod@worldbank.org.
the supply of native labor to that industry-region: φn = dd ln ln w Nn<sup>.Itdependsprimarily</sup> on the willingness and ability of native workers to change their industry or region of employment, as well as flexibility to enter and exit the labor force.<sup>13</sup> The effect of immigration on the wage and employment levels of native workers in an industry-region is given by: where sm and sn is the shares of immigrant and native labor, respectively, in the total wage bill. Whether immigration increases or decreases the demand for labor depends on whether the elasticity of labor demand or the elasticity of substitution is greater. When η > σmn, then immigration will increase native wages and employment. The intuition for this result is that the inflow of immigrants reduces the cost of immigrant labor and hence results in two countervailing effects. First, the substitution effect: for a given level of output, firms will substitute immigrant workers for native labor. Second, the scale effect: the decline in the cost of production results in output expansion and hence, for a given relative wage, firms will employ more native workers. The difference in the magnitude of these effects determines whether immigration is a positive or negative shock to the demand for native labor, as seen in equations (3) and (4). The elasticity of native labor

> 13The elasticity of native labor supply across industry-regions can be thought of as deriving from a worker’s discrete choice problem that takes the form of a two-level nested logit. Workers can be thought of as first choosing a region and then an industry, reflecting the fact it is likely easier to move across industries within a region than across regions. The elasticity of labor supply across industry-regions is the weighted sum of the elasticity across industries (within the same region) and across regions. With data on worker flows across industries and regions it could be decomposed into these two components.

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(2’) which relates the number of hours optimally supplied to .

## **Wage equation**

Finally we state a usual Mincer equation to model the relationship between log wages and human capital inputs.

This specification is usually adopted to represent the fact that a worker’s productivity (and hence his wage) depends on his own human capital and the fact that wages are dispersed also among people holding the same occupation.

# **Empirical specification**

So far we have defined the model in very general terms. Nonetheless important hints about which variables to include have already emerged. For instance, the fact that wages are a function of individual characteristics, wages can be substituted out in both the employment and the labor supply equations. Moreover, there is a clear indication that the (only) set of variables to be included in our reduced form equations are those influencing individual wages (productivity) and those influencing the disutility from labor ( and ). We stress here that both observable and unobservable/non measurable variables do enter these sets.

As mentioned earlier, it is reasonable to assume that poor health reduces worker productivity and increases the disutility of labor. Specifically, we are interested in the role of chronic diseases and disabilities and the presence of such conditions is our measure of health conditions. In the dataset we use, Egypt Household Health Utilization and Expenditure Survey 2002 (EHHUES2002), a representative sample of people is asked “During the last 12 months have you been complaining from any persistent health problem for at least 3 months (including disability, disease, injury) or any other chronic disease?”. About 25% of the subsample of people aged 16-64 (working age) answer affirmatively (27% of females and 22% of males). Chronic conditions are also the most important single factor influencing people’s negative health conditions, according to EHHUES2002. For instance the probability of reporting bad health increases by 25% in case of chronic diseases, after controlling for age, gender and governatorate of residence.

Following the literature of labor economics, the set includes human capital dimensions such as gender, education, experience accrued on the labor market (proxied by age), current health conditions ( ) and endowed ability ( ). Hence 6
**TABLE 2—EXAMPLE OF CALCULATIONS FOR BHUTAN, 2005-06**

||Over-spent<br>group|Under-spent<br>group|Accurately-<br>spentgroup|Total|
|---|---|---|---|---|
|Number of budget heads (count)|1|7|4|12|
|Budgetary allocations (Ngultrum millions)|682|5,478|3,494|9,654|
|Actual expenditures (Ngultrum millions)|740|4,815|3,425|8,980|
|Deviations(Ngultrum millions)|58|-663|-69|-674|
|Deviations w.r.t. to own base(%)|8.50|-12.10|-1.97|-6.98|
||**_eo_**|**_eu_**|**_ea_**|**_e_**|
|Deviations w.r.t. to total spending (%)|0.60|-6.87|-0.71|-6.98|
||**_fo_**|**_fu_**|**_fb_**||
|Fraction of budget heads(%)|8.33|58.33|33.33|100.00|
||**_do_**|**_du_**|**_db_**||
|Averagepercent deviationper share of heads(%)|7.21|-11.77|-2.14||

Source: Bhutan Public Financial Management Accountability Assessment, World Bank Report No. 58444-BT.

Even the best performers are not perfect. South Africa between 2005/06 and 2007/08 was the best performer, with the largest share of the budget accurately spent, within the sample. The government managed to keep only 17 of 21 budget heads within 5 percent of approved allocations in 2006/07 when the deviation in net resources was only -0.6 percent. In that year, three heads were under-spent while one was over-spent. In 2005/06, only 14 out of 21 budget heads were within 5 percent of approved allocations when the deviation in net resources was +0.4 percent. Four heads were over-spent and three were under-spent. In 2007/08, only 15 of 21 budget heads were close to approved allocations, four over-spent, and 2 under-spent. One finds simultaneous under- and over-spending even in the best performing governments.

**DISTRIBUTIONS FOR MEASURES OF COMPOSITIONAL DEVIATION**

<!-- Start of picture text --> Figure 6a. Over-spent group, eo Figure 6b. Under-spent group, eu Figure 6c. Accurately-spent group, eb<br>90 90 90<br>80 80 80<br>70 70 70<br>60 60 60<br>50 50 50<br>40 40 40<br>30 30 30<br>20 20 20<br>10 10 10<br>0 0 0<br>-50 -40 -30 -20 -10 0 10 20 30 40 50 -50 -40 -30 -20 -10 0 10 20 30 40 50 -50 -40 -30 -20 -10 0 10 20 30 40 50<br>Frequency Frequency Frequency<br><!-- End of picture text --> Compositional deviations tend to be larger than the deviations in total net resources because of simultaneous over- and under-spending. For example, over-spending some budget heads during a period of an unanticipated resource shortfall necessarily requires that the remaining budget heads be cut beyond what the shortfall would have otherwise required. As shown in Table 3, for the 49 cases of resource shortfalls, the average deviation in net resources was -10.7 percent while the average deviation within under-spent budget heads, _eu_ , was -13.1 percent, and the average deviation in over-spent heads, _eo_ , was 2.5 percent. For the 56 cases of surplus resources, the average deviation in net resources was 14.6 percent while the average deviation within over-spent budget heads was 17.4 percent and the average deviation within under-spent heads was -3.0 percent.

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- 21 -

2.05 Progressive Budgetary Displacement. Incremental urban investments are now urgently needed in LAC countries. However, a sudden escalation in the volume of subnational investment would remain neutral to the objectives cf macroeconomic adjustment only if fiaanced out of marginal local revenues, lest it become an additional source of inflation. Our analysis shows that LAC rapid urbanization had a substantial impact on the structure of expenditures by governments. This effect of progressive budgetary displacement is visible in national accounts (Tables 2.5a & b). It shows in the proportional increase of expenditures by subnational authorities (Table 1.13) in response to growing pressures by their constituencies, and in the corresponding increase of central government outlays, both in terms of transfers (Table 4.12a) and of direct investments (Table 3.10b). Ultimately, it shows in the progressive swelling of the subnational deficit (Table 4.2).

2.06 Revenue Effects of the Recession. In practice, the aspiration to preserve existing standard of service in LAC cities, confronted all levels of government with severe financial problems. This was mainly due to the slow growth of their economies in recent years, and to the steady erosion of the local 1 revenue bases under the combined effects of inflation and declining incomes. 8 At the same time, the volume and predictability of resource transfers to subnational governments was challenged by the contraction of the central government revenues, which are even more directly related to the cyclical fluctuations of economic activity. Nevertheless, intergovernmental flows (generally the largest item in local revenues) did not decline in real terms as drastically as one would expect (Table 4.12b), even when the sources of institutional crtdit were gradually dried up by financial constraints or official restrictions (See Section 4.13 on Borrowing).

2.07 Centralized Economic Policy and Political Democratization. ln a number of LAC countries, the contraction of public revenues at all levels of government came at a time of explosive urban growth and often in coincidence with the claims of greater subnational autonomy engendered by the re-establishment of elective governments. Yet, the national situation called for reductions in transfer payments, tightened credit controls and adjustments in public service rates. Crisis management also required central direction of all instruments of fiscal policy, including the authority to tax or not to tax certain activities,

- 1j To this one could add the diminishing tax compliance by city dwellers in the expectation of tax amrnesties (blanqueos) or simply of the inflationinduced depreciation of arrear payments. For instance, in Argentina, delinquent tax obligations were totally or partially forgiven by new governments on repeated occasions.
4.1)<sup>32</sup> . In fact, several of the interviewed credit institutions had partnered up with other organizations (e.g. universities, Fundes, ANIF, the National Training Service SENA etc.) in order to provide free/low-cost non-lending technical assistance to their SME clients on issues such as financial statements preparation, business plan design, registration procedures, tax policy etc. Although these problems lie beyond the realm of the financial sector per se, these institutions perceive the need to deal with them in order to expand their SME borrower base.

Table 4.1: Main Obstacles Cited by Banks for SME Financing (February 2007)

|**Obstacle**|**Importance***|
|---|---|
|Quality of financial information|1.4|
|Informality of company|1.4|
|Lack of payment history|1.8|
|Low capacity to pay|2.4|
|Weak financial structure of project|2.4|
|Quality of management|2.6|
|Lack of guarantees|2.8|
|Internal bank risk policies|3.4|
|ent<br>Lack of specialization in market segm|3.8|
|<br>Government regulations|4|
|<br>Lowprofitabilityfor this type of loan|4.2|

_Source: Encuesta Pymes Asobancaria. * Importance is ranked from 1 to 5, with 1 being the most important._ Credit institutions perceive the interest rate ceiling as a significant source of legal and regulatory uncertainty. The ceiling, whose violation represents a penal code offense, applies to all loans granted by supervised financial institutions. Its technical method of calculation has been frequently modified in recent years in order to achieve specific policy objectives, but often in a non-transparent manner<sup>33</sup> . The existence and continuous re-interpretation of the ceiling (as well as the manner in which it is modified) also creates legal uncertainty for credit institutions – for example, with respect to whether the current ceiling applies only to newly-originated loans or to the outstanding credit stock as well.

> 32 However, the Asobancaria survey results need to be treated with caution. Other important SME financing obstacles are either not mentioned (e.g. legal and contractual environment, macroeconomic stability) or are not deemed to be very important (e.g. lack of guarantees, government regulations), which is a surprising result in light of credit institutions’ responses to the questionnaire in this study.

> 33 See Asobancaria (2007) for a description. The methodology for estimating the interest rate ceiling is now publicly available from the website of the SF (www.superfinanciera.gov.co).

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Table 2: Shares used in the World Bank’s Commodity Price Index, in percentage

|Commodity|Share|Commodity|Share|
|---|---|---|---|
|_Energy_||_Non-energy Com_|_modities_|
|Coal|4.7|Agriculture|64.9|
|Crude Oil|84.6|Food|40.0|
|Natural Gas|10.8|Others|24.8|
|||Metals and Minerals|31.6|
|||Aluminum|8.4|
|_Precious Me_|_tals_|Copper|12.1|
|Gold|77.8|Iron Ore|6.0|
|Silver|18.9|Others|5.1|
|Platinum|3.3|Fertilizers|3.6|

Source: World Bank, Development Prospects Group. Based on 2002-04 developing countries’ export values. November 24, 2008.

ties” group is further divided into “agriculture”, “metals and minerals”, and “fertilizers”. The sum of “agriculture” and “metals and minerals” adds up to 96.5%. Since China is a major importer of metals and agricultural products, it is worth investigating the importance of including these indices in **d** _it_ , which allows us to model the multiple channels of impact propagation through the international commodity markets.

Table 3 summarizes the covariance analysis of three commodity prices (in log-differences). The price of oil is the most volatile and the price of agricultural products is the least volatile. In terms of the instantaneous correlation coefficients, Corr(∆ _p_<sup>_M_</sup> _t_<sup>_,_∆</sup><sup>_pA_</sup> _t_<sup>) = 0</sup><sup>_._5506 is the high-</sup> est, Corr(∆ _p_<sup>_M_</sup> _t_<sup>_,_∆</sup><sup>_pO_</sup> _t_<sup>)=0</sup><sup>_._4506isthesecondhighest,andCorr(∆</sup><sup>_pA_</sup> _t_<sup>_,_∆</sup><sup>_pO_</sup> _t_<sup>)=0</sup><sup>_._3171isthe</sup> lowest. It is often reported that both metals and agricultural products require a fair amount of energy as an intermediate input. There is higher correlation between metals and oil, but this is not supported by the correlation between agriculture and oil.

In order to further investigate the inter-variable relationship, a plain three-variate VAR model is estimated for ∆ _p_<sup>_O_</sup> _t_<sup>,∆</sup><sup>_pA_</sup> _t_<sup>,∆</sup><sup>_pM_</sup> _t_<sup>,and the causal relation is examined by the Granger</sup> causality test. The optimal lag length of VAR, three, is selected by AIC. Table 4 reports the results. The second column is the equation for “oil”, where the exclusion hypothesis of “metal” is rejected at a 10% significance level. For the “agri” equation, neither “oil” nor “metal” is a significant causal variable. Lastly, for the “metal” price, there is strong Granger 14