rafmacalaba/datause-encoder-data huggingface dataset:
Figure 1: Gold production of 10 largest mines in Ghana, 1991-2010 <!-- Start of picture text --> 30000<br>25000<br>20000<br>15000<br>10000<br>5000<br>0<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Obuasi Tarkwa Damang Iduapriem Bogoso Teberebie Bibiani Ahafo (Yamfo-Semfi) Wassa Chirano<br><!-- End of picture text -->
mapping of tertiary and residential road networks. Here, we make two hypothesises: 1) settlement clusters with denser highways/motorways also have denser tertiary/residential roads and 2) strong differences in percentile rankings between the two network types, speficially lower percentile ranks for tertiary and residential roads, suggest incompleteness in the OSM road network. Our results show that approximately 44.7% of the clusters have percentile ratios greater than one, with a maximum ratio of 21.3. This suggests that the density of tertiary and residential roads for many settlement clusters is relatively lower compared to the network density of higher-level roads. Therefore, we decided to exclude tertiary and residential road types in this study due to incompleteness in the mapping of tertiary and residential roads (3.3% and 92.7% of the clusters contain no records for tertiary and residential roads respectively) as well as imbalance in network density distribution.

In addition, we examine the same percentile rank patterns for clusters that belong to economies at different development stages and find that clusters that belong to developed regions (e.g., G7<sup>6</sup> countries) have relatively higher road network densities than those that belong to emerging and less developed economies (Fig. 3c-i). Such results are aligned with the findings from Barrington-Leigh et al. and raise the issue of data bias in this study. To minimize the error introduced by this bias, we remove clusters with inadequate network coverage in addition to excluding tertiary and residential roads from extracted OSM. For each cluster, we calculate the percentage difference between the area of the convex hull created from the OSM data set and that created from the original settlement cluster boundaries. Clusters with percentage differences above the inflection point or “knee”<sup>74</sup> are removed, resulting in 3,128 clusters (Supplementary Figure 2). Furthermore, 564 clusters (among the 3,128 clusters) have insufficient speed limit assignment and are left out in the subsequent travel simulation. Consequently, a total of 2,564 settlement clusters distributed across 177 countries are included in the subsequent travel simulation analysis.

To summarize, we use the following criteria to narrow down the total number of clusters from 1,222,827 to 2,564: (1) area larger than 100 km<sup>2</sup> (1,218,590 clusters removed), (2) not extremely large clusters (eight clusters removed), (3) sufficient network coverage within convex hull (1,101 clusters removed), and (4) complete speed limit assignment (564 clusters removed). For each of the 2,564 clusters, we create directed graphs in which road intersections are represented as nodes and road segments are represented as links. Key information about road segment geometry, road type, segment length, number of lanes, one-way restrictions, and speed assignment is embedded in the link attributes.

> 6 The G7 (Group of Seven) is an informal grouping of seven of the world’s advanced economies: Canada, France, Germany, Italy, Japan, the United Kingdom, the United States, and the European Union.

8
# **Annex 1: Matching program data to administrative data on formalization**

This Annex describes the protocol to match businesses in the administrative database on formalization provided by the GUFE (around 550 businesses every month) with the program data (3,600 informal businesses prior to the program start).

# **Information available:**

We had the following information in both databases:

- Surname of the business owner. It can be written with different spellings in each database.

- Between 1 and 5 first names. In GUFE data we usually have more than one first name. In the program data we only have one first name in most of the cases.

- The business activity as described by the owner (no codes). The business activity is missing for 30% of businesses in the GUFE data.

- Business addresses. In the GUFE data addresses were given by the business owner whereas in the program data, we are using “official” addresses used by the tax administration (there are 144 neighborhoods in Cotonou). In practice only neighborhoods can be matched. In GUFE data, there are few missing variables and some cases for which the neighborhood does not belong to the official list of neighborhoods.

- Gender of the business owner.

- Phone number of the business owner

# **Definition of a match:**

We consider it to be a match if: (i) the surname, at least one first name, the activity, and the neighborhood match or (ii) if surname and at least one first name match and either the activity, the neighborhood or the phone number also match, and the others are missing (or does not exist for the neighborhood or the phone number).

# **Method of matching:**

We used first the Stata command “reclinck” designed for fuzzy name matching. This command uses record linkage methods to generate matching scores. For this first step, we used tree variables: surname, first name and gender. As a second step, we looked manually (in an Excel 28
2015 to March 2016, whereas data collection for NPHC 2014 was pursued in August/September and the UNHS 2016/17 data collection was implemented between June 2016 and June 2017.

Table 6.2 presents the comparison estimate indicators on individual and household characteristics at the national level. The first three columns show the indicators from the UNPS 2015/16 without using sampling weights (unweighted estimator), using the original UNPS weights (current UNPS estimator), and using the calibrated GWSM base weights (calibrated GWSM base estimator). The column of unweighted estimates is helpful because it indicates the different effects by which various weighting strategies act on the final estimates, thus changing the unweighted estimates.

In the last two columns, the table presents the official statistics and their source. In general terms, estimates are more accurate using the calibrated GWSM base estimator rather than the current UNPS estimator for indicators at the individual level. The share of female population and the share of children below 18 years old weighted using the calibrated base GWSM weights closely approximate the official statistics. This result is expected as the weights are calibrated using the population totals projection based on the 2014 census that we are using as a benchmark.

Regardless of the weight we apply, the UNPS 2015/16 overestimates the official “true” value of the literate population above 10 years. Although the three estimates are consistent with each other, the differences reported in the table are statistically significant because they are based on a sample of about 16,000 individuals. For such a sample size, considering a deft value(Kish, 1966) ranging from 2 to 3 (implying the deff value ranges from 4 to 9), the sampling standard deviation of a proportion varies around values between 0.007 and 0.010.

The unweighted statistic, which has a much higher value (80.7%) than the official statistics (72.2%), suggests that the non-response affects the non-literate population. For such reason, this subpopulation is under-represented in the panel. However, the observed difference could be an issue of comparability in how this information was asked in the questionnaires. The different weighting systems seem unable to deal with this problem. Finally, the calibrated GWSM base weight estimator appears to be more accurate for two key indicators of the survey, namely the working population and the poverty head count at the national level.

At the household level, the current UNPS and the calibrated GWSM base estimators seem more comparable, even though the former produces more accurate statistics, particularly for the variables on share of urban households and share of agricultural households. In a few cases – namely, access to electricity and ownership of the dwelling – the UNPS 2019/20 produces inaccurate estimates of the official statistics, regardless of the weight used.

27
beliefs. The results further indicate that, as expected, higher education and lower household income drive more women into the labor force. The positive relation between reservation wages for women and labor force participation is interesting, indicating possibly that women only enter the work force once their expectations of higher wages are met, or that working women have higher reservation wages. Column 2 shows that the reservation wage calculation is different and lower for non-working women who want to work compared to those who do not.<sup>11</sup>

**Table 8: Determinants of whether women work**

||(1)<br>Workingfemale|(2)<br>Want to work|
|---|---|---|
|Education|0.0404***|0.0225***|
||[0.00455]|[0.00637]|
|Log HH Income (minus female|||
|income)|‐0.144***|‐0.0713**|
||[0.0222]|[0.0350]|
|Reservation wage for female|0.000601***|‐0.000995***|
||[0.000123]|[0.000240]|
|PB index|0.0317**|0.115***|
||[0.0148]|[0.0193]|
|SE index|0.0398***|0.00720|
||[0.0140]|[0.0192]|
|SN index|‐0.00501|‐0.0476**|
||[0.0144]|[0.0188]|
|Observations|949|685|
|Adjusted R‐squared|0.167|0.085|

Note: *** denotes significance at 1%, ** at 5%, and * at 10% level. Standard errors are in parentheses. All columns report results from OLS regressions. The PB, SE, and SN indices are the standardized index of items for the personal belief, social empirical, and social normative components after factor analysis, as specified in Annex 2, Table 2.8.

# **Discussion**

Social norms clearly influence FLFP all over the world, but they have not been systematically studied nor measured. We show that social norms can be measured in ways that exhibit reasonable features with respect to internal consistency, external validity, test-retest reliability, and predictive usefulness.

There are multiple social norms influencing FLFP. Social norms are best measured not through yes/no binary questions, but as expectations regarding a diverse set of roles and expectations, including women’s role in public life, occupational segregation, women’s role in the household, and the male role in the household. Although respondents express support for FLFP in general, support declines when respondents are asked about specific scenarios, such as when a working woman can return from work and the age at which it is appropriate for a mother to leave a child in order to work. For married women, the strongest correlate of FLFP is not the generalized social norm but rather the interpersonal expectations within the household and the husband’s personal beliefs. For unmarried women, social empirical expectations appear important. Personal beliefs, and not only social norms strictly understood, affect the extent to which women

> 11 This is consistent with the results in general, for example as seen in Table 5, personal belief items, but not social expectations, appear to be correlated with willingness to work.

23
Three important, recent papers examine fishing cooperatives empirically. Deacon et al. (2008) and Deacon et al. (2013) develop a model incorporating concerns specific to cooperatives and then empirically test this model. They examine the intraseasonal allocation of fishing effort across space, time, and fishers in a salmon fishery with one cooperative and independent fishers. Our work provides less detail on the location of fishing effort and instead focuses on the allocation of effort across time in the face of species-specific differences in mobility and large-scale environmental oscillations that cycle over several years. Ovando et al. (2013) use a survey of 67 cooperatives from around the world to examine what management tools cooperatives use and how this is shaped by differing economic, political, and ecological contexts. We focus on a particular management instrument—the choice of what price to pay cooperative members—and we complement our empirical analysis with a detailed theoretical model that delivers clear predictions for how this instrument should respond to a variety of circumstances.

The paper proceeds as follows. Section 1 describes the setting of Mexico’s fisheries in more detail. Section 2 describes the data used in the empirical analysis. Section 3 develops the theoretical model and derives three testable implications. Section 4 uses the model to develop empirical tests of these implications and presents empirical results from these tests. Section 5 concludes.

# I MEXICO’S GULF OF CALIFORNIA FISHERIES

Bordering five Mexican states, the Gulf of California is one of the most biologically productive areas of the world’s oceans.<sup>4</sup> While the region’s remarkable biodiversity has considerable conservation value, it also is of substantial social and economic importance. The states surrounding the Gulf contribute 71% of Mexico’s total fisheries volume and 57% of total value (OECD 2006). As in many parts of the world, the Gulf’s fleet is characterized by small-scale subsistence or commercial fishing on small two or three-person boats. Small-scale fisheries are a major source of employment and income, as well as a safety net in times of economic or environmental uncertainty (Pauly 1997; Allison and Ellis 2001; Basurto and Coleman 2010). However, a number of commercially valuable species have declined in recent years due to several factors, including improved technology, population and income growth, and increased export opportunities (Sala et al. 2004; Sáenz-Arroyo et al. 2005; Dong et al. 2004).

Several ecological factors make the Gulf of California an appropriate focus for our study. The species targeted by small-scale fishers have diverse life histories, ranging from those with

> 4. In addition to the Gulf proper, here we also consider the Pacific Coast of B.C.S. as part of the “Gulf region,” in keeping with previous work as in COBI/TNC (2006).

5
This information is taken from Kirilenko and Summers (2003). They report that in 2001 Brazil’s bank debit tax yield was 1.45% of GDP with a statutory tax rate of 0.37%. Figure 2 depicts this simple rootfinding problem. The equation has two roots, being n ≈ 5 the economically meaningful one. Again, sensitivity analysis will be conducted to assess how results are affected by more complex transaction environments.

From the steady state version of the law of motion of the aggregate capital stock, equation (SYS.4), an expression for the rate of depreciation of the stock of physical capital is obtained: δ = 1− (1− ((1− COR) KOR))⋅ ηss , where η<sup>ss</sup> −1 is the steady state growth rate of per capita GDP, COR is the consumption-output ratio and KOR is the capital-output ratio. The rate of growth of per capita GDP is evaluated at 2.22% which corresponds to the average value calculated for the 1972-1993 sample using World Bank data on GDP and population. From the same database and for the same time period, COR is estimated at 73.6%. KOR is fixed at 2.68 which corresponds to the average ratio for the 1972-1990 period using Dhareshwar and Nehru’s (1993) data. Thus, δ is set at 7.84%.

Using time series of the Selic rate (return on government bonds) for 1980-2000, Kanczuk (2002) finds that the real interest rate is close to 8% (1.9% on a quarterly basis). This estimate is taken as proxy for the economy’s real rate of return RRR . The real rate of return and the marginal product of capital are related simply as RRR = r ss − δ . This along with (SYS.6) yields an expression for the capital’s share in output, α = ( RRR + δ) ⋅( KOR ηss ). α is then set equal to 41.55%. τ is set to 5.57% since, by K definition, τ<sup>K</sup> = KRO (RRR ⋅KOR) where KRO is the ratio of capital income tax to output. KRO is calculated as the average ratio of taxes on corporate income, profits and capital gains and nominal GDP for the 1980-1993 period. The sources for tax data are the World Bank’s World Development Indicators database and the IMF’s Government Finance Statistics (GFS) database. Nominal GDP is taken from World Bank data. The level of government on which tax revenue data is based is what the IMF defines as “consolidated central government.” In addition, equation (SYS.2 ́) delivers an expression to compute β , (ηss β) = 1+ (1− τ K ) ⋅ RRR . This implies β = .095 .

On the preference side, an expression for θ in the utility function can be determined from (SYS.1 ́): Values for average tax rates included in the preceding expression are computed from accounting identities, i.e., τ<sup>C</sup> = CRO COR and τ<sup>W</sup> = WRO (1− α) where CRO stands for the average collection of consumption taxes relative to GDP and WRO represents a similar ratio for labor income tax revenue. The definition of consumption taxes corresponds to domestic taxes on goods and services in the GFS database while that of 15
Policy Research Working Paper 10885

# **Abstract**

Sub-Saharan Africa’s low agricultural productivity exacerbates rural poverty. An important investment, the sustainable use of groundwater for irrigation, has the potential to increase agricultural productivity, but the region has been much slower to adopt this irrigation method compared to other regions, despite abundant reserves. This study uses a simulation-driven approach to examine the benefits of sustainably utilizing groundwater for irrigation. By mapping data from 291,798 global agro-ecological zones to 8,099 groundwater grids, the study models the production gains from groundwater irrigation for rain-fed croplands. Simulation results indicate that groundwater access could increase output by 27.97 to 129.42 percent, contingent on crop and model conditions. This research facilitates the assessment of the transformative potential of groundwater irrigation and identifies areas in Sub-Saharan Africa where investments can yield significant returns without depleting the groundwater table.

This paper is a product of the Office of the Chief Economist, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at bs1088@georgetown.edu.

_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
- 21 -

|(B) RURAL Areas|Mean|Standard Deviation|
|---|---|---|
|Expenditure Variables<br>|||
|- Total household expenditure, 1987 (1000 CFA)|980.560|668.146|
|- Per capita expenditure, 1987 (1000 CFA)<br>|176.900<br>|96.689<br>|
|- Change of total hh exp, 1987-88 (1000 CFA)|-180.912|450.444|
|- Change of per capita exp, 1987-88 (1000 CFA)|-35.217|75.934|
|Human Capital|||
|- # of children in hh|3.484|3.233|
|- # of male adults in hh<br>|1.117<br>|0.821<br>|
|-# of female adults in hh|1.569|1.269|
|- # of elderly in hh<br>|0.455<br>|0.760<br>|
|- Age of head of hh|48.698|14.773|
|- Age of head of hh squared<br>|2589.090<br>|1517.330<br>|
|- Diploma (yes= 1)|0.065|0.246|
|- Total years of education of hh members|5.991|8.737|
|Physical Capital|||
|- House ownership (no= 1)|0.138|0.345|
|- Land used (hectares)|6.452|6.376|
|- # of farm equipments|0.211|0.591|
|- Value of farm equipment (1000 CFA)|17.680|64.719|
|<br>- Non-farming business (yes=l)|0.179|0.384|
|- Revenue of nonfarm business (1000 CFA)|178.496|913.500|
|- # of durable goods in hh|1.390|1.590|
|- Value of durable goods (1000 CFA)|70.053|207.133|
|Region|||
|- East Forest|0.328|0.470|
|- West Forest|0.226|0.419|
|- Savanahh|0.446|0.498|
|Socio-Economic Status<br>|||
|- Export crop farmer|0.258|0.438|
|- Food crop farmer|0.569<br>|0.496<br>|
|-Other|0.173|0.379|
|Segmentation Variables<br>- Non-Ivorian|0.123|0.329|
|- Head's gender (female= 1)|0.070|0.256|
|Income composition (%)<br>|||
|- Share of wage income|4.798|19.770|
|- Share of farm income|63.612|28.937|
|- Share of nonfarm income|7.809|21.386|
|- Share of other income|23.782|20.191|
|Change Variables between 1987 and 88|||
|<br>- Change of # of children in hh|0.012|1.295|
|- Change of # of male adults in hh<br>|-0.009<br>|0.576<br>|
|- Change of # of female adults in hh|-0.021|0.791|
|-Change of # of elderly in hh|0015|0347|
|<br>- Change of total years of education|.<br>-0.144|.<br>4.814|
|-Change of house ownership|0.003|0.224|
|<br>- Change of land used (hectares)|<br>0.235|4.672|
|-Change of # of farm equipments|0012|0479|
|<br>- Change of value of farm equipment (1000 CFA)|.<br>7.522|.<br>59.102|
|-Change of # of nonfarm business|-0009|0606|
|<br>- Change of revenue of nonfarm bus (1000 CFA)|.<br>-0.428|.<br>1050.150|
|-Change of # of durable goods<br>|0.067<br>|0.907<br>|
|-Change of value of durable goods (1000 CFA)|17.845|366.313|
|- Change of share of wage income<br>|-0.950<br>|13.012<br>|
|- Change of share of farm income<br>Ch f h f f i|4.021<br>0566|21.216<br>16173|
|- ange o sare o nonarn ncome<br>- Change of share of other income|.<br>-4.223|.<br>18.202|
Figure 2 <!-- Start of picture text --> Inflation and the Real Value of Debt<br>(1989 - 1993)<br><!-- End of picture text --> <!-- Start of picture text --> 100 r<br>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _<br>90 \Hnay-.- _-- -<br>*<br>70 \ \ zech-Repub1ic- -.<br>60-<br>aR 50 ' . | index of real value of 1989 debt = (1 +average lending rate)1price index<br>40<br>30 land--. _. _ _ -<br>20<br>10 - ussia__<br>O _ I - q - -- - -i - - - -- - - - 1~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~- - - - -- - - -<br>1989 1990 1991 1992 1993<br>year<br>-U- Poland --- Hungary C-*--Czech Republic ---- Russia<br><!-- End of picture text -->