rafmacalaba/datause-encoder-data huggingface dataset:
- **Giles, John, and Ren Mu.** 2018. “Village Political Economy, Land Tenure Insecurity, and the Rural to Urban Migration Decision: Evidence from China.” _American Journal of Agricultural Economics_ , 100(2): 521–544.

- **Goldsmith-Pinkham, Paul, Isaac Sorkin, and Henry Swift.** 2020. “Bartik instruments: What, When, Why and How?” _American Economic Review_ , 110: 2586–2624.

- **Gore, Madhav Sadashiv.** 1970. _Immigrants and Neighbourhoods: Two aspects of Life in a Metropolitican City._ Tata Institute of Social Sciences.

- **Government of India.** 2017. _Economic Survey 2016-17._ Government of India, Ministry of Finance, Department of Economic Affairs, Economic Division.

- **Hallagan, William.** 1978. “Self-Selection by Contractual Choice and the Theory of Sharecropping.” _Bell Journal of Economics_ , 9(2): 344–354.

- **Herrendorf, Berthold, Richard Rogerson, and Akos**<sup>** ́**</sup> **Valentinyi.** 2014. “Chapter 6 - Growth and Structural Transformation.” In _Handbook of Economic Growth_ . Vol. 2 of _Handbook of Economic Growth_ , , ed. Philippe Aghion and Steven N. Durlauf, 855–941. Elsevier.

- **Hnatkovska, Viktoria, and Amartya Lahiri.** 2015. “Rural and Urban Migrants in India: 1983–2008.” _World Bank Economic Review_ , 29(suppl ~~1~~ ): S257–S270.

- **Hornbeck, Richard, and Suresh Naidu.** 2014. “When the Levee Breaks: Black Migration and Economic Development in the American South.” _American Economic Review_ , 104(3): 963–90.

- **Imbert, Clement, and John Papp.** 2015. “Labor Market Effects of Social Programs: Evidence from India’s employment Guarantee.” _American Economic Journal: Applied Economics_ , 7(2): 233–263.

- **Jacoby, Hanan G., and Ghazala Mansuri.** 2009. “Incentives, Supervision, and Sharecropper Productivity.” _Journal of Development Economics_ , 88(2): 232–241.

- **Jayachandran, Seema.** 2006. “Selling Labor Low: Wage Responses to Productivity Shocks in Developing Countries.” _Journal of Political Economy_ , 114(3): 538–575.

40
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**Table 9. Financing from External and Informal Sources, OLS and FE estimates**

|Dependent Variable|**EXT**|**EXT**|**INF**|**INF**|
|---|---|---|---|---|
|Estimation|**OLS**|**FE**|**OLS**|**FE**|
||**(1)**|**(2)**|**(3)**|**(4)**|
|Formality|0.066**|0.033**|-0.048***|-0.057*|
||(2.508)|(2.224)|(-1.708)|(-5.163)|
|Fowned|-0.101*|-0.087*|-0.048*|-0.050*|
||(-3.472)|(-6.455)|(-2.895)|(-4.992)|
|Age|0.002*|0.000|-0.001*|-0.001*|
||(2.795)|(0.780)|(-3.397)|(-2.765)|
|Exporter|0.086*|0.053*|-0.007|-0.014***|
||(3.555)|(4.648)|(-0.460)|(-1.729)|
|Manager’s education|0.105*|0.052*|0.028***|0.023*|
||(4.268)|(5.127)|(1.887)|(3.019)|
|Size dummies: Small|0.120*|0.092*|-0.021|-0.018***|
||(4.687)|(6.840)|(-1.172)|(-1.773)|
|Medium|0.224*|0.180*|-0.037|-0.058*|
||(7.643)|(10.324)|(-1.637)|(-4.484)|
|Large|0.198*|0.183*|-0.038|-0.081*|
||(5.164)|(10.159)|(-1.270)|(-6.102)|
|Very Large|0.208*|0.219*|-0.031|-0.101*|
||(5.723)|(10.964)|(-0.589)|(-6.880)|
|Location dummies: City pop> 1mil|0.071***|0.018|0.024|-0.009|
||(1.784)|(1.216)|(0.557)|(-0.768)|
|250,000<Pop<1,000,000|-0.014|0.023|-0.019|-0.026**|
||(-0.489)|(1.594)|(-0.750)|(-2.483)|
|50,000<Pop<250,000|0.062|0.042*|-0.025|-0.032*|
||(1.581)|(2.995)|(-1.188)|(-3.051)|
|Pop<250,000|0.111*|-0.016|-0.030|0.004|
||(2.959)|(-0.880)|(-1.179)|(0.295)|
|Industry dummies|Yes|Yes|Yes|Yes|
|Cons|0.120**|0.258*|0.218*|0.250*|
||(2.290)|(11.125)|(7.351)|(14.659)|
|Number of observations|11889|11889|11889|11889|
|Number of countries|49|49|49|49|
|Adjusted R2|0.116|0.0648|0.0245|0.0293|

Notes: t-statistics in parenthesis. Significance: 01 - ***; .05 - **; .1 - *; Size dummies: reference category: micro firms; location dummies: reference category: capital city.

30
Table 3: Summary of costs associated with dietary risk factors for nutrition and health outcomes in Search 1 studies

|Dietary risk factor<br>(Author,Year)|Geographic<br>context, year|Nutrition/health<br>outcomes|Mortality cost|DALYs cost|Health care and other<br>economic costs|
|---|---|---|---|---|---|
|*Dietary risks<br>(Zhang et al. 2023)|Global, 1990-<br>2019|CVD|2019: 41% of CVD mortality<br>attributable to dietary risk<br>factors<br>6.86 million deaths<br>(ASDR 87.38)<br>High sodium 21.51<br>Low whole grains 20.48<br>Low legumes 14.30|2019: 39% of CVD DALYs<br>attributable to dietary risk<br>factors<br>153.19 million DALYs<br>(ASR 1870.06 per 100,000)<br>High sodium 490.68<br>Low whole grains 433.82<br>Low legumes 297.51|n/a|
|*Dietary risks – 13<br>relevant toCVD<br>(Dong et al. 2022)|Global, 1990-<br>2019|CVD|6.9 million (37%) CVD deaths<br>attributed to dietary risks|153.2 million (39%) CVD DALYs<br>attributed to dietary risks<br>(ASR 1870.1 per 100,000)<br>Low whole grains 20.1%<br>High sodium 18.1%<br>Low fruits 14.9%<br>Low nuts and seeds 12.2%<br>Low vegetables 9.8%<br>Low seafood OmFA 9.3%|n/a|
|*Dietary risks – 13<br>related to CVD<br>(Qiao et al. 2022)|Global, 1990-<br>2019|_NCDs attributable to_<br>_dietary risks (CVD,_<br>_cancers and_T8DM_)_|2019:7.9 million deaths<br>(ASDR 101.0)|2019:187.7 million DALYs<br>Top fve dietary risks<br>High sodium<br>Low whole grains<br>Low legumes<br>Low fruits<br>High red meat|n/a|
|*Dietary risks - 15<br>(Davila-Cervantes<br>2020)|Mexico, 1990-<br>2017|CVD|n/a|Risk factors contributing to CVD<br>age-standardized DALY rate per<br>100,000:<br>High systolic BP 1423.8<br>Dietary risks 1397.9<br>High BMI 883.4|n/a|
|*Dietary risks for<br>diabetes - 15<br>(Duncan et al. 2017)|Brazil, 1990-<br>2015<br>Whole<br>population|Diabetesand<br>hyperglycemia|62,466 diabetes deaths|Total 2.11 million<br>(ASR 1102.8 per 100,000)<br>High BMI610.7 (60.1%)<br>Low whole grains 217.3 (21.4%)<br>High SSB 11.9 (1.2%)<br>Low nuts and seeds 138.1<br>(13.6%)<br>High red meat 42.0(4.1%)|n/a|

23
3 income for each individual under several scenarios.<sup>3</sup> One scenario would be the "counterfactual", the policies which would have been followed in the absence of an adjustment program, and this would be used to compare other scenarios of various adjustment policy packages. The time profile would provide a measure of the short-run costs and long-run benefits of stabilization and structural adjustment, and the discount factor would reflect the individual's preferences between consumption today and tomorrow. These present values could then be weighted according to a social welfare furction to determine the optimal scenario.<sup>4</sup> rhis, of course, is an ideal methodology.

The problem is essentially how to link aggregate macrovariables to the microlevel d-stribution of income. There have been a variety of approaches, which reflects the large scope and complexity of the question. Varying data availability in countries have also led to the development of different approaches. Economic theory provides some guidance but it does not provide unambiguous answers, and the distributional results of particular polic will ultimately depend on country-specific economic structures and parameters.

There are at least three main difficulties in trying to isolate the effects of adjustment policies on income distribution. First, there is the problem of identifying the counterfactual: it is inherently dif_icult to determine what would have been the distributional outcome in the absence of adjustment policies.

3 More realistically, the present value of real returns to different socioeconomic groups would be estimated, along with some measure of how the variance of the intragroup distribution changes, uader different scenarios. The price deflators should reflect the consumption basket of each socioeconomic group.

4For analytic ease, the information contained in the income vector could be collapsed so that the social welfare function is a function of mean per capita income, an inequality measure, and an absolute poverty measure.
where _ξit_ are district-time fixed effects, _Ejt_ denotes the equity shock received by bank _j_ as a result of the bailout and _Bijt_ denotes program exposure (the share of total credit written off under the program) at all branches of bank _j_ in district _i_ .

As we show in more detail below, the estimates from the two alternative identification strategies are quantitatively similar, although standard errors are expectedly narrower when we use bank-district level data.

# **4.1 Impact on Credit Allocation**

In this subsection, we use the methodology described above to estimate the effect of the bailout on credit market outcomes. We begin by estimating the impact of the bailout on credit allocation and present evidence to corroborate the hypothesis that the patterns we observe are driven by changes in the supply of credit that occur in response to the bailout.

Table IV reports the results, using the specification in equation 4.1. The dependent variable in columns [1] to [4] is the log number of loans, while in columns [5] to [8] the dependent variable is the log amount of credit. For each dependent variable, the first two columns report OLS estimates while the second set of columns present two-stage least squares (2SLS) results using the instrumental variable strategy described in the previous section. Within each set of estimates, the first column controls for district fixed effects as well as time varying controls. The second column presents results from a more flexible specification that allows each district to follow its own linear time trend in the pre-program period.

We find that that bank lending becomes more conservative as a result of the bailout. While overall lending increases after the program, the results indicate that banks reallocate credit away from districts with greater program exposure. The magnitude of credit reallocation in response to the bailout is economically large. Estimates from our preferred specification in Table IV, columns [4] and [8] indicate that a one standard deviation increase 18
# **5. Results**

# **5.1. Estimation specification**

Our estimation is at the business level, and involves the following specification for business _i_ : Where ��,��� is the outcome variable (formalization), �1� is an indicator for being assigned to treatment group 1, �2� an indicator for being assigned to treatment group 2 and �3� an indicator for being assigned to treatment group 3. �� is a vector of strata dummy variables (one dummy variable for each triplet of businesses) (Bruhn and McKenzie, 2009) and ��,� is the error term. ��, �� and �� provide the intent-to-treat effect of being assigned to group 1, 2 and 3, respectively. This is the effect of being a business assigned to treatment 1, 2 or 3 relative to being a business in the control group.

# **5.2. Impact on formalization**

As detailed in section 3.4, our main measure of formalization correspond to the registration of the business with the chamber of commerce at GUFE (i.e. the registration was found in GUFE data). We think that this definition of formalization is preferable over others that use midline survey data because administrative data included information on all study population, whereas survey data only have information on those who were surveyed. Moreover, survey data are subject to declaration bias. However, the correlation between survey data and administrative data was high (0.64), and we show similar results using the survey data as well.

Table 6 presents the results on formalization one year after the program started. The Impact of the program on the formalization rate was 9.1 percentage points in group 1, 13 percentage points in group 2, and 15.8 percentage points in group 3. All these effects are statistically significant at one percent level. The effects in groups 2 and 3 are significantly higher than in group 1, and the effect in group 3 is significantly higher than in group 2: both sets of additional incentives included in package B (counselling, trainings and bank services) and in package C (tax mediation) seemed to be valued by informal businesses as incentives to register.

The formalization rate in the control group was only 0.9 percent. Therefore, in the absence of the program, only few businesses would have formalized. Alternative measures of formalization that combine survey and administrative data show consistent results on 20
- 32 -

already heavily dollarized economies, that they come too late in an already crowded marketplace where the dollar has taken a dominant position, the counterargument is that the local currency should have a natural constituency in countries where monetary independence makes sense. Because of its unique shock-buffering capacity, the local currency, if well managed, should gain a high and stable market share. However, policymakers should resist the tendency to respond to high dollarization with a fear of floating that limits the benefits of their currency and boosts instead dollarization.

The importance of institutional variables in explaining dollarization suggests that the policy reform agenda should also include measures to strengthen the institutional environment and send a clear signal that good policies are here to stay. In particular, the government's capacity and willingness to minimize the other risks of financial contracting in local currency should be of importance. Aspects that could help create a favorable environment for contracting include enforcement of adequate legal rights for creditors, quality of accounting, political stability, relatively undistorted goods markets and the overall quality of government, as has been shown by empirical studies of the determinants of financial depth more generally (including those cited above). The relevance of institutional variables could also be interpreted as sounding a note of caution, however, if viewed as a manifestation of deeply rooted hindrances to achieving monetary credibility. The degree of policy persistence and consistency needed to remove these roadblocks could be considerable in many cases.
|_Improved Seed_|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Log of distance to markets index, winsorized at 5%|0.00654*|0.00636*|0.00909*|0.00842|
||(0.00284)|(0.00264)|(0.00263)|(0.00368)|
|Log of WRSI for maize, lagged one period|0.316**|0.326**|0.203|0.337*|
||(0.0983)|(0.0886)|(0.0751)|(0.126)|
|Area of parcel planted in Ha, self‐reported|0.00347|0.00323|0.00402|0.00588*|
||(0.00188)|(0.00191)|(0.00261)|(0.00244)|
|Household owns any of the land cultivated|0.00582|0.00396|0.0137|‐0.00887|
||(0.0344)|(0.0297)|(0.0594)|(0.0323)|
|Use of any extension services in the community|0.0631***||0.0530**|0.0532**|
||(0.0128)||(0.0109)|(0.0110)|
|The household participated in a NAADs program||0.0847***<br>(0.0184)|||
|Any HH member obtained loan in past 12 months|||0.0411||
||||(0.0277)||
|Average monthly real price of maize, lagged one period||||‐0.000327<br>(0.000192)|
|Constant|‐1.261**|‐1.295**|‐0.746|‐1.192|
||(0.446)|(0.394)|(0.353)|(0.585)|
|Observations|2,979|2,979|1,906|1,784|
|R‐squared|0.021|0.024|0.021|0.032|

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

# 5. Conclusions

Financial returns to input use in Uganda are considerable. This report analyzes some of the factors associated with the low input adoption observed in Uganda from 2005/06 to 2013/14. Despite favorable weather conditions and favorable agricultural crop prices, use rates of fertilizer, pesticide and hybrid seeds in Uganda remained stubbornly low throughout this period, particularly in comparison with other countries in Sub‐Saharan Africa. We first analyze the financial returns to using inorganic fertilizer and improved seeds under normal conditions, across the distribution of observed prices in Uganda from 2000 onwards, along with yield parameters identified in the agronomic literature. The main finding is that financial gains from using inputs in Uganda are substantial, despite observed output price volatility. The economic return to fertilizer is always positive (across the entire price range) for beans, maize and matooke, and positive for the top 75 percent of prices for coffee. Median returns are 180 percent, 74 percent, 64 percent and 39 percent respectively. In the case of hybrid seeds, yields increased by 32 percent on average. However, the cost of using maize hybrid seeds often exceeds the extra revenue gained, reducing financial returns.

However, once the quality of inputs is considered, the returns decline substantially, becoming negative for a considerable segment of the price range. Given the substandard quality of inputs typically available in local markets, measured quantitively by several studies, the yield gain from using hybrid seeds and nitrogen falls by 75‐87 percent. As a result, the sizable positive financial returns observed for using authentic inputs in maize crops disappear and even become negative at many prices. The rate of return 30
average consumption expenditure (which are taken from the caiTbrated database) are imposed as mean and as standard deviation. For skille-& workers in the formal urban sector and for capitalist and rentiers, a standard deviation of 0.8 times the mean is assumned. Figure 5 shows the distribtion **of** consumption in each group. Third, the incorne poverty line for the rural sector is set (somewhat arbitrarily) such that the percentage of rural houiseholds in poverty is 50 percent. The poverty line in urban areas is then assumed to be 15 percent higher. The rural and urban poverty lines for consumption expenditure are calculated in the same manner. This procedure produces an economy-wide, income-based headcount poverty index of 38.6 percent and an economy-wide consumption-based headcount index of 41.1 percent. For income distribution, the overall Gini index is 0.48 (consumption based) and 0.49 (income based).<sup>2 2</sup> The within-group Theil inequality decomposition is 78 percent (consumption based) and 73 percent (income based).

# 3 Nhcy Mxp'men(t

Mini-IMMPA can be used to analyze a variety of policy ana exogenous shocks. For illustrative purposes, the growth, unemployment and poverty effects of two types of labor market policies are examined in this section: a cut in the minimum wage and a reduction in the payroll tax rate on unskilled labor.<sup>23</sup> Both experiments relate to critical policy issues in developing countries. Economists have long debated the roie of minimnum wage legislation in labor market adjustment. Advocates have oLten viewed minimum wages as being beneficial in various ways-through its positive effect on- nutrition or productivity, or as an instrunent of income redistribution and social justice. By contrast, opponents argue that minimum wage legislation, bI preventing wages from adjusting downward to excess supply of labor, impos^-s an implicit tax on employers in the formal economy, leads to misallocation of labor (by preventing wages from adjusting downward to excess supply of **a** la nr) d creates unemployment (particularly for unskilled workers), induces .labor market

> 2 2 The income-based Gini coefficients are 0.45 for the rural sector, 0.48 for the urban sector, 0.44 for the informal sector households, 0.43 for urban unskilled households, 0.38 for urban skilled households, and 0.39 for capitalists and rentiers.

> 2 3 1n these simulations, **_PD3_** is assumed to be fully flexible, the skilled wage-setting equation (29) and the effort function (38) are used, and both the unskilled public sector wage and the minimum wage are taken to be fixed in nominal terms _(idxu_ **_idxM_** _=_ **0).**

34
-20-

# IV. Ouantification of PoErty_P.

ILA

# IV.1.A. The Income-EXDenditure Survey of,1984.

The most recently available income-expenditure survey (IES) for Mexico, carried out by the Instituto Nacional de Estadistica, Geografia e Informatica (INEGI), is for 1984 (SPP (1984))23. I make five observations on the data. First, about 5,000 households (the unit of observation) during each trimester of 1984 were surveyed. However, since some questionnaires were improperly filled out, the actual size of the sample for the whole year is 18,958. Second, the IES does not directly distinguish between urban and rural areas. Rather, households are characterized by the population density of the area in which they live, and grouped into two categories: 'high' and 'low' density areas. The former are municipios with at least one of the following characteristics (see INEGI, 1984, p. 30): (i) at least one locality with more than 15,000 inhabitants, (ii) a total of more than 100,000 inhabitants, (iii) be the capital of the state, or (iv) be part of any of the twelve largest metropolitan areas of the country. In this paper I use 'low' ('high') density areas as equivalent to rural (urban) areas24.

Third, the sample was designed such that results are representative of the urban and rural regions only at the national level; unfortunately, the sample is not representative at the state level. Table 1 summarizes the number of households surveyed in each state and region; note that there are eleven states where rural households were not surveyed 23Although a similar survey was carried out in 1989. Unfortunately, the results of this latter survey are not yet available.

> 2 4 0ther authors divide the population into urban and rural using the occupation of the household head: rural households are those where the household head is a 'Jornalero rural o peon de campo' (roughly, landless agricultural worker). I find this procedure inadequate because it misses an important category of rural inhabitants, the ejidatarios, who are sometimes classified as 'patron' (employer with one to five employees) or as 'trabajador por cuenta propia' (self-employed worker); see INEGI (1984, p. 144). On the other hand, if self-employed workers or employers with one to five employees are included in the definition of rural, one then adds incorrectly all the urban self-employed as well as small scale urban employers. Thus, the low/high density approximation seems to be better since it focuses directly on a geographical concept.
The Syrian refugee crisis is one of the largest refugee crises ever recorded in history if we consider the numbers of displaced people relatively to the country of origin and the countries of destination. The crisis started in the spring of 2011 following clashes between protestors and government forces in several major cities and quickly descended into a complex civil war. By 2014, 6.7 million people had been displaced internally in the country, about 1.5 million people fled the country with their own means, and an additional 3.7 million people were hosted as refugees mostly in neighboring countries. As a result, about half of the Syrian population was considered displaced in 2014. For some countries, Syrian refugees also represented a major population shock. In 2014, Syrian refugees accounted for about 20% of the population of Lebanon and about 10% of the population in Jordan. The incidence of such immigration for these countries is among the highest ever recorded in history (Verme and Schuettler, 2019).

The UNHCR has the mandate to protect and assist refugees in host countries and its role in the aftermath of a crisis is to find shelter, provide food and cash assistance and assist with basic services such as health and education. In order to provide these services, the UNHCR employs a system of mandatory registration for all refugees or asylum seekers requiring assistance that implies the collection of personal biometric and socio-economic information. This proGres registration system is the most comprehensive database on refugees in any country where the UNHCR manages the registration of refugees.<sup>7</sup> This is the case of Jordan, the country we consider in this paper.

In addition to the registration system, the UNHCR conducts sample surveys and home visits for a variety of purposes, such as protection of different categories of vulnerable populations or assistance of targeted programs such as the cash or food assistance program. In the case of Jordan

> 7 In some countries, such as Turkey, the host government or other agencies manage the registration process.

8