rafmacalaba/data-use-mentions-extended huggingface dataset:
households include; an adult PA with both parents and adult siblings; an adult PA with nephews/nieces all of whom are adults; adult PA with nephews/nieces some of which are children, some of whom are adults; adult PA with adult sons/daughters of his/her own, plus minors/disabled/elderly

- **Polygamous** : Families with one adult male who has registered more than one wife with or without children

<mark>Finally, to examine poverty, we match each individual household with its respective case (household) in the JD‐HV dataset and separate the households into those with male and female principal applicants.</mark> Table 1. Characteristics of male and female principal applicant households

||**Male PA**|**Female PA**|**All PAs**|**Male PA**|**Frequency**<br>**Female PA**|**All PAs**|
|---|---|---|---|---|---|---|
|All PAs, %|66.2|33.8|||||
|Elderly PA (65+)|5.37|10.64|7.15|1,934|1,958|3,892|
|**Marital status (%)**|||||||
|Married with spouse in HH|69.00|6.84|47.99|24,850|1,258|26,108|
|Married without spouse in HH|12.80|52.70|26.29|4,609|9,694|14,303|
|Widowed|1.05|24.59|9.01|379|4,523|4,902|
|Single or engaged|16.52
# **3. Research Design**

# 3.1. Data

The key variables for our analysis include indicators of structural change, productivity convergence, and imported capital and intermediates. The first two variables are the outcome variables—which we employ as key components of economic transformation. Indicators of imported capital and intermediates, on the other hand, are the main explanatory variables. To minimize potential omitted variable bias, it is important that we control for other variables in the empirical model specification. Guided by a closely related literature on the drivers of structural change and productivity efficiency (see Danquah & Ouattara, 2015; Owusu, 2024; Ndubuisi _et al.,_ 2022; Ndubuisi & Owusu, 2023), our specified empirical model will control for human capital, institutional quality, inflation rate, natural resource, and infrastructure (mobile broadband and transport). We also control for initial industrial structure since this has strong implication on structural change and productivity growth. To operationalize this variable, we use each country’s initial agricultural value-added share. Table A1 in the appendix describes and lists the data sources of these variables.

Structural change involves a compositional shift in the sectoral structure of an economy. From a production standpoint, structural change can be assessed by examining the evolution of sectoral value-added shares or employment shares (Herrendorf _et al.,_ 2014). Due to limited manufacturing employment data for Africa, our empirical analysis relies on value added data. Both the employment and value-added data, whenever available are used interchangeably to measure structural change. Following Kaba et al. (2022), we measure structural change as the ratio of manufacturing value-added to agriculture value added. Higher values of the ratio indicate a compositional shift of an economy’s sectoral structure from agriculture to manufacturing. We adopt a similar measure in our study. The sectoral value-added data used to compute the structural change index is taken from the UNCTAD Statistical Database.

<mark>For the productivity convergence</mark> , we use a measure
in March 2015, in which state of the Republic or in which country did (NAME) live?”_ While this question enables the identification of international migrants, the analysis is limited to internal migrants contrasted with non-internal migrants, identified as those who resided in Mexico five years ago and stayed in the same state.

**Using Census data to identify internal migrants in Mexico has some limitations.** The question that allows identifying internal migrants has the inevitable shortcoming that any 2-stage-migration during the five years is considered as a one-stage migration. Moreover, any intermediate migration within the five years cannot be identified. Therefore, the analysis of internal migration in this study considers as “permanent” any migration decision from the original state of residence.

**Additionally, the questionnaire elicits information about the personal reasons for migration** . This data is an essential input for the analysis since it allows a better understanding of the internal migration patterns and the identification of heterogeneous migration decisions among women and men. However, the units of analysis are limited to the individual and dwelling levels. This fact leaves aside the possibility of analyses at the household level.

**Around 3.4 percent of the population residing in Mexico in 2015 moved to another state during the period 2015-2020.** In 2020, 91.4 percent of the people living in Mexico resided in the country five years ago (in March 2015). Of these, 3.4 percent migrated from another state between 2015 and 2020, representing nearly four million people. The internal migration rate is higher among men than women (3.6 against 3.3 percent). However, women represented almost half (49.8 percent) of the total internal migrants in 2020 (Figure 4).

9
> whether the child wanted become a health worker at age 12 or not, while _PARENT_ indicates whether the parents had an influence on the decision to become a health worker.

To investigate the importance psychosocial characteristics, we also included several psychosocial characteristics captured by series of questions during the survey. The variables and reliability of the scales constructed for the student responses are listed in Table 5. The first construct, _INTEGRITY_ , reflects the extent to which the students disapprove of individuals accepting pay for more hours than they worked or reimbursement for more than actual expenses.<sup>32</sup> Table 5 shows that with a CronbachAlpha of 0.70, the scale is quite reliable. Somewhat less reliable is the variable based on past trusting behaviour. We use the measure suggested by Glaeser, Liabson, Scheinkman and Soutter . The Cronbach-Alpha for _TRUST1_ is 0.42. If we reduce the items to the first two we obtain _TRUST2_ , for which the Cronbach-Alpha raises to 0.51. To measure initiative we calculate the interitem correlation of three questions. The items for _INITIAT_ also show a moderate level of reliability (Cronbach-Alpha=0.47).

Another approach to investigate the importance of psychosocial characteristics is to measure the individual’s behavior in a game. We played two behavioral games with the subjects: a generalized trust game and a third party punishment game. The first measures generalised trust while the second measures the individual’s judgement of fairness. The games are explained in detail in the appendix.<sup>33</sup> In the first game the subject receives an amount _x_ from player A and passes any portion of this amount on to Player C. The student’s behavior in the game reflects trustworthiness to an unknown party or what is called _generalized reciprocity_ . There is strong evidence for the role of reciprocity in labor market choices and performance, both from lab experiments as well as from combining survey and experimental data . The game was selected because the extent to which the students respond
# **End note**

1 For example, see the survey on free trade in Initiative on Global Markets (University of Chicago Booth School of Business): http://www.igmchicago.org/surveys/free-trade.

2 If changes in tariffs are correlated with changes in non-tariff barriers (NTBs), there may be a concern with omitted variable bias. To test this, we run panel regressions on the relationship between three different measures of NTBs (anti-dumping cases initiated, safeguards, and WTO disputes) and changes in tariffs, controlling for country- and time-fixed effects (as in our estimation framework described in equation 1). We do not find evidence of any strong correlation, mitigating this concern in our set-up. However, the results should be taken with a pinch of salt since NTBs are difficult to measure and data is scarce for this exercise.

3 Kehoe (2003) compares the predictions of three ex-ante CGE studies of NAFTA with observed outcomes and concludes that trade increases in most sectors surpassed the predictions more than ten-fold. Corcos et. al (2012) find same results for the EU.

4 Since the set of control variables includes lags of output growth as well as the real exchange rate and trade balance, this approach is equivalent to a VAR approach in which tariff shocks do not respond to shocks in other variables within a year. We relax this assumption later as a robustness check.

26
employment decisions, government pay policies may also lead **to,** growing wage bill shares which distort the labor/non-labor input mix in government production. Pay offers in excess of worker opportunity costs may "crowd-out" expenditures which would otherwise have gone to finance non-Labor inputs. A rising ratio of wage to non-wage expenditures may be a signal of a growing imbalance between labor and non-labor inputs in government production. However, care must be exercised in drawing such a conclusion based on expenditure data alone. After all, relative increases in labor to non-labor payments may teflect changes in relative input prices or an intended shift X. -- the compositiorn of public output towards more labor-intensive products e.g., a shift towards social sectors and away from infrastructure development. While no simple test of the appropriate labor intensity of government production exists, sudden shifts in the wage bills relative share of total expenditures may be a warning sigA of a growing imbalance in the input mix employed.

Beyond the relationship between non-labor and labor inputs lies the problem of reaching appropriate complementarities between labor skills. An imbalance between numbers of managers and numbers of workers (or between professionals, skilled and unskilled labor) may prove as inefficient a use of fiscal resources as a lack of complementary equipment or raw materials. The choice of both pay and employment policy, of course, will influence the mix of labor skills in public employment. In the area of employment policy, a tendency to use government jobs as a means f lowering urban unemployment can result in an employment structure which is bottom heavy, that is, with employment concentrated in the lowest salary grades to a degree unwarranted by government production requirements.

9
|
|Age at frst birth|5737|28.33|(4.177)|15|58|
|Years of education|5541|11.08|(3.426)|0|20|
|Rural|5737|0.478|(0.499)|0|1|
|Household size|5737|5.210|(1.424)|1|16|
|Extended family|5737|0.364|(0.481)|0|1|
|Marital status (married)|5737|0.998|(0.032)|0|1|
|Muslims|5737|0.798|(0.401)|0|1|
|Catholics|5737|0.082|(0.275)|0|1|
|Orthodox|5737|0.069|(0.254)|0|1|
|Coastal region|5737|0.262|(0.440)|0|1|
|Mountain region|5737|0.285|(0.451)|0|1|
|Tirana region|5737|0.164|(0.370)|0|1|

_Notes:_ The data used are the 2002 LSMS, the 2005 LSMS, the 2008-09 DHS and the 2012 LSMS 36
4 have focused on micro-foundations for political and economic decision making by establishing stronger links between theory and empirics, emphasizing forces such as governance structures, political institutions, and ideology. These developments include extensions of the Grossman and Helpman (1994; 1995) model on political economy of trade policies: work by Acemoglu and Robinson (2001; 2006) on the interactions between institutions and policymaking; applications of Baron and Ferejohn’s (1989) model of decisionmaking rules and the role of agenda setting; work by Roland (1994; 2000) on the political economy of transition; by Shleifer (1997) on the role of bureaucracies and corruption in policymaking; by Persson and Tabellini (2000; 2003) on the political economics of fiscal policy and macro-economic policy and on the role of constitutions; and by Rausser, Swinnen, and Zusman (2011) on how integrating the four major analytical dimensions of public-policy analysis (incidence, mechanism design, political economy, and governance structures) can facilitate the separation of the public interest from specialized interests in any attempts to sustain policy reforms conditioned on political-economic equilibria.

In our presentation, we first document the key data and stylized facts that have emerged from the new global five-decade database. We present a series of tables and figures that show quantitative indicators of the extent of policy interventions, as well as statistical estimates of the degree of price distortion. We then review the political-economic hypotheses that have been explored in the literature to explain these patterns and assess the empirical evidence that has emerged. Finally, we draw implications from this empirical evidence and identify directions for new research, with an emphasis on the potential for agricultural-policy reforms.

# **2. Stylized Facts**

Many agricultural- and trade-policy developments of the past half-century have happened quite suddenly and been transformational. Such events include decolonization in Africa and elsewhere around 1960; the creation of the Common Agricultural Policy (CAP) in Europe in 1962; the introduction of flexible exchange rates from the 1970s; liberalization, deregulation, privatization, and
**III. Data, methodology, and assumptions**

## i. Data

### **_The National Household Budget Survey (ENPH)_**

The ENPH is a nationally representative survey conducted by the National Statistical Office (DANE). This survey captures information about the resources that Colombian households receive and how they spend them. It includes variables related to physical characteristics and fixtures of the homes, attributes of the people of the household such as age, gender, educational level, enrollment in the social security health care system, occupation, monetary income (permanent and occasional), non-monetary and other, goods and services acquired frequently and less frequently by urban and rural households, as well as those obtained on a personal basis, meals bought outside home, quantities purchased, amounts paid and estimated from the acquisition, forms of procurement, places of purchase, purchase frequency, among others.

The ENPH interviewed 87,201 households. Of this total, 81,438 households were interviewed in urban areas, and the remaining 5,736 correspond to households located in rural areas. The ENPH has national

> 25 The tax reform (Law 1607 of 2012) reduced the corporate income tax from 33 percent to 25 percent while exempting them from making contributions to SENA, ICBF and 8.5 percent for health care for employees who earn less than 10 monthly minimum wages. The CREE equity tax was created to compensate for this reduction in parafiscal costs, which seeks to guarantee social programs such as SENA, ICBF and health care.

> 26 In the case of the Subsidized Regime, people are classified in level I of the SISBEN III if their score is lower than: i) 47.99 and they live in the 14 main cities, ii) 44.79 and live in the other administrative seats, iii) 32.98 and live in a rural zone. Similarly, those who are classified as level II have scores: i) between 48 and 54.86 and live in the
domestic work is extremely low, either migrants or not (Figure 18). Furthermore, most women who migrated work as salaried workers compared to those who did not migrate (71.1 against 66.0 percent). A lower proportion of them work as self-employed (18.6 against 22.7 percent). The same holds for men, although the differences across migration status are more significant (Figure 19).

Figure 18. Distribution of women and men (15+) who did and did not migrate by labor force status <!-- Start of picture text --> 100<br>7.6<br>80 2.7 9.6 Not working<br>37.3<br>3.1 41.2<br>Unable to work<br>60<br>8.2 Unpaid housework<br>1.4 9.4<br>40 80.6 72.1 0.9 Studying<br>20 46.8 40.2 Retired / Pensioner<br>Unemployed<br>0<br>Men who Men who did not Women who Women who did Employed<br>migrated migrate migrated not migrate<br><!-- End of picture text --> Source: Own estimates based on Population and Housing Census 2020.

Figure 19. Distribution of employed women and men (15+) who did and did not migrate by type of employment <!-- Start of picture text --> 100<br>14.1<br>21.0 18.6 22.7<br>80<br>4.4<br>Unpaid worker<br>7.2<br>60 Self-employed<br>Employer<br>40<br>73.1 71.1 Paid trainee<br>66.0<br>60.0<br>Daily or farm workers<br>20<br>
# **5 Conclusion**

Quantifying who benefits and who loses from trade reform and by how much is of crucial policy interest, but often challenging because of a lack of suitable data. The Household Impacts of Tariffs (HIT) data introduced in this paper are a publicly available household survey based data set covering 54 developing countries that enables researchers to analyze the distributional impacts of tariffs. It contains granular data for each percentile of the income distribution on both the income derived from and consumption of 53 agricultural products. In addition, it keeps track of spending on five different types of manufacturing goods and services, as well as transfers, and wage income disaggregated by 1-digit sector, 10 different types of non-farm household enterprise sales and various types of transfers.

Using a stylized agricultural model and a first-order effects approach we have illustrated potential applications of the data and shown that the prevailing structure of agricultural tariffs represses household incomes by 2.50% percentage points across countries. However, the costs of protectionism vary enormously across countries and across households within countries, because households in different parts of the income distribution tend to have very different income and consumption portfolios; the average standard deviation of the gains from trade within a country is 1.01 percentage points. We also show that using disaggregated data is important, because using more aggregated data yields biased estimates of the average gains from trade.

While we have focused on the elimination of agricultural tariffs, the HIT data set has a much wider set of potential applications and can accommodate richer and more sophisticated modeling assumptions. Examples of potential applications include assessing how EU and U.S. agricultural tariffs or regional trade-agreements, such as AGOA, impact households in low-income countries.<sup>13</sup> An analysis of poverty and inequality impacts of food price shocks is also possible. Moreover, the data can be used to study issues that are not related to trade reform, such as food subsidies or value-added tax reforms.

> 13In the online Appendix we analyze the impact of
3. Decreasing real transportation costs allow firms to search over wider geographies for inputs and markets.

4. Labor force skills and quality, physical infrastructure, and other industry-specific requirements must be sufficient to support targeted industries.

The ability of regional policy makers to effect significant change is in many cases constrained by national policy and global changes. Accordingly, attention is often directed to changes at the margin – enhancing a region’s connectivity, investment in human capital and the provision of incentives or indirect support to enable local firms to upgrade technologically. Much less attention has been given to addressing the market failures that are in the scope of most regional and local governments, such as ensuring the fluidity of land markets or promoting a favorable business environment. The recent attention directed to the idea of smart specialization provides an example of an innovation in policy thinking that has been clearly motivated by the difficulties of measuring the contributions of past policy initiatives.

# **5 Regional Accounts and IO Software**

## **Regional Accounts**

Regional accounts are to regions what national accounts are to nations. These accounts provide the foundations for a wide array of analytical tools and techniques. The accounts detail the interactions among economic agents within the system and with other regions and the rest of the world. These economic agents are the establishments and firms that compose the production sector of the economy, and the consumers of the goods and services that they produce, which includes households, governments, investment, and trade. The establishments are grouped in industries, and their products are grouped into corresponding commodities.

Because region economic systems are typically more open than national counterparts, an accurate identification of which interactions take place within the region and which cross the regions borders – in the form of imports and exports – is critical for the generation of meaningful analyses using the suite of tools and techniques that are available. Analysts’ initial challenge is often the 35
Roads

### **Achievements**

Zimbabwe has a relatively dense national road network. Total road density is 100 km/1,000 square kilometers (km<sup>2</sup> )—double the figure for neighboring Zambia and almost triple that of Mozambique—and the network spanned almost 44,000 km in 2008.<sup>8</sup> The classified network—roads under state jurisdiction— totaled 18,253 km in 2010, according to the recent Zimbabwe Road Condition Survey, while there are an additional 5,000 km of urban roads, and around 39,000 km of unclassified roads. The rural-accessibility index in Zimbabwe, at 46 percent, is more than double the regional averages (table 9).

Zimbabwe has strong road connections with the SADC. Zimbabwe is linked to its neighbor South Africa—the largest regional economy and home to the busiest port in Africa—and other countries such as Mozambique, Botswana, Zambia, the Democratic Republic of Congo, Malawi, and Tanzania through the North–South Corridor, the most extensive corridor system in the region. In addition, the North–South

> 8 Alternative sources, including several World Bank project documents, mention a total of more than 88,000 km, doubling AICD‘s estimates.

29
assumption of price-taking in output markets seems unrealistic as well, because the loan markets in the five East Asian countries are not highly competitive.

Berger and Mester (1997) derive alternative profit efficiency from the following prespecified profit function, in log _form,_ _ln(Qr_ + S) _= f(w, y, z, e)_ + In _v,_ - In u, , where xr is the variable profits of the firm, 4 is a constant added to every firm's profit so that the natural log is taken of a positive number, _f_ denotes some functional form, w is the vector of prices of variable inputs, y is the vector of quantities of variable outputs, z indicates the quantities of any fixed inputs, _e_ is a set of environmental or market variables that may affect efficiency, _u,r_ denotes an inefficiency factor that may lower profits below the best-practice level, and v,f denotes the random error that incorporates measurement error and luck that may temporarily give banks high or low profits. The inefficiency factor u, incorporates both allocative inefficiencies from failing to react optimally to relative prices of inputs, and technical inefficiencies from employing too much of the inputs to produce y or setting sub-optimal output prices. As Mester (1996) points out, the profit concept assumes that all financial institutions use the same technology. This assumption seems to be a strong one, because it is likely that foreign banks use superior technology compared to domestic financial institutions.

Application of both techniques on the same banking data can lead to very different results as Ferrier and Lovell (1990) have shown. The choice of technique depends on the situation. Possible reasons to prefer SFA over DEA are plentiful. Firstly, as pointed out by Schmidt (1986), DEA estimates give only an upper bound to efficiency measures so that it is difficult to use DEA to compare efficiency among frms. Secondly, DEA does not assume statistical noise which means that all the error term in the estimation is attributed to