rafmacalaba/datause-encoder-data huggingface dataset:
10 measured as the ratio of minimum to average firm wages-- comes from the variation in wage levels across firms, and not from variation in the minimum wage. Disentangling "minimum wage effects" from "firm wage effects" is difficult in this case. The existence of regional minimum wages in Mexico,<sup>1 4</sup> and the dual structure of minimum wage payments in Colombia<sup>1 5</sup> (with differentiation for large cities versus other areas) makes these countries appealing to study since there is greater measured variation in minimum wages. Panel data from manufacturing firms provide variation over time and a changing trend in the real value of the minimum wage in each country adds a further element of heterogeneity to the minimum wage variable.

In order to isolate the impact of the minimum wage on the employment of unskilled and skilled labor, I consider the firm conceptually as employing three types of labor, namely skilled, high paid unskilled, and low paid unskilled labor. Corresponding to each type. of labor is a factor price-- the skilled wage, the unskilled wage, and the minimum wage, respectively. Following Hamermesh (1982) an appropriate specification of the effect of a minimum wage change on low paid unskilled labor must hold fixed for the effect of such a change on the higher paid tail of the unskilled distribution and on the wages of the more skilled. Assuming that the supply of each type of labor to the firm is perfectly elastic,<sup>16</sup> firm employment (for either unskilled or skilled labor) is given as a function of the price of all inputs (including the minimum wage), and output prices (which is available at the 4-digit ISIC level and is comfortably thought of as an instrument for firm output). Specifically, we have:

# (5) Employment = f [min, PI, Pk, Pm' Pe]

14Fourteen regional minimum wages were in place in Mexico in 1984, although this number has diminished through time as the Mexican govemment has gradually tried to equalize differences across regions. For example, by 1990 only five regional minimum were differentiated and enforced in Mexico. 15In Columbia the dual-minimum wage structure which differentiated between large cities and smaller cities and agricultural areas was replaced with a single minimum wage in 1985.

1<sup>6</sup> Even with plant level data for the most unskilled workers this assumption is troublesome, although the existence of large urban informal sectors in most developing economies offers hope that it may be a better approximation to reality in a developing country context. Certainly for the unskilled and minimum wage labor (whose wages are set by fiat) the assumption seems most reasonable.
patterns, exacerbated by the ongoing climate change, which is expected to alter the regional composition of commodity production and increase yield volatility (Wing et al. 2021). Public investment in developing new cultivars resilient to temperature and precipitation variations will be crucial for maintaining historical yield growth trajectories. The significance of such cultivars will only grow, as most global food supplies rely heavily on a few crops, with the Big-4 accounting for nearly half of the global calorie supplies (Zabel et al. 2019). Additionally, climate change is likely to increase yield volatility. Indeed, yield growth in advanced economies has been more volatile compared to emerging markets and developing economies, primarily due to weather-induced factors such as the La Niña episodes of 2010-11 and 2011-12 (Rippey 2015). Addressing these challenges will require substantial investment, research and development, and diffusion efforts, similar to those that led to the development and use of hybrid maize varieties in the United States and the grain varieties promoted by the Consultative Group on International Agricultural Research, which spearheaded the Green Revolution in East Asia (Crow 1998; Evenson and Gollin 2003; Renkow and Byerlee 2010).

Another challenge is removing distorting trade policies, which restrict food availability in regions experiencing food deficits (Baffes and de Gorter 2005; Rutten et al. 2013; Smith and Glauber 2020). Despite agricultural policy reforms undertaken in recent decades, global agricultural trade is still subjected to trade barriers and domestic policies (OECD 2021). Policies diverting food commodities to biofuels should balance environmental and energy security concerns and food availability (Graham-Rowe 2011). Likewise, policies to reduce greenhouse gas emissions should not compromise productivity. This is especially important since agriculture accounts for a quarter of global emissions (Laborde et al. 2021).

Finally, even if adequate food supplies can be ensured globally, access to food remains a concern, especially in low-income countries where food insecurity is associated with conflict and extreme weather events (FAO 2021). The number of people subjected to acute food insecurity worldwide has more than doubled during the past five years, exceeding an estimated 280 million in 2023 (Food Security Information Network 2024). Achieving equitable access to food across income groups will entail financial assistance, food delivery through aid, as well as investments and policies aimed at increasing productivity but also reducing food waste at the production level (Nature 2020).

The calorific-based indices along with the estimation procedure presented in this paper not only deepen our comprehension at both aggregate and regional levels, but also pave the way for multifaceted avenues of future research. For instance, by incorporating additional factors, like water usage or chemical input use, the aggregation of production _— 17 —_
As expected, pro...ts rise with ': only ...rms with ' > '<sup>�</sup> BD;j<sup>earnnon-negativepro...tson</sup> average once their demand shocks are realized, and therefore, only such ...rms try their luck in market j. This gives the cuto¤ productivity '<sup>�</sup> BD;j<sup>intermsofthemodel’sparameters.</sup> (See equations (45) and (46) in the Appendix.) Knowing '<sup>�</sup> BD;j<sup>and v ('; PBD;j) allows us to</sup> depict the trade-o¤ between the demand shocks and productivities of ...rms in each market, as done in Figures 2 and 3: the downward sloping locus re‡ects the fact that the demand shock needs to be really low to force a very e¢ cient ...rm to exit the market.<sup>27</sup>

## 3.2.3 Stage 1

In Stage 1 we use the free entry condition to derive the mass of entrants in the equilibrium. Our solutions for '<sup>�</sup> BD;j<sup>; j2 fEU; USg ; depend on the aggregate price indices in the markets,</sup> which fall with increases in the mass of entrants. This reduces pro...ts at any given ' and v; which shifts the cuto¤ locus upward and raises the cuto¤ productivity in each market, thereby reducing ex-ante expected pro...ts from entry. The equilibrium entry level is such that the expected pro...ts from entering the industry, obtaining a productivity draw, and choosing optimally from there onwards equal the cost of doing so, fe: (See equation (47) in the Appendix.) We use the model and the available data on Bangladeshi ...rms to estimate the model’s parameters. The solution of the model is described in the Appendix.

# 4 Estimation Outline

Identi...cation of the parameters is conditional on a number of basic assumptions stated and brie‡y discussed below. First, the model is structured so that decisions across markets are made separately. This simpli...es the derivations signi...cantly. We assume in particular that marginal costs are constant, but they could fall or the ...rm could be subject to capacity

> on invoking EBA preferences multiplied by the probability of getting a demand shock high enough to warrant meeting the ROOs needed to obtain EBA preferences.

> 27This is analogous to the setup in Lileeva and Tre‡er (2010), where there are also two sources of ...rm heterogeneity, namely, initial productivity and productivity gains from investing. In their analysis, ...rms with initial productivity and productivity gains from investing that are above a downward sloping locus select into making an additional investment.

18
treatment communities. The remaining two individuals in the treated communities are people associated with the program but not necessarily directly affected by it.

The data from the household survey include assets, including animals and household durables; whether the individuals had made complaints to local leaders about their NUSAF2 project or other projects in the community; and the individuals’ level of trust in local leaders. The descriptive statistics for the project assessment and household data collections are presented in Table 3. The description is separated by whether data were collected at the project or household level. While NUSAF2 targeted very low-income households, most had livestock in their home, with the mean household having 2.45 cattle at the endline.<sup>12</sup> The sample size for the household survey was determined to provide the highest statistical power given a fixed budget. The intra-cluster correlation (ICC) for the main outcome of interest, number of cattle, is 0.045. For the scorecard sample, which includes 574 clusters, the minimum detectible effect (MDE) size is below 10%. For total assets, the ICC is 0.35 and so the MDE is approximately 15%.

# **4.2 Balance tests**

Tables 4 and 5 present balance tests for the social accountability training and project quality scorecard samples, respectively. Due to the project timeline and funding, a full baseline with communities was not feasible. We do have four indicators that were available before the beginning of the NUSAF2 projects in the sample: the amount of money approved per community, the type of project, when the program grants were received, and the level of corruption and mismanagement in the areas where the communities exist. We also present tests for whether the randomly drawn respondent in the household survey was a man, whether that person could write or read, and the distance from the respondent’s household to the sub-county headquarters. We include these last four measures because we believe they are not likely to have changed due to the program and so reflect the characteristics of the communities before the social accountability training treatment.

> 12 As part of a separate experiment, the enumeration teams were randomly assigned to villages during the second data collection. This was done to test for enumerator effects on reported household characteristics and outcomes. There is no or very little enumerator bias introduced on the main outcomes of interest, especially number of animals. While the experiment is not able to directly test for Hawthorne effects, the lack of enumerator bias and the fact that the enumeration team was separate from the implementation team reduces the likelihood of such issues impacting the main results.

18
20 MFN tariff cuts?

Table 7 provides relevant information by tabulating the value of African non-fuel exports to all OECD countries and the share (percentage) of this trade subject to a nontariff measure. Similar data for the EEC, Japan and United States is also shown separately.<sup>18</sup> As indicated, about 18 percent of the $16.8 billion of African exports encounter nontariff measures, but there is considerable variation in this share across OECD markets and over African countries. For example, only about 10 percent of African exports to the United States encounter NTMs (implying about $163 million in affected trade) while the corresponding trade coverage ratios for Japan and the EEC are between 22 to 23 percent.

An examination of the statistics in Table 7 shows that a relatively few countries may encounter important non-tariff measures. Almost 65 percent of Mauritius' exports encounter NTMs -- largely variable import levies on sugar and Multifiber Arrangement (MFA) quotas on textile and clothing. Mauritius is exceptional in that it (and to a much lessor extent Nigeria) is the only Sub-Saharan country whose exports are restricted by the MFA (see Table 'These tabulations utilize an inventory of nontariff trade measures maintained by the UNCTAD Secretariat in Geneva. For a description of the inventory and how it is compiled see Laird and Yeats (1991, chapter 4). One weakness of NTB coverage ratios reported in Table 7 is that they are computed using "own" trade weights. As such, products facing restrictive NTMs enter the calculation of the overall coverage ratio with relatively low (or zero) weights. Also, the inventory data (and Table 7) tell nothing about the restrictiveness or ad valorem equivalents of different NTBs (i.e., quotas, variable import levies, licensing requirements, "voluntary" export restraints, etc.), but merely indicate whethe; a measure is present or not. In spite of these limitations, however, NTM inventories have been widely used in research and analysis of trade barriers. See Laird and Yeats (1989), Walter (1969)(1972), or World Bank (1993) for illustrative applications.
4

# **2.  Severance Pay and Employment Rigidity**

The first concern about severance pay in Indonesia is that the costs associated with the program are high and, as a consequence, distort firms’ hiring and firing decisions. Business representatives argue that these high costs discourage them from hiring new employees on a permanent basis, influence their lay-off decisions in response to contemporaneous economic conditions and lead to a deterioration in the investment climate.

Severance pay compensation rates in Indonesia began to increase in the mid1990s. There were three changes to these regulations in the last decade of the last century and he first decade of this century: 1996, 2000, and 2003. Figure 1 shows the severance pay rates associated with these three legislative changes to the regulations. In 1996, the severance payment was estimated to be equivalent to a “hiring tax” of about 2.0 monthly wages per employee.<sup>4</sup> The legislative changes in 2000 led to an increase in the “hiring tax” to an average of 3.4 months. With the changes introduced in 2003, the average severance payment is about 4.1 monthly wages per employee or 34 percent of an employee’s annual wage. The maximum severance pay is close to 30 months of wages in the case of a dismissal for an economic cause beyond the control of the employee.

The current employment protection legislation, Manpower Law (No. 13/2003), regulates severance pay rates in Indonesia. In terms of severance regulations, Indonesia falls into the group of economies in East and South Asia with more generous statutes, in the same range as Bangladesh, Korea, Nepal, the Philippines, and the People’s Republic of China. As stated above, business representatives in Indonesian claim that severance pay costs are high and constrain the workforce decisions that firms make.

One way to evaluate these claims is to examine the trends in various indicators of labor market rigidity. Using unemployment data and work experience data from the annual Sakernas labor force surveys from 1999 to 2008 for turnover in the labor market,

> 4 The hiring tax measures the discounted expected cost, at a time a worker is hired, of dismissing him/her in the future or if the worker quits.  It shows the severance payment an employer expects to pay in the future for hiring a new worker.  The calculation is based on the probability a new worker leaves in a particular year based on the reason for the separation.  Different rates apply depending on the specific reason for the separation (quit, dismissal for minor violations or economic cause).  See Alihsjahbana (2007) and Padjadjaran University (2004).
By organizing the data in spells – where a spell represents the number of years that a credit boom lasts and it is denoted by _Dur_ – we are able to compute their mean duration.<sup>6</sup> According to the MT-criteria, credit booms last on average around 6.1 quarters, but they last longer in the group of industrial countries (7.5) than in the group of developing countries (5.6) – see Table 1. According to the GVL-criteria they tend to last a bit more (about 8.5 quarters), either for industrial or developing countries. Whether there is any significant difference or not in the duration of credit booms between these groups of countries is something that we will test below in the empirical analysis. In particular, we will test whether there is a significant difference in the average duration of credit booms, as well as in the duration dependence parameter ( _p_ ) between these two groups of countries. This will be done by including the dummy _D_Indus_ in the model, which takes the value of 1 for industrial countries and 0 otherwise. Moreover, some separate regressions for each of these groups will also be considered.

Additionally, we observe in Table 1 that the average duration of credit booms has increased over the last decades, independently of the criteria used. Whether this evidence is statistically solid is another issue that we will explore in the empirical analysis using dummies for each decade ( _Dec70_ , _Dec80_ , _Dec90_ , _Dec00_ ). Following Castro (2012), we also consider a kind of a trend variable for the credit boom spells, labeled as Event, to check whether their duration has become gradually longer or shorter over time. This variable reports the order or observation number of each event over time and for every single country: it is equal to 1 for the first event, 2 for the second, and so on. If the coefficient on this variable is significantly smaller (larger) than zero, phase durations get longer (shorter) over time or, better, from spell to spell.

# **5. Empirical Analysis**

This section investigates whether there is positive duration dependence in credit boom episodes using quarterly data from 1975q1 to 2010q4 for both industrial and developing countries (23 and 48, respectively). We also conduct a test whether there is positive duration

> 6 The variable Dur corresponds to t_{i} in the model described in the previous Section.

19
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A.8
suggesting that the increase of violence has not been a response to the government's aggressive policy to fight DTOs: Rios (2012) and Sota and Messmacher (2012) argue that it does not appear to be the case that violence has increased as a response to a targeted security policy, which suggests that counties where violence increased the most need not be affected by different government policies as those with no increase in violence.

Rios (2012) suggests that the main reason behind the recent escalation of violence is that the illegal drug industry evolved from one in which DTOs were stable oligopolies into one in which DTOs wanted to compete against each other. Rios (2012) provides empirical evidence that the propensity of criminal organizations to engage in damaging criminal activities increases when municipal and state governments are not coordinated. She argues that coordinated political institutions lead criminal organizations to behave and organize in less violent ways. Mexico’s political decentralization has decreased the coordination between levels of government which in turn has increased drug related violence.

Dube et al (2012) analyze the effect of an increase of the availability of guns on violence in Mexican municipalities near the U.S. border. They find differential increases in homicides in municipalities that were exposed to the spillover of an increased gun supply. They find that the increments were most marked in municipalities with a high degree of political competition in high drug trafficking areas.

Whatever the reason that violence grew substantially in some places and not in others, it is not obvious that trends on education enrollment would have caused and or predicted them. The suddenness and drastic nature of the violence spikes on violence in selected municipalities (which did not have particularly different trends in educational attainment) give us confidence in our methodology, which assumes that changes in violence are orthogonal to what the changes in education enrollment would have been.

# **4. Data and Key Variables**

# **Violence Indicators**

We use the municipal homicide rate per 100,000 inhabitants as our measure of violence. Using homicides as our measure for violence has several advantages. First, homicides are a form of crime which is generally both violent and visible. Additionally, it does not suffer from reporting bias as other types of crime. Finally, homicides have been consistently reported at the municipality level. Homicide statistics were gathered from administrative data and cover the period from 1990 to 2011. They include all homicides at the municipality (municipal) level. Its source is the vital statistics registry from INEGI, the Mexican National Institute of Statistics and Geography.

An additional variable we use is the drug-related homicide rate. These data on total number of homicides at the municipal level comes from official figures made public by Mexico’s Technical 8