rafmacalaba/datause-encoder-data huggingface dataset:
_Table 5: OLS Imputation Model Results for Malawi - Dependent Variable: GPS-Based Plot Area (HA)_

||**Full Sample**|**Plots[d <= 1km]**|**Plots[d <= 500m]**|
|---|---|---|---|
|**_Plot Area_**||||
|<br>Farmer-reported plot area (Ha)|0.583***|0.583***|0.613***|
||(0.006)|(0.007)|(0.008)|
|**_Plot Manager Characteristics_**||||
|Female †|-0.060***|-0.069***|-0.069***|
||(0.013)|(0.015)|(0.016)|
|Age (Years)|0.003***|0.003***|0.002***|
||(0.001)|(0.001)|(0.001)|
|Education (Years)|-0.003**|-0.004**|-0.005**|
||(0.001)|(0.002)|(0.002)|
|Plot manager is respondent †|0.032***|0.042***|0.039***|
||(0.011)|(0.013)|(0.014)|
|Has a chronic disease †|-0.039**|-0.043**|-0.054**|
||(0.017)|(0.020)|(0.022)|
|Religion: Christian †|0.054**|0.062**|0.037|
||(0.025)|(0.029)|(0.032)|
|Religion: Muslim †|-0.018|-0.005|-0.042|
||(0.030)|(0.036)|(0.039)|
|Religion: Traditional †|0.012|0.045|-0.014|
||(0.047)|(0.057)|(0.070)|
|**_Plot Characteristics_**||||
|Soil quality good †|-0.025|-0.021|-0.015|
||(0.016)|(0.019)|(0.021)|
|Use of organic fertilizer †|0.036**|0.048***|0.033*|
||(0.016)|(0.018)|(0.020)|
|Use of inorganic fertilizer †|0.086***|0.085***|0.064***|
||(0.011)|(0.012)|(0.013)|
|Use of hired labor †|0.113***|0.104***|0.109***|
||(0.013)|(0.015)|(0.017)|
|Irrigated †|-0.142**|-0.088|-0.035|
||(0.072)|(0.088)|(0.111)|
|**_Household Characteristics_**||||
|# of HH members - [0,5]|0.006|0.005|0.008|
||(0.006)|(0.007)|(0.007)|
|# of HH members - [6,14]|0.022***|0.022***|0.022***|
||(0.004)|(0.005)|(0.005)|
|# of female HH members - [15,39]|0.011|0.001|0.001|
||(0.008)|(0.009)|(0.010)|
|# of female HH members - [40,59]|0.057***|0.055***|0.068***|
||(0.013)|(0.015)|(0.017)|
|# of male HH members - [15,39]|0.023***|0.022***|0.016*|
||(0.006)|(0.008)|(0.008)|
|# of male HH members - [40,59]|<br>0.039***|<br>0.040**|<br>0.044**|
||(0.014)|(0.016)|(0.017)|
|# of HH members – 60 & above|0.030**|0.025|0.041**|
||(0.015)|(0.017)|(0.018)|
|Wealth index|0.010***|0.008**|0.006|
||(0.003)|(0.003)|(0.004)|

16
# **2.3 Design of Supply-Side Experiment**

In November 2023, alongside our demand-side experiment, we conducted a supply-side experiment at the same four restaurants to explore how changes in the price of Moambe Chicken affect wild meat consumption. Moambe Chicken, a dish specific to Congolese cuisine, is popular in the region and may fill a similar cultural role as wild meat. Our field team, primarily composed of Kinshasa residents, advised us that Moambe Chicken was the most likely substitute dish for wild meat at restaurants in Kinshasa. On randomly selected days, the price of Moambe Chicken was reduced by $2 (5,000 Congolese Francs). On these days, we compensated restaurants $2 for every plate of Moambe Chicken they sold (to all customers, not only those participating in our demand-side experiment). The goal of this experiment was to determine whether making a non-wild meat option more affordable would reduce customers’ decisions to order wild meat. This relationship between wild meat consumption and the price of alternatives is the rationale for supply-side alternative protein programs, such as promoting poultry production, where the aim is to reduce wild meat consumption by increasing the availability and affordability of substitutes (Foerster et al., 2012; Moro et al., 2015).

The restaurants provided daily sales data, which included all customers, not only those participating in our demand-side experiment. These data encompassed the number of plates sold in three categories: wild meat, Moambe Chicken, and all other dishes, along with the corresponding revenue for each category. For the purpose of our analysis, “wild meat dishes” referred to all types of wild meat combined into a single category. For instance, if a restaurant sold 2 dishes of antelope and 2 dishes of monkey on a day, it was recorded as 4 dishes of wild meat sold that day.

During the supply-side experiment, we gathered data across 68 restaurant-day observations, comprising 17 days for each of the four participating restaurants. Among these, 11 restaurantdays were randomly chosen to lower the price of Moambe Chicken, creating the treatment 15
# **Figure 3: Cumulative rate of program participation**

Figure 3 also shows the participation rates of regular microfinance in areas with PRIME (MFI1 curve) and without PRIME (MFI2). Participation in regular microfinance gradually increases over time in both PRIME and non-PRIME areas, but participation in regular microfinance is much higher in PRIME areas compared to non-PRIME areas. PRIME was introduced in areas that already had a higher rate of regular-microfinance participation among the ultra-poor. This is not surprising, given that PKSF’s POs, who introduced PRIME, were providers of regular microfinance before PRIME was introduced by PKSF. As PKSF provides grants and loans on better, more flexible terms for PRIME than for regular microfinance, the POs understandably introduced PRIME initially in areas of their current operation. Higher participation rates of the ultra-poor with PRIME may also reflect greater demand for PRIME services by the ultra-poor. The PRIME program is perhaps better designed than regular microfinance to reach the ultra-poor.

<!-- Start of picture text --> .6<br>.5<br>MFI1<br>.4<br>.3 PRIME<br>MFI2<br>.2<br>.1<br>0 0-5 5-10 10-15 15-20 20-25 25-50 50+<br>Landholding (decimals)<br>MFI1: MFI in PRIME area MFI2: MFI in non-PRIME area<br><!-- End of picture text --> 23
- who had the lowest household roster number (this chose the household head as the owner if he or she was among the owners), if no other business was reported for at least one of the owners over the whole observation period; or

- who had already been identified as the owner of a different business, observed during one or more different waves over the whole observation period;

and in the remaining cases we chose the owner who was reported as the owner in most of the waves in which we observed the firm, or, if this was the case for both owners, who had the lowest household roster number.

# **_Specialized firm impact evaluation surveys:_**

We use data from the control group only in a number of panel data surveys collected to measure the impacts of firm interventions in randomized experiments.

# _Cotonou Informal Enterprise Survey (BJINFORMALITY)_

The sample surveyed for the Cotonou Informal Enterprise Survey (BJINFORMALITY) consists of 3,596 informal businesses and is representative of informal businesses operating in Cotonou, Benin. The survey was designed to evaluate the effect of a simple free business registration process on formalization, and test the effectiveness of supplementary efforts to enhance the presumed benefits of formalization by facilitating its links to government training programs, support to open bank accounts, and tax mediation services. Details on sampling and the interventions tested can be found in Benhassine et al., (2016). Survey participants were randomly allocated into three treatment groups and one control group, with the control group consisting of 1,197 informal firms. The baseline survey was conducted between March and April 2014. Two follow-up surveys followed, the first was conducted between April and June 2015, and the second between May and June 2016. Attrition rates at first and second follow-up were 11.8 percent and 15.9 respectively and were not correlated with treatment status.

We coded _survival_ to one if:

- the owner reported to be still operating in the same sector as in the previous wave; or

- the business was said by a person knowledgeable on this matter to be still operating, if the owner refused to be re-interviewed or could not be interviewed when survey attempts were made.

We coded _survival_ to zero if:

- the owner reported not to be operating anymore in the same sector as in the previous wave because of a change in activity; or

- the survey was not completed because the business was found to have been closed definitely; or

- the business was said by a person knowledgeable on this matter not to be operating anymore, if the owner refused to be re-interviewed or could not be interviewed when survey attempts were made.

We coded _newfirmstart_ to one if the owner reported not to be operating anymore in the same sector as in the previous wave because of a change in activity. For the endline wave, we additionally coded new firm start to one if the enterprise had closed definitely and the owner was now operating a different enterprises.

_Newfirmstart_ was coded to zero if:

- an enterprise survived; or

46
"A3OR COSTS, in which employer contributions to legally required eployees' benefit plans and other prevailing taxes existing on payroll or employment are added to labor earnings. Among those, the main statutory payments are contributions to certain funds, wiLch &,a generally **expressed as a** proportion of total labor earnings.2 Second, we use the concept of labor coste net of labor earnings as a synonym of 0ON-WAGE COSTS Or LARol (MfC). It is important to nate that, because of lack of adequate information, **we** do not account for other costs associated vith employment - like training and hiring costs **as** well **as** outlays associated vith the provision of certain plant facilities for the use of the workers. Consideration of these items would form the concept of CO MSIV LABOR COSTS.

In order to construct the serLes of total labor costs presented in this paper, we used information on **wages** and non-wage costs of labor provided by national sourcea. However, for the latter, we complemented these data with information from international agencies **(e.g.** ILO or BLS)<sup>3</sup> concerning social security contributions or other regular payments made by employers in LDC6.<sup>4</sup> The informtion arrived at is basically comparable among LDCs, given that we used the sae definitions with regard to WC and similar INC items. A basic difference, however, appears in connection with the industrial countries, in whose case ve reproduced the informtion prepared by the Bureau of Labor Statistics of the U.- Department of Labor (BLS), in which the Implicit cost of days of vacations and the value of vacations bonuses **are** not Included In the calculation of NUC.

**The** labor cost levels presented b,low **are** In dollars, and they correspond to WC and NHC expressed in hourly terms.5 Data on hourly labor costs In each country are also reported in domestLc currency units. In order
involved from the very beginning in selecting sites, building structures, and maintaining them.'<sup>9</sup> In many countries of the world, peoples' participation has transformed the yields of water projects. In Sri Lanka, the Integrated Management of Major Irrigation Settlements (INMAS) scheme has worked fairly well. The program was based on participatory management, using project managers, farmers organizations, institutional organizers, and a system of committees to make decisions on scheduling of cultivation, maintenance, etc.<sup>20</sup> Peoples' participation has to be institutionalized for effective benefit. In many countries, beneficiaries serve on agency boards, outside government experts serve on technical committees, and public figures serve on policy and oversight commissions.<sup>2</sup>

# **m1. The Strategy for Bangladesh**

The principal elements of a water sector management system, presented in the previous sections, could be used to propose a strategic approach for Bangladesh to improve its water management. Some of the elements (shown in Figure 1) are already present in the system, and for these specific improvements to increase efficiency will be examined in the following sections. Others, which are currently missing, will have to developed.

## **_Formulating the_** _National Water Policy_

This is the first step towards a comprehensive water management program. There have been many ad-hoc directives from the government in the past, but these are disjointed and often conflicting. The initial task will be to review all the existing polices in conjunction with:

- i. Development objectives and strategies for the agricultural, industrial, energy and power, fisheries, livestock, forestry, navigation, and public health sub-sectors.

- ii. Flood and drought management objectives and strategies.

- iii. Environmental protection objectives and strategies.

- iv. Poverty alleviation and women-in-development objectives and strategies.

- v. Decentralization objectives and strategies.

- vi. Private use of water

The review should reveal any inconsistency between existing water policies and the needs of the different sectors and targeted development areas. It should also identify need for new policies to integrate and harmonize the objectives of different development agencies and the private sector.

> **'9** See Water Resource Management, World Bank, op cit.

> **20** See Comprehensive Water Resources Management in Sri Lanka, Asian Development Bank, January 1994.

> 21 See Water Resource Management in Asia, World Bank, op cit.

14
reduction of consumption of food and selling productive assets or land. We also analyze whether the scheme caused changes in the use of less costly coping mechanisms. Specifically, we estimate effects on the consumption of non-food goods, utilization of informal loans, receipt of remittances, and supply of labor by non-sick adult household members.

Our analysis exploits the introduction of the national health insurance scheme (NHIS) in Ghana, which was the first in Sub-Saharan Africa and served as a precedent for many countries of the continent (Alhassan et al., 2016). The main identification problem when analyzing the effects of a national policy is finding a suitable comparison group. In this paper, we exploit the fact that the staggered implementation of the NHIS overlapped with the rollout of the 5fth round of the Ghanaian Living Standards Survey. We are able to observe, within the same district, subdistricts interviewed right before the NHIS introduction, and subdistricts interviewed right thereafter. This allows us to use a regression discontinuity design with time and district fixed effects, where the running variable is months to the NHIS implementation at the district level. Because the timing of interviews of households was external to the timing of the NHIS adoption, we argue that, within the same district, whether a household was observed before or after NHIS implementation is as good as random. We indeed find no systematic discontinuities in the characteristics of households that were interviewed around the time of the NHIS introduction.

Our first finding is that the NHIS resulted in a reduction of out-of-pocket (OOP) payments for medical care of about 20 percent. To gauge the extent to which this effect is driven by NHIS cushioning households from the financial consequences of more severe health shocks, we split the sample by the length of a sickness episode. We show that this measure was not affected by the introduction of the NHIS in the period studied. Estimation results reveal that the drop in OOP payments is larger for households experiencing higher sickness intensity (35 percent).

Our second finding is that households experiencing higher sickness intensity increased food consumption by roughly 1 percent as a result of the insurance. This effect only accounts for a tiny fraction of the estimated OOP savings. Thus, for the most part, food consumption was already protected against expenditure shocks in the absence of health insurance. According to the overly narrow traditional perspective, this implies that the scheme had limited social value. Yet, the third and central result of this study is that the incidence of child labor decreased by 8 percentage points as a consequence of insurance coverage among households with higher sickness intensity. Also, class attendance of children increased by 2.5 percent for the same group of households. Considering that disinvestment in the human capital of children is associated with detrimental long term effects according to previous literature (e.g., Beegle et al., 2009), this rather suggests that the scheme had a large social 2
# 3.1 Cglombja's Leather Goods Industry and its Exporters

As recently as thirty years ago, Colombia's production of leather goods for local consumption was carried out by artisans using simple sewing machines and tables, most often located in a corner of the house. Luggage and harnesses for dmf1 animals were the main products. The industry remains dominated by small and medium plants in its export activity as well as in production. Of an estimated 4807 establishments in 1991, only four had 200 or more workers. Total employment, reached 9,000-1 1,000 by early 19933 about two-thirds employed in plants of less than 50 workers. The main items of production and export are women's handbags, luggage, and "maroquineria", which together represent about 76 percent of sales (Salmon, 1990, 119). During 1974-8B, output growth averaged just 1.9 percent per year and registered employment actually fell subsequently the industry received. Exports rose from under US $14 million in 1984 to $106 million in 1991.

The sectors increasingly outward orientation has brought the export/output ratio up to about 50 percent (compared to about 10 percent for manufacturing as a whole), making this the most export-oriented thre-digit sector of Colombian manutfacturing. The share of output exported directly is higher in the larger plants than in SMEs, although this relationship might be reversed when indirect exports are taken into account Some interviewees noted that it was impossible to compete in the domestic market with an "army" of very small firms, which have the advantages of lower labour costs and almost no fixed costs.

Colombia has a long history as an important producer of cattle, and has exported both hides (in an earlier era) and leather in significant quantities. Exports of leather goods, primarily luggage, began in the 3 Official DANE statistics (which in theory cover plants with 10 pemianent workers or more but in practice miss some of the smaller plants in the 10-25 worker range) show a continuous employment decline from 1981-85, followed by a rapid increase, especially marked in 1990 (the last year for which data are available) at over 30 percent. Total employment in the sector, including the many people engaged in "microenterprise," has probably followed the same general pattem. If the figure of 8,000 is a good estimate for 1988, the share of employment in plants with less than 50 workers was about two-thirds at that time, although it may have fallen a little since then as plants expanded.

Employment in the Leather Goods (Excluding Shoes) Industry, By Size of Plant, 1981-90

|Year|Small|Medium|<br>Lar|ge Total|
|---|---|---|---|---|
|1981|926|1,847|473|3,246|
|1985|693|1,508|256|2,457|
|1988|898|2,069|691|3.674|
|1989|987|2,396|256|3,639|
|1990|895|2,525|1,327|4,807|

Source: DANE, Anuarios de Industria Manufacturera.

S
effect on women is around 50 percentage points.<sup>35</sup> Second, we explore heterogeneity by respondent’s availability of digital resources. Evidence on the gap in ownership of mobile phones by gender or income is limited, and this heterogeneity analysis addresses efforts to link phone ownership among women and poor people to obtain information from them (LeFevre et al., 2020). To proxy for respondent’s availability of digital resources, we use indicators of whether the respondent shares a phone with someone else and if she has Tigo Money, and we test for heterogeneity based on these two variables. As we show in columns (2) and (3) in Table 4, there are no differences relative to those with different availability of digital resources—that is, relative to those with phone exclusivity or without Tigo Money. Yet, the sign and the magnitude of the interaction between having Tigo Money and being assigned to the phone-based survey are consistent with the idea that respondents who are more prone to use digital resources are less responsive to a phone-based survey than to a survey delivered via WhatsApp.

We also examine treatment effect heterogeneity by age. As we emphasized in the introduction, phone usage and survey completion can differ substantially by age (Gigliotti and Dietsch, 2014). Thus we can expect that the effect of receiving a phone call on answering the survey increases with age. We test that hypothesis by estimating specification (2)—for example, by interacting our treatment indicator with an “old group” indicator for whether the respondent is older than a certain age _i_ , where _i ∈{_ 22 _,_ 23 _, ...,_ 43 _}_ . Figure 2 summarizes the age heterogeneity by modifying the age threshold for the “old group” indicator we include in each separate regression. We can see that coefficients are increasing with the age threshold that defines the old-group indicator variable, suggesting that older respondents are more responsive to the treatment. For example, although we observe no difference when comparing respondents older and younger than 25 years old, respondents who are older than 40 years old are 41 percentage points more likely to answer the survey.

> 35Although existing evidence has shown differences in the quality of data that can be collected from women using different survey modes, further research is required to understand differences in the quality of data or response rate by gender. For example, Lamanna et al. (2019) find that the use of mobile phones to collect nutrition data from rural women in Africa may result in differences of between 0 and 25 percent in nutrition estimates, relative to collecting this information in face-to-face interviews.

18
18 value of 0.387. For Dar es Salaam, rather than declining to 0.176, the simulation shows poverty rising to more than twice that level, 0.358.

It is unclear why using sectoral population growth rates produces such implausible results. We suspect this is related to population growth assumptions underlying the national accounts data. However, without a detailed understanding of how the national accounts data were constructed, we are unable to say conclusively. For purposes of this paper, we focus on the sectoral per capita growth rates calculated with the _national_ population growth rate, on the basis that they provide a much better match to the 2001 survey data.

**Figure 7**

<!-- Start of picture text --> Simulated changes in poverty<br>using alternative urban and rural growth rates<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>Year<br>Mainland Tanzania Dar es Salaam<br>Other urban areas Rural areas<br>.45<br>.4<br>.35<br>.3<br>.25<br>Fraction below poverty line<br>.2<br><!-- End of picture text -->

# **5.5 Simulation with Regional Growth Rates**

Another alternative is to apply growth data at the regional level, using annual GDP data by region. This data, in current prices, was converted to real values using the GDP deflator in Table 2, and the annual levels were used to calculate regional GDP growth rates for each year. Separately, regional population growth rates were calculated using the regional totals from the 1988 and 2002 national censuses. These regional GDP and population growth rate estimates are shown in Appendix Table A6. Regional GDP per capita growth rates were then calculated year-by-year by subtracting the population growth rates from the GDP growth rates.<sup>8</sup> Population changes were also taken into account to adjust the weights used in the calculation of poverty rates.

Results from this simulation are shown in Appendix Table A7 and in Figure 8 below. The resulting simulation closely tracks the results using only rural/urban growth rates, with the exception

> 8 While the regional GDP changes are different values year-by-year, the population growth rate by region is assumed constant over time.